From 32042e760feba34102dc88c5594d2496567a26ea Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Sat, 10 Feb 2018 09:43:01 -0800 Subject: [PATCH 01/87] Update README.md (#4) --- README.md | 51 +++++++++++++++++++++++---------------------------- 1 file changed, 23 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 6e0bf29c..97c8ee2d 100644 --- a/README.md +++ b/README.md @@ -21,35 +21,10 @@ FfDL is a collaboration platform for: * `docker`: the Docker command-line interface (https://www.docker.com/) -* `aws`: the AWS command-line interface for storing test data in a local S3 mock server - -* `wget`: the command-line interface for downloading web resources - * An existing Kubernetes cluster (e.g., [Minikube](https://github.com/kubernetes/minikube) for local testing). Once installed, use the command `make minikube` to start Minikube and set up local network routes. Alternatively, use the Vagrant based setup to automatically install a local Kubernetes cluster. -Optional: - -* `go`: a working [Go](https://golang.org/) environment is required to build the code - -* `glide`: the glide package manager for Go (https://glide.sh) - -* `npm`: the Node.js package manager (https://www.npmjs.com) for building the Web UI - -Important: - -* `Go` is very specific about directory layouts. Make sure to set your `$GOPATH` and clone this repo to a directory -`$GOPATH/src/github.com/IBM/FfDL` before proceeding with the next steps. - -* Also, when testing locally with Minikube, make sure to point the `docker` CLI to Minikube's Docker daemon: - - ``` - eval $(minikube docker-env) - ``` - -* FfDL has only been tested under Mac OS and Linux (we plan to add support for Windows in the near future). - ## Steps 1. [Quick Start](#1-quick-start) - 1.1 [Installation using Vagrant](#11-installation-using-vagrant) @@ -122,12 +97,25 @@ The platform ships with a simple Grafana monitoring dashboard. The URL is printe ## 4. Development Use the following instructions if you want to run a full development build, compile the code, and build the -Docker images locally. First, fetch the dependencies via: +Docker images locally. + +Install: + +* `go`: a working [Go](https://golang.org/) environment is required to build the code + +* `glide`: the glide package manager for Go (https://glide.sh) + +* `npm`: the Node.js package manager (https://www.npmjs.com) for building the Web UI + +* `Go` is very specific about directory layouts. Make sure to set your `$GOPATH` and clone this repo to a directory +`$GOPATH/src/github.com/IBM/FfDL` before proceeding with the next steps. + + +Then, fetch the dependencies via: ``` glide install ``` - -Then compile the code and build the Docker images via: +Compile the code and build the Docker images via: ``` make build make docker-build @@ -297,9 +285,16 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) ## 8. Troubleshooting +* FfDL has only been tested under Mac OS and Linux + * The default Minikube driver under Mac OS is VirtualBox, which is known for having issues with networking. We generally recommend Mac OS users to install Minikube using the xhyve driver. + +* Also, when testing locally with Minikube, make sure to point the `docker` CLI to Minikube's Docker daemon: + ``` + eval $(minikube docker-env) + ``` * If you run into DNS name resolution issues using Minikube, make sure that the system uses only `10.0.0.10` as the single nameserver. Using multiple nameservers can result in problems, in particular under Mac OS. From a9167d2fd67e7969209b674e90130a9b9f5bb38a Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Mon, 12 Feb 2018 10:29:05 -0800 Subject: [PATCH 02/87] Adding paper references - changing main title (#5) --- README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 97c8ee2d..383aabb0 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![build status](https://travis-ci.org/IBM/FfDL.svg?branch=master)](https://travis-ci.org/IBM/FfDL) -# FfDL Core Services +# Fabric for Deep Learning (FfDL) This repository contains the core services of the *FfDL* (Fabric for Deep Learning) platform. FfDL is an operating system "fabric" for Deep Learning @@ -38,6 +38,7 @@ FfDL is a collaboration platform for: 6. [Detailed Testing Instructions](#6-detailed-testing-instructions) 7. [Clean Up](#7-clean-up) 8. [Troubleshooting](#8-troubleshooting) +9. [References](#9-references)) ## 1. Quick Start @@ -302,3 +303,12 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) make sure to follow the standard Go directory layout (see [Prerequisites section]{#Prerequisites}). * To remove FfDL on your Cluster, simply run `make undeploy` + +## 9. References + +Based on IBM Research work in Deep Learning. + +* B. Bhattacharjee et al., "IBM Deep Learning Service," in IBM Journal of Research and Development, vol. 61, no. 4, pp. 10:1-10:11, July-Sept. 1 2017. https://arxiv.org/abs/1709.05871 + +* Scott Boag, et al. Scalable Multi-Framework Multi-Tenant Lifecycle Management of Deep Learning Training Jobs, In Workshop on ML Systems at NIPS'17, 2017. http://learningsys.org/nips17/assets/papers/paper_29.pdf + From 209572ac657b59f0dc509f78bfd3ac0b0a4b3cb2 Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Mon, 12 Feb 2018 12:10:06 -0800 Subject: [PATCH 03/87] Removing the extra bracket around references (#7) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 383aabb0..f1af9f90 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ FfDL is a collaboration platform for: 6. [Detailed Testing Instructions](#6-detailed-testing-instructions) 7. [Clean Up](#7-clean-up) 8. [Troubleshooting](#8-troubleshooting) -9. [References](#9-references)) +9. [References](#9-references) ## 1. Quick Start From 7057c84c431411d180e4a0add81d5b9ae63452c1 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Mon, 12 Feb 2018 12:57:32 -0800 Subject: [PATCH 04/87] Update instructions with Cloud Object Storage (#3) Update instructions with Cloud Object Storage --- README.md | 90 ++++++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 82 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index f1af9f90..708975c8 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ This repository contains the core services of the *FfDL* (Fabric for Deep Learni FfDL is a collaboration platform for: - Framework-independent training of Deep Learning models on distributed hardware -- Open Deep Learning APIs   +- Open Deep Learning APIs - Common instrumentation - Inferencing in the cloud - Running Deep Learning hosting in user's private or public cloud @@ -36,6 +36,8 @@ FfDL is a collaboration platform for: 4. [Development](#4-development) 5. [Detailed Installation Instructions](#5-detailed-installation-instructions) 6. [Detailed Testing Instructions](#6-detailed-testing-instructions) + - 6.1 [Using FfDL Local S3 Based Object Storage](#61-using-ffdl-local-s3-based-object-storage) + - 6.2 [Using Cloud Object Storage](#62-using-cloud-object-storage) 7. [Clean Up](#7-clean-up) 8. [Troubleshooting](#8-troubleshooting) 9. [References](#9-references) @@ -197,18 +199,20 @@ Congratulation, FfDL is now running on your Cluster. ## 6. Detailed Testing Instructions In this example, we will run some simple jobs to train a convolutional network model using TensorFlow and Caffe. We will download a set of -MNIST handwritten digit images, store them in the S3 storage pod, and use the FfDL CLI to train a handwritten digit classification model. +MNIST handwritten digit images, store them with Object Storage, and use the FfDL CLI to train a handwritten digit classification model. > Note: For Minikube, make sure you have the latest TensorFlow Docker image by running `docker pull tensorflow/tensorflow` -1. Run the following commands to obtain the S3 storage endpoint from your cluster. +### 6.1. Using FfDL Local S3 Based Object Storage + +1. Run the following commands to obtain the object storage endpoint from your cluster. ```shell node_ip=$(make --no-print-directory kubernetes-ip) s3_port=$(kubectl get service s3 -o jsonpath='{.spec.ports[0].nodePort}') s3_url=http://$node_ip:$s3_port ``` -2. Next, set up the default S3 storage access ID and KEY. Then create S3 buckets for all the necessary training data and models. +2. Next, set up the default object storage access ID and KEY. Then create buckets for all the necessary training data and models. ```shell export AWS_ACCESS_KEY_ID=test; export AWS_SECRET_ACCESS_KEY=test; export AWS_DEFAULT_REGION=us-east-1; @@ -220,7 +224,7 @@ $s3cmd mb s3://dlaas-trained-models ``` 3. Now, create a temporary repository, download the necessary images for training and labeling our TensorFlow model, and upload those images -to your S3 tf_training_data bucket. +to your tf_training_data bucket. ```shell mkdir tmp @@ -231,8 +235,8 @@ do done ``` -4. Next, let's download all the necessary training and testing images in LMDB format for our Caffe model -and upload those images to your S3 mnist_lmdb_data bucket. +4. Next, let's download all the necessary training and testing images in [LMDB format](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database) for our Caffe model +and upload those images to your mnist_lmdb_data bucket. ```shell for phase in train test; @@ -246,7 +250,7 @@ do done ``` -5. Now you should have all the necessary training data set in your S3 storage. Let's go ahead to set up your restapi endpoint +5. Now you should have all the necessary training data set in your object storage. Let's go ahead to set up your restapi endpoint and default credentials for Deep Learning as a Service. Once you done that, you can start running jobs using the FfDL CLI(executable binary). @@ -278,6 +282,76 @@ Then, click `Submit Training Job` to run your job. ![ui-example](docs/images/ui-example.png) +### 6.2. Using Cloud Object Storage + +In this section we will demonstrate how to run a TensorFlow job with training data stored in Cloud Object Storage. + +> Note: This also can be done with other Cloud providers' Object Storage, but we will demonstrate how to use IBM Cloud Object Storage in this instructions. + +1. Provision an S3 based Object Storage from your Cloud provider. Take note of your Authentication Endpoints, Access Key ID and Secret. + +> For IBM Cloud, you can provision an Object Storage from [IBM Cloud Dashboard](https://console.bluemix.net/catalog/infrastructure/cloud-object-storage?taxonomyNavigation=apps) or from [SoftLayer Portal](https://control.softlayer.com/storage/objectstorage). + +2. Setup your S3 command with the Object Storage credentials you just obtained. + +```shell +s3_url=http:// +export AWS_ACCESS_KEY_ID= +export AWS_SECRET_ACCESS_KEY= + +s3cmd="aws --endpoint-url=$s3_url s3" +``` + +3. Next, let us create 2 buckets, one for storing the training data and another one for storing the training result. +```shell +trainingDataBucket= +trainingResultBucket= + +$s3cmd mb s3://$trainingDataBucket +$s3cmd mb s3://$trainingResultBucket +``` + +4. Now, create a temporary repository, download the necessary images for training and labeling our TensorFlow model, and upload those images to your training data bucket. + +```shell +mkdir tmp +for file in t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz; +do + test -e tmp/$file || wget -q -O tmp/$file http://yann.lecun.com/exdb/mnist/$file + $s3cmd cp tmp/$file s3://$trainingDataBucket/$file +done +``` + +5. Next, we need to modify our example job to use your Cloud Object Storage using the following sed commands. +```shell +if [ "$(uname)" = "Darwin" ]; then + sed -i '' s#"tf_training_data"#"$trainingDataBucket"# etc/examples/tf-model/manifest.yml + sed -i '' s#"tf_trained_model"#"$trainingResultBucket"# etc/examples/tf-model/manifest.yml + sed -i '' s#"http://s3.default.svc.cluster.local"#"$s3_url"# etc/examples/tf-model/manifest.yml + sed -i '' s#"user_name: test"#"user_name: $AWS_ACCESS_KEY_ID"# etc/examples/tf-model/manifest.yml + sed -i '' s#"password: test"#"password: $AWS_SECRET_ACCESS_KEY"# etc/examples/tf-model/manifest.yml +else + sed -i s#"tf_training_data"#"$trainingDataBucket"# etc/examples/tf-model/manifest.yml + sed -i s#"tf_trained_model"#"$trainingResultBucket"# etc/examples/tf-model/manifest.yml + sed -i s#"http://s3.default.svc.cluster.local"#"$s3_url"# etc/examples/tf-model/manifest.yml + sed -i s#"user_name: test"#"user_name: $AWS_ACCESS_KEY_ID"# etc/examples/tf-model/manifest.yml + sed -i s#"password: test"#"password: $AWS_SECRET_ACCESS_KEY"# etc/examples/tf-model/manifest.yml +fi +``` + +6. Now you should have all the necessary training data set in your training data bucket. Let's go ahead to set up your restapi endpoint +and default credentials for Deep Learning as a Service. Once you done that, you can start running jobs using the FfDL CLI(executable +binary). + +```shell +restapi_port=$(kubectl get service ffdl-restapi -o jsonpath='{.spec.ports[0].nodePort}') +export DLAAS_URL=http://$node_ip:$restapi_port; export DLAAS_USERNAME=test-user; export DLAAS_PASSWORD=test; + +# Obtain the correct CLI for your machine and run the training job with our default TensorFlow model +CLI_CMD=cli/bin/ffdl-$(if [ "$(uname)" = "Darwin" ]; then echo 'osx'; else echo 'linux'; fi) +$CLI_CMD train etc/examples/tf-model/manifest.yml etc/examples/tf-model +``` + ## 7. Clean Up If you want to remove FfDL from your cluster, simply use the command below or run `helm delete ` ```shell From 85ef00cde79853f9002c3843122a63c71e82b648 Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Wed, 14 Feb 2018 15:42:08 -0800 Subject: [PATCH 05/87] Architecture diagram + adjusted specs for memory and cpu (#12) * arch diag * arch diag * Update README.md * adding specs * adding specs --- README.md | 4 +++- docs/images/ffdl-architecture.png | Bin 0 -> 396132 bytes lcm/service/lcm/constants.go | 2 +- 3 files changed, 4 insertions(+), 2 deletions(-) create mode 100644 docs/images/ffdl-architecture.png diff --git a/README.md b/README.md index 708975c8..e03373a0 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ FfDL is a collaboration platform for: - Inferencing in the cloud - Running Deep Learning hosting in user's private or public cloud -**Note:** This repository is currently work in progress, it is only used for internal testing at the moment. +![ffdl-architecture](docs/images/ffdl-architecture.png) ## Prerequisites @@ -25,6 +25,8 @@ FfDL is a collaboration platform for: Once installed, use the command `make minikube` to start Minikube and set up local network routes. Alternatively, use the Vagrant based setup to automatically install a local Kubernetes cluster. +* The minimum recommended capacity for FfDL is 4GB Memory and 2 CPUs. + ## Steps 1. 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logCollectorMilliCPU=20 logCollectorMemInMB=100 controllerMilliCPU=20 From 2fc89660350a2ec96123af4560a0ac0aa61eb76f Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Fri, 16 Feb 2018 12:51:03 -0800 Subject: [PATCH 06/87] Add support to none VM_TYPE Clusters (#15) * update none VM_TYPE * polish export commands --- Makefile | 3 +++ README.md | 29 ++++++++++++++++++----------- bin/grafana.init.sh | 2 ++ 3 files changed, 23 insertions(+), 11 deletions(-) diff --git a/Makefile b/Makefile index 30c5f384..3ae6c2e3 100644 --- a/Makefile +++ b/Makefile @@ -21,6 +21,7 @@ MINIKUBE_BRIDGE ?= $(shell (ifconfig | grep -e "^bridge100:" || ifconfig | grep UI_REPO = git@github.com:IBM/FfDL-dashboard.git CLI_CMD = $(shell pwd)/cli/bin/ffdl-$(UNAME_SHORT) CLUSTER_NAME ?= mycluster +PUBLIC_IP ?= 127.0.0.1 IMAGE_DIR := $(IMAGE_NAME) ifneq ($(filter $(IMAGE_NAME),controller ),) @@ -222,6 +223,8 @@ kubernetes-ip: echo $$(minikube ip); \ elif [ "$(VM_TYPE)" = "ibmcloud" ]; then \ echo $$(bx cs workers $(CLUSTER_NAME) | grep Ready | awk '{ print $$2;exit }'); \ + elif [ "$(VM_TYPE)" = "none" ]; then \ + echo "$(PUBLIC_IP)"; \ fi install-minikube-in-ci: diff --git a/README.md b/README.md index e03373a0..753e859b 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ into an existing Kubernetes cluster. This is the simplest and recommended option for local testing. The following commands will automatically spin up a Vagrant box with Kubernetes and the FfDL platform deployed on top of it: -``` +``` shell export VM_TYPE=vagrant vagrant up make deploy @@ -62,7 +62,7 @@ make deploy ### 1.2 Installation using Minikube If you have Minikube installed on your machine, use these commands to deploy the FfDL platform: -``` +``` shell export VM_TYPE=minikube make minikube make deploy @@ -72,8 +72,11 @@ make deploy To install FfDL to a proper Kubernetes cluster, make sure `kubectl` points to the right namespace, then deploy the platform services: -``` +> Note: For PUBLIC_IP, put down one of your Cluster Public IP that can access your Cluster's NodePorts. + +``` shell export VM_TYPE=none +export PUBLIC_IP= make deploy ``` @@ -83,7 +86,7 @@ To install FfDL to a proper IBM Cloud Kubernetes cluster, make sure `kubectl` po and your machine is logged in with `bx login`, then deploy the platform services: ``` shell export VM_TYPE=ibmcloud -export CLUSTER_NAME=yourClusterName # Replace yourClusterName with your IBM Cloud Cluster Name +export CLUSTER_NAME= # Replace with your IBM Cloud Cluster Name make deploy ``` @@ -102,7 +105,7 @@ The platform ships with a simple Grafana monitoring dashboard. The URL is printe ## 4. Development Use the following instructions if you want to run a full development build, compile the code, and build the -Docker images locally. +Docker images locally. Install: @@ -175,9 +178,14 @@ helm status $(helm list | grep ffdl | awk '{print $1}' | head -n 1) | grep STATU ``` shell # If your Cluster is running on Vagrant or Minikube, replace "ibmcloud" to "vagrant" | "minikube" +# If your Cluster is not running on Vagrant, Minikube, or IBM Cloud, replace "ibmcloud" to "none" export VM_TYPE=ibmcloud -# Replace yourClusterName with your IBM Cloud Cluster Name if your cluster is on IBM Cloud. -export CLUSTER_NAME=yourClusterName + +# Replace with your IBM Cloud Cluster Name if your cluster is on IBM Cloud. +# Use export PUBLIC_IP if you are using a none VM_TYPE. A Cluster Public IP that can access your Cluster's NodePorts. +export CLUSTER_NAME= +export PUBLIC_IP= + ./bin/grafana.init.sh ``` @@ -362,11 +370,11 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) ## 8. Troubleshooting -* FfDL has only been tested under Mac OS and Linux +* FfDL has only been tested under Mac OS and Linux * The default Minikube driver under Mac OS is VirtualBox, which is known for having issues with networking. We generally recommend Mac OS users to install Minikube using the xhyve driver. - + * Also, when testing locally with Minikube, make sure to point the `docker` CLI to Minikube's Docker daemon: ``` @@ -382,9 +390,8 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) ## 9. References -Based on IBM Research work in Deep Learning. +Based on IBM Research work in Deep Learning. * B. Bhattacharjee et al., "IBM Deep Learning Service," in IBM Journal of Research and Development, vol. 61, no. 4, pp. 10:1-10:11, July-Sept. 1 2017. https://arxiv.org/abs/1709.05871 * Scott Boag, et al. Scalable Multi-Framework Multi-Tenant Lifecycle Management of Deep Learning Training Jobs, In Workshop on ML Systems at NIPS'17, 2017. http://learningsys.org/nips17/assets/papers/paper_29.pdf - diff --git a/bin/grafana.init.sh b/bin/grafana.init.sh index f1c35884..96a8dc9b 100755 --- a/bin/grafana.init.sh +++ b/bin/grafana.init.sh @@ -10,6 +10,8 @@ elif [ "$VM_TYPE" == "minikube" ]; then elif [ "$VM_TYPE" == "vagrant" ]; then node_ip_line=$(vagrant ssh master -c 'ifconfig eth1 | grep "inet "' 2> /dev/null) node_ip=$(echo $node_ip_line | sed "s/.*inet \([^ ]*\) .*/\1/") +elif [ "$VM_TYPE" == "none" ]; then + node_ip=$PUBLIC_IP fi grafana_port=$(kubectl get service grafana -o jsonpath='{.spec.ports[0].nodePort}') grafana_url="http://$node_ip:$grafana_port" From 51b6e58f82d4fa56abfd7a3395b01d570da66e75 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Sun, 18 Feb 2018 12:59:18 -0800 Subject: [PATCH 07/87] add user guide (#10) --- README.md | 4 +- docs/user-guide.md | 159 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 162 insertions(+), 1 deletion(-) create mode 100644 docs/user-guide.md diff --git a/README.md b/README.md index 753e859b..47ec2adc 100644 --- a/README.md +++ b/README.md @@ -204,7 +204,7 @@ echo "Monitoring dashboard: http://$node_ip:$grafana_port/ (login: admin/admin)" echo "Web UI: http://$node_ip:$ui_port/#/login?endpoint=$node_ip:$restapi_port&username=test-user" ``` -Congratulation, FfDL is now running on your Cluster. +Congratulation, FfDL is now running on your Cluster. Now you can go to [Step 6](#6-detailed-testing-instructions) to run some sample jobs or go to the [user guide](docs/user-guide.md) to learn about how to run and deploy your custom models. ## 6. Detailed Testing Instructions @@ -275,6 +275,8 @@ $CLI_CMD train etc/examples/tf-model/manifest.yml etc/examples/tf-model Congratulation, you had submitted your first job on FfDL. You can check your FfDL status either from the FfDL UI or simply run `$CLI_CMD list` +> You can learn about how to create your own model definition files and manifest.yaml at [user guild](docs/user-guide.md#2-create-new-models-with-ffdl). + 6. Since it's simple and straightforward to submit jobs with different deep learning framework on FfDL, let's try to run a Caffe Job. ```shell diff --git a/docs/user-guide.md b/docs/user-guide.md new file mode 100644 index 00000000..6c70599f --- /dev/null +++ b/docs/user-guide.md @@ -0,0 +1,159 @@ +# Fabric for Deep Learning (FfDL) User Guide + +## Table of Contents +1. [Supported Deep Learning Frameworks](#1-supported-deep-learning-frameworks) +2. [Create New Models with FfDL](#2-create-new-models-with-ffdl) + * 2.1. [Model Definition Files](#21-model-definition-files) + * 2.2. [Data Formatting](#22-data-formatting) + * 2.3. [Uploading Data in Object Store](#23-uploading-data-in-object-store) + * 2.4. [Creating Manifest file](#24-creating-manifest-file) + * 2.5. [Creating Model zip file](#25-creating-model-zip-file) + * 2.6. [Model Deployment and Training](#26-model-deployment-and-training) + * 2.6.1. [Train models using FfDL CLI](#261-train-models-using-ffdl-cli) + * 2.6.2. [Train models using FfDL UI](#262-train-models-using-ffdl-ui) +3. [Object Store for FfDL](#3-object-store-for-ffdl) + * 3.1. [FfDL Local Object Store](#31-ffdl-local-object-store) + * 3.2. [Cloud Object Store](#32-cloud-object-store) + +### Prerequisites + +* You need to have [FfDL](../README.md#5-detailed-installation-instructions) running on your cluster. + +## 1. Supported Deep learning frameworks +Currently, Fabric for Deep Learning supports following frameworks + +* [Tensorflow (with Keras 2)](https://www.tensorflow.org/) version "1.3-py3" +* [Caffe](http://caffe.berkeleyvision.org/) version "1.0-py2" + +You can deploy models based on these frameworks and then train your models using the FfDL CLI or FfDL UI. + +## 2. Create New Models with FfDL + +To create new models you first need to create model definition files and data files for training and testing. + +### 2.1. Model Definition Files + +Different deep learning frameworks support different languages to define their models. For example, [Torch](http://torch.ch/) models are defined in [LuaJIT](http://luajit.org/) whereas [Caffe](http://caffe.berkeleyvision.org/) models are defined using config files written in [Protocol Buffer Language](https://developers.google.com/protocol-buffers/docs/proto). Details on how to write model definition files is beyond the scope of this document. + +### 2.2. Data Formatting +Different frameworks require train and test datasets in different formats. For example, [Caffe requires datasets in LevelDB or LMDB format](http://caffe.berkeleyvision.org/tutorial/layers.html#data-layers) while Torch requires datasets in Torch proprietary format. We assume that data is already in the format needed by the specific framework. Details on how to convert raw data to framework specific format is beyond the scope of this document. + +### 2.3. Uploading Data in Object Store +Follow the instructions under [Object Store for FfDL](#3-object-store-for-ffdl). You can then use the object store credentials to upload your data. The object store is also used to store the trained model. + +### 2.4. Creating Manifest file +The manifest file contains different fields describing the model in FfDL , its object store information, its resource requirements, and several arguments (including hyperparameters) required for model execution during training and testing. +Here are [example manifest files](../etc/examples/tf-model/manifest.yml) for Caffe and TensorFlow models. You can use these templates to create manifest file for your models. Below we describe different fields of the manifest file for FfDL. +* ```name:``` After a model is deployed in FfDL a unique id for the model is created. The model id is +, where is a string of alphanumeric characters to uniquely identify the deployed model. is a prefix of the model id. You can provide any value to name. +* ```version:``` This is version of the manifest file. This field is currently not used. +* ```description:``` This is for users to keep track of their deployed models. Users can use in future and get information about particular model. FfDL does not interpret it. You can put anything here. +* ```learners:``` Number of learners to use in training. As FfDL supports distributed learning, you can have more than one learner for your training job. +* ```gpus:``` Number of gpus used by each learner during training. +* ```cpus:``` Number of cpus used by each learner during training. The default cpu number is 8. +* ```memory:``` Memory assigned to each learner during training. The default memory is 60Gb. +* ```data_stores:```You can specify as many data stores as you want in the manifest file. Each data store has the following fields. + * ```id:``` Data store id (**which you make up**), to be used when creating a training job. + * ```type:``` Type of data store, values can be "swift_datastore", "s3_datastore", or "mount_cos" (details below). + * ```training_data:``` Location of the training data in the data store. + * ```container:``` The container where the training data resides. + * ```training_results:``` Location of the training results in the data store. After training the trained model and training logs will be stored here, under "training-TRAININGJOBID". + * ```container:``` The container where the training results will be stored. **This filed is recommended to define by users. e.g. `mnist_trained_model`.** If not, the default location of trained models is FfDL object store. + * ```connection:``` The connection variables for the data store. The list of connection variables supported is data store type dependent. At present, the following connection variables are supported: + * swift_datastore (softlayer credential form): ```auth_url```, ```user_name```, and ```password``` + * swift_datastore (IBM Cloud credential form): ```auth_url```, ```user_name```, ```password```, ```domain_name```, ```region``` and ```project_id``` + * s3_datastore: ```auth_url```, ```user_name``` (AWS Access Key), and ```password``` (AWS Secret Access Key) + * mount_cos: ```auth_url```, ```user_name``` (AWS Access Key), and ```password``` (AWS Secret Access Key), ```region``` (optional) + +* ```framework:``` This field provides deep learning framework specific information. + * ```name:``` Name of framework, values can be "caffe", or "tensorflow". + * ```version:``` Version of framework. + * ```command:``` This field identifies the main program file along with any arguments that FfDL needs to execute. For example, the command to run a TensorFlow training can be as follows ```python mnist_with_summaries.py --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --max_steps 400 --learning_rate 0.001``` where `python mnist_with_summaries.py` is the model code to execute while the remainder are arguments to the model. `train_images_file`, `train_labels_file`, `test_images_file`, `test_labels_file` refers to the dataset path in learner, `max_steps`, `learning_rate` are training parameters and hyperparameters. + +**Note**: If the user's model and manifest files refer to some training data, they shouldn't use absolute paths. They should either: + + * use relative paths, like + + --train_images_file ./train-images-idx3-ubyte.gz + + --test_images_file ./t10k-images-idx3-ubyte.gz + + * or, use the $DATA_DIR environment variable, like + + --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz + + --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz + +### 2.5. Creating Model zip file +**Note** that FfDL CLI can take both zip or unzip files. + +You need to zip all the model definition files and create a model zip file for jobs submitting on FfDL UI. At present, FfDL UI only supports zip format for model files, other compression formats like gzip, bzip, tar etc., are not supported. **Note** that all model definition files has to be in the first level of the zip file and there are no nested directories in the zip file. + +### 2.6. Model Deployment and Training +After creating the manifest file and model definition file, you can either use the FfDL CLI or FfDL UI to deploy your model. + +#### 2.6.1. Train models using FfDL CLI +> Note: Right now FfDL CLI only available on Mac and Linux. + +In order to use the FfDL CLI, you will need your FfDL's restapi endpoint. Currently, the FfDL CLI is an executable binary located at `cli/bin` +```shell +restapi_port=$(kubectl get service ffdl-restapi -o jsonpath='{.spec.ports[0].nodePort}') +export DLAAS_URL=http://$node_ip:$restapi_port; export DLAAS_USERNAME=test-user; export DLAAS_PASSWORD=test; +``` + +Next, you need to get the correct executable binary for FfDL CLI located at `cli/bin`. +```shell +CLI_CMD=cli/bin/ffdl-$(if [ "$(uname)" = "Darwin" ]; then echo 'osx'; else echo 'linux'; fi) +``` + +Now, you can use the following command to train your models. +```shell +$CLI_CMD train +``` + +After training your models, you can run `$CLI_CMD logs ` to view your model's logs and `$CLI_CMD list` to view the list of models your had trained. You can also run `$CLI_CMD -h` to learn more about the FfDL CLI. + +#### 2.6.2. Train models using FfDL UI +To train your models using FfDL UI, simply upload your manifest file and model definition zip in the correspond fields and click `Submit Training Job` + +![ui-example](images/ui-example.png) + +## 3. Object Store for FfDL +We will use the [Amazon's S3 command line interface](https://aws.amazon.com/cli/) to access the object store. To set up a user environment to access object store, please follow instructions at [AWS cli setup page](http://docs.aws.amazon.com/cli/latest/userguide/installing.html) and [Using Amazon S3 with the AWS cli](http://docs.aws.amazon.com/cli/latest/userguide/cli-s3.html). + +### 3.1 FfDL Local Object Store +By default FfDL will use its local object store for storing any training and result data. You need to set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables using the default Local Object Store credentials before using this client. +```shell +export AWS_ACCESS_KEY_ID=test +export AWS_SECRET_ACCESS_KEY=test +export AWS_DEFAULT_REGION=us-east-1 + +# Create your training data and result buckets +aws --endpoint-url=http://$(make --no-print-directory kubernetes-ip):$(kubectl get service s3 -o jsonpath='{.spec.ports[0].nodePort}') s3 mb +aws --endpoint-url=http://$(make --no-print-directory kubernetes-ip):$(kubectl get service s3 -o jsonpath='{.spec.ports[0].nodePort}') s3 mb +``` + +Now, upload all you datasets to the training data bucket. +```shell +aws --endpoint-url=http://$(make --no-print-directory kubernetes-ip):$(kubectl get service s3 -o jsonpath='{.spec.ports[0].nodePort}') s3 cp s3:/// +``` + +### 3.2 Cloud Object Store +Provision an S3 based Object Storage from your Cloud provider. Take note of your Authentication Endpoints, Access Key ID and Secret. + +> For IBM Cloud, you can provision an Object Storage from [IBM Cloud Dashboard](https://console.bluemix.net/catalog/infrastructure/cloud-object-storage?taxonomyNavigation=apps) or from [SoftLayer Portal](https://control.softlayer.com/storage/objectstorage). + +You need to set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables using your Cloud Object Store credentials before using this client. + +```shell +export AWS_ACCESS_KEY_ID=***************** +export AWS_SECRET_ACCESS_KEY=******************** + +# Create your training data and result buckets +aws --endpoint-url=http:// s3 mb +aws --endpoint-url=http:// s3 mb +``` + +Now, upload all you datasets to the training data bucket. +```shell +aws --endpoint-url=http:// s3 cp s3:/// +``` From 2dc0b34553b8d371123fb30c46a48cc9473418a3 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Tue, 20 Feb 2018 16:51:21 -0800 Subject: [PATCH 08/87] * Fix Caffe Example (#19) * Extent make test-submit waiting time --- Makefile | 2 +- etc/examples/caffe-model/lenet_train_test.prototxt | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index 3ae6c2e3..b0be21c0 100644 --- a/Makefile +++ b/Makefile @@ -191,7 +191,7 @@ test-submit: ## Submit test training job (cd etc/examples/$(TEST_SAMPLE); $(CLI_CMD) train manifest.yml .); \ echo Test job submitted. Track the status via '"'DLAAS_URL=$$DLAAS_URL DLAAS_USERNAME=$(TEST_USER) DLAAS_PASSWORD=test $(CLI_CMD) list'"'. ; \ sleep 10; \ - (for i in $$(seq 1 25); do output=$$($(CLI_CMD) list 2>&1 | grep training-); \ + (for i in $$(seq 1 50); do output=$$($(CLI_CMD) list 2>&1 | grep training-); \ if echo $$output | grep 'FAILED'; then echo 'Job failed'; exit 1; fi; \ if echo $$output | grep 'COMPLETED'; then echo 'Job completed'; exit 0; fi; \ echo $$output; \ diff --git a/etc/examples/caffe-model/lenet_train_test.prototxt b/etc/examples/caffe-model/lenet_train_test.prototxt index a8d98f8c..386ee31b 100644 --- a/etc/examples/caffe-model/lenet_train_test.prototxt +++ b/etc/examples/caffe-model/lenet_train_test.prototxt @@ -11,7 +11,7 @@ layer { scale: 0.00390625 } data_param { - source: "/job/dl-data/train/" + source: "/job/mnist_lmdb_data/train/" batch_size: 64 backend: LMDB } @@ -28,7 +28,7 @@ layer { scale: 0.00390625 } data_param { - source: "/job/dl-data/test/" + source: "/job/mnist_lmdb_data/test/" batch_size: 100 backend: LMDB } From 8581fec29029da0a1f2857e838d31cf5c3d0a606 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Thu, 22 Feb 2018 11:19:24 -0800 Subject: [PATCH 09/87] Assign default edit role to lcm to support kubernetes 1.9 and above (#22) * assign default edit role to lcm * add helm value options for 1.7 and below --- README.md | 4 ++++ templates/services/lcm-deployment.yml | 4 ++++ templates/services/lcm-rbac.yml | 20 ++++++++++++++++++++ values.yaml | 3 ++- 4 files changed, 30 insertions(+), 1 deletion(-) create mode 100644 templates/services/lcm-rbac.yml diff --git a/README.md b/README.md index 47ec2adc..07aec756 100644 --- a/README.md +++ b/README.md @@ -49,6 +49,8 @@ FfDL is a collaboration platform for: There are multiple installation paths for installing FfDL locally ("1-click-install") or into an existing Kubernetes cluster. +> Note: If your Kubernetes Cluster version is 1.7 or below, please go to the [values.yaml](values.yaml) and change `k8s_1dot8_or_above` to **false**. + ### 1.1 Installation using Vagrant This is the simplest and recommended option for local testing. The following commands will automatically @@ -147,6 +149,8 @@ kubectl get pods --all-namespaces | grep tiller-deploy ``` 2. Now let's install all the necessary FfDL components using helm install. +> Note: If your Kubernetes Cluster version is 1.7 or below, please go to the [values.yaml](values.yaml) and change `k8s_1dot8_or_above` to **false**. + ``` shell helm install . ``` diff --git a/templates/services/lcm-deployment.yml b/templates/services/lcm-deployment.yml index 83ccf39d..8209647c 100644 --- a/templates/services/lcm-deployment.yml +++ b/templates/services/lcm-deployment.yml @@ -34,6 +34,10 @@ spec: key: DLAAS_ETCD_CERT, path: etcd/etcd.cert }] +{{ if .Values.k8s_1dot8_or_above }} + serviceAccount: {{.Values.docker.image_prefix}}lcm + serviceAccountName: {{.Values.docker.image_prefix}}lcm +{{ end }} containers: - name: ffdl-lcm-container image: {{.Values.docker.registry}}/{{.Values.docker.namespace}}/{{.Values.docker.image_prefix}}lcm:{{.Values.trainer.version}} diff --git a/templates/services/lcm-rbac.yml b/templates/services/lcm-rbac.yml new file mode 100644 index 00000000..db3da0ae --- /dev/null +++ b/templates/services/lcm-rbac.yml @@ -0,0 +1,20 @@ +{{ if .Values.k8s_1dot8_or_above }} +apiVersion: v1 +kind: ServiceAccount +metadata: + name: {{.Values.docker.image_prefix}}lcm + namespace: {{.Values.namespace}} +--- +apiVersion: rbac.authorization.k8s.io/v1beta1 +kind: ClusterRoleBinding +metadata: + name: {{.Values.docker.image_prefix}}lcm +roleRef: + apiGroup: rbac.authorization.k8s.io + kind: ClusterRole + name: edit +subjects: + - kind: ServiceAccount + name: {{.Values.docker.image_prefix}}lcm + namespace: {{.Values.namespace}} +{{ end }} diff --git a/values.yaml b/values.yaml index 79dba44a..8b187455 100644 --- a/values.yaml +++ b/values.yaml @@ -1,5 +1,6 @@ -namespace: test +namespace: default env: dev +k8s_1dot8_or_above: true services: expose_node_port: true docker: From 7749f82f8ee30d0b5147752bda62118ab333a2e0 Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Thu, 22 Feb 2018 15:49:17 -0800 Subject: [PATCH 10/87] Some changes in Prereqs + Pointers to ICP (#23) * adding prereqs and bumping user guide in front * adding prereqs and bumping user guide in front --- README.md | 5 ++++- docs/images/ffdl-pattern-arch.png | Bin 0 -> 422318 bytes 2 files changed, 4 insertions(+), 1 deletion(-) create mode 100644 docs/images/ffdl-pattern-arch.png diff --git a/README.md b/README.md index 07aec756..023aa9db 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ FfDL is a collaboration platform for: - Framework-independent training of Deep Learning models on distributed hardware - Open Deep Learning APIs - Common instrumentation -- Inferencing in the cloud - Running Deep Learning hosting in user's private or public cloud ![ffdl-architecture](docs/images/ffdl-architecture.png) @@ -25,8 +24,12 @@ FfDL is a collaboration platform for: Once installed, use the command `make minikube` to start Minikube and set up local network routes. Alternatively, use the Vagrant based setup to automatically install a local Kubernetes cluster. +* Follow the appropriate instructions for deployment on [IBM Cloud Public](https://github.com/IBM/container-journey-template/blob/master/README.md) or [IBM Cloud Private](https://github.com/IBM/deploy-ibm-cloud-private/blob/master/README.md) + * The minimum recommended capacity for FfDL is 4GB Memory and 2 CPUs. +* If you already have a FfDL deployment up and running, you can jump to [User Guide](docs/user-guide.md) to use FfDL for training your models. + ## Steps 1. 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Once installed, use the command `make minikube` to start Minikube and set up local network routes. Alternatively, diff --git a/docs/gpu-guide.md b/docs/gpu-guide.md new file mode 100644 index 00000000..df006e11 --- /dev/null +++ b/docs/gpu-guide.md @@ -0,0 +1,33 @@ +# Train Models using GPUs + +***This is still under development, any of the following instructions may have significant changes in the future.*** + +### Prerequisites + +* You need to have [FfDL](../README.md#5-detailed-installation-instructions) running on your Cluster. + +* You need to have a Kubernetes cluster configured to use GPUs. Currently tested with Kubernetes configured using [feature gate `Accelerators`](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/). + +* Currently Tensorflow and Caffe tested with GPUs + +## Instructions + +### TensorFlow example + +To run the TensorFlow job with GPU, simply go to the [tf-model's manifest file](../etc/examples/tf-model/manifest.yml) and do the following changes +* Change the framework version from `latest` to `latest-gpu` for CUDA 9 Driver or `1.3.0-gpu` for CUDA 8 Driver. +* Change the `gpus` section to be greater than 0, so the learner can get GPU resource to train the job. + +The `etc/examples/tf-model/gpu-manifest.yml` is the example manifest file for running the TensorFlow example with GPU. Once you have done the above changes, you can following the same [testing instructions](../README.md#6-detailed-testing-instructions) on the main README to run the sample TensorFlow job on GPU. + +### Caffe example + +To run the Caffe job with GPU, simply go to the [caffe-model's manifest file](../etc/examples/caffe-model/manifest.yml) and do the following changes +* Change the Framework version from `cpu` to `gpu`. +* Change the `gpus` section to be greater than 0, so the learner can get GPU resource to train the job. +* Add the caffe GPU flag in the `command` section (e.g. Change the `command` from `caffe train -solver lenet_solver.prototxt` to `caffe train -gpu all -solver lenet_solver.prototxt`). +* Lastly, go to the `lenet_solver.prototxt` file and change `solver_mode` to GPU to enable Caffe to run on GPU. + +The `etc/examples/caffe-model/gpu-manifest.yml` is the example manifest file for running the Caffe example with GPU. Once you have done the above changes, you can following the same [testing instructions](../README.md#6-detailed-testing-instructions) on the main README to run the sample TensorFlow job on GPU. + +You can go to the [user guide](user-guide.md) to learn more about how to modify the model manifest file and run GPU jobs with your own setting. Note that you must select the framework versions that support GPU and set the `gpus` section greater than 0 in order to execute your job with GPU in the manifest file. diff --git a/docs/user-guide.md b/docs/user-guide.md index 6c70599f..5237c16a 100644 --- a/docs/user-guide.md +++ b/docs/user-guide.md @@ -20,10 +20,17 @@ * You need to have [FfDL](../README.md#5-detailed-installation-instructions) running on your cluster. ## 1. Supported Deep learning frameworks -Currently, Fabric for Deep Learning supports following frameworks - -* [Tensorflow (with Keras 2)](https://www.tensorflow.org/) version "1.3-py3" -* [Caffe](http://caffe.berkeleyvision.org/) version "1.0-py2" +Currently, Fabric for Deep Learning supports following community frameworks + +| Framework | Versions | Processing Unit | +| ------------- | ------------- | --------------- | +| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0, 1.3.0-py3, 1.4.0, 1.4.0-py3, 1.5.0, 1.5.0-py3, latest, latest-py3 | CPU | +| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0-gpu, 1.3.0-gpu-py3, 1.4.0-gpu, 1.4.0-gpu-py3, 1.5.0-gpu, 1.5.0-gpu-py3, latest-gpu, latest-gpu-py3 | GPU | +| [caffe](https://hub.docker.com/r/bvlc/caffe/) | cpu, intel | CPU | +| [caffe](https://hub.docker.com/r/bvlc/caffe/) | gpu | GPU | +| [pytorch](https://hub.docker.com/r/pytorch/pytorch/) | v0.2, latest | CPU, GPU | +| [caffe2](https://hub.docker.com/r/caffe2ai/caffe2/) | c2v0.8.1.cpu.full.ubuntu14.04, c2v0.8.0.cpu.full.ubuntu16.04 | CPU | +| [caffe2](https://hub.docker.com/r/caffe2ai/caffe2/) | c2v0.8.1.cuda8.cudnn7.ubuntu16.04, latest | GPU | You can deploy models based on these frameworks and then train your models using the FfDL CLI or FfDL UI. @@ -65,8 +72,8 @@ Here are [example manifest files](../etc/examples/tf-model/manifest.yml) for Caf * mount_cos: ```auth_url```, ```user_name``` (AWS Access Key), and ```password``` (AWS Secret Access Key), ```region``` (optional) * ```framework:``` This field provides deep learning framework specific information. - * ```name:``` Name of framework, values can be "caffe", or "tensorflow". - * ```version:``` Version of framework. + * ```name:``` Name of framework, values can be "caffe", "tensorflow" , "pytorch", or "caffe2". + * ```version:``` Version of framework. List of available versions are in [section 1](#1-supported-deep-learning-frameworks). You must pick the version with the correct processing unit in order to run your jobs in GPU/CPU. * ```command:``` This field identifies the main program file along with any arguments that FfDL needs to execute. For example, the command to run a TensorFlow training can be as follows ```python mnist_with_summaries.py --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --max_steps 400 --learning_rate 0.001``` where `python mnist_with_summaries.py` is the model code to execute while the remainder are arguments to the model. `train_images_file`, `train_labels_file`, `test_images_file`, `test_labels_file` refers to the dataset path in learner, `max_steps`, `learning_rate` are training parameters and hyperparameters. **Note**: If the user's model and manifest files refer to some training data, they shouldn't use absolute paths. They should either: diff --git a/etc/examples/caffe-model/gpu-manifest.yml b/etc/examples/caffe-model/gpu-manifest.yml new file mode 100644 index 00000000..65759ba0 --- /dev/null +++ b/etc/examples/caffe-model/gpu-manifest.yml @@ -0,0 +1,25 @@ +name: mnist-caffe-gpu-model +description: Caffe MNIST model (GPU) +version: "1.0" +gpus: 1 +cpus: 0.005 +learners: 1 +memory: 1000MiB + +# Object stores that allow the system to retrieve training data. +data_stores: + - id: test-datastore + type: s3_datastore + training_data: + container: mnist_lmdb_data + connection: + type: s3_datastore + auth_url: http://s3.default.svc.cluster.local + user_name: test + password: test + +framework: + name: caffe + version: "gpu" + command: > + caffe train -gpu all -solver lenet_solver.prototxt diff --git a/etc/examples/caffe-model/manifest.yml b/etc/examples/caffe-model/manifest.yml index 5ac9b075..8b456a94 100644 --- a/etc/examples/caffe-model/manifest.yml +++ b/etc/examples/caffe-model/manifest.yml @@ -19,6 +19,6 @@ data_stores: framework: name: caffe - version: "1.0-py2" + version: "cpu" command: > caffe train -solver lenet_solver.prototxt diff --git a/etc/examples/caffe2-model/README.md b/etc/examples/caffe2-model/README.md new file mode 100644 index 00000000..a9cab7f1 --- /dev/null +++ b/etc/examples/caffe2-model/README.md @@ -0,0 +1,6 @@ +# Caffe2 MNIST example + +Using the FfDL CLI, run the below command at the top level of this repository. +```shell +$CLI_CMD train etc/examples/caffe2-model/manifest.yml etc/examples/caffe2-model +``` diff --git a/etc/examples/caffe2-model/manifest.yml b/etc/examples/caffe2-model/manifest.yml new file mode 100644 index 00000000..91b018a9 --- /dev/null +++ b/etc/examples/caffe2-model/manifest.yml @@ -0,0 +1,26 @@ +name: mnist-caffe2-model +description: Caffe2 MNIST model +version: "1.0" +gpus: 0 +cpus: 1 +memory: 1000MiB + +# Object stores that allow the system to retrieve training data. +data_stores: + - id: test-datastore + type: s3_datastore + training_data: + container: mnist_lmdb_data + training_results: + container: dlaas-trained-models + connection: + type: s3_datastore + auth_url: http://s3.default.svc.cluster.local + user_name: test + password: test + +framework: + name: caffe2 + version: "c2v0.8.1.cpu.full.ubuntu14.04" + command: > + python mnist.py --train_data ${DATA_DIR}/train/ --test_data ${DATA_DIR}/test/ --num_labels 120 --epoch_size 1000 --num_epochs 0 --use_cpu True --save_model_name mnist_caffe2model --file_store_path ${RESULT_DIR} diff --git a/etc/examples/caffe2-model/mnist.py b/etc/examples/caffe2-model/mnist.py new file mode 100644 index 00000000..c0ca1200 --- /dev/null +++ b/etc/examples/caffe2-model/mnist.py @@ -0,0 +1,628 @@ +# Copyright (c) 2016-present, Facebook, Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +############################################################################## + +# Module caffe2.python.examples.resnet50_trainer +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import argparse +import logging +import numpy as np +import time +import os + +from caffe2.python import core, workspace, experiment_util, data_parallel_model +from caffe2.python import dyndep, optimizer +from caffe2.python import timeout_guard, model_helper, brew +from caffe2.proto import caffe2_pb2 + +import caffe2.python.models.resnet as resnet +from caffe2.python.modeling.initializers import Initializer, pFP16Initializer +import caffe2.python.predictor.predictor_exporter as pred_exp +import caffe2.python.predictor.predictor_py_utils as pred_utils +from caffe2.python.predictor_constants import predictor_constants as predictor_constants + +''' +Parallelized multi-GPU distributed trainer for Resnet 50. Can be used to train +on imagenet data, for example. + +To run the trainer in single-machine multi-gpu mode by setting num_shards = 1. + +To run the trainer in multi-machine multi-gpu mode with M machines, +run the same program on all machines, specifying num_shards = M, and +shard_id = a unique integer in the set [0, M-1]. + +For rendezvous (the trainer processes have to know about each other), +you can either use a directory path that is visible to all processes +(e.g. NFS directory), or use a Redis instance. Use the former by +passing the `file_store_path` argument. Use the latter by passing the +`redis_host` and `redis_port` arguments. +''' + +logging.basicConfig() +log = logging.getLogger("resnet50_trainer") +log.setLevel(logging.DEBUG) + +dyndep.InitOpsLibrary('@/caffe2/caffe2/distributed:file_store_handler_ops') +dyndep.InitOpsLibrary('@/caffe2/caffe2/distributed:redis_store_handler_ops') + + +def AddImageInput(model, reader, batch_size, img_size, dtype, is_test): + ''' + The image input operator loads image and label data from the reader and + applies transformations to the images (random cropping, mirroring, ...). + ''' + data, label = brew.image_input( + model, + reader, ["data", "label"], + batch_size=batch_size, + output_type=dtype, + use_gpu_transform=True if model._device_type == 1 else False, + use_caffe_datum=True, + mean=128., + std=128., + scale=256, + crop=img_size, + mirror=1, + is_test=is_test, + ) + + data = model.StopGradient(data, data) + + +def AddNullInput(model, reader, batch_size, img_size, dtype): + ''' + The null input function uses a gaussian fill operator to emulate real image + input. A label blob is hardcoded to a single value. This is useful if you + want to test compute throughput or don't have a dataset available. + ''' + suffix = "_fp16" if dtype == "float16" else "" + model.param_init_net.GaussianFill( + [], + ["data" + suffix], + shape=[batch_size, 3, img_size, img_size], + ) + if dtype == "float16": + model.param_init_net.FloatToHalf("data" + suffix, "data") + + model.param_init_net.ConstantFill( + [], + ["label"], + shape=[batch_size], + value=1, + dtype=core.DataType.INT32, + ) + + +def SaveModel(args, train_model, epoch): + prefix = "[]_{}".format(train_model._device_prefix, train_model._devices[0]) + predictor_export_meta = pred_exp.PredictorExportMeta( + predict_net=train_model.net.Proto(), + parameters=data_parallel_model.GetCheckpointParams(train_model), + inputs=[prefix + "/data"], + outputs=[prefix + "/softmax"], + shapes={ + prefix + "/softmax": (1, args.num_labels), + prefix + "/data": (args.num_channels, args.image_size, args.image_size) + } + ) + + # save the train_model for the current epoch + model_path = "%s/%s_%d.mdl" % ( + args.file_store_path, + args.save_model_name, + epoch, + ) + + # set db_type to be "minidb" instead of "log_file_db", which breaks + # the serialization in save_to_db. Need to switch back to log_file_db + # after migration + pred_exp.save_to_db( + db_type="minidb", + db_destination=model_path, + predictor_export_meta=predictor_export_meta, + ) + + +def LoadModel(path, model): + ''' + Load pretrained model from file + ''' + log.info("Loading path: {}".format(path)) + meta_net_def = pred_exp.load_from_db(path, 'minidb') + init_net = core.Net(pred_utils.GetNet( + meta_net_def, predictor_constants.GLOBAL_INIT_NET_TYPE)) + predict_init_net = core.Net(pred_utils.GetNet( + meta_net_def, predictor_constants.PREDICT_INIT_NET_TYPE)) + + predict_init_net.RunAllOnGPU() + init_net.RunAllOnGPU() + + assert workspace.RunNetOnce(predict_init_net) + assert workspace.RunNetOnce(init_net) + + # Hack: fix iteration counter which is in CUDA context after load model + itercnt = workspace.FetchBlob("optimizer_iteration") + workspace.FeedBlob( + "optimizer_iteration", + itercnt, + device_option=core.DeviceOption(caffe2_pb2.CPU, 0) + ) + + +def RunEpoch( + args, + epoch, + train_model, + test_model, + total_batch_size, + num_shards, + expname, + explog, +): + ''' + Run one epoch of the trainer. + TODO: add checkpointing here. + ''' + # TODO: add loading from checkpoint + log.info("Starting epoch {}/{}".format(epoch, args.num_epochs)) + epoch_iters = int(args.epoch_size / total_batch_size / num_shards) + for i in range(epoch_iters): + # This timeout is required (temporarily) since CUDA-NCCL + # operators might deadlock when synchronizing between GPUs. + timeout = 600.0 if i == 0 else 60.0 + with timeout_guard.CompleteInTimeOrDie(timeout): + t1 = time.time() + workspace.RunNet(train_model.net.Proto().name) + t2 = time.time() + dt = t2 - t1 + + fmt = "Finished iteration {}/{} of epoch {} ({:.2f} images/sec)" + log.info(fmt.format(i + 1, epoch_iters, epoch, total_batch_size / dt)) + prefix = "{}_{}".format( + train_model._device_prefix, + train_model._devices[0]) + accuracy = workspace.FetchBlob(prefix + '/accuracy') + loss = workspace.FetchBlob(prefix + '/loss') + train_fmt = "Training loss: {}, accuracy: {}" + log.info(train_fmt.format(loss, accuracy)) + + num_images = epoch * epoch_iters * total_batch_size + prefix = "{}_{}".format(train_model._device_prefix, train_model._devices[0]) + accuracy = workspace.FetchBlob(prefix + '/accuracy') + loss = workspace.FetchBlob(prefix + '/loss') + learning_rate = workspace.FetchBlob( + data_parallel_model.GetLearningRateBlobNames(train_model)[0] + ) + test_accuracy = 0 + if (test_model is not None): + # Run 100 iters of testing + ntests = 0 + for _ in range(0, 100): + workspace.RunNet(test_model.net.Proto().name) + for g in test_model._devices: + test_accuracy += np.asscalar(workspace.FetchBlob( + "{}_{}".format(test_model._device_prefix, g) + '/accuracy' + )) + ntests += 1 + test_accuracy /= ntests + else: + test_accuracy = (-1) + + explog.log( + input_count=num_images, + batch_count=(i + epoch * epoch_iters), + additional_values={ + 'accuracy': accuracy, + 'loss': loss, + 'learning_rate': learning_rate, + 'epoch': epoch, + 'test_accuracy': test_accuracy, + } + ) + assert loss < 40, "Exploded gradients :(" + + # TODO: add checkpointing + return epoch + 1 + + +def Train(args): + # Either use specified device list or generate one + if args.gpus is not None: + gpus = [int(x) for x in args.gpus.split(',')] + num_gpus = len(gpus) + else: + gpus = list(range(args.num_gpus)) + num_gpus = args.num_gpus + + log.info("Running on GPUs: {}".format(gpus)) + + # Verify valid batch size + total_batch_size = args.batch_size + batch_per_device = total_batch_size // num_gpus + assert \ + total_batch_size % num_gpus == 0, \ + "Number of GPUs must divide batch size" + + # Round down epoch size to closest multiple of batch size across machines + global_batch_size = total_batch_size * args.num_shards + epoch_iters = int(args.epoch_size / global_batch_size) + + assert \ + epoch_iters > 0, \ + "Epoch size must be larger than batch size times shard count" + + args.epoch_size = epoch_iters * global_batch_size + log.info("Using epoch size: {}".format(args.epoch_size)) + + # Create ModelHelper object + train_arg_scope = { + 'order': 'NCHW', + 'use_cudnn': True, + 'cudnn_exhaustive_search': True, + 'ws_nbytes_limit': (args.cudnn_workspace_limit_mb * 1024 * 1024), + } + train_model = model_helper.ModelHelper( + name="resnet50", arg_scope=train_arg_scope + ) + + num_shards = args.num_shards + shard_id = args.shard_id + + # Expect interfaces to be comma separated. + # Use of multiple network interfaces is not yet complete, + # so simply use the first one in the list. + interfaces = args.distributed_interfaces.split(",") + + # Rendezvous using MPI when run with mpirun + if os.getenv("OMPI_COMM_WORLD_SIZE") is not None: + num_shards = int(os.getenv("OMPI_COMM_WORLD_SIZE", 1)) + shard_id = int(os.getenv("OMPI_COMM_WORLD_RANK", 0)) + if num_shards > 1: + rendezvous = dict( + kv_handler=None, + num_shards=num_shards, + shard_id=shard_id, + engine="GLOO", + transport=args.distributed_transport, + interface=interfaces[0], + mpi_rendezvous=True, + exit_nets=None) + + elif num_shards > 1: + # Create rendezvous for distributed computation + store_handler = "store_handler" + if args.redis_host is not None: + # Use Redis for rendezvous if Redis host is specified + workspace.RunOperatorOnce( + core.CreateOperator( + "RedisStoreHandlerCreate", [], [store_handler], + host=args.redis_host, + port=args.redis_port, + prefix=args.run_id, + ) + ) + else: + # Use filesystem for rendezvous otherwise + workspace.RunOperatorOnce( + core.CreateOperator( + "FileStoreHandlerCreate", [], [store_handler], + path=args.file_store_path, + prefix=args.run_id, + ) + ) + + rendezvous = dict( + kv_handler=store_handler, + shard_id=shard_id, + num_shards=num_shards, + engine="GLOO", + transport=args.distributed_transport, + interface=interfaces[0], + exit_nets=None) + + else: + rendezvous = None + + # Model building functions + def create_resnet50_model_ops(model, loss_scale): + initializer = (pFP16Initializer if args.dtype == 'float16' + else Initializer) + + with brew.arg_scope([brew.conv, brew.fc], + WeightInitializer=initializer, + BiasInitializer=initializer, + enable_tensor_core=args.enable_tensor_core, + float16_compute=args.float16_compute): + pred = resnet.create_resnet50( + model, + "data", + num_input_channels=args.num_channels, + num_labels=args.num_labels, + no_bias=True, + no_loss=True, + ) + + if args.dtype == 'float16': + pred = model.net.HalfToFloat(pred, pred + '_fp32') + + softmax, loss = model.SoftmaxWithLoss([pred, 'label'], + ['softmax', 'loss']) + loss = model.Scale(loss, scale=loss_scale) + brew.accuracy(model, [softmax, "label"], "accuracy") + return [loss] + + def add_optimizer(model): + stepsz = int(30 * args.epoch_size / total_batch_size / num_shards) + + if args.float16_compute: + # TODO: merge with multi-prceision optimizer + opt = optimizer.build_fp16_sgd( + model, + args.base_learning_rate, + momentum=0.9, + nesterov=1, + weight_decay=args.weight_decay, # weight decay included + policy="step", + stepsize=stepsz, + gamma=0.1 + ) + else: + optimizer.add_weight_decay(model, args.weight_decay) + opt = optimizer.build_multi_precision_sgd( + model, + args.base_learning_rate, + momentum=0.9, + nesterov=1, + policy="step", + stepsize=stepsz, + gamma=0.1 + ) + return opt + + # Define add_image_input function. + # Depends on the "train_data" argument. + # Note that the reader will be shared with between all GPUS. + if args.train_data == "null": + def add_image_input(model): + AddNullInput( + model, + None, + batch_size=batch_per_device, + img_size=args.image_size, + dtype=args.dtype, + ) + else: + reader = train_model.CreateDB( + "reader", + db=args.train_data, + db_type=args.db_type, + num_shards=num_shards, + shard_id=shard_id, + ) + + def add_image_input(model): + AddImageInput( + model, + reader, + batch_size=batch_per_device, + img_size=args.image_size, + dtype=args.dtype, + is_test=False, + ) + + def add_post_sync_ops(model): + """Add ops applied after initial parameter sync.""" + for param_info in model.GetOptimizationParamInfo(model.GetParams()): + if param_info.blob_copy is not None: + model.param_init_net.HalfToFloat( + param_info.blob, + param_info.blob_copy[core.DataType.FLOAT] + ) + + # Create parallelized model + data_parallel_model.Parallelize( + train_model, + input_builder_fun=add_image_input, + forward_pass_builder_fun=create_resnet50_model_ops, + optimizer_builder_fun=add_optimizer, + post_sync_builder_fun=add_post_sync_ops, + devices=gpus, + rendezvous=rendezvous, + optimize_gradient_memory=False, + cpu_device=args.use_cpu, + ) + + if args.model_parallel: + # Shift half of the activations to another GPU + assert workspace.NumCudaDevices() >= 2 * args.num_gpus + activations = data_parallel_model_utils.GetActivationBlobs(train_model) + data_parallel_model_utils.ShiftActivationDevices( + train_model, + activations=activations[len(activations) // 2:], + shifts={g: args.num_gpus + g for g in range(args.num_gpus)}, + ) + + data_parallel_model.OptimizeGradientMemory(train_model, {}, set(), False) + + workspace.RunNetOnce(train_model.param_init_net) + workspace.CreateNet(train_model.net) + + # Add test model, if specified + test_model = None + if (args.test_data is not None): + log.info("----- Create test net ----") + test_arg_scope = { + 'order': "NCHW", + 'use_cudnn': True, + 'cudnn_exhaustive_search': True, + } + test_model = model_helper.ModelHelper( + name="resnet50_test", arg_scope=test_arg_scope, init_params=False + ) + + test_reader = test_model.CreateDB( + "test_reader", + db=args.test_data, + db_type=args.db_type, + ) + + def test_input_fn(model): + AddImageInput( + model, + test_reader, + batch_size=batch_per_device, + img_size=args.image_size, + dtype=args.dtype, + is_test=True, + ) + + data_parallel_model.Parallelize( + test_model, + input_builder_fun=test_input_fn, + forward_pass_builder_fun=create_resnet50_model_ops, + post_sync_builder_fun=add_post_sync_ops, + param_update_builder_fun=None, + devices=gpus, + cpu_device=args.use_cpu, + ) + workspace.RunNetOnce(test_model.param_init_net) + workspace.CreateNet(test_model.net) + + epoch = 0 + # load the pre-trained model and reset epoch + if args.load_model_path is not None: + LoadModel(args.load_model_path, train_model) + + # Sync the model params + data_parallel_model.FinalizeAfterCheckpoint(train_model) + + # reset epoch. load_model_path should end with *_X.mdl, + # where X is the epoch number + last_str = args.load_model_path.split('_')[-1] + if last_str.endswith('.mdl'): + epoch = int(last_str[:-4]) + log.info("Reset epoch to {}".format(epoch)) + else: + log.warning("The format of load_model_path doesn't match!") + + expname = "resnet50_gpu%d_b%d_L%d_lr%.2f_v2" % ( + args.num_gpus, + total_batch_size, + args.num_labels, + args.base_learning_rate, + ) + + explog = experiment_util.ModelTrainerLog(expname, args) + + # Run the training one epoch a time + while epoch < args.num_epochs: + epoch = RunEpoch( + args, + epoch, + train_model, + test_model, + total_batch_size, + num_shards, + expname, + explog + ) + + # Save the model for each epoch + SaveModel(args, train_model, epoch) + + model_path = "%s/%s_" % ( + args.file_store_path, + args.save_model_name + ) + # remove the saved model from the previous epoch if it exists + if os.path.isfile(model_path + str(epoch - 1) + ".mdl"): + os.remove(model_path + str(epoch - 1) + ".mdl") + + +def main(): + # TODO: use argv + parser = argparse.ArgumentParser( + description="Caffe2: Resnet-50 training" + ) + parser.add_argument("--train_data", type=str, default=None, required=True, + help="Path to training data (or 'null' to simulate)") + parser.add_argument("--test_data", type=str, default=None, + help="Path to test data") + parser.add_argument("--db_type", type=str, default="lmdb", + help="Database type (such as lmdb or leveldb)") + parser.add_argument("--gpus", type=str, + help="Comma separated list of GPU devices to use") + parser.add_argument("--num_gpus", type=int, default=1, + help="Number of GPU devices (instead of --gpus)") + parser.add_argument("--model_parallel", type=bool, default=False, + help="Split model over 2 x num_gpus") + parser.add_argument("--num_channels", type=int, default=3, + help="Number of color channels") + parser.add_argument("--image_size", type=int, default=227, + help="Input image size (to crop to)") + parser.add_argument("--num_labels", type=int, default=1000, + help="Number of labels") + parser.add_argument("--batch_size", type=int, default=32, + help="Batch size, total over all GPUs") + parser.add_argument("--epoch_size", type=int, default=1500000, + help="Number of images/epoch, total over all machines") + parser.add_argument("--num_epochs", type=int, default=1000, + help="Num epochs.") + parser.add_argument("--base_learning_rate", type=float, default=0.1, + help="Initial learning rate.") + parser.add_argument("--weight_decay", type=float, default=1e-4, + help="Weight decay (L2 regularization)") + parser.add_argument("--cudnn_workspace_limit_mb", type=int, default=64, + help="CuDNN workspace limit in MBs") + parser.add_argument("--num_shards", type=int, default=1, + help="Number of machines in distributed run") + parser.add_argument("--shard_id", type=int, default=0, + help="Shard id.") + parser.add_argument("--run_id", type=str, + help="Unique run identifier (e.g. uuid)") + parser.add_argument("--redis_host", type=str, + help="Host of Redis server (for rendezvous)") + parser.add_argument("--redis_port", type=int, default=6379, + help="Port of Redis server (for rendezvous)") + parser.add_argument("--file_store_path", type=str, default="/tmp", + help="Path to directory to use for rendezvous") + parser.add_argument("--save_model_name", type=str, default="resnet50_model", + help="Save the trained model to a given name") + parser.add_argument("--load_model_path", type=str, default=None, + help="Load previously saved model to continue training") + parser.add_argument("--use_cpu", type=bool, default=False, + help="Use CPU instead of GPU") + parser.add_argument('--dtype', default='float', + choices=['float', 'float16'], + help='Data type used for training') + parser.add_argument('--float16_compute', action='store_true', + help="Use float 16 compute, if available") + parser.add_argument('--enable-tensor-core', action='store_true', + help='Enable Tensor Core math for Conv and FC ops') + parser.add_argument("--distributed_transport", type=str, default="tcp", + help="Transport to use for distributed run [tcp|ibverbs]") + parser.add_argument("--distributed_interfaces", type=str, default="", + help="Network interfaces to use for distributed run") + + args = parser.parse_args() + + Train(args) + +if __name__ == '__main__': + workspace.GlobalInit(['caffe2', '--caffe2_log_level=2']) + main() diff --git a/etc/examples/pytorch-model/README.md b/etc/examples/pytorch-model/README.md new file mode 100644 index 00000000..86fdf77d --- /dev/null +++ b/etc/examples/pytorch-model/README.md @@ -0,0 +1,6 @@ +# PyTorch MNIST example + +Using the FfDL CLI, run the below command at the top level of this repository. +```shell +$CLI_CMD train etc/examples/pytorch-model/manifest.yml etc/examples/pytorch-model +``` diff --git a/etc/examples/pytorch-model/manifest.yml b/etc/examples/pytorch-model/manifest.yml new file mode 100644 index 00000000..c8996ea9 --- /dev/null +++ b/etc/examples/pytorch-model/manifest.yml @@ -0,0 +1,26 @@ +name: mnist-pytorch-model +description: pytorch MNIST model +version: "1.0" +gpus: 0 +cpus: 1 +memory: 1000MiB + +# Object stores that allow the system to retrieve training data. +data_stores: + - id: test-datastore + type: s3_datastore + training_data: + container: tf_training_data + training_results: + container: tf_trained_model + connection: + type: s3_datastore + auth_url: http://s3.default.svc.cluster.local + user_name: test + password: test + +framework: + name: pytorch + version: "latest" + command: > + python mnist.py --epochs 5 diff --git a/etc/examples/pytorch-model/mnist.py b/etc/examples/pytorch-model/mnist.py new file mode 100644 index 00000000..93fafa47 --- /dev/null +++ b/etc/examples/pytorch-model/mnist.py @@ -0,0 +1,173 @@ +from __future__ import print_function +import argparse +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import os +import gzip +import codecs + +from torchvision import datasets, transforms +from torch.autograd import Variable +def get_int(b): + return int(codecs.encode(b, 'hex'), 16) + +def parse_byte(b): + if isinstance(b, str): + return ord(b) + return b + +def read_label_file(path): + with open(path, 'rb') as f: + data = f.read() + assert get_int(data[:4]) == 2049 + length = get_int(data[4:8]) + labels = [parse_byte(b) for b in data[8:]] + assert len(labels) == length + return torch.LongTensor(labels) + + +def read_image_file(path): + with open(path, 'rb') as f: + data = f.read() + assert get_int(data[:4]) == 2051 + length = get_int(data[4:8]) + num_rows = get_int(data[8:12]) + num_cols = get_int(data[12:16]) + images = [] + idx = 16 + for l in range(length): + img = [] + images.append(img) + for r in range(num_rows): + row = [] + img.append(row) + for c in range(num_cols): + row.append(parse_byte(data[idx])) + idx += 1 + assert len(images) == length + return torch.ByteTensor(images).view(-1, 28, 28) + +# Training settings +parser = argparse.ArgumentParser(description='PyTorch MNIST Example') +parser.add_argument('--batch-size', type=int, default=64, metavar='N', + help='input batch size for training (default: 64)') +parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', + help='input batch size for testing (default: 1000)') +parser.add_argument('--epochs', type=int, default=10, metavar='N', + help='number of epochs to train (default: 10)') +parser.add_argument('--lr', type=float, default=0.01, metavar='LR', + help='learning rate (default: 0.01)') +parser.add_argument('--momentum', type=float, default=0.5, metavar='M', + help='SGD momentum (default: 0.5)') +parser.add_argument('--no-cuda', action='store_true', default=False, + help='disables CUDA training') +parser.add_argument('--seed', type=int, default=1, metavar='S', + help='random seed (default: 1)') +parser.add_argument('--log-interval', type=int, default=10, metavar='N', + help='how many batches to wait before logging training status') +args = parser.parse_args() +args.cuda = not args.no_cuda and torch.cuda.is_available() + +torch.manual_seed(args.seed) +if args.cuda: + torch.cuda.manual_seed(args.seed) + +files=os.listdir(os.environ["DATA_DIR"]) +for filename in files: + if '.gz' in filename: + print(filename) + file_path=os.environ["DATA_DIR"]+'/'+filename + print(file_path) + with open(file_path.replace('.gz', ''), 'wb') as out_f, gzip.GzipFile(file_path) as zip_f: + out_f.write(zip_f.read()) + +training_set = (read_image_file(os.environ["DATA_DIR"]+'/train-images-idx3-ubyte'),read_label_file(os.environ["DATA_DIR"]+'/train-labels-idx1-ubyte')) +test_set = (read_image_file(os.environ["DATA_DIR"]+'/t10k-images-idx3-ubyte'),read_label_file(os.environ["DATA_DIR"]+'/t10k-labels-idx1-ubyte')) + +os.makedirs(os.environ["RESULT_DIR"]+'/processed') + +with open(os.environ["RESULT_DIR"]+'/processed/training.pt', 'wb') as f: + torch.save(training_set, f) + +with open(os.environ["RESULT_DIR"]+'/processed/test.pt', 'wb') as f: + torch.save(test_set, f) + +kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {} +train_loader = torch.utils.data.DataLoader( + datasets.MNIST(os.environ["RESULT_DIR"], train=True, download=False, + transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ])), + batch_size=args.batch_size, shuffle=True, **kwargs) +test_loader = torch.utils.data.DataLoader( + datasets.MNIST(os.environ["RESULT_DIR"], train=False, transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ])), + batch_size=args.test_batch_size, shuffle=True, **kwargs) + + +class Net(nn.Module): + def __init__(self): + super(Net, self).__init__() + self.conv1 = nn.Conv2d(1, 10, kernel_size=5) + self.conv2 = nn.Conv2d(10, 20, kernel_size=5) + self.conv2_drop = nn.Dropout2d() + self.fc1 = nn.Linear(320, 50) + self.fc2 = nn.Linear(50, 10) + + def forward(self, x): + x = F.relu(F.max_pool2d(self.conv1(x), 2)) + x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) + x = x.view(-1, 320) + x = F.relu(self.fc1(x)) + x = F.dropout(x, training=self.training) + x = self.fc2(x) + return F.log_softmax(x) + +model = Net() +if args.cuda: + model.cuda() + +optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) + +def train(epoch): + model.train() + for batch_idx, (data, target) in enumerate(train_loader): + if args.cuda: + data, target = data.cuda(), target.cuda() + data, target = Variable(data), Variable(target) + optimizer.zero_grad() + output = model(data) + loss = F.nll_loss(output, target) + loss.backward() + optimizer.step() + if batch_idx % args.log_interval == 0: + print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( + epoch, batch_idx * len(data), len(train_loader.dataset), + 100. * batch_idx / len(train_loader), loss.data[0])) + +def test(): + model.eval() + test_loss = 0 + correct = 0 + for data, target in test_loader: + if args.cuda: + data, target = data.cuda(), target.cuda() + data, target = Variable(data, volatile=True), Variable(target) + output = model(data) + test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss + pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability + correct += pred.eq(target.data.view_as(pred)).cpu().sum() + + test_loss /= len(test_loader.dataset) + print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( + test_loss, correct, len(test_loader.dataset), + 100. * correct / len(test_loader.dataset))) + +for epoch in range(1, args.epochs + 1): + train(epoch) + test() diff --git a/etc/examples/tf-model/gpu-manifest.yml b/etc/examples/tf-model/gpu-manifest.yml new file mode 100644 index 00000000..eef4a1ff --- /dev/null +++ b/etc/examples/tf-model/gpu-manifest.yml @@ -0,0 +1,39 @@ +name: tf_mnist_with_summaries_gpu_tutorial +description: Convolutional network model using tensorflow (GPU) +version: "1.0" +gpus: 1 +cpus: 0.005 +memory: 2000MiB + +# Object stores that allow the system to retrieve training data. +data_stores: + - id: sl-internal-os + type: s3_datastore + training_data: + container: tf_training_data + training_results: + container: tf_trained_model + connection: + type: s3_datastore + auth_url: http://s3.default.svc.cluster.local + user_name: test + password: test + +framework: + name: tensorflow + version: "1.3.0-gpu" + command: > + python mnist_with_summaries.py + --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz + --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz + --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz + --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz + --max_steps 400 +# --learning_rate 0.001 + +evaluation_metrics: + type: tensorboard + in: "$JOB_STATE_DIR/logs/tb" + # (Eventual) Available event types: 'images', 'distributions', 'histograms', 'images' + # 'audio', 'scalars', 'tensors', 'graph', 'meta_graph', 'run_metadata' + # event_types: [scalars] diff --git a/etc/examples/tf-model/manifest.yml b/etc/examples/tf-model/manifest.yml index 98e9c49e..cd75c866 100644 --- a/etc/examples/tf-model/manifest.yml +++ b/etc/examples/tf-model/manifest.yml @@ -21,12 +21,12 @@ data_stores: framework: name: tensorflow - version: "1.3-py3" + version: "latest" command: > python mnist_with_summaries.py --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz - --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz - --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz + --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz + --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --max_steps 400 # --learning_rate 0.001 @@ -36,4 +36,4 @@ evaluation_metrics: in: "$JOB_STATE_DIR/logs/tb" # (Eventual) Available event types: 'images', 'distributions', 'histograms', 'images' # 'audio', 'scalars', 'tensors', 'graph', 'meta_graph', 'run_metadata' - # event_types: [scalars] \ No newline at end of file + # event_types: [scalars] diff --git a/etc/examples/tf-simple-model/convolutional_network.py b/etc/examples/tf-simple-model/convolutional_network.py deleted file mode 100644 index c960900f..00000000 --- a/etc/examples/tf-simple-model/convolutional_network.py +++ /dev/null @@ -1,220 +0,0 @@ -''' -A Convolutional Network implementation example using TensorFlow library. -This example is using the MNIST database of handwritten digits -(http://yann.lecun.com/exdb/mnist/) - -Author: Aymeric Damien -Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' - -import tensorflow as tf -import input_data -import sys -import os -import itertools -import re -import time -from random import randint - -train_images_file = "" -train_labels_file = "" -test_images_file = "" -test_labels_file = "" - -# Parameters -learning_rate = 0.001 -training_iters = 200000 -batch_size = 128 -display_step = 10 - -model_path = os.environ["RESULT_DIR"] + "/model.ckpt" - -# This helps distinguish instances when the training job is restarted. -instance_id = randint(0, 9999) - -def main(argv): - - if len(argv) < 12: - sys.exit("Not enough arguments provided.") - - global train_images_file, train_labels_file, test_images_file, test_labels_file, learning_rate, training_iters - - i = 1 - while i <= 12: - arg = str(argv[i]) - if arg == "--trainImagesFile": - train_images_file = str(argv[i+1]) - elif arg == "--trainLabelsFile": - train_labels_file = str(argv[i+1]) - elif arg == "--testImagesFile": - test_images_file = str(argv[i+1]) - elif arg == "--testLabelsFile": - test_labels_file = str(argv[i+1]) - elif arg == "--learningRate": - learning_rate = float(argv[i+1]) - elif arg =="--trainingIters": - training_iters = int(argv[i+1]) - i += 2 - -if __name__ == "__main__": - main(sys.argv) - -# Import MINST data -mnist = input_data.read_data_sets(train_images_file, - train_labels_file, test_images_file, test_labels_file, one_hot=True) - -# Network Parameters -n_input = 784 # MNIST data input (img shape: 28*28) -n_classes = 10 # MNIST total classes (0-9 digits) -dropout = 0.75 # Dropout, probability to keep units - -# tf Graph input -x = tf.placeholder(tf.float32, [None, n_input]) -y = tf.placeholder(tf.float32, [None, n_classes]) -keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) - - -# Create some wrappers for simplicity -def conv2d(x, W, b, strides=1): - # Conv2D wrapper, with bias and relu activation - x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') - x = tf.nn.bias_add(x, b) - return tf.nn.relu(x) - - -def maxpool2d(x, k=2): - # MaxPool2D wrapper - return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], - padding='SAME') - - -# Create model -def conv_net(x, weights, biases, dropout): - # Reshape input picture - x = tf.reshape(x, shape=[-1, 28, 28, 1]) - - # Convolution Layer - conv1 = conv2d(x, weights['wc1'], biases['bc1']) - # Max Pooling (down-sampling) - conv1 = maxpool2d(conv1, k=2) - - # Convolution Layer - conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) - # Max Pooling (down-sampling) - conv2 = maxpool2d(conv2, k=2) - - # Fully connected layer - # Reshape conv2 output to fit fully connected layer input - fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) - fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) - fc1 = tf.nn.relu(fc1) - # Apply Dropout - fc1 = tf.nn.dropout(fc1, dropout) - - # Output, class prediction - out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) - return out - -# Store layers weight & bias -weights = { - # 5x5 conv, 1 input, 32 outputs - 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), - # 5x5 conv, 32 inputs, 64 outputs - 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), - # fully connected, 7*7*64 inputs, 1024 outputs - 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), - # 1024 inputs, 10 outputs (class prediction) - 'out': tf.Variable(tf.random_normal([1024, n_classes])) -} - -biases = { - 'bc1': tf.Variable(tf.random_normal([32])), - 'bc2': tf.Variable(tf.random_normal([64])), - 'bd1': tf.Variable(tf.random_normal([1024])), - 'out': tf.Variable(tf.random_normal([n_classes])) -} - -# Construct model -pred = conv_net(x, weights, biases, keep_prob) - -# Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) - -# Evaluate model -correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) -accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) - -# Initializing the variables -init = tf.global_variables_initializer() - -saver = tf.train.Saver(max_to_keep=1) - -# Assuming the checkpoint filename is "xyz-nnn", return nnn (as an int). -def get_step_from_checkpoint_name(filename): - m = re.match(".*-([0-9]+)$", filename) - if m is not None: - return int(m.group(1)) - else: - return None - -# Restore from a checkpoint if possible, and return the step count of the checkpoint. -def restore_from_checkpoint(): - cs = tf.train.get_checkpoint_state(os.environ["RESULT_DIR"]) - checkpoints = [] - if cs is not None: - checkpoints = itertools.chain([cs.model_checkpoint_path], reversed(cs.all_model_checkpoint_paths)) - step = None - for c in checkpoints: - try: - saver.restore(sess, c) - print("Restored from checkpoint: %s" % c) - step = get_step_from_checkpoint_name(c) - break - except tf.errors.NotFoundError: - print("Warning: checkpoint not found: %s" % c) - except tf.errors.DataLossError: - print("Warning: checkpoint corrupted: %s" % c) - return step - -def save_checkpoint(step): - path = saver.save(sess, model_path, global_step=step) - print("Saved checkpoint %s" % path) - - -# Launch the graph -with tf.Session() as sess: - sess.run(init) - step = restore_from_checkpoint() - if step is None: - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: - batch_x, batch_y = mnist.train.next_batch(batch_size) - # Run optimization op (backprop) - sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, - keep_prob: dropout}) - if step % display_step == 0: - save_checkpoint(step) - # Calculate batch loss and accuracy - loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, - y: batch_y, - keep_prob: 1.}) - print("Time " + "{:.4f}".format(time.time()) + \ - ", instance " + str(instance_id) + \ - ", Iter " + str(step * batch_size) + \ - ", Minibatch Loss= " + "{:.6f}".format(loss) + \ - ", Training Accuracy= " + "{:.5f}".format(acc)) - sys.stdout.flush() - step += 1 - print("Optimization Finished!") - - # Calculate accuracy for 256 mnist test images - print("Testing Accuracy:", \ - sess.run(accuracy, feed_dict={x: mnist.test.images[:256], - y: mnist.test.labels[:256], - keep_prob: 1.})) - - save_path = saver.save(sess, model_path) - print("Model saved in file: %s" % save_path) - sys.stdout.flush() diff --git a/etc/examples/tf-simple-model/input_data.py b/etc/examples/tf-simple-model/input_data.py deleted file mode 100644 index 889dd8cb..00000000 --- a/etc/examples/tf-simple-model/input_data.py +++ /dev/null @@ -1,147 +0,0 @@ - -#!/usr/bin/env python - -"""Functions for downloading and reading MNIST data.""" -import gzip -import os -from six.moves.urllib.request import urlretrieve -import numpy - -def _read32(bytestream): - dt = numpy.dtype(numpy.uint32).newbyteorder('>') - return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] - - -def extract_images(filename): - """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2051: - raise ValueError( - 'Invalid magic number %d in MNIST image file: %s' % - (magic, filename)) - num_images = _read32(bytestream) - rows = _read32(bytestream) - cols = _read32(bytestream) - buf = bytestream.read(rows * cols * num_images) - data = numpy.frombuffer(buf, dtype=numpy.uint8) - data = data.reshape(num_images, rows, cols, 1) - return data - - -def dense_to_one_hot(labels_dense, num_classes=10): - """Convert class labels from scalars to one-hot vectors.""" - num_labels = labels_dense.shape[0] - index_offset = numpy.arange(num_labels) * num_classes - labels_one_hot = numpy.zeros((num_labels, num_classes)) - labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 - return labels_one_hot - - -def extract_labels(filename, one_hot=False): - """Extract the labels into a 1D uint8 numpy array [index].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2049: - raise ValueError( - 'Invalid magic number %d in MNIST label file: %s' % - (magic, filename)) - num_items = _read32(bytestream) - buf = bytestream.read(num_items) - labels = numpy.frombuffer(buf, dtype=numpy.uint8) - if one_hot: - return dense_to_one_hot(labels) - return labels - - -class DataSet(object): - def __init__(self, images, labels, fake_data=False): - if fake_data: - self._num_examples = 10000 - else: - assert images.shape[0] == labels.shape[0], ( - "images.shape: %s labels.shape: %s" % (images.shape, - labels.shape)) - self._num_examples = images.shape[0] - # Convert shape from [num examples, rows, columns, depth] - # to [num examples, rows*columns] (assuming depth == 1) - assert images.shape[3] == 1 - images = images.reshape(images.shape[0], - images.shape[1] * images.shape[2]) - # Convert from [0, 255] -> [0.0, 1.0]. - images = images.astype(numpy.float32) - images = numpy.multiply(images, 1.0 / 255.0) - self._images = images - self._labels = labels - self._epochs_completed = 0 - self._index_in_epoch = 0 - - @property - def images(self): - return self._images - - @property - def labels(self): - return self._labels - - @property - def num_examples(self): - return self._num_examples - - @property - def epochs_completed(self): - return self._epochs_completed - - def next_batch(self, batch_size, fake_data=False): - """Return the next `batch_size` examples from this data set.""" - if fake_data: - fake_image = [1.0 for _ in xrange(784)] - fake_label = 0 - return [fake_image for _ in xrange(batch_size)], [ - fake_label for _ in xrange(batch_size)] - start = self._index_in_epoch - self._index_in_epoch += batch_size - if self._index_in_epoch > self._num_examples: - # Finished epoch - self._epochs_completed += 1 - # Shuffle the data - perm = numpy.arange(self._num_examples) - numpy.random.shuffle(perm) - self._images = self._images[perm] - self._labels = self._labels[perm] - # Start next epoch - start = 0 - self._index_in_epoch = batch_size - assert batch_size <= self._num_examples - end = self._index_in_epoch - return self._images[start:end], self._labels[start:end] - - -def read_data_sets(train_images_file, train_labels_file, test_images_file, test_labels_file, fake_data=False, one_hot=False): - class DataSets(object): - pass - data_sets = DataSets() - if fake_data: - data_sets.train = DataSet([], [], fake_data=True) - data_sets.validation = DataSet([], [], fake_data=True) - data_sets.test = DataSet([], [], fake_data=True) - return data_sets - TRAIN_IMAGES = train_images_file - TRAIN_LABELS = train_labels_file - TEST_IMAGES = test_images_file - TEST_LABELS = test_labels_file - VALIDATION_SIZE = 3000 - train_images = extract_images(TRAIN_IMAGES) - train_labels = extract_labels(TRAIN_LABELS, one_hot=one_hot) - test_images = extract_images(TEST_IMAGES) - test_labels = extract_labels(TEST_LABELS, one_hot=one_hot) - validation_images = train_images[:VALIDATION_SIZE] - validation_labels = train_labels[:VALIDATION_SIZE] - train_images = train_images[VALIDATION_SIZE:] - train_labels = train_labels[VALIDATION_SIZE:] - data_sets.train = DataSet(train_images, train_labels) - data_sets.validation = DataSet(validation_images, validation_labels) - data_sets.test = DataSet(test_images, test_labels) - return data_sets \ No newline at end of file diff --git a/etc/examples/tf-simple-model/manifest.yml b/etc/examples/tf-simple-model/manifest.yml deleted file mode 100644 index 07de8109..00000000 --- a/etc/examples/tf-simple-model/manifest.yml +++ /dev/null @@ -1,31 +0,0 @@ -name: sample-tensorflow-model -description: Convolutional network model using tensorflow -version: "1.0" -gpus: 0 -cpus: 0.5 -memory: 500MiB - -# Object stores that allow the system to retrieve training data. -data_stores: - - id: sl-internal-os - type: s3_datastore - training_data: - container: tf_training_data - training_results: - container: tf_trained_model - connection: - type: s3_datastore - auth_url: http://s3.default.svc.cluster.local - user_name: test - password: test - -framework: - name: tensorflow - version: "1.3-py3" - command: > - python convolutional_network.py - --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz - --trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz - --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz - --testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz - --learningRate 0.001 --trainingIters 200 diff --git a/lcm/service/lcm/constants.go b/lcm/service/lcm/constants.go index 1c3c8c7c..16fcbe28 100644 --- a/lcm/service/lcm/constants.go +++ b/lcm/service/lcm/constants.go @@ -54,6 +54,8 @@ const ( caffeFrameworkName string = "caffe" tfFrameworkName string = "tensorflow" torchFrameworkName string = "torch" + caffe2FrameworkName string = "caffe2" + pytorchFrameworkName string = "pytorch" numRetries = 5 maxGPUsPerNode = 4 errCodeNormal = "000" diff --git a/lcm/service/lcm/container_helper.go b/lcm/service/lcm/container_helper.go index 48eebb53..b2a4eb28 100644 --- a/lcm/service/lcm/container_helper.go +++ b/lcm/service/lcm/container_helper.go @@ -230,9 +230,9 @@ func fetchImageNameFromEvaluationMetrics(evalMetricsString string, if logCollectorImageShortName == "tensorboard" || logCollectorImageShortName == "tensorboard_extractor" { // TODO: Fix tensorflow/board docker image version matrix nightmare tensorBoardVersion := "1.3-py3" - if framework == tfFrameworkName { - tensorBoardVersion = version - } + // if framework == tfFrameworkName { + // tensorBoardVersion = version + // } // Try to match the best version logCollectorImageShortName = fmt.Sprintf("%s_extract_%s", "tensorboard", tensorBoardVersion) } diff --git a/lcm/service/lcm/container_helper_extensions.go b/lcm/service/lcm/container_helper_extensions.go index 31c6cb2d..04de99fc 100644 --- a/lcm/service/lcm/container_helper_extensions.go +++ b/lcm/service/lcm/container_helper_extensions.go @@ -24,9 +24,13 @@ func extendLearnerContainer(learner *v1core.Container, req *service.JobDeploymen switch req.Framework { case caffeFrameworkName: - learnerImage = "bvlc/caffe:cpu" + learnerImage = "bvlc/caffe:" + req.Version case tfFrameworkName: - learnerImage = "tensorflow/tensorflow" + learnerImage = "tensorflow/tensorflow:" + req.Version + case caffe2FrameworkName: + learnerImage = "caffe2ai/caffe2:" + req.Version + case pytorchFrameworkName: + learnerImage = "pytorch/pytorch:" + req.Version default: // TODO! } diff --git a/templates/services/learner-configmap.yml b/templates/services/learner-configmap.yml index 203b093d..5f76478f 100644 --- a/templates/services/learner-configmap.yml +++ b/templates/services/learner-configmap.yml @@ -3,12 +3,31 @@ kind: ConfigMap metadata: name: learner-config data: - tensorflow_gpu_1.3-py3_CURRENT: manual - caffe_cpu_1.0-ddl_CURRENT: master-7 - caffe_cpu_1.0-py2_CURRENT: master-39 - caffe_gpu_1.0-ddl_CURRENT: master-7 - caffe_gpu_1.0-py2_CURRENT: master-39 - + tensorflow_cpu_latest_CURRENT: manual + tensorflow_cpu_latest-py3_CURRENT: manual + tensorflow_gpu_latest-gpu-py3_CURRENT: manual + tensorflow_gpu_latest-gpu_CURRENT: manual + tensorflow_gpu_1.5.0-gpu-py3_CURRENT: manual + tensorflow_gpu_1.5.0-gpu_CURRENT: manual + tensorflow_cpu_1.5.0_CURRENT: manual + tensorflow_cpu_1.5.0-py3_CURRENT: manual + tensorflow_gpu_1.4.0-gpu-py3_CURRENT: manual + tensorflow_gpu_1.4.0-gpu_CURRENT: manual + tensorflow_cpu_1.4.0_CURRENT: manual + tensorflow_cpu_1.4.0-py3_CURRENT: manual + tensorflow_gpu_1.3.0-gpu-py3_CURRENT: manual + tensorflow_gpu_1.3.0-gpu_CURRENT: manual + tensorflow_cpu_1.3.0_CURRENT: manual + tensorflow_cpu_1.3.0-py3_CURRENT: manual + caffe_cpu_cpu_CURRENT: master-39 + caffe_gpu_gpu_CURRENT: master-39 + caffe_cpu_intel_CURRENT: master-39 + pytorch_gpu_v0.2_CURRENT: master-39 + pytorch_gpu_latest_CURRENT: master-39 + caffe2_cpu_c2v0.8.1.cpu.full.ubuntu14.04_CURRENT: master-39 + caffe2_gpu_c2v0.8.1.cuda8.cudnn7.ubuntu16.04_CURRENT: master-39 + caffe2_cpu_c2v0.8.0.cpu.full.ubuntu16.04_CURRENT: master-39 + caffe2_gpu_latest_CURRENT: master-39 --- apiVersion: v1 From 008655ae271cdfa102702eb4a18cb799becd6012 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Wed, 28 Feb 2018 22:09:40 -0800 Subject: [PATCH 12/87] Update UI instructions to manually create model zip files (#29) * update UI instructions * fix command --- README.md | 10 ++++++++-- etc/examples/tf-model.zip | Bin 7429 -> 0 bytes 2 files changed, 8 insertions(+), 2 deletions(-) delete mode 100644 etc/examples/tf-model.zip diff --git a/README.md b/README.md index b274a978..991f4bea 100644 --- a/README.md +++ b/README.md @@ -297,8 +297,14 @@ you can simply run `$CLI_CMD logs ` > If you no longer need any of the MNIST dataset we used in this example, you can simply delete the tmp repository. -7. (Experimental) After you done with step 4, if you want to run your job via the FfDL UI, simply upload -`tf-model.zip` and `manifest.yml` (The default TensorFlow model) in the `etc/examples/` repository as shown below. +7. If you want to run your job via the FfDL UI, simply run the below command to create your model zip file. + +```shell +# Replace tf-model with the model you want to zip +pushd etc/examples/tf-model && zip ../tf-model.zip * && popd +``` + +Then, upload `tf-model.zip` and `manifest.yml` (The default TensorFlow model) in the `etc/examples/` repository as shown below. Then, click `Submit Training Job` to run your job. ![ui-example](docs/images/ui-example.png) diff --git a/etc/examples/tf-model.zip b/etc/examples/tf-model.zip deleted file mode 100644 index 2ab024a3ccb0edbb66bc8c70d03407bd24cfd035..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 7429 zcma)>bx>T}y6qbc!GjYZ!Gk-E6M_Y491rkH%}Y@3i>4q0Px?-$8-&#%Rmyp>ndTD zXR@|BeX7LeINN56Y515+_|}1vqdeM6dFBqyo{U?)_m^U=3I*1SHZ2k$f^d-E_omZ~ z9`o0}F8VNc(eYs|eMi$aQbZXwZgh0?+T4~jL}v)B$E$$2>Z?p^fjBI9=Agdw%gW&m=M@M7G(^zsfIVww&1`R%KMfg!w+3Zw){W~R~af> zJ@SJi)7U8{Mor{MooPGiU>AX$rwG7D6U7`aQme*jTB>7SkJUIU#98r#snCqoqHE|N zK|M}gV@ydVVQ%bTiwNbN7sn;W4j5f&Uzil>DokZP`6|Ih=8Qv7b7EPkHO&auH<4*{QYl`SpC+iaLkX*C%Oc z%@O2`uOiWy`6GM+=)p4Nd1kFDx~wzm{Xh0UB{9@SYKIPGsFiKSYZ-J0$DOI(m8Ir} zm)B_WMu9doNA9Kd`p`ZupN=!CrBHFh=!N$ca?j6Q@8f`F-G|^iYoj0d8PdyY|kXX@l zP8yTZu~j~XL#miZZY$>{GM?Bfz+^M@3+-}(5JZIXDkO;+OE9bC;cQ`Vs>Ep;wHoNHO-0UL_0#lAjVvAKLp-ACpT6*0i>dyi(E|l;>y7 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2001 From: Tommy Li Date: Wed, 14 Mar 2018 00:26:37 -0700 Subject: [PATCH 15/87] update UI dependencies and add font-awesome (#33) --- dashboard/package-lock.json | 9268 ++++++++++------- dashboard/package.json | 16 +- .../src/assets/font-awesome/HELP-US-OUT.txt | 7 + .../assets/font-awesome/css/font-awesome.css | 2337 +++++ .../font-awesome/css/font-awesome.min.css | 4 + .../assets/font-awesome/fonts/FontAwesome.otf | Bin 0 -> 134808 bytes .../fonts/fontawesome-webfont.eot | Bin 0 -> 165742 bytes .../fonts/fontawesome-webfont.svg | 2671 +++++ .../fonts/fontawesome-webfont.ttf | Bin 0 -> 165548 bytes .../fonts/fontawesome-webfont.woff | Bin 0 -> 98024 bytes .../fonts/fontawesome-webfont.woff2 | Bin 0 -> 77160 bytes .../assets/font-awesome/less/animated.less | 34 + .../font-awesome/less/bordered-pulled.less | 25 + .../src/assets/font-awesome/less/core.less | 12 + .../assets/font-awesome/less/fixed-width.less | 6 + .../font-awesome/less/font-awesome.less | 18 + .../src/assets/font-awesome/less/icons.less | 789 ++ .../src/assets/font-awesome/less/larger.less | 13 + .../src/assets/font-awesome/less/list.less | 19 + .../src/assets/font-awesome/less/mixins.less | 60 + .../src/assets/font-awesome/less/path.less | 15 + .../font-awesome/less/rotated-flipped.less | 20 + .../font-awesome/less/screen-reader.less | 5 + .../src/assets/font-awesome/less/stacked.less | 20 + .../assets/font-awesome/less/variables.less | 800 ++ .../assets/font-awesome/scss/_animated.scss | 34 + .../font-awesome/scss/_bordered-pulled.scss | 25 + .../src/assets/font-awesome/scss/_core.scss | 12 + .../font-awesome/scss/_fixed-width.scss | 6 + .../src/assets/font-awesome/scss/_icons.scss | 789 ++ .../src/assets/font-awesome/scss/_larger.scss | 13 + .../src/assets/font-awesome/scss/_list.scss | 19 + .../src/assets/font-awesome/scss/_mixins.scss | 60 + .../src/assets/font-awesome/scss/_path.scss | 15 + .../font-awesome/scss/_rotated-flipped.scss | 20 + .../font-awesome/scss/_screen-reader.scss | 5 + .../assets/font-awesome/scss/_stacked.scss | 20 + .../assets/font-awesome/scss/_variables.scss | 800 ++ .../font-awesome/scss/font-awesome.scss | 18 + dashboard/src/index.html | 2 +- 40 files changed, 14243 insertions(+), 3734 deletions(-) create mode 100644 dashboard/src/assets/font-awesome/HELP-US-OUT.txt create mode 100644 dashboard/src/assets/font-awesome/css/font-awesome.css create mode 100644 dashboard/src/assets/font-awesome/css/font-awesome.min.css create mode 100644 dashboard/src/assets/font-awesome/fonts/FontAwesome.otf create mode 100644 dashboard/src/assets/font-awesome/fonts/fontawesome-webfont.eot create mode 100644 dashboard/src/assets/font-awesome/fonts/fontawesome-webfont.svg create mode 100644 dashboard/src/assets/font-awesome/fonts/fontawesome-webfont.ttf create mode 100644 dashboard/src/assets/font-awesome/fonts/fontawesome-webfont.woff create mode 100644 dashboard/src/assets/font-awesome/fonts/fontawesome-webfont.woff2 create mode 100644 dashboard/src/assets/font-awesome/less/animated.less create mode 100644 dashboard/src/assets/font-awesome/less/bordered-pulled.less create mode 100644 dashboard/src/assets/font-awesome/less/core.less create mode 100644 dashboard/src/assets/font-awesome/less/fixed-width.less create mode 100644 dashboard/src/assets/font-awesome/less/font-awesome.less create mode 100644 dashboard/src/assets/font-awesome/less/icons.less create mode 100644 dashboard/src/assets/font-awesome/less/larger.less create mode 100644 dashboard/src/assets/font-awesome/less/list.less create mode 100644 dashboard/src/assets/font-awesome/less/mixins.less create mode 100644 dashboard/src/assets/font-awesome/less/path.less create mode 100644 dashboard/src/assets/font-awesome/less/rotated-flipped.less create mode 100644 dashboard/src/assets/font-awesome/less/screen-reader.less create mode 100644 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"dependencies": { + "lodash": { + "version": "4.17.5", + "resolved": "https://registry.npmjs.org/lodash/-/lodash-4.17.5.tgz", + "integrity": "sha512-svL3uiZf1RwhH+cWrfZn3A4+U58wbP0tGVTLQPbjplZxZ8ROD9VLuNgsRniTlLe7OlSqR79RUehXgpBW/s0IQw==", + "dev": true + } } }, "webpack-sources": { - "version": "1.0.2", - "resolved": "https://registry.npmjs.org/webpack-sources/-/webpack-sources-1.0.2.tgz", - "integrity": "sha512-Y7UddMCv6dGjy81nBv6nuQeFFIt5aalHm7uyDsAsW86nZwfOVPGRr3XMjEQLaT+WKo8rlzhC9qtbJvYKLtAwaw==", + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/webpack-sources/-/webpack-sources-1.1.0.tgz", + "integrity": "sha512-aqYp18kPphgoO5c/+NaUvEeACtZjMESmDChuD3NBciVpah3XpMEU9VAAtIaB1BsfJWWTSdv8Vv1m3T0aRk2dUw==", "dev": true, "requires": { "source-list-map": "2.0.0", @@ -9281,9 +11099,9 @@ } }, "webpack-subresource-integrity": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/webpack-subresource-integrity/-/webpack-subresource-integrity-1.0.1.tgz", - "integrity": "sha1-H8CdRkl9pm5GdDoqUdLMOFucsO0=", + "version": "1.0.4", + "resolved": "https://registry.npmjs.org/webpack-subresource-integrity/-/webpack-subresource-integrity-1.0.4.tgz", + "integrity": "sha1-j6yKfo61n8ahZ2ioXJ2U7n+dDts=", "dev": true, "requires": { "webpack-core": "0.6.9" @@ -9295,7 +11113,7 @@ "integrity": "sha1-DK+dLXVdk67gSdS90NP+LMoqJOs=", "dev": true, "requires": { - "http-parser-js": "0.4.9", + "http-parser-js": "0.4.11", "websocket-extensions": "0.1.3" } }, @@ -9311,12 +11129,6 @@ "integrity": "sha1-RztRfsFZ4rhQBUl6E5g/CVQS404=", "dev": true }, - "whet.extend": { - "version": "0.9.9", - "resolved": "https://registry.npmjs.org/whet.extend/-/whet.extend-0.9.9.tgz", - "integrity": "sha1-+HfVv2SMl+WqVC+twW1qJZucEaE=", - "dev": true - }, "which": { "version": "1.3.0", "resolved": "https://registry.npmjs.org/which/-/which-1.3.0.tgz", @@ -9354,13 +11166,12 @@ "dev": true }, "worker-farm": { - "version": "1.5.2", - "resolved": "https://registry.npmjs.org/worker-farm/-/worker-farm-1.5.2.tgz", - "integrity": "sha512-XxiQ9kZN5n6mmnW+mFJ+wXjNNI/Nx4DIdaAKLX1Bn6LYBWlN/zaBhu34DQYPZ1AJobQuu67S2OfDdNSVULvXkQ==", + "version": "1.6.0", + "resolved": "https://registry.npmjs.org/worker-farm/-/worker-farm-1.6.0.tgz", + "integrity": "sha512-6w+3tHbM87WnSWnENBUvA2pxJPLhQUg5LKwUQHq3r+XPhIM+Gh2R5ycbwPCyuGbNg+lPgdcnQUhuC02kJCvffQ==", "dev": true, "requires": { - "errno": "0.1.4", - "xtend": "4.0.1" + "errno": "0.1.7" } }, "wrap-ansi": { @@ -9430,18 +11241,18 @@ "dev": true }, "xxhashjs": { - "version": "0.2.1", - "resolved": "https://registry.npmjs.org/xxhashjs/-/xxhashjs-0.2.1.tgz", - "integrity": "sha1-m76b6JYUKXbfo0wGGy0GjEPTDeA=", + "version": "0.2.2", + "resolved": "https://registry.npmjs.org/xxhashjs/-/xxhashjs-0.2.2.tgz", + "integrity": "sha512-AkTuIuVTET12tpsVIQo+ZU6f/qDmKuRUcjaqR+OIvm+aCBsZ95i7UVY5WJ9TMsSaZ0DA2WxoZ4acu0sPH+OKAw==", "dev": true, "requires": { "cuint": "0.2.2" } }, "y18n": { - "version": "3.2.1", - "resolved": "https://registry.npmjs.org/y18n/-/y18n-3.2.1.tgz", - "integrity": "sha1-bRX7qITAhnnA136I53WegR4H+kE=", + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/y18n/-/y18n-4.0.0.tgz", + "integrity": "sha512-r9S/ZyXu/Xu9q1tYlpsLIsa3EeLXXk0VwlxqTcFRfg9EhMW+17kbt9G0NrgCmhGb5vT2hyhJZLfDGx+7+5Uj/w==", "dev": true }, "yallist": { @@ -9478,6 +11289,13 @@ "integrity": "sha1-MvxLn82vhF/N9+c7uXysImHwqwo=", "dev": true, "optional": true + }, + "y18n": { + "version": "3.2.1", + "resolved": "https://registry.npmjs.org/y18n/-/y18n-3.2.1.tgz", + "integrity": "sha1-bRX7qITAhnnA136I53WegR4H+kE=", + "dev": true, + "optional": true } } }, @@ -9513,9 +11331,9 @@ "dev": true }, "zone.js": { - "version": "0.8.18", - "resolved": "https://registry.npmjs.org/zone.js/-/zone.js-0.8.18.tgz", - "integrity": "sha512-knKOBQM0oea3/x9pdyDuDi7RhxDlJhOIkeixXSiTKWLgs4LpK37iBc+1HaHwzlciHUKT172CymJFKo8Xgh+44Q==" + "version": "0.8.20", + "resolved": "https://registry.npmjs.org/zone.js/-/zone.js-0.8.20.tgz", + "integrity": "sha512-FXlA37ErSXCMy5RNBcGFgCI/Zivqzr0D19GuvDxhcYIJc7xkFp6c29DKyODJu0Zo+EMyur/WPPgcBh1EHjB9jA==" } } } diff --git a/dashboard/package.json b/dashboard/package.json index 8d43d390..2aae9497 100644 --- a/dashboard/package.json +++ b/dashboard/package.json @@ -35,22 +35,22 @@ "zone.js": "^0.8.14" }, "devDependencies": { - "@angular/cli": "^1.5.3", - "@angular/compiler-cli": "^5.0.0", - "@angular/language-service": "^5.0.0", - "@types/chart.js": "^2.6.8", + "@angular/cli": "^1.7.3", + "@angular/compiler-cli": "^5.2.8", + "@angular/language-service": "^5.2.8", + "@types/chart.js": "^2.7.10", "@types/jasmine": "~2.5.53", "@types/jasminewd2": "~2.0.2", - "@types/jquery": "^3.2.12", - "@types/node": "~6.0.60", + "@types/jquery": "^3.3.1", + "@types/node": "^6.0.102", "codelyzer": "~3.2.0", "jasmine-core": "~2.6.2", "jasmine-spec-reporter": "~4.1.0", "karma": "~1.7.0", "karma-chrome-launcher": "~2.1.1", "karma-cli": "~1.0.1", - "karma-coverage-istanbul-reporter": "^1.2.1", - "karma-jasmine": "~1.1.0", + "karma-coverage-istanbul-reporter": "^1.4.2", + "karma-jasmine": "^1.1.1", "karma-jasmine-html-reporter": "^0.2.2", "protractor": "~5.1.2", "ts-node": "~3.2.0", diff --git a/dashboard/src/assets/font-awesome/HELP-US-OUT.txt b/dashboard/src/assets/font-awesome/HELP-US-OUT.txt new file mode 100644 index 00000000..83d083dd --- /dev/null +++ b/dashboard/src/assets/font-awesome/HELP-US-OUT.txt @@ -0,0 +1,7 @@ +I hope you love Font Awesome. If you've found it useful, please do me a favor and check out my latest project, +Fort Awesome (https://fortawesome.com). It makes it easy to put the perfect icons on your website. Choose from our awesome, +comprehensive icon sets or copy and paste your own. + +Please. Check it out. + +-Dave Gandy diff --git a/dashboard/src/assets/font-awesome/css/font-awesome.css b/dashboard/src/assets/font-awesome/css/font-awesome.css new file mode 100644 index 00000000..ee906a81 --- /dev/null +++ b/dashboard/src/assets/font-awesome/css/font-awesome.css @@ -0,0 +1,2337 @@ +/*! + * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome + * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) + */ +/* FONT PATH + * -------------------------- */ +@font-face { + font-family: 'FontAwesome'; + src: url('../fonts/fontawesome-webfont.eot?v=4.7.0'); + src: url('../fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg'); + font-weight: normal; + font-style: normal; +} +.fa { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} +/* makes the font 33% larger relative to the icon container */ +.fa-lg { + font-size: 1.33333333em; + line-height: 0.75em; + vertical-align: -15%; +} +.fa-2x { + font-size: 2em; +} +.fa-3x { + font-size: 3em; +} +.fa-4x { + font-size: 4em; +} +.fa-5x { + font-size: 5em; +} +.fa-fw { + width: 1.28571429em; + text-align: center; +} +.fa-ul { + padding-left: 0; + margin-left: 2.14285714em; + list-style-type: none; +} +.fa-ul > li { + position: relative; +} +.fa-li { + position: absolute; + left: -2.14285714em; + width: 2.14285714em; + top: 0.14285714em; + text-align: center; +} +.fa-li.fa-lg { + left: -1.85714286em; +} +.fa-border { + padding: .2em .25em .15em; + border: solid 0.08em #eeeeee; + border-radius: .1em; +} +.fa-pull-left { + float: left; +} +.fa-pull-right { + float: right; +} +.fa.fa-pull-left { + margin-right: .3em; +} +.fa.fa-pull-right { + margin-left: .3em; +} +/* Deprecated as of 4.4.0 */ +.pull-right { + float: right; +} +.pull-left { + float: left; +} +.fa.pull-left { + margin-right: .3em; +} +.fa.pull-right { + margin-left: .3em; +} +.fa-spin { + -webkit-animation: fa-spin 2s infinite linear; + animation: fa-spin 2s infinite linear; +} +.fa-pulse { + -webkit-animation: fa-spin 1s infinite steps(8); + animation: fa-spin 1s infinite steps(8); +} +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} +.fa-rotate-90 { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)"; + -webkit-transform: rotate(90deg); + -ms-transform: rotate(90deg); + transform: rotate(90deg); +} +.fa-rotate-180 { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)"; + -webkit-transform: rotate(180deg); + -ms-transform: rotate(180deg); + transform: rotate(180deg); +} +.fa-rotate-270 { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)"; + -webkit-transform: rotate(270deg); + -ms-transform: rotate(270deg); + transform: rotate(270deg); +} +.fa-flip-horizontal { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)"; + -webkit-transform: scale(-1, 1); + -ms-transform: scale(-1, 1); + transform: scale(-1, 1); +} +.fa-flip-vertical { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"; + -webkit-transform: scale(1, -1); + -ms-transform: scale(1, -1); + transform: scale(1, -1); +} +:root .fa-rotate-90, +:root .fa-rotate-180, +:root .fa-rotate-270, +:root .fa-flip-horizontal, +:root .fa-flip-vertical { + filter: none; +} +.fa-stack { + position: relative; + display: inline-block; + width: 2em; + height: 2em; + line-height: 2em; + vertical-align: middle; +} +.fa-stack-1x, +.fa-stack-2x { + position: absolute; + left: 0; + width: 100%; + text-align: center; +} +.fa-stack-1x { + line-height: inherit; +} +.fa-stack-2x { + font-size: 2em; +} +.fa-inverse { + color: #ffffff; +} +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen + readers do not read off random characters that represent icons */ +.fa-glass:before { + content: "\f000"; +} +.fa-music:before { + content: "\f001"; +} +.fa-search:before { + content: "\f002"; +} +.fa-envelope-o:before { + content: "\f003"; +} +.fa-heart:before { + content: "\f004"; +} +.fa-star:before { + content: "\f005"; +} +.fa-star-o:before { + content: "\f006"; +} +.fa-user:before { + content: "\f007"; +} +.fa-film:before { + content: "\f008"; +} +.fa-th-large:before { + content: "\f009"; +} +.fa-th:before { + content: "\f00a"; +} +.fa-th-list:before { + content: "\f00b"; +} +.fa-check:before { + content: "\f00c"; +} +.fa-remove:before, +.fa-close:before, +.fa-times:before { + content: "\f00d"; +} +.fa-search-plus:before { + content: "\f00e"; +} +.fa-search-minus:before { + content: "\f010"; +} +.fa-power-off:before { + content: "\f011"; +} +.fa-signal:before { + content: "\f012"; +} +.fa-gear:before, +.fa-cog:before { + content: "\f013"; +} +.fa-trash-o:before { + content: "\f014"; +} +.fa-home:before { + content: "\f015"; +} +.fa-file-o:before { + content: "\f016"; +} +.fa-clock-o:before { + content: "\f017"; +} +.fa-road:before { + content: "\f018"; +} +.fa-download:before { + content: "\f019"; +} +.fa-arrow-circle-o-down:before { + content: "\f01a"; +} +.fa-arrow-circle-o-up:before { + content: "\f01b"; +} +.fa-inbox:before { + content: "\f01c"; +} +.fa-play-circle-o:before { + content: "\f01d"; +} +.fa-rotate-right:before, +.fa-repeat:before { + content: "\f01e"; +} +.fa-refresh:before { + content: "\f021"; +} +.fa-list-alt:before { + content: "\f022"; +} +.fa-lock:before { + content: "\f023"; +} +.fa-flag:before { + content: "\f024"; +} +.fa-headphones:before { + content: "\f025"; +} +.fa-volume-off:before { + content: "\f026"; +} +.fa-volume-down:before { + content: "\f027"; +} +.fa-volume-up:before { + content: "\f028"; +} +.fa-qrcode:before { + content: "\f029"; +} +.fa-barcode:before { + content: "\f02a"; +} +.fa-tag:before { + content: "\f02b"; +} +.fa-tags:before { + content: "\f02c"; +} +.fa-book:before { + content: "\f02d"; +} +.fa-bookmark:before { + content: "\f02e"; +} +.fa-print:before { + content: "\f02f"; +} +.fa-camera:before { + content: "\f030"; +} +.fa-font:before { + content: "\f031"; +} +.fa-bold:before { + content: "\f032"; +} +.fa-italic:before { + content: "\f033"; +} +.fa-text-height:before { + content: "\f034"; +} +.fa-text-width:before { + content: "\f035"; +} +.fa-align-left:before { + content: "\f036"; +} +.fa-align-center:before { + content: "\f037"; +} +.fa-align-right:before { + content: "\f038"; +} +.fa-align-justify:before { + content: "\f039"; +} +.fa-list:before { + content: "\f03a"; +} +.fa-dedent:before, +.fa-outdent:before { + content: "\f03b"; +} +.fa-indent:before { + content: "\f03c"; +} +.fa-video-camera:before { + content: "\f03d"; +} +.fa-photo:before, +.fa-image:before, +.fa-picture-o:before { + content: "\f03e"; +} +.fa-pencil:before { + content: "\f040"; +} +.fa-map-marker:before { + content: "\f041"; +} +.fa-adjust:before { + content: "\f042"; +} +.fa-tint:before { + content: "\f043"; +} +.fa-edit:before, +.fa-pencil-square-o:before { + content: "\f044"; +} +.fa-share-square-o:before { + content: "\f045"; +} +.fa-check-square-o:before { + content: "\f046"; +} +.fa-arrows:before { + content: "\f047"; +} +.fa-step-backward:before { + content: "\f048"; +} +.fa-fast-backward:before { + content: "\f049"; +} +.fa-backward:before { + content: "\f04a"; +} +.fa-play:before { + content: "\f04b"; +} +.fa-pause:before { + content: "\f04c"; +} +.fa-stop:before { + content: "\f04d"; +} +.fa-forward:before { + content: "\f04e"; +} +.fa-fast-forward:before { + content: "\f050"; +} +.fa-step-forward:before { + content: "\f051"; +} +.fa-eject:before { + content: "\f052"; +} +.fa-chevron-left:before { + content: "\f053"; +} +.fa-chevron-right:before { + content: "\f054"; +} +.fa-plus-circle:before { + content: "\f055"; +} +.fa-minus-circle:before { + content: "\f056"; +} +.fa-times-circle:before { + content: "\f057"; +} +.fa-check-circle:before { + content: "\f058"; +} +.fa-question-circle:before { + content: "\f059"; +} +.fa-info-circle:before { + content: "\f05a"; +} +.fa-crosshairs:before { + content: "\f05b"; +} +.fa-times-circle-o:before { + content: "\f05c"; +} +.fa-check-circle-o:before { + content: "\f05d"; +} +.fa-ban:before { + content: "\f05e"; +} +.fa-arrow-left:before { + content: "\f060"; +} +.fa-arrow-right:before { + content: "\f061"; +} +.fa-arrow-up:before { + content: "\f062"; +} +.fa-arrow-down:before { + content: "\f063"; +} +.fa-mail-forward:before, +.fa-share:before { + content: "\f064"; +} +.fa-expand:before { + content: "\f065"; +} +.fa-compress:before { + content: "\f066"; +} +.fa-plus:before { + content: "\f067"; +} +.fa-minus:before { + content: "\f068"; +} +.fa-asterisk:before { + content: "\f069"; +} +.fa-exclamation-circle:before { + content: "\f06a"; +} +.fa-gift:before { + content: "\f06b"; +} +.fa-leaf:before { + content: "\f06c"; +} +.fa-fire:before { + content: "\f06d"; +} +.fa-eye:before { + content: "\f06e"; +} +.fa-eye-slash:before { + content: "\f070"; +} +.fa-warning:before, +.fa-exclamation-triangle:before { + content: "\f071"; +} +.fa-plane:before { + content: "\f072"; +} +.fa-calendar:before { + content: "\f073"; +} +.fa-random:before { + content: "\f074"; +} +.fa-comment:before { + content: "\f075"; +} +.fa-magnet:before { + content: "\f076"; +} +.fa-chevron-up:before { + content: "\f077"; +} +.fa-chevron-down:before { + content: "\f078"; +} +.fa-retweet:before { + content: "\f079"; +} +.fa-shopping-cart:before { + content: "\f07a"; +} +.fa-folder:before { + content: "\f07b"; +} +.fa-folder-open:before { + content: "\f07c"; +} +.fa-arrows-v:before { + content: "\f07d"; +} +.fa-arrows-h:before { + content: "\f07e"; +} +.fa-bar-chart-o:before, +.fa-bar-chart:before { + content: "\f080"; +} +.fa-twitter-square:before { + content: "\f081"; +} +.fa-facebook-square:before { + content: "\f082"; +} +.fa-camera-retro:before { + content: "\f083"; +} +.fa-key:before { + content: "\f084"; +} +.fa-gears:before, +.fa-cogs:before { + content: "\f085"; +} +.fa-comments:before { + content: "\f086"; +} +.fa-thumbs-o-up:before { + content: "\f087"; +} +.fa-thumbs-o-down:before { + content: "\f088"; +} +.fa-star-half:before { + content: "\f089"; +} +.fa-heart-o:before { + content: "\f08a"; +} +.fa-sign-out:before { + content: "\f08b"; +} +.fa-linkedin-square:before { + content: "\f08c"; +} +.fa-thumb-tack:before { + content: "\f08d"; +} +.fa-external-link:before { + content: "\f08e"; +} +.fa-sign-in:before { + content: "\f090"; +} +.fa-trophy:before { + content: "\f091"; +} +.fa-github-square:before { + content: "\f092"; +} +.fa-upload:before { + content: "\f093"; +} +.fa-lemon-o:before { + content: "\f094"; +} +.fa-phone:before { + content: "\f095"; +} +.fa-square-o:before { + content: "\f096"; +} +.fa-bookmark-o:before { + content: "\f097"; +} +.fa-phone-square:before { + content: "\f098"; +} +.fa-twitter:before { + content: "\f099"; +} +.fa-facebook-f:before, +.fa-facebook:before { + content: "\f09a"; +} +.fa-github:before { + content: "\f09b"; +} +.fa-unlock:before { + content: "\f09c"; +} +.fa-credit-card:before { + content: "\f09d"; +} +.fa-feed:before, +.fa-rss:before { + content: "\f09e"; +} +.fa-hdd-o:before { + content: "\f0a0"; +} +.fa-bullhorn:before { + content: "\f0a1"; +} +.fa-bell:before { + content: "\f0f3"; +} +.fa-certificate:before { + content: "\f0a3"; +} +.fa-hand-o-right:before { + content: "\f0a4"; +} +.fa-hand-o-left:before { + content: "\f0a5"; +} +.fa-hand-o-up:before { + content: "\f0a6"; +} +.fa-hand-o-down:before { + content: "\f0a7"; +} +.fa-arrow-circle-left:before { + content: "\f0a8"; +} +.fa-arrow-circle-right:before { + content: "\f0a9"; +} +.fa-arrow-circle-up:before { + content: "\f0aa"; +} +.fa-arrow-circle-down:before { + content: "\f0ab"; +} +.fa-globe:before { + content: "\f0ac"; +} +.fa-wrench:before { + content: "\f0ad"; +} +.fa-tasks:before { + content: "\f0ae"; +} +.fa-filter:before { + content: "\f0b0"; +} +.fa-briefcase:before { + content: "\f0b1"; +} +.fa-arrows-alt:before { + content: "\f0b2"; +} +.fa-group:before, +.fa-users:before { + content: "\f0c0"; +} +.fa-chain:before, +.fa-link:before { + content: "\f0c1"; +} +.fa-cloud:before { + content: "\f0c2"; +} +.fa-flask:before { + content: "\f0c3"; +} +.fa-cut:before, +.fa-scissors:before { + content: "\f0c4"; +} +.fa-copy:before, +.fa-files-o:before { + content: "\f0c5"; +} 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"\f104"; +} +.fa-angle-right:before { + content: "\f105"; +} +.fa-angle-up:before { + content: "\f106"; +} +.fa-angle-down:before { + content: "\f107"; +} +.fa-desktop:before { + content: "\f108"; +} +.fa-laptop:before { + content: "\f109"; +} +.fa-tablet:before { + content: "\f10a"; +} +.fa-mobile-phone:before, +.fa-mobile:before { + content: "\f10b"; +} +.fa-circle-o:before { + content: "\f10c"; +} +.fa-quote-left:before { + content: "\f10d"; +} +.fa-quote-right:before { + content: "\f10e"; +} +.fa-spinner:before { + content: "\f110"; +} +.fa-circle:before { + content: "\f111"; +} +.fa-mail-reply:before, +.fa-reply:before { + content: "\f112"; +} +.fa-github-alt:before { + content: "\f113"; +} +.fa-folder-o:before { + content: "\f114"; +} +.fa-folder-open-o:before { + content: "\f115"; +} +.fa-smile-o:before { + content: "\f118"; +} +.fa-frown-o:before { + content: "\f119"; +} +.fa-meh-o:before { + content: "\f11a"; +} +.fa-gamepad:before { + content: "\f11b"; +} 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{ + content: "\f146"; +} +.fa-minus-square-o:before { + content: "\f147"; +} +.fa-level-up:before { + content: "\f148"; +} +.fa-level-down:before { + content: "\f149"; +} +.fa-check-square:before { + content: "\f14a"; +} +.fa-pencil-square:before { + content: "\f14b"; +} +.fa-external-link-square:before { + content: "\f14c"; +} +.fa-share-square:before { + content: "\f14d"; +} +.fa-compass:before { + content: "\f14e"; +} +.fa-toggle-down:before, +.fa-caret-square-o-down:before { + content: "\f150"; +} +.fa-toggle-up:before, +.fa-caret-square-o-up:before { + content: "\f151"; +} +.fa-toggle-right:before, +.fa-caret-square-o-right:before { + content: "\f152"; +} +.fa-euro:before, +.fa-eur:before { + content: "\f153"; +} +.fa-gbp:before { + content: "\f154"; +} +.fa-dollar:before, +.fa-usd:before { + content: "\f155"; +} +.fa-rupee:before, +.fa-inr:before { + content: "\f156"; +} +.fa-cny:before, +.fa-rmb:before, +.fa-yen:before, +.fa-jpy:before { + content: "\f157"; +} +.fa-ruble:before, 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content: "\f16c"; +} +.fa-instagram:before { + content: "\f16d"; +} +.fa-flickr:before { + content: "\f16e"; +} +.fa-adn:before { + content: "\f170"; +} +.fa-bitbucket:before { + content: "\f171"; +} +.fa-bitbucket-square:before { + content: "\f172"; +} +.fa-tumblr:before { + content: "\f173"; +} +.fa-tumblr-square:before { + content: "\f174"; +} +.fa-long-arrow-down:before { + content: "\f175"; +} +.fa-long-arrow-up:before { + content: "\f176"; +} +.fa-long-arrow-left:before { + content: "\f177"; +} +.fa-long-arrow-right:before { + content: "\f178"; +} +.fa-apple:before { + content: "\f179"; +} +.fa-windows:before { + content: "\f17a"; +} +.fa-android:before { + content: "\f17b"; +} +.fa-linux:before { + content: "\f17c"; +} +.fa-dribbble:before { + content: "\f17d"; +} +.fa-skype:before { + content: "\f17e"; +} +.fa-foursquare:before { + content: "\f180"; +} +.fa-trello:before { + content: "\f181"; +} +.fa-female:before { + content: "\f182"; +} +.fa-male:before { + content: "\f183"; +} +.fa-gittip:before, +.fa-gratipay:before { + content: "\f184"; +} +.fa-sun-o:before { + content: "\f185"; +} +.fa-moon-o:before { + content: "\f186"; +} +.fa-archive:before { + content: "\f187"; +} +.fa-bug:before { + content: "\f188"; +} +.fa-vk:before { + content: "\f189"; +} +.fa-weibo:before { + content: "\f18a"; +} +.fa-renren:before { + content: "\f18b"; +} +.fa-pagelines:before { + content: "\f18c"; +} +.fa-stack-exchange:before { + content: "\f18d"; +} +.fa-arrow-circle-o-right:before { + content: "\f18e"; +} +.fa-arrow-circle-o-left:before { + content: "\f190"; +} +.fa-toggle-left:before, +.fa-caret-square-o-left:before { + content: "\f191"; +} +.fa-dot-circle-o:before { + content: "\f192"; +} +.fa-wheelchair:before { + content: "\f193"; +} +.fa-vimeo-square:before { + content: "\f194"; +} +.fa-turkish-lira:before, +.fa-try:before { + content: "\f195"; +} +.fa-plus-square-o:before { + content: "\f196"; +} +.fa-space-shuttle:before { + content: "\f197"; +} +.fa-slack:before { + content: "\f198"; +} +.fa-envelope-square:before { + content: "\f199"; +} +.fa-wordpress:before { + content: "\f19a"; +} +.fa-openid:before { + content: "\f19b"; +} +.fa-institution:before, +.fa-bank:before, +.fa-university:before { + content: "\f19c"; +} +.fa-mortar-board:before, +.fa-graduation-cap:before { + content: "\f19d"; +} +.fa-yahoo:before { + content: "\f19e"; +} +.fa-google:before { + content: "\f1a0"; +} +.fa-reddit:before { + content: "\f1a1"; +} +.fa-reddit-square:before { + content: "\f1a2"; +} +.fa-stumbleupon-circle:before { + content: "\f1a3"; +} +.fa-stumbleupon:before { + content: "\f1a4"; +} +.fa-delicious:before { + content: "\f1a5"; +} +.fa-digg:before { + content: "\f1a6"; +} +.fa-pied-piper-pp:before { + content: "\f1a7"; +} +.fa-pied-piper-alt:before { + content: "\f1a8"; +} +.fa-drupal:before { + content: "\f1a9"; +} +.fa-joomla:before { + content: "\f1aa"; +} +.fa-language:before { + content: "\f1ab"; +} +.fa-fax:before { + content: "\f1ac"; +} 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+.fa-y-combinator-square:before, +.fa-yc-square:before, +.fa-hacker-news:before { + content: "\f1d4"; +} +.fa-tencent-weibo:before { + content: "\f1d5"; +} +.fa-qq:before { + content: "\f1d6"; +} +.fa-wechat:before, +.fa-weixin:before { + content: "\f1d7"; +} +.fa-send:before, +.fa-paper-plane:before { + content: "\f1d8"; +} +.fa-send-o:before, +.fa-paper-plane-o:before { + content: "\f1d9"; +} +.fa-history:before { + content: "\f1da"; +} +.fa-circle-thin:before { + content: "\f1db"; +} +.fa-header:before { + content: "\f1dc"; +} +.fa-paragraph:before { + content: "\f1dd"; +} +.fa-sliders:before { + content: "\f1de"; +} +.fa-share-alt:before { + content: "\f1e0"; +} +.fa-share-alt-square:before { + content: "\f1e1"; +} +.fa-bomb:before { + content: "\f1e2"; +} +.fa-soccer-ball-o:before, +.fa-futbol-o:before { + content: "\f1e3"; +} +.fa-tty:before { + content: "\f1e4"; +} +.fa-binoculars:before { + content: "\f1e5"; +} +.fa-plug:before { + content: "\f1e6"; +} +.fa-slideshare:before { + content: "\f1e7"; +} +.fa-twitch:before { + content: "\f1e8"; +} +.fa-yelp:before { + content: "\f1e9"; +} +.fa-newspaper-o:before { + content: "\f1ea"; +} +.fa-wifi:before { + content: "\f1eb"; +} +.fa-calculator:before { + content: "\f1ec"; +} +.fa-paypal:before { + content: "\f1ed"; +} +.fa-google-wallet:before { + content: "\f1ee"; +} +.fa-cc-visa:before { + content: "\f1f0"; +} +.fa-cc-mastercard:before { + content: "\f1f1"; +} +.fa-cc-discover:before { + content: "\f1f2"; +} +.fa-cc-amex:before { + content: "\f1f3"; +} +.fa-cc-paypal:before { + content: "\f1f4"; +} +.fa-cc-stripe:before { + content: "\f1f5"; +} +.fa-bell-slash:before { + content: "\f1f6"; +} +.fa-bell-slash-o:before { + content: "\f1f7"; +} +.fa-trash:before { + content: "\f1f8"; +} +.fa-copyright:before { + content: "\f1f9"; +} +.fa-at:before { + content: "\f1fa"; +} +.fa-eyedropper:before { + content: "\f1fb"; +} +.fa-paint-brush:before { + content: "\f1fc"; +} +.fa-birthday-cake:before { + content: "\f1fd"; +} +.fa-area-chart:before { + content: "\f1fe"; +} +.fa-pie-chart:before { + content: "\f200"; +} +.fa-line-chart:before { + content: "\f201"; +} +.fa-lastfm:before { + content: "\f202"; +} +.fa-lastfm-square:before { + content: "\f203"; +} +.fa-toggle-off:before { + content: "\f204"; +} +.fa-toggle-on:before { + content: "\f205"; +} +.fa-bicycle:before { + content: "\f206"; +} +.fa-bus:before { + content: "\f207"; +} +.fa-ioxhost:before { + content: "\f208"; +} +.fa-angellist:before { + content: "\f209"; +} +.fa-cc:before { + content: "\f20a"; +} +.fa-shekel:before, +.fa-sheqel:before, +.fa-ils:before { + content: "\f20b"; +} +.fa-meanpath:before { + content: "\f20c"; +} +.fa-buysellads:before { + content: "\f20d"; +} +.fa-connectdevelop:before { + content: "\f20e"; +} +.fa-dashcube:before { + content: "\f210"; +} +.fa-forumbee:before { + content: "\f211"; +} +.fa-leanpub:before { + content: "\f212"; +} +.fa-sellsy:before { + content: "\f213"; +} +.fa-shirtsinbulk:before { + content: "\f214"; +} +.fa-simplybuilt:before { + content: "\f215"; +} +.fa-skyatlas:before { + content: "\f216"; +} +.fa-cart-plus:before { + content: "\f217"; +} +.fa-cart-arrow-down:before { + content: "\f218"; +} +.fa-diamond:before { + content: "\f219"; +} +.fa-ship:before { + content: "\f21a"; +} +.fa-user-secret:before { + content: "\f21b"; +} +.fa-motorcycle:before { + content: "\f21c"; +} +.fa-street-view:before { + content: "\f21d"; +} +.fa-heartbeat:before { + content: "\f21e"; +} +.fa-venus:before { + content: "\f221"; +} +.fa-mars:before { + content: "\f222"; +} +.fa-mercury:before { + content: "\f223"; +} +.fa-intersex:before, +.fa-transgender:before { + content: "\f224"; +} +.fa-transgender-alt:before { + content: "\f225"; +} +.fa-venus-double:before { + content: "\f226"; +} +.fa-mars-double:before { + content: "\f227"; +} +.fa-venus-mars:before { + content: "\f228"; +} +.fa-mars-stroke:before { + content: "\f229"; +} +.fa-mars-stroke-v:before { + content: "\f22a"; +} +.fa-mars-stroke-h:before { + content: "\f22b"; +} +.fa-neuter:before { + content: "\f22c"; +} +.fa-genderless:before { + content: "\f22d"; +} +.fa-facebook-official:before { + content: "\f230"; +} +.fa-pinterest-p:before { + content: "\f231"; +} +.fa-whatsapp:before { + content: "\f232"; +} +.fa-server:before { + content: "\f233"; +} +.fa-user-plus:before { + content: "\f234"; +} +.fa-user-times:before { + content: "\f235"; +} +.fa-hotel:before, +.fa-bed:before { + content: "\f236"; +} +.fa-viacoin:before { + content: "\f237"; +} +.fa-train:before { + content: "\f238"; +} +.fa-subway:before { + content: "\f239"; +} +.fa-medium:before { + content: "\f23a"; +} +.fa-yc:before, +.fa-y-combinator:before { + content: "\f23b"; +} +.fa-optin-monster:before { + content: "\f23c"; +} +.fa-opencart:before { + content: "\f23d"; +} +.fa-expeditedssl:before { + content: "\f23e"; +} +.fa-battery-4:before, +.fa-battery:before, +.fa-battery-full:before { + content: "\f240"; +} +.fa-battery-3:before, +.fa-battery-three-quarters:before { + content: "\f241"; +} +.fa-battery-2:before, +.fa-battery-half:before { + content: "\f242"; +} +.fa-battery-1:before, +.fa-battery-quarter:before { + content: "\f243"; +} +.fa-battery-0:before, +.fa-battery-empty:before { + content: "\f244"; +} +.fa-mouse-pointer:before { + content: "\f245"; +} +.fa-i-cursor:before { + content: "\f246"; +} +.fa-object-group:before { + content: "\f247"; +} +.fa-object-ungroup:before { + content: "\f248"; +} +.fa-sticky-note:before { + content: "\f249"; +} +.fa-sticky-note-o:before { + content: "\f24a"; +} +.fa-cc-jcb:before { + content: "\f24b"; +} +.fa-cc-diners-club:before { + content: "\f24c"; +} +.fa-clone:before { + content: "\f24d"; +} +.fa-balance-scale:before { + content: "\f24e"; +} +.fa-hourglass-o:before { + content: "\f250"; +} +.fa-hourglass-1:before, +.fa-hourglass-start:before { + content: "\f251"; +} +.fa-hourglass-2:before, +.fa-hourglass-half:before { + content: "\f252"; +} +.fa-hourglass-3:before, +.fa-hourglass-end:before { + content: "\f253"; +} +.fa-hourglass:before { + content: "\f254"; +} +.fa-hand-grab-o:before, +.fa-hand-rock-o:before { + content: "\f255"; +} +.fa-hand-stop-o:before, +.fa-hand-paper-o:before { + content: "\f256"; +} +.fa-hand-scissors-o:before { + content: "\f257"; +} +.fa-hand-lizard-o:before { + content: "\f258"; +} +.fa-hand-spock-o:before { + content: "\f259"; +} +.fa-hand-pointer-o:before { + content: "\f25a"; +} +.fa-hand-peace-o:before { + content: "\f25b"; +} +.fa-trademark:before { + content: "\f25c"; +} +.fa-registered:before { + content: "\f25d"; +} +.fa-creative-commons:before { + content: "\f25e"; +} +.fa-gg:before { + content: "\f260"; +} +.fa-gg-circle:before { + content: "\f261"; +} +.fa-tripadvisor:before { + content: "\f262"; +} +.fa-odnoklassniki:before { + content: "\f263"; +} +.fa-odnoklassniki-square:before { + content: "\f264"; +} +.fa-get-pocket:before { + content: "\f265"; +} +.fa-wikipedia-w:before { + content: "\f266"; +} +.fa-safari:before { + content: "\f267"; +} +.fa-chrome:before { + content: "\f268"; +} +.fa-firefox:before { + content: "\f269"; +} +.fa-opera:before { + content: "\f26a"; +} +.fa-internet-explorer:before { + content: "\f26b"; +} +.fa-tv:before, +.fa-television:before { + content: "\f26c"; +} +.fa-contao:before { + content: "\f26d"; +} +.fa-500px:before { + content: "\f26e"; +} +.fa-amazon:before { + content: "\f270"; +} +.fa-calendar-plus-o:before { + content: "\f271"; +} +.fa-calendar-minus-o:before { + content: "\f272"; +} +.fa-calendar-times-o:before { + content: "\f273"; +} +.fa-calendar-check-o:before { + content: "\f274"; +} +.fa-industry:before { + content: "\f275"; +} +.fa-map-pin:before { + content: "\f276"; +} +.fa-map-signs:before { + content: "\f277"; +} +.fa-map-o:before { + content: "\f278"; +} +.fa-map:before { + content: "\f279"; +} +.fa-commenting:before { + content: "\f27a"; +} +.fa-commenting-o:before { + content: "\f27b"; +} +.fa-houzz:before { + content: "\f27c"; +} +.fa-vimeo:before { + content: "\f27d"; +} +.fa-black-tie:before { + content: "\f27e"; +} +.fa-fonticons:before { + content: "\f280"; +} +.fa-reddit-alien:before { + content: "\f281"; +} +.fa-edge:before { + content: "\f282"; +} +.fa-credit-card-alt:before { + content: "\f283"; +} +.fa-codiepie:before { + content: "\f284"; +} +.fa-modx:before { + content: "\f285"; +} +.fa-fort-awesome:before { + content: "\f286"; +} +.fa-usb:before { + content: "\f287"; +} +.fa-product-hunt:before { + content: "\f288"; +} +.fa-mixcloud:before { + content: "\f289"; +} +.fa-scribd:before { + content: "\f28a"; +} +.fa-pause-circle:before { + content: "\f28b"; +} +.fa-pause-circle-o:before { + content: "\f28c"; +} +.fa-stop-circle:before { + content: "\f28d"; +} +.fa-stop-circle-o:before { + content: "\f28e"; +} +.fa-shopping-bag:before { + content: "\f290"; +} +.fa-shopping-basket:before { + content: "\f291"; +} +.fa-hashtag:before { + content: "\f292"; +} +.fa-bluetooth:before { + content: "\f293"; +} +.fa-bluetooth-b:before { + content: "\f294"; +} +.fa-percent:before { + content: "\f295"; +} +.fa-gitlab:before { + content: "\f296"; +} +.fa-wpbeginner:before { + content: "\f297"; +} +.fa-wpforms:before { + content: "\f298"; +} +.fa-envira:before { + content: "\f299"; +} +.fa-universal-access:before { + content: "\f29a"; +} +.fa-wheelchair-alt:before { + content: "\f29b"; +} +.fa-question-circle-o:before { + content: "\f29c"; +} +.fa-blind:before { + content: "\f29d"; +} +.fa-audio-description:before { + content: "\f29e"; +} +.fa-volume-control-phone:before { + content: "\f2a0"; +} +.fa-braille:before { + content: "\f2a1"; +} +.fa-assistive-listening-systems:before { + content: "\f2a2"; +} +.fa-asl-interpreting:before, +.fa-american-sign-language-interpreting:before { + content: "\f2a3"; +} +.fa-deafness:before, +.fa-hard-of-hearing:before, +.fa-deaf:before { + content: "\f2a4"; +} +.fa-glide:before { + content: "\f2a5"; +} +.fa-glide-g:before { + content: "\f2a6"; +} +.fa-signing:before, +.fa-sign-language:before { + content: "\f2a7"; +} +.fa-low-vision:before { + content: "\f2a8"; +} +.fa-viadeo:before { + content: "\f2a9"; +} +.fa-viadeo-square:before { + content: "\f2aa"; +} +.fa-snapchat:before { + content: "\f2ab"; +} +.fa-snapchat-ghost:before { + content: "\f2ac"; +} +.fa-snapchat-square:before { + content: "\f2ad"; +} +.fa-pied-piper:before { + content: "\f2ae"; +} +.fa-first-order:before { + content: "\f2b0"; +} +.fa-yoast:before { + content: "\f2b1"; +} +.fa-themeisle:before { + content: "\f2b2"; +} +.fa-google-plus-circle:before, +.fa-google-plus-official:before { + content: "\f2b3"; +} +.fa-fa:before, +.fa-font-awesome:before { + content: "\f2b4"; +} +.fa-handshake-o:before { + content: "\f2b5"; +} +.fa-envelope-open:before { + content: "\f2b6"; +} +.fa-envelope-open-o:before { + content: "\f2b7"; +} +.fa-linode:before { + content: "\f2b8"; +} +.fa-address-book:before { + content: "\f2b9"; +} +.fa-address-book-o:before { + content: "\f2ba"; +} +.fa-vcard:before, +.fa-address-card:before { + content: "\f2bb"; +} +.fa-vcard-o:before, +.fa-address-card-o:before { + content: "\f2bc"; +} +.fa-user-circle:before { + content: "\f2bd"; +} +.fa-user-circle-o:before { + content: "\f2be"; +} +.fa-user-o:before { + content: "\f2c0"; +} +.fa-id-badge:before { + content: "\f2c1"; +} +.fa-drivers-license:before, +.fa-id-card:before { + content: "\f2c2"; +} +.fa-drivers-license-o:before, +.fa-id-card-o:before { + content: "\f2c3"; +} +.fa-quora:before { + content: "\f2c4"; +} +.fa-free-code-camp:before { + content: "\f2c5"; +} +.fa-telegram:before { + content: "\f2c6"; +} +.fa-thermometer-4:before, +.fa-thermometer:before, +.fa-thermometer-full:before { + content: "\f2c7"; +} +.fa-thermometer-3:before, +.fa-thermometer-three-quarters:before { + content: "\f2c8"; +} +.fa-thermometer-2:before, +.fa-thermometer-half:before { + content: "\f2c9"; +} +.fa-thermometer-1:before, +.fa-thermometer-quarter:before { + content: "\f2ca"; +} +.fa-thermometer-0:before, +.fa-thermometer-empty:before { + content: "\f2cb"; +} +.fa-shower:before { + content: "\f2cc"; +} +.fa-bathtub:before, +.fa-s15:before, +.fa-bath:before { + content: "\f2cd"; +} +.fa-podcast:before { + content: "\f2ce"; +} +.fa-window-maximize:before { + content: "\f2d0"; +} +.fa-window-minimize:before { + content: "\f2d1"; +} +.fa-window-restore:before { + content: "\f2d2"; +} +.fa-times-rectangle:before, +.fa-window-close:before { + content: "\f2d3"; +} +.fa-times-rectangle-o:before, +.fa-window-close-o:before { + content: "\f2d4"; +} +.fa-bandcamp:before { + content: "\f2d5"; +} +.fa-grav:before { + content: "\f2d6"; +} +.fa-etsy:before { + content: "\f2d7"; +} +.fa-imdb:before { + content: "\f2d8"; +} +.fa-ravelry:before { + content: "\f2d9"; +} +.fa-eercast:before { + content: "\f2da"; +} +.fa-microchip:before { + content: "\f2db"; +} +.fa-snowflake-o:before { + content: "\f2dc"; +} +.fa-superpowers:before { + content: "\f2dd"; +} +.fa-wpexplorer:before { + content: "\f2de"; +} +.fa-meetup:before { + content: "\f2e0"; +} +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} +.sr-only-focusable:active, +.sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; +} diff --git a/dashboard/src/assets/font-awesome/css/font-awesome.min.css b/dashboard/src/assets/font-awesome/css/font-awesome.min.css new file mode 100644 index 00000000..540440ce --- /dev/null +++ b/dashboard/src/assets/font-awesome/css/font-awesome.min.css @@ -0,0 +1,4 @@ +/*! + * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome + * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) + */@font-face{font-family:'FontAwesome';src:url('../fonts/fontawesome-webfont.eot?v=4.7.0');src:url('../fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') 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+@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} diff --git a/dashboard/src/assets/font-awesome/less/bordered-pulled.less b/dashboard/src/assets/font-awesome/less/bordered-pulled.less new file mode 100644 index 00000000..f1c8ad75 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/bordered-pulled.less @@ -0,0 +1,25 @@ +// Bordered & Pulled +// ------------------------- + +.@{fa-css-prefix}-border { + padding: .2em .25em .15em; + border: solid .08em @fa-border-color; + border-radius: .1em; +} + +.@{fa-css-prefix}-pull-left { float: left; } +.@{fa-css-prefix}-pull-right { float: right; } + +.@{fa-css-prefix} { + &.@{fa-css-prefix}-pull-left { margin-right: .3em; } + &.@{fa-css-prefix}-pull-right { margin-left: .3em; } +} + +/* Deprecated as of 4.4.0 */ +.pull-right { float: right; } +.pull-left { float: left; } + +.@{fa-css-prefix} { + &.pull-left { margin-right: .3em; } + &.pull-right { margin-left: .3em; } +} diff --git a/dashboard/src/assets/font-awesome/less/core.less b/dashboard/src/assets/font-awesome/less/core.less new file mode 100644 index 00000000..c577ac84 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/core.less @@ -0,0 +1,12 @@ +// Base Class Definition +// ------------------------- + +.@{fa-css-prefix} { + display: inline-block; + font: normal normal normal @fa-font-size-base/@fa-line-height-base FontAwesome; // shortening font declaration + font-size: inherit; // can't have font-size inherit on line above, so need to override + text-rendering: auto; // optimizelegibility throws things off #1094 + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + +} diff --git a/dashboard/src/assets/font-awesome/less/fixed-width.less b/dashboard/src/assets/font-awesome/less/fixed-width.less new file mode 100644 index 00000000..110289f2 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/fixed-width.less @@ -0,0 +1,6 @@ +// Fixed Width Icons +// ------------------------- +.@{fa-css-prefix}-fw { + width: (18em / 14); + text-align: center; +} diff --git a/dashboard/src/assets/font-awesome/less/font-awesome.less b/dashboard/src/assets/font-awesome/less/font-awesome.less new file mode 100644 index 00000000..c3677def --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/font-awesome.less @@ -0,0 +1,18 @@ +/*! + * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome + * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) + */ + +@import "variables.less"; +@import "mixins.less"; +@import "path.less"; +@import "core.less"; +@import "larger.less"; +@import "fixed-width.less"; +@import "list.less"; +@import "bordered-pulled.less"; +@import "animated.less"; +@import "rotated-flipped.less"; +@import "stacked.less"; +@import "icons.less"; +@import "screen-reader.less"; diff --git a/dashboard/src/assets/font-awesome/less/icons.less b/dashboard/src/assets/font-awesome/less/icons.less new file mode 100644 index 00000000..159d6004 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/icons.less @@ -0,0 +1,789 @@ +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen + readers do not read off random characters that represent icons */ + +.@{fa-css-prefix}-glass:before { content: @fa-var-glass; } +.@{fa-css-prefix}-music:before { content: @fa-var-music; } +.@{fa-css-prefix}-search:before { content: @fa-var-search; } +.@{fa-css-prefix}-envelope-o:before { content: @fa-var-envelope-o; } +.@{fa-css-prefix}-heart:before { content: @fa-var-heart; } +.@{fa-css-prefix}-star:before { content: @fa-var-star; } +.@{fa-css-prefix}-star-o:before { content: @fa-var-star-o; } +.@{fa-css-prefix}-user:before { content: @fa-var-user; } +.@{fa-css-prefix}-film:before { content: @fa-var-film; } +.@{fa-css-prefix}-th-large:before { content: @fa-var-th-large; } +.@{fa-css-prefix}-th:before { content: @fa-var-th; } +.@{fa-css-prefix}-th-list:before { content: @fa-var-th-list; } +.@{fa-css-prefix}-check:before { content: @fa-var-check; } +.@{fa-css-prefix}-remove:before, +.@{fa-css-prefix}-close:before, +.@{fa-css-prefix}-times:before { content: @fa-var-times; } +.@{fa-css-prefix}-search-plus:before { content: @fa-var-search-plus; } +.@{fa-css-prefix}-search-minus:before { content: @fa-var-search-minus; } +.@{fa-css-prefix}-power-off:before { content: @fa-var-power-off; } +.@{fa-css-prefix}-signal:before { content: @fa-var-signal; } +.@{fa-css-prefix}-gear:before, +.@{fa-css-prefix}-cog:before { content: @fa-var-cog; } +.@{fa-css-prefix}-trash-o:before { content: @fa-var-trash-o; } +.@{fa-css-prefix}-home:before { content: @fa-var-home; } +.@{fa-css-prefix}-file-o:before { content: @fa-var-file-o; } +.@{fa-css-prefix}-clock-o:before { content: @fa-var-clock-o; } +.@{fa-css-prefix}-road:before { content: @fa-var-road; } +.@{fa-css-prefix}-download:before { content: @fa-var-download; } +.@{fa-css-prefix}-arrow-circle-o-down:before { content: @fa-var-arrow-circle-o-down; } +.@{fa-css-prefix}-arrow-circle-o-up:before { content: @fa-var-arrow-circle-o-up; } +.@{fa-css-prefix}-inbox:before { content: @fa-var-inbox; } +.@{fa-css-prefix}-play-circle-o:before { content: @fa-var-play-circle-o; } +.@{fa-css-prefix}-rotate-right:before, +.@{fa-css-prefix}-repeat:before { content: @fa-var-repeat; } +.@{fa-css-prefix}-refresh:before { content: @fa-var-refresh; } +.@{fa-css-prefix}-list-alt:before { content: @fa-var-list-alt; } +.@{fa-css-prefix}-lock:before { content: @fa-var-lock; } +.@{fa-css-prefix}-flag:before { content: @fa-var-flag; } +.@{fa-css-prefix}-headphones:before { content: @fa-var-headphones; } +.@{fa-css-prefix}-volume-off:before { content: @fa-var-volume-off; } +.@{fa-css-prefix}-volume-down:before { content: @fa-var-volume-down; } +.@{fa-css-prefix}-volume-up:before { content: @fa-var-volume-up; } +.@{fa-css-prefix}-qrcode:before { content: @fa-var-qrcode; } +.@{fa-css-prefix}-barcode:before { content: @fa-var-barcode; } +.@{fa-css-prefix}-tag:before { content: @fa-var-tag; } +.@{fa-css-prefix}-tags:before { content: @fa-var-tags; } +.@{fa-css-prefix}-book:before { content: @fa-var-book; } +.@{fa-css-prefix}-bookmark:before { content: @fa-var-bookmark; } +.@{fa-css-prefix}-print:before { content: @fa-var-print; } +.@{fa-css-prefix}-camera:before { content: @fa-var-camera; } +.@{fa-css-prefix}-font:before { content: @fa-var-font; } +.@{fa-css-prefix}-bold:before { content: @fa-var-bold; } +.@{fa-css-prefix}-italic:before { content: @fa-var-italic; } +.@{fa-css-prefix}-text-height:before { content: @fa-var-text-height; } +.@{fa-css-prefix}-text-width:before { content: @fa-var-text-width; } +.@{fa-css-prefix}-align-left:before { content: @fa-var-align-left; } +.@{fa-css-prefix}-align-center:before { content: @fa-var-align-center; } +.@{fa-css-prefix}-align-right:before { content: @fa-var-align-right; } +.@{fa-css-prefix}-align-justify:before { content: @fa-var-align-justify; } +.@{fa-css-prefix}-list:before { content: @fa-var-list; } +.@{fa-css-prefix}-dedent:before, +.@{fa-css-prefix}-outdent:before { content: @fa-var-outdent; } +.@{fa-css-prefix}-indent:before { content: @fa-var-indent; } +.@{fa-css-prefix}-video-camera:before { content: @fa-var-video-camera; } +.@{fa-css-prefix}-photo:before, +.@{fa-css-prefix}-image:before, +.@{fa-css-prefix}-picture-o:before { content: @fa-var-picture-o; } +.@{fa-css-prefix}-pencil:before { content: @fa-var-pencil; } +.@{fa-css-prefix}-map-marker:before { content: @fa-var-map-marker; } +.@{fa-css-prefix}-adjust:before { content: @fa-var-adjust; } +.@{fa-css-prefix}-tint:before { content: @fa-var-tint; } +.@{fa-css-prefix}-edit:before, +.@{fa-css-prefix}-pencil-square-o:before { content: @fa-var-pencil-square-o; } +.@{fa-css-prefix}-share-square-o:before { content: @fa-var-share-square-o; } +.@{fa-css-prefix}-check-square-o:before { content: @fa-var-check-square-o; } +.@{fa-css-prefix}-arrows:before { content: @fa-var-arrows; } +.@{fa-css-prefix}-step-backward:before { content: @fa-var-step-backward; } +.@{fa-css-prefix}-fast-backward:before { content: @fa-var-fast-backward; } +.@{fa-css-prefix}-backward:before { content: @fa-var-backward; } +.@{fa-css-prefix}-play:before { content: @fa-var-play; } +.@{fa-css-prefix}-pause:before { content: @fa-var-pause; } +.@{fa-css-prefix}-stop:before { content: @fa-var-stop; } +.@{fa-css-prefix}-forward:before { content: @fa-var-forward; } +.@{fa-css-prefix}-fast-forward:before { content: @fa-var-fast-forward; } +.@{fa-css-prefix}-step-forward:before { content: @fa-var-step-forward; } +.@{fa-css-prefix}-eject:before { content: @fa-var-eject; } +.@{fa-css-prefix}-chevron-left:before { content: @fa-var-chevron-left; } +.@{fa-css-prefix}-chevron-right:before { content: @fa-var-chevron-right; } +.@{fa-css-prefix}-plus-circle:before { content: @fa-var-plus-circle; } +.@{fa-css-prefix}-minus-circle:before { content: @fa-var-minus-circle; } +.@{fa-css-prefix}-times-circle:before { content: @fa-var-times-circle; } +.@{fa-css-prefix}-check-circle:before { content: @fa-var-check-circle; } +.@{fa-css-prefix}-question-circle:before { content: @fa-var-question-circle; } +.@{fa-css-prefix}-info-circle:before { content: @fa-var-info-circle; } +.@{fa-css-prefix}-crosshairs:before { content: @fa-var-crosshairs; } +.@{fa-css-prefix}-times-circle-o:before { content: @fa-var-times-circle-o; } +.@{fa-css-prefix}-check-circle-o:before { content: @fa-var-check-circle-o; } +.@{fa-css-prefix}-ban:before { content: @fa-var-ban; } +.@{fa-css-prefix}-arrow-left:before { content: @fa-var-arrow-left; } +.@{fa-css-prefix}-arrow-right:before { content: @fa-var-arrow-right; } +.@{fa-css-prefix}-arrow-up:before { content: @fa-var-arrow-up; } +.@{fa-css-prefix}-arrow-down:before { content: @fa-var-arrow-down; } +.@{fa-css-prefix}-mail-forward:before, +.@{fa-css-prefix}-share:before { content: @fa-var-share; } +.@{fa-css-prefix}-expand:before { content: @fa-var-expand; } +.@{fa-css-prefix}-compress:before { content: @fa-var-compress; } +.@{fa-css-prefix}-plus:before { content: @fa-var-plus; } +.@{fa-css-prefix}-minus:before { content: @fa-var-minus; } +.@{fa-css-prefix}-asterisk:before { content: @fa-var-asterisk; } +.@{fa-css-prefix}-exclamation-circle:before { content: @fa-var-exclamation-circle; } +.@{fa-css-prefix}-gift:before { content: @fa-var-gift; } +.@{fa-css-prefix}-leaf:before { content: @fa-var-leaf; } +.@{fa-css-prefix}-fire:before { content: @fa-var-fire; } +.@{fa-css-prefix}-eye:before { content: @fa-var-eye; } +.@{fa-css-prefix}-eye-slash:before { content: @fa-var-eye-slash; } +.@{fa-css-prefix}-warning:before, +.@{fa-css-prefix}-exclamation-triangle:before { content: @fa-var-exclamation-triangle; } +.@{fa-css-prefix}-plane:before { content: @fa-var-plane; } +.@{fa-css-prefix}-calendar:before { content: @fa-var-calendar; } +.@{fa-css-prefix}-random:before { content: @fa-var-random; } +.@{fa-css-prefix}-comment:before { content: @fa-var-comment; } +.@{fa-css-prefix}-magnet:before { content: @fa-var-magnet; } +.@{fa-css-prefix}-chevron-up:before { content: @fa-var-chevron-up; } +.@{fa-css-prefix}-chevron-down:before { content: @fa-var-chevron-down; } +.@{fa-css-prefix}-retweet:before { content: @fa-var-retweet; } +.@{fa-css-prefix}-shopping-cart:before { content: @fa-var-shopping-cart; } +.@{fa-css-prefix}-folder:before { content: @fa-var-folder; } +.@{fa-css-prefix}-folder-open:before { content: @fa-var-folder-open; } +.@{fa-css-prefix}-arrows-v:before { content: @fa-var-arrows-v; } +.@{fa-css-prefix}-arrows-h:before { content: @fa-var-arrows-h; } +.@{fa-css-prefix}-bar-chart-o:before, +.@{fa-css-prefix}-bar-chart:before { content: @fa-var-bar-chart; } +.@{fa-css-prefix}-twitter-square:before { content: @fa-var-twitter-square; } +.@{fa-css-prefix}-facebook-square:before { content: @fa-var-facebook-square; } +.@{fa-css-prefix}-camera-retro:before { content: @fa-var-camera-retro; } +.@{fa-css-prefix}-key:before { content: @fa-var-key; } +.@{fa-css-prefix}-gears:before, +.@{fa-css-prefix}-cogs:before { content: @fa-var-cogs; } +.@{fa-css-prefix}-comments:before { content: @fa-var-comments; } +.@{fa-css-prefix}-thumbs-o-up:before { content: @fa-var-thumbs-o-up; } +.@{fa-css-prefix}-thumbs-o-down:before { content: @fa-var-thumbs-o-down; } +.@{fa-css-prefix}-star-half:before { content: @fa-var-star-half; } +.@{fa-css-prefix}-heart-o:before { content: @fa-var-heart-o; } +.@{fa-css-prefix}-sign-out:before { content: @fa-var-sign-out; } +.@{fa-css-prefix}-linkedin-square:before { content: @fa-var-linkedin-square; } +.@{fa-css-prefix}-thumb-tack:before { content: @fa-var-thumb-tack; } +.@{fa-css-prefix}-external-link:before { content: @fa-var-external-link; } +.@{fa-css-prefix}-sign-in:before { content: @fa-var-sign-in; } +.@{fa-css-prefix}-trophy:before { content: @fa-var-trophy; } +.@{fa-css-prefix}-github-square:before { content: @fa-var-github-square; } +.@{fa-css-prefix}-upload:before { content: @fa-var-upload; } +.@{fa-css-prefix}-lemon-o:before { content: @fa-var-lemon-o; } +.@{fa-css-prefix}-phone:before { content: @fa-var-phone; } +.@{fa-css-prefix}-square-o:before { content: @fa-var-square-o; } +.@{fa-css-prefix}-bookmark-o:before { content: @fa-var-bookmark-o; } +.@{fa-css-prefix}-phone-square:before { content: @fa-var-phone-square; } +.@{fa-css-prefix}-twitter:before { content: @fa-var-twitter; } +.@{fa-css-prefix}-facebook-f:before, +.@{fa-css-prefix}-facebook:before { content: @fa-var-facebook; } +.@{fa-css-prefix}-github:before { content: @fa-var-github; } +.@{fa-css-prefix}-unlock:before { content: @fa-var-unlock; } +.@{fa-css-prefix}-credit-card:before { content: @fa-var-credit-card; } +.@{fa-css-prefix}-feed:before, +.@{fa-css-prefix}-rss:before { content: @fa-var-rss; } +.@{fa-css-prefix}-hdd-o:before { content: @fa-var-hdd-o; } +.@{fa-css-prefix}-bullhorn:before { content: @fa-var-bullhorn; } +.@{fa-css-prefix}-bell:before { content: @fa-var-bell; } +.@{fa-css-prefix}-certificate:before { content: @fa-var-certificate; } +.@{fa-css-prefix}-hand-o-right:before { content: @fa-var-hand-o-right; } +.@{fa-css-prefix}-hand-o-left:before { content: @fa-var-hand-o-left; } +.@{fa-css-prefix}-hand-o-up:before { content: @fa-var-hand-o-up; } +.@{fa-css-prefix}-hand-o-down:before { content: @fa-var-hand-o-down; } +.@{fa-css-prefix}-arrow-circle-left:before { content: @fa-var-arrow-circle-left; } +.@{fa-css-prefix}-arrow-circle-right:before { content: @fa-var-arrow-circle-right; } +.@{fa-css-prefix}-arrow-circle-up:before { content: @fa-var-arrow-circle-up; } +.@{fa-css-prefix}-arrow-circle-down:before { content: @fa-var-arrow-circle-down; } +.@{fa-css-prefix}-globe:before { content: @fa-var-globe; } +.@{fa-css-prefix}-wrench:before { content: @fa-var-wrench; } +.@{fa-css-prefix}-tasks:before { content: @fa-var-tasks; } +.@{fa-css-prefix}-filter:before { content: @fa-var-filter; } +.@{fa-css-prefix}-briefcase:before { content: @fa-var-briefcase; } +.@{fa-css-prefix}-arrows-alt:before { content: @fa-var-arrows-alt; } +.@{fa-css-prefix}-group:before, +.@{fa-css-prefix}-users:before { content: @fa-var-users; } +.@{fa-css-prefix}-chain:before, +.@{fa-css-prefix}-link:before { content: @fa-var-link; } +.@{fa-css-prefix}-cloud:before { content: @fa-var-cloud; } +.@{fa-css-prefix}-flask:before { content: @fa-var-flask; } +.@{fa-css-prefix}-cut:before, +.@{fa-css-prefix}-scissors:before { content: @fa-var-scissors; } +.@{fa-css-prefix}-copy:before, +.@{fa-css-prefix}-files-o:before { content: @fa-var-files-o; } +.@{fa-css-prefix}-paperclip:before { content: @fa-var-paperclip; } +.@{fa-css-prefix}-save:before, +.@{fa-css-prefix}-floppy-o:before { content: @fa-var-floppy-o; } +.@{fa-css-prefix}-square:before { content: @fa-var-square; } +.@{fa-css-prefix}-navicon:before, +.@{fa-css-prefix}-reorder:before, +.@{fa-css-prefix}-bars:before { content: @fa-var-bars; } +.@{fa-css-prefix}-list-ul:before { content: @fa-var-list-ul; } +.@{fa-css-prefix}-list-ol:before { content: @fa-var-list-ol; } +.@{fa-css-prefix}-strikethrough:before { content: @fa-var-strikethrough; } +.@{fa-css-prefix}-underline:before { content: @fa-var-underline; } +.@{fa-css-prefix}-table:before { content: @fa-var-table; } +.@{fa-css-prefix}-magic:before { content: @fa-var-magic; } +.@{fa-css-prefix}-truck:before { content: @fa-var-truck; } +.@{fa-css-prefix}-pinterest:before { content: @fa-var-pinterest; } +.@{fa-css-prefix}-pinterest-square:before { content: @fa-var-pinterest-square; } +.@{fa-css-prefix}-google-plus-square:before { content: @fa-var-google-plus-square; } +.@{fa-css-prefix}-google-plus:before { content: @fa-var-google-plus; } +.@{fa-css-prefix}-money:before { content: @fa-var-money; } +.@{fa-css-prefix}-caret-down:before { content: @fa-var-caret-down; } +.@{fa-css-prefix}-caret-up:before { content: @fa-var-caret-up; } +.@{fa-css-prefix}-caret-left:before { content: @fa-var-caret-left; } +.@{fa-css-prefix}-caret-right:before { content: @fa-var-caret-right; } +.@{fa-css-prefix}-columns:before { content: @fa-var-columns; } +.@{fa-css-prefix}-unsorted:before, +.@{fa-css-prefix}-sort:before { content: @fa-var-sort; } +.@{fa-css-prefix}-sort-down:before, +.@{fa-css-prefix}-sort-desc:before { content: @fa-var-sort-desc; } +.@{fa-css-prefix}-sort-up:before, +.@{fa-css-prefix}-sort-asc:before { content: @fa-var-sort-asc; } +.@{fa-css-prefix}-envelope:before { content: @fa-var-envelope; } +.@{fa-css-prefix}-linkedin:before { content: @fa-var-linkedin; } +.@{fa-css-prefix}-rotate-left:before, +.@{fa-css-prefix}-undo:before { content: @fa-var-undo; } +.@{fa-css-prefix}-legal:before, +.@{fa-css-prefix}-gavel:before { content: @fa-var-gavel; } +.@{fa-css-prefix}-dashboard:before, +.@{fa-css-prefix}-tachometer:before { content: @fa-var-tachometer; } +.@{fa-css-prefix}-comment-o:before { content: @fa-var-comment-o; } +.@{fa-css-prefix}-comments-o:before { content: @fa-var-comments-o; } +.@{fa-css-prefix}-flash:before, +.@{fa-css-prefix}-bolt:before { content: @fa-var-bolt; } +.@{fa-css-prefix}-sitemap:before { content: @fa-var-sitemap; } +.@{fa-css-prefix}-umbrella:before { content: @fa-var-umbrella; } +.@{fa-css-prefix}-paste:before, +.@{fa-css-prefix}-clipboard:before { content: @fa-var-clipboard; } +.@{fa-css-prefix}-lightbulb-o:before { content: @fa-var-lightbulb-o; } +.@{fa-css-prefix}-exchange:before { content: @fa-var-exchange; } +.@{fa-css-prefix}-cloud-download:before { content: @fa-var-cloud-download; } +.@{fa-css-prefix}-cloud-upload:before { content: @fa-var-cloud-upload; } +.@{fa-css-prefix}-user-md:before { content: @fa-var-user-md; } +.@{fa-css-prefix}-stethoscope:before { content: @fa-var-stethoscope; } +.@{fa-css-prefix}-suitcase:before { content: @fa-var-suitcase; } +.@{fa-css-prefix}-bell-o:before { content: @fa-var-bell-o; } +.@{fa-css-prefix}-coffee:before { content: @fa-var-coffee; } +.@{fa-css-prefix}-cutlery:before { content: @fa-var-cutlery; } +.@{fa-css-prefix}-file-text-o:before { content: @fa-var-file-text-o; } +.@{fa-css-prefix}-building-o:before { content: @fa-var-building-o; } +.@{fa-css-prefix}-hospital-o:before { content: @fa-var-hospital-o; } +.@{fa-css-prefix}-ambulance:before { content: @fa-var-ambulance; } +.@{fa-css-prefix}-medkit:before { content: @fa-var-medkit; } +.@{fa-css-prefix}-fighter-jet:before { content: @fa-var-fighter-jet; } +.@{fa-css-prefix}-beer:before { content: @fa-var-beer; } +.@{fa-css-prefix}-h-square:before { content: @fa-var-h-square; } +.@{fa-css-prefix}-plus-square:before { content: @fa-var-plus-square; } +.@{fa-css-prefix}-angle-double-left:before { content: @fa-var-angle-double-left; } +.@{fa-css-prefix}-angle-double-right:before { content: @fa-var-angle-double-right; } +.@{fa-css-prefix}-angle-double-up:before { content: @fa-var-angle-double-up; } +.@{fa-css-prefix}-angle-double-down:before { content: @fa-var-angle-double-down; } +.@{fa-css-prefix}-angle-left:before { content: @fa-var-angle-left; } +.@{fa-css-prefix}-angle-right:before { content: @fa-var-angle-right; } +.@{fa-css-prefix}-angle-up:before { content: @fa-var-angle-up; } +.@{fa-css-prefix}-angle-down:before { content: @fa-var-angle-down; } +.@{fa-css-prefix}-desktop:before { content: @fa-var-desktop; } +.@{fa-css-prefix}-laptop:before { content: @fa-var-laptop; } +.@{fa-css-prefix}-tablet:before { content: @fa-var-tablet; } +.@{fa-css-prefix}-mobile-phone:before, +.@{fa-css-prefix}-mobile:before { content: @fa-var-mobile; } +.@{fa-css-prefix}-circle-o:before { content: @fa-var-circle-o; } +.@{fa-css-prefix}-quote-left:before { content: @fa-var-quote-left; } +.@{fa-css-prefix}-quote-right:before { content: @fa-var-quote-right; } +.@{fa-css-prefix}-spinner:before { content: @fa-var-spinner; } +.@{fa-css-prefix}-circle:before { content: @fa-var-circle; } +.@{fa-css-prefix}-mail-reply:before, +.@{fa-css-prefix}-reply:before { content: @fa-var-reply; } +.@{fa-css-prefix}-github-alt:before { content: @fa-var-github-alt; } +.@{fa-css-prefix}-folder-o:before { content: @fa-var-folder-o; } +.@{fa-css-prefix}-folder-open-o:before { content: @fa-var-folder-open-o; } +.@{fa-css-prefix}-smile-o:before { content: @fa-var-smile-o; } +.@{fa-css-prefix}-frown-o:before { content: @fa-var-frown-o; } +.@{fa-css-prefix}-meh-o:before { content: @fa-var-meh-o; } +.@{fa-css-prefix}-gamepad:before { content: @fa-var-gamepad; } +.@{fa-css-prefix}-keyboard-o:before { content: @fa-var-keyboard-o; } +.@{fa-css-prefix}-flag-o:before { content: @fa-var-flag-o; } +.@{fa-css-prefix}-flag-checkered:before { content: @fa-var-flag-checkered; } +.@{fa-css-prefix}-terminal:before { content: @fa-var-terminal; } +.@{fa-css-prefix}-code:before { content: @fa-var-code; } +.@{fa-css-prefix}-mail-reply-all:before, +.@{fa-css-prefix}-reply-all:before { content: @fa-var-reply-all; } +.@{fa-css-prefix}-star-half-empty:before, +.@{fa-css-prefix}-star-half-full:before, +.@{fa-css-prefix}-star-half-o:before { content: @fa-var-star-half-o; } +.@{fa-css-prefix}-location-arrow:before { content: @fa-var-location-arrow; } +.@{fa-css-prefix}-crop:before { content: @fa-var-crop; } +.@{fa-css-prefix}-code-fork:before { content: @fa-var-code-fork; } +.@{fa-css-prefix}-unlink:before, +.@{fa-css-prefix}-chain-broken:before { content: @fa-var-chain-broken; } +.@{fa-css-prefix}-question:before { content: @fa-var-question; } +.@{fa-css-prefix}-info:before { content: @fa-var-info; } +.@{fa-css-prefix}-exclamation:before { content: @fa-var-exclamation; } +.@{fa-css-prefix}-superscript:before { content: @fa-var-superscript; } +.@{fa-css-prefix}-subscript:before { content: @fa-var-subscript; } +.@{fa-css-prefix}-eraser:before { content: @fa-var-eraser; } +.@{fa-css-prefix}-puzzle-piece:before { content: @fa-var-puzzle-piece; } +.@{fa-css-prefix}-microphone:before { content: @fa-var-microphone; } +.@{fa-css-prefix}-microphone-slash:before { content: @fa-var-microphone-slash; } +.@{fa-css-prefix}-shield:before { content: @fa-var-shield; } +.@{fa-css-prefix}-calendar-o:before { content: @fa-var-calendar-o; } +.@{fa-css-prefix}-fire-extinguisher:before { content: @fa-var-fire-extinguisher; } +.@{fa-css-prefix}-rocket:before { content: @fa-var-rocket; } +.@{fa-css-prefix}-maxcdn:before { content: @fa-var-maxcdn; } +.@{fa-css-prefix}-chevron-circle-left:before { content: @fa-var-chevron-circle-left; } +.@{fa-css-prefix}-chevron-circle-right:before { content: @fa-var-chevron-circle-right; } +.@{fa-css-prefix}-chevron-circle-up:before { content: @fa-var-chevron-circle-up; } +.@{fa-css-prefix}-chevron-circle-down:before { content: @fa-var-chevron-circle-down; } +.@{fa-css-prefix}-html5:before { content: @fa-var-html5; } +.@{fa-css-prefix}-css3:before { content: @fa-var-css3; } +.@{fa-css-prefix}-anchor:before { content: @fa-var-anchor; } +.@{fa-css-prefix}-unlock-alt:before { content: @fa-var-unlock-alt; } +.@{fa-css-prefix}-bullseye:before { content: @fa-var-bullseye; } +.@{fa-css-prefix}-ellipsis-h:before { content: @fa-var-ellipsis-h; } +.@{fa-css-prefix}-ellipsis-v:before { content: @fa-var-ellipsis-v; } +.@{fa-css-prefix}-rss-square:before { content: @fa-var-rss-square; } +.@{fa-css-prefix}-play-circle:before { content: @fa-var-play-circle; } +.@{fa-css-prefix}-ticket:before { content: @fa-var-ticket; } +.@{fa-css-prefix}-minus-square:before { content: @fa-var-minus-square; } +.@{fa-css-prefix}-minus-square-o:before { content: @fa-var-minus-square-o; } +.@{fa-css-prefix}-level-up:before { content: @fa-var-level-up; } +.@{fa-css-prefix}-level-down:before { content: @fa-var-level-down; } +.@{fa-css-prefix}-check-square:before { content: @fa-var-check-square; } +.@{fa-css-prefix}-pencil-square:before { content: @fa-var-pencil-square; } +.@{fa-css-prefix}-external-link-square:before { content: @fa-var-external-link-square; } +.@{fa-css-prefix}-share-square:before { content: @fa-var-share-square; } +.@{fa-css-prefix}-compass:before { content: @fa-var-compass; } +.@{fa-css-prefix}-toggle-down:before, +.@{fa-css-prefix}-caret-square-o-down:before { content: @fa-var-caret-square-o-down; } +.@{fa-css-prefix}-toggle-up:before, +.@{fa-css-prefix}-caret-square-o-up:before { content: @fa-var-caret-square-o-up; } +.@{fa-css-prefix}-toggle-right:before, +.@{fa-css-prefix}-caret-square-o-right:before { content: @fa-var-caret-square-o-right; } +.@{fa-css-prefix}-euro:before, +.@{fa-css-prefix}-eur:before { content: @fa-var-eur; } +.@{fa-css-prefix}-gbp:before { content: @fa-var-gbp; } +.@{fa-css-prefix}-dollar:before, +.@{fa-css-prefix}-usd:before { content: @fa-var-usd; } +.@{fa-css-prefix}-rupee:before, +.@{fa-css-prefix}-inr:before { content: @fa-var-inr; } +.@{fa-css-prefix}-cny:before, +.@{fa-css-prefix}-rmb:before, +.@{fa-css-prefix}-yen:before, +.@{fa-css-prefix}-jpy:before { content: @fa-var-jpy; } +.@{fa-css-prefix}-ruble:before, +.@{fa-css-prefix}-rouble:before, +.@{fa-css-prefix}-rub:before { content: @fa-var-rub; } +.@{fa-css-prefix}-won:before, +.@{fa-css-prefix}-krw:before { content: @fa-var-krw; } +.@{fa-css-prefix}-bitcoin:before, +.@{fa-css-prefix}-btc:before { content: @fa-var-btc; } +.@{fa-css-prefix}-file:before { content: @fa-var-file; } +.@{fa-css-prefix}-file-text:before { content: @fa-var-file-text; } +.@{fa-css-prefix}-sort-alpha-asc:before { content: @fa-var-sort-alpha-asc; } +.@{fa-css-prefix}-sort-alpha-desc:before { content: @fa-var-sort-alpha-desc; } +.@{fa-css-prefix}-sort-amount-asc:before { content: @fa-var-sort-amount-asc; } 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content: @fa-var-rebel; } +.@{fa-css-prefix}-ge:before, +.@{fa-css-prefix}-empire:before { content: @fa-var-empire; } +.@{fa-css-prefix}-git-square:before { content: @fa-var-git-square; } +.@{fa-css-prefix}-git:before { content: @fa-var-git; } +.@{fa-css-prefix}-y-combinator-square:before, +.@{fa-css-prefix}-yc-square:before, +.@{fa-css-prefix}-hacker-news:before { content: @fa-var-hacker-news; } +.@{fa-css-prefix}-tencent-weibo:before { content: @fa-var-tencent-weibo; } +.@{fa-css-prefix}-qq:before { content: @fa-var-qq; } +.@{fa-css-prefix}-wechat:before, +.@{fa-css-prefix}-weixin:before { content: @fa-var-weixin; } +.@{fa-css-prefix}-send:before, +.@{fa-css-prefix}-paper-plane:before { content: @fa-var-paper-plane; } +.@{fa-css-prefix}-send-o:before, +.@{fa-css-prefix}-paper-plane-o:before { content: @fa-var-paper-plane-o; } +.@{fa-css-prefix}-history:before { content: @fa-var-history; } +.@{fa-css-prefix}-circle-thin:before { content: @fa-var-circle-thin; } 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@fa-var-wifi; } +.@{fa-css-prefix}-calculator:before { content: @fa-var-calculator; } +.@{fa-css-prefix}-paypal:before { content: @fa-var-paypal; } +.@{fa-css-prefix}-google-wallet:before { content: @fa-var-google-wallet; } +.@{fa-css-prefix}-cc-visa:before { content: @fa-var-cc-visa; } +.@{fa-css-prefix}-cc-mastercard:before { content: @fa-var-cc-mastercard; } +.@{fa-css-prefix}-cc-discover:before { content: @fa-var-cc-discover; } +.@{fa-css-prefix}-cc-amex:before { content: @fa-var-cc-amex; } +.@{fa-css-prefix}-cc-paypal:before { content: @fa-var-cc-paypal; } +.@{fa-css-prefix}-cc-stripe:before { content: @fa-var-cc-stripe; } +.@{fa-css-prefix}-bell-slash:before { content: @fa-var-bell-slash; } +.@{fa-css-prefix}-bell-slash-o:before { content: @fa-var-bell-slash-o; } +.@{fa-css-prefix}-trash:before { content: @fa-var-trash; } +.@{fa-css-prefix}-copyright:before { content: @fa-var-copyright; } +.@{fa-css-prefix}-at:before { content: @fa-var-at; } +.@{fa-css-prefix}-eyedropper:before { content: @fa-var-eyedropper; } +.@{fa-css-prefix}-paint-brush:before { content: @fa-var-paint-brush; } +.@{fa-css-prefix}-birthday-cake:before { content: @fa-var-birthday-cake; } +.@{fa-css-prefix}-area-chart:before { content: @fa-var-area-chart; } +.@{fa-css-prefix}-pie-chart:before { content: @fa-var-pie-chart; } +.@{fa-css-prefix}-line-chart:before { content: @fa-var-line-chart; } +.@{fa-css-prefix}-lastfm:before { content: @fa-var-lastfm; } +.@{fa-css-prefix}-lastfm-square:before { content: @fa-var-lastfm-square; } +.@{fa-css-prefix}-toggle-off:before { content: @fa-var-toggle-off; } +.@{fa-css-prefix}-toggle-on:before { content: @fa-var-toggle-on; } +.@{fa-css-prefix}-bicycle:before { content: @fa-var-bicycle; } +.@{fa-css-prefix}-bus:before { content: @fa-var-bus; } +.@{fa-css-prefix}-ioxhost:before { content: @fa-var-ioxhost; } +.@{fa-css-prefix}-angellist:before { content: @fa-var-angellist; } +.@{fa-css-prefix}-cc:before { content: @fa-var-cc; } 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@fa-var-diamond; } +.@{fa-css-prefix}-ship:before { content: @fa-var-ship; } +.@{fa-css-prefix}-user-secret:before { content: @fa-var-user-secret; } +.@{fa-css-prefix}-motorcycle:before { content: @fa-var-motorcycle; } +.@{fa-css-prefix}-street-view:before { content: @fa-var-street-view; } +.@{fa-css-prefix}-heartbeat:before { content: @fa-var-heartbeat; } +.@{fa-css-prefix}-venus:before { content: @fa-var-venus; } +.@{fa-css-prefix}-mars:before { content: @fa-var-mars; } +.@{fa-css-prefix}-mercury:before { content: @fa-var-mercury; } +.@{fa-css-prefix}-intersex:before, +.@{fa-css-prefix}-transgender:before { content: @fa-var-transgender; } +.@{fa-css-prefix}-transgender-alt:before { content: @fa-var-transgender-alt; } +.@{fa-css-prefix}-venus-double:before { content: @fa-var-venus-double; } +.@{fa-css-prefix}-mars-double:before { content: @fa-var-mars-double; } +.@{fa-css-prefix}-venus-mars:before { content: @fa-var-venus-mars; } +.@{fa-css-prefix}-mars-stroke:before { content: @fa-var-mars-stroke; } +.@{fa-css-prefix}-mars-stroke-v:before { content: @fa-var-mars-stroke-v; } +.@{fa-css-prefix}-mars-stroke-h:before { content: @fa-var-mars-stroke-h; } +.@{fa-css-prefix}-neuter:before { content: @fa-var-neuter; } +.@{fa-css-prefix}-genderless:before { content: @fa-var-genderless; } +.@{fa-css-prefix}-facebook-official:before { content: @fa-var-facebook-official; } +.@{fa-css-prefix}-pinterest-p:before { content: @fa-var-pinterest-p; } +.@{fa-css-prefix}-whatsapp:before { content: @fa-var-whatsapp; } +.@{fa-css-prefix}-server:before { content: @fa-var-server; } +.@{fa-css-prefix}-user-plus:before { content: @fa-var-user-plus; } +.@{fa-css-prefix}-user-times:before { content: @fa-var-user-times; } +.@{fa-css-prefix}-hotel:before, +.@{fa-css-prefix}-bed:before { content: @fa-var-bed; } +.@{fa-css-prefix}-viacoin:before { content: @fa-var-viacoin; } +.@{fa-css-prefix}-train:before { content: @fa-var-train; } +.@{fa-css-prefix}-subway:before { content: @fa-var-subway; } +.@{fa-css-prefix}-medium:before { content: @fa-var-medium; } +.@{fa-css-prefix}-yc:before, +.@{fa-css-prefix}-y-combinator:before { content: @fa-var-y-combinator; } +.@{fa-css-prefix}-optin-monster:before { content: @fa-var-optin-monster; } +.@{fa-css-prefix}-opencart:before { content: @fa-var-opencart; } +.@{fa-css-prefix}-expeditedssl:before { content: @fa-var-expeditedssl; } +.@{fa-css-prefix}-battery-4:before, +.@{fa-css-prefix}-battery:before, +.@{fa-css-prefix}-battery-full:before { content: @fa-var-battery-full; } +.@{fa-css-prefix}-battery-3:before, +.@{fa-css-prefix}-battery-three-quarters:before { content: @fa-var-battery-three-quarters; } +.@{fa-css-prefix}-battery-2:before, +.@{fa-css-prefix}-battery-half:before { content: @fa-var-battery-half; } +.@{fa-css-prefix}-battery-1:before, +.@{fa-css-prefix}-battery-quarter:before { content: @fa-var-battery-quarter; } +.@{fa-css-prefix}-battery-0:before, +.@{fa-css-prefix}-battery-empty:before { content: @fa-var-battery-empty; } +.@{fa-css-prefix}-mouse-pointer:before { content: @fa-var-mouse-pointer; } +.@{fa-css-prefix}-i-cursor:before { content: @fa-var-i-cursor; } +.@{fa-css-prefix}-object-group:before { content: @fa-var-object-group; } +.@{fa-css-prefix}-object-ungroup:before { content: @fa-var-object-ungroup; } +.@{fa-css-prefix}-sticky-note:before { content: @fa-var-sticky-note; } +.@{fa-css-prefix}-sticky-note-o:before { content: @fa-var-sticky-note-o; } +.@{fa-css-prefix}-cc-jcb:before { content: @fa-var-cc-jcb; } +.@{fa-css-prefix}-cc-diners-club:before { content: @fa-var-cc-diners-club; } +.@{fa-css-prefix}-clone:before { content: @fa-var-clone; } +.@{fa-css-prefix}-balance-scale:before { content: @fa-var-balance-scale; } +.@{fa-css-prefix}-hourglass-o:before { content: @fa-var-hourglass-o; } +.@{fa-css-prefix}-hourglass-1:before, +.@{fa-css-prefix}-hourglass-start:before { content: @fa-var-hourglass-start; } +.@{fa-css-prefix}-hourglass-2:before, +.@{fa-css-prefix}-hourglass-half:before { content: @fa-var-hourglass-half; } +.@{fa-css-prefix}-hourglass-3:before, +.@{fa-css-prefix}-hourglass-end:before { content: @fa-var-hourglass-end; } +.@{fa-css-prefix}-hourglass:before { content: @fa-var-hourglass; } +.@{fa-css-prefix}-hand-grab-o:before, +.@{fa-css-prefix}-hand-rock-o:before { content: @fa-var-hand-rock-o; } +.@{fa-css-prefix}-hand-stop-o:before, +.@{fa-css-prefix}-hand-paper-o:before { content: @fa-var-hand-paper-o; } +.@{fa-css-prefix}-hand-scissors-o:before { content: @fa-var-hand-scissors-o; } +.@{fa-css-prefix}-hand-lizard-o:before { content: @fa-var-hand-lizard-o; } +.@{fa-css-prefix}-hand-spock-o:before { content: @fa-var-hand-spock-o; } +.@{fa-css-prefix}-hand-pointer-o:before { content: @fa-var-hand-pointer-o; } +.@{fa-css-prefix}-hand-peace-o:before { content: @fa-var-hand-peace-o; } +.@{fa-css-prefix}-trademark:before { content: @fa-var-trademark; } +.@{fa-css-prefix}-registered:before { content: @fa-var-registered; } +.@{fa-css-prefix}-creative-commons:before { content: @fa-var-creative-commons; } +.@{fa-css-prefix}-gg:before { content: @fa-var-gg; } +.@{fa-css-prefix}-gg-circle:before { content: @fa-var-gg-circle; } +.@{fa-css-prefix}-tripadvisor:before { content: @fa-var-tripadvisor; } +.@{fa-css-prefix}-odnoklassniki:before { content: @fa-var-odnoklassniki; } +.@{fa-css-prefix}-odnoklassniki-square:before { content: @fa-var-odnoklassniki-square; } +.@{fa-css-prefix}-get-pocket:before { content: @fa-var-get-pocket; } +.@{fa-css-prefix}-wikipedia-w:before { content: @fa-var-wikipedia-w; } +.@{fa-css-prefix}-safari:before { content: @fa-var-safari; } +.@{fa-css-prefix}-chrome:before { content: @fa-var-chrome; } +.@{fa-css-prefix}-firefox:before { content: @fa-var-firefox; } +.@{fa-css-prefix}-opera:before { content: @fa-var-opera; } +.@{fa-css-prefix}-internet-explorer:before { content: @fa-var-internet-explorer; } +.@{fa-css-prefix}-tv:before, +.@{fa-css-prefix}-television:before { content: @fa-var-television; } +.@{fa-css-prefix}-contao:before { content: @fa-var-contao; } +.@{fa-css-prefix}-500px:before { content: @fa-var-500px; } +.@{fa-css-prefix}-amazon:before { content: @fa-var-amazon; } +.@{fa-css-prefix}-calendar-plus-o:before { content: @fa-var-calendar-plus-o; } +.@{fa-css-prefix}-calendar-minus-o:before { content: @fa-var-calendar-minus-o; } +.@{fa-css-prefix}-calendar-times-o:before { content: @fa-var-calendar-times-o; } +.@{fa-css-prefix}-calendar-check-o:before { content: @fa-var-calendar-check-o; } +.@{fa-css-prefix}-industry:before { content: @fa-var-industry; } +.@{fa-css-prefix}-map-pin:before { content: @fa-var-map-pin; } +.@{fa-css-prefix}-map-signs:before { content: @fa-var-map-signs; } +.@{fa-css-prefix}-map-o:before { content: @fa-var-map-o; } +.@{fa-css-prefix}-map:before { content: @fa-var-map; } +.@{fa-css-prefix}-commenting:before { content: @fa-var-commenting; } +.@{fa-css-prefix}-commenting-o:before { content: @fa-var-commenting-o; } +.@{fa-css-prefix}-houzz:before { content: @fa-var-houzz; } +.@{fa-css-prefix}-vimeo:before { content: @fa-var-vimeo; } +.@{fa-css-prefix}-black-tie:before { content: @fa-var-black-tie; } +.@{fa-css-prefix}-fonticons:before { content: @fa-var-fonticons; } +.@{fa-css-prefix}-reddit-alien:before { content: @fa-var-reddit-alien; } +.@{fa-css-prefix}-edge:before { content: @fa-var-edge; } +.@{fa-css-prefix}-credit-card-alt:before { content: @fa-var-credit-card-alt; } +.@{fa-css-prefix}-codiepie:before { content: @fa-var-codiepie; } +.@{fa-css-prefix}-modx:before { content: @fa-var-modx; } +.@{fa-css-prefix}-fort-awesome:before { content: @fa-var-fort-awesome; } +.@{fa-css-prefix}-usb:before { content: @fa-var-usb; } +.@{fa-css-prefix}-product-hunt:before { content: @fa-var-product-hunt; } +.@{fa-css-prefix}-mixcloud:before { content: @fa-var-mixcloud; } +.@{fa-css-prefix}-scribd:before { content: @fa-var-scribd; } +.@{fa-css-prefix}-pause-circle:before { content: @fa-var-pause-circle; } +.@{fa-css-prefix}-pause-circle-o:before { content: @fa-var-pause-circle-o; } +.@{fa-css-prefix}-stop-circle:before { content: @fa-var-stop-circle; } +.@{fa-css-prefix}-stop-circle-o:before { content: @fa-var-stop-circle-o; } +.@{fa-css-prefix}-shopping-bag:before { content: @fa-var-shopping-bag; } +.@{fa-css-prefix}-shopping-basket:before { content: @fa-var-shopping-basket; } +.@{fa-css-prefix}-hashtag:before { content: @fa-var-hashtag; } +.@{fa-css-prefix}-bluetooth:before { content: @fa-var-bluetooth; } +.@{fa-css-prefix}-bluetooth-b:before { content: @fa-var-bluetooth-b; } +.@{fa-css-prefix}-percent:before { content: @fa-var-percent; } +.@{fa-css-prefix}-gitlab:before { content: @fa-var-gitlab; } +.@{fa-css-prefix}-wpbeginner:before { content: @fa-var-wpbeginner; } +.@{fa-css-prefix}-wpforms:before { content: @fa-var-wpforms; } +.@{fa-css-prefix}-envira:before { content: @fa-var-envira; } +.@{fa-css-prefix}-universal-access:before { content: @fa-var-universal-access; } +.@{fa-css-prefix}-wheelchair-alt:before { content: @fa-var-wheelchair-alt; } +.@{fa-css-prefix}-question-circle-o:before { content: @fa-var-question-circle-o; } +.@{fa-css-prefix}-blind:before { content: @fa-var-blind; } +.@{fa-css-prefix}-audio-description:before { content: @fa-var-audio-description; } +.@{fa-css-prefix}-volume-control-phone:before { content: @fa-var-volume-control-phone; } +.@{fa-css-prefix}-braille:before { content: @fa-var-braille; } +.@{fa-css-prefix}-assistive-listening-systems:before { content: @fa-var-assistive-listening-systems; } +.@{fa-css-prefix}-asl-interpreting:before, +.@{fa-css-prefix}-american-sign-language-interpreting:before { content: @fa-var-american-sign-language-interpreting; } +.@{fa-css-prefix}-deafness:before, +.@{fa-css-prefix}-hard-of-hearing:before, +.@{fa-css-prefix}-deaf:before { content: @fa-var-deaf; } +.@{fa-css-prefix}-glide:before { content: @fa-var-glide; } +.@{fa-css-prefix}-glide-g:before { content: @fa-var-glide-g; } +.@{fa-css-prefix}-signing:before, +.@{fa-css-prefix}-sign-language:before { content: @fa-var-sign-language; } +.@{fa-css-prefix}-low-vision:before { content: @fa-var-low-vision; } +.@{fa-css-prefix}-viadeo:before { content: @fa-var-viadeo; } +.@{fa-css-prefix}-viadeo-square:before { content: @fa-var-viadeo-square; } +.@{fa-css-prefix}-snapchat:before { content: @fa-var-snapchat; } +.@{fa-css-prefix}-snapchat-ghost:before { content: @fa-var-snapchat-ghost; } +.@{fa-css-prefix}-snapchat-square:before { content: @fa-var-snapchat-square; } +.@{fa-css-prefix}-pied-piper:before { content: @fa-var-pied-piper; } +.@{fa-css-prefix}-first-order:before { content: @fa-var-first-order; } +.@{fa-css-prefix}-yoast:before { content: @fa-var-yoast; } +.@{fa-css-prefix}-themeisle:before { content: @fa-var-themeisle; } +.@{fa-css-prefix}-google-plus-circle:before, +.@{fa-css-prefix}-google-plus-official:before { content: @fa-var-google-plus-official; } +.@{fa-css-prefix}-fa:before, +.@{fa-css-prefix}-font-awesome:before { content: @fa-var-font-awesome; } +.@{fa-css-prefix}-handshake-o:before { content: @fa-var-handshake-o; } +.@{fa-css-prefix}-envelope-open:before { content: @fa-var-envelope-open; } +.@{fa-css-prefix}-envelope-open-o:before { content: @fa-var-envelope-open-o; } +.@{fa-css-prefix}-linode:before { content: @fa-var-linode; } +.@{fa-css-prefix}-address-book:before { content: @fa-var-address-book; } +.@{fa-css-prefix}-address-book-o:before { content: @fa-var-address-book-o; } +.@{fa-css-prefix}-vcard:before, +.@{fa-css-prefix}-address-card:before { content: @fa-var-address-card; } +.@{fa-css-prefix}-vcard-o:before, +.@{fa-css-prefix}-address-card-o:before { content: @fa-var-address-card-o; } +.@{fa-css-prefix}-user-circle:before { content: @fa-var-user-circle; } +.@{fa-css-prefix}-user-circle-o:before { content: @fa-var-user-circle-o; } +.@{fa-css-prefix}-user-o:before { content: @fa-var-user-o; } +.@{fa-css-prefix}-id-badge:before { content: @fa-var-id-badge; } +.@{fa-css-prefix}-drivers-license:before, +.@{fa-css-prefix}-id-card:before { content: @fa-var-id-card; } +.@{fa-css-prefix}-drivers-license-o:before, +.@{fa-css-prefix}-id-card-o:before { content: @fa-var-id-card-o; } +.@{fa-css-prefix}-quora:before { content: @fa-var-quora; } +.@{fa-css-prefix}-free-code-camp:before { content: @fa-var-free-code-camp; } +.@{fa-css-prefix}-telegram:before { content: @fa-var-telegram; } +.@{fa-css-prefix}-thermometer-4:before, +.@{fa-css-prefix}-thermometer:before, +.@{fa-css-prefix}-thermometer-full:before { content: @fa-var-thermometer-full; } +.@{fa-css-prefix}-thermometer-3:before, +.@{fa-css-prefix}-thermometer-three-quarters:before { content: @fa-var-thermometer-three-quarters; } +.@{fa-css-prefix}-thermometer-2:before, +.@{fa-css-prefix}-thermometer-half:before { content: @fa-var-thermometer-half; } +.@{fa-css-prefix}-thermometer-1:before, +.@{fa-css-prefix}-thermometer-quarter:before { content: @fa-var-thermometer-quarter; } +.@{fa-css-prefix}-thermometer-0:before, +.@{fa-css-prefix}-thermometer-empty:before { content: @fa-var-thermometer-empty; } +.@{fa-css-prefix}-shower:before { content: @fa-var-shower; } +.@{fa-css-prefix}-bathtub:before, +.@{fa-css-prefix}-s15:before, +.@{fa-css-prefix}-bath:before { content: @fa-var-bath; } +.@{fa-css-prefix}-podcast:before { content: @fa-var-podcast; } +.@{fa-css-prefix}-window-maximize:before { content: @fa-var-window-maximize; } +.@{fa-css-prefix}-window-minimize:before { content: @fa-var-window-minimize; } +.@{fa-css-prefix}-window-restore:before { content: @fa-var-window-restore; } +.@{fa-css-prefix}-times-rectangle:before, +.@{fa-css-prefix}-window-close:before { content: @fa-var-window-close; } +.@{fa-css-prefix}-times-rectangle-o:before, +.@{fa-css-prefix}-window-close-o:before { content: @fa-var-window-close-o; } +.@{fa-css-prefix}-bandcamp:before { content: @fa-var-bandcamp; } +.@{fa-css-prefix}-grav:before { content: @fa-var-grav; } +.@{fa-css-prefix}-etsy:before { content: @fa-var-etsy; } +.@{fa-css-prefix}-imdb:before { content: @fa-var-imdb; } +.@{fa-css-prefix}-ravelry:before { content: @fa-var-ravelry; } +.@{fa-css-prefix}-eercast:before { content: @fa-var-eercast; } +.@{fa-css-prefix}-microchip:before { content: @fa-var-microchip; } +.@{fa-css-prefix}-snowflake-o:before { content: @fa-var-snowflake-o; } +.@{fa-css-prefix}-superpowers:before { content: @fa-var-superpowers; } +.@{fa-css-prefix}-wpexplorer:before { content: @fa-var-wpexplorer; } +.@{fa-css-prefix}-meetup:before { content: @fa-var-meetup; } diff --git a/dashboard/src/assets/font-awesome/less/larger.less b/dashboard/src/assets/font-awesome/less/larger.less new file mode 100644 index 00000000..c9d64677 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/larger.less @@ -0,0 +1,13 @@ +// Icon Sizes +// ------------------------- + +/* makes the font 33% larger relative to the icon container */ +.@{fa-css-prefix}-lg { + font-size: (4em / 3); + line-height: (3em / 4); + vertical-align: -15%; +} +.@{fa-css-prefix}-2x { font-size: 2em; } +.@{fa-css-prefix}-3x { font-size: 3em; } +.@{fa-css-prefix}-4x { font-size: 4em; } +.@{fa-css-prefix}-5x { font-size: 5em; } diff --git a/dashboard/src/assets/font-awesome/less/list.less b/dashboard/src/assets/font-awesome/less/list.less new file mode 100644 index 00000000..0b440382 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/list.less @@ -0,0 +1,19 @@ +// List Icons +// ------------------------- + +.@{fa-css-prefix}-ul { + padding-left: 0; + margin-left: @fa-li-width; + list-style-type: none; + > li { position: relative; } +} +.@{fa-css-prefix}-li { + position: absolute; + left: -@fa-li-width; + width: @fa-li-width; + top: (2em / 14); + text-align: center; + &.@{fa-css-prefix}-lg { + left: (-@fa-li-width + (4em / 14)); + } +} diff --git a/dashboard/src/assets/font-awesome/less/mixins.less b/dashboard/src/assets/font-awesome/less/mixins.less new file mode 100644 index 00000000..beef231d --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/mixins.less @@ -0,0 +1,60 @@ +// Mixins +// -------------------------- + +.fa-icon() { + display: inline-block; + font: normal normal normal @fa-font-size-base/@fa-line-height-base FontAwesome; // shortening font declaration + font-size: inherit; // can't have font-size inherit on line above, so need to override + text-rendering: auto; // optimizelegibility throws things off #1094 + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + +} + +.fa-icon-rotate(@degrees, @rotation) { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=@{rotation})"; + -webkit-transform: rotate(@degrees); + -ms-transform: rotate(@degrees); + transform: rotate(@degrees); +} + +.fa-icon-flip(@horiz, @vert, @rotation) { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=@{rotation}, mirror=1)"; + -webkit-transform: scale(@horiz, @vert); + -ms-transform: scale(@horiz, @vert); + transform: scale(@horiz, @vert); +} + + +// Only display content to screen readers. A la Bootstrap 4. +// +// See: http://a11yproject.com/posts/how-to-hide-content/ + +.sr-only() { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0,0,0,0); + border: 0; +} + +// Use in conjunction with .sr-only to only display content when it's focused. +// +// Useful for "Skip to main content" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1 +// +// Credit: HTML5 Boilerplate + +.sr-only-focusable() { + &:active, + &:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; + } +} diff --git a/dashboard/src/assets/font-awesome/less/path.less b/dashboard/src/assets/font-awesome/less/path.less new file mode 100644 index 00000000..835be41f --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/path.less @@ -0,0 +1,15 @@ +/* FONT PATH + * -------------------------- */ + +@font-face { + font-family: 'FontAwesome'; + src: url('@{fa-font-path}/fontawesome-webfont.eot?v=@{fa-version}'); + src: url('@{fa-font-path}/fontawesome-webfont.eot?#iefix&v=@{fa-version}') format('embedded-opentype'), + url('@{fa-font-path}/fontawesome-webfont.woff2?v=@{fa-version}') format('woff2'), + url('@{fa-font-path}/fontawesome-webfont.woff?v=@{fa-version}') format('woff'), + url('@{fa-font-path}/fontawesome-webfont.ttf?v=@{fa-version}') format('truetype'), + url('@{fa-font-path}/fontawesome-webfont.svg?v=@{fa-version}#fontawesomeregular') format('svg'); + // src: url('@{fa-font-path}/FontAwesome.otf') format('opentype'); // used when developing fonts + font-weight: normal; + font-style: normal; +} diff --git a/dashboard/src/assets/font-awesome/less/rotated-flipped.less b/dashboard/src/assets/font-awesome/less/rotated-flipped.less new file mode 100644 index 00000000..f6ba8147 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/rotated-flipped.less @@ -0,0 +1,20 @@ +// Rotated & Flipped Icons +// ------------------------- + +.@{fa-css-prefix}-rotate-90 { .fa-icon-rotate(90deg, 1); } +.@{fa-css-prefix}-rotate-180 { .fa-icon-rotate(180deg, 2); } +.@{fa-css-prefix}-rotate-270 { .fa-icon-rotate(270deg, 3); } + +.@{fa-css-prefix}-flip-horizontal { .fa-icon-flip(-1, 1, 0); } +.@{fa-css-prefix}-flip-vertical { .fa-icon-flip(1, -1, 2); } + +// Hook for IE8-9 +// ------------------------- + +:root .@{fa-css-prefix}-rotate-90, +:root .@{fa-css-prefix}-rotate-180, +:root .@{fa-css-prefix}-rotate-270, +:root .@{fa-css-prefix}-flip-horizontal, +:root .@{fa-css-prefix}-flip-vertical { + filter: none; +} diff --git a/dashboard/src/assets/font-awesome/less/screen-reader.less b/dashboard/src/assets/font-awesome/less/screen-reader.less new file mode 100644 index 00000000..11c18819 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/screen-reader.less @@ -0,0 +1,5 @@ +// Screen Readers +// ------------------------- + +.sr-only { .sr-only(); } +.sr-only-focusable { .sr-only-focusable(); } diff --git a/dashboard/src/assets/font-awesome/less/stacked.less b/dashboard/src/assets/font-awesome/less/stacked.less new file mode 100644 index 00000000..fc53fb0e --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/stacked.less @@ -0,0 +1,20 @@ +// Stacked Icons +// ------------------------- + +.@{fa-css-prefix}-stack { + position: relative; + display: inline-block; + width: 2em; + height: 2em; + line-height: 2em; + vertical-align: middle; +} +.@{fa-css-prefix}-stack-1x, .@{fa-css-prefix}-stack-2x { + position: absolute; + left: 0; + width: 100%; + text-align: center; +} +.@{fa-css-prefix}-stack-1x { line-height: inherit; } +.@{fa-css-prefix}-stack-2x { font-size: 2em; } +.@{fa-css-prefix}-inverse { color: @fa-inverse; } diff --git a/dashboard/src/assets/font-awesome/less/variables.less b/dashboard/src/assets/font-awesome/less/variables.less new file mode 100644 index 00000000..7ddbbc01 --- /dev/null +++ b/dashboard/src/assets/font-awesome/less/variables.less @@ -0,0 +1,800 @@ +// Variables +// -------------------------- + +@fa-font-path: "../fonts"; +@fa-font-size-base: 14px; +@fa-line-height-base: 1; +//@fa-font-path: "//netdna.bootstrapcdn.com/font-awesome/4.7.0/fonts"; // for referencing Bootstrap CDN font files directly +@fa-css-prefix: fa; +@fa-version: "4.7.0"; +@fa-border-color: #eee; +@fa-inverse: #fff; +@fa-li-width: (30em / 14); + +@fa-var-500px: "\f26e"; +@fa-var-address-book: "\f2b9"; +@fa-var-address-book-o: "\f2ba"; +@fa-var-address-card: "\f2bb"; +@fa-var-address-card-o: "\f2bc"; +@fa-var-adjust: "\f042"; +@fa-var-adn: "\f170"; +@fa-var-align-center: "\f037"; +@fa-var-align-justify: "\f039"; +@fa-var-align-left: "\f036"; +@fa-var-align-right: "\f038"; +@fa-var-amazon: "\f270"; +@fa-var-ambulance: "\f0f9"; +@fa-var-american-sign-language-interpreting: "\f2a3"; +@fa-var-anchor: "\f13d"; +@fa-var-android: "\f17b"; +@fa-var-angellist: "\f209"; +@fa-var-angle-double-down: "\f103"; +@fa-var-angle-double-left: "\f100"; +@fa-var-angle-double-right: "\f101"; +@fa-var-angle-double-up: "\f102"; +@fa-var-angle-down: "\f107"; +@fa-var-angle-left: "\f104"; +@fa-var-angle-right: "\f105"; +@fa-var-angle-up: "\f106"; +@fa-var-apple: "\f179"; +@fa-var-archive: "\f187"; +@fa-var-area-chart: "\f1fe"; +@fa-var-arrow-circle-down: "\f0ab"; +@fa-var-arrow-circle-left: "\f0a8"; +@fa-var-arrow-circle-o-down: "\f01a"; +@fa-var-arrow-circle-o-left: "\f190"; +@fa-var-arrow-circle-o-right: "\f18e"; +@fa-var-arrow-circle-o-up: "\f01b"; +@fa-var-arrow-circle-right: "\f0a9"; +@fa-var-arrow-circle-up: "\f0aa"; +@fa-var-arrow-down: "\f063"; +@fa-var-arrow-left: "\f060"; +@fa-var-arrow-right: "\f061"; +@fa-var-arrow-up: "\f062"; +@fa-var-arrows: "\f047"; +@fa-var-arrows-alt: "\f0b2"; +@fa-var-arrows-h: "\f07e"; +@fa-var-arrows-v: "\f07d"; +@fa-var-asl-interpreting: "\f2a3"; +@fa-var-assistive-listening-systems: "\f2a2"; +@fa-var-asterisk: "\f069"; +@fa-var-at: "\f1fa"; +@fa-var-audio-description: "\f29e"; +@fa-var-automobile: "\f1b9"; +@fa-var-backward: "\f04a"; +@fa-var-balance-scale: "\f24e"; +@fa-var-ban: "\f05e"; +@fa-var-bandcamp: "\f2d5"; +@fa-var-bank: "\f19c"; +@fa-var-bar-chart: "\f080"; +@fa-var-bar-chart-o: "\f080"; +@fa-var-barcode: "\f02a"; +@fa-var-bars: "\f0c9"; +@fa-var-bath: "\f2cd"; +@fa-var-bathtub: "\f2cd"; +@fa-var-battery: "\f240"; +@fa-var-battery-0: "\f244"; +@fa-var-battery-1: "\f243"; +@fa-var-battery-2: "\f242"; +@fa-var-battery-3: "\f241"; +@fa-var-battery-4: "\f240"; +@fa-var-battery-empty: "\f244"; +@fa-var-battery-full: "\f240"; +@fa-var-battery-half: "\f242"; +@fa-var-battery-quarter: "\f243"; +@fa-var-battery-three-quarters: "\f241"; +@fa-var-bed: "\f236"; +@fa-var-beer: "\f0fc"; +@fa-var-behance: "\f1b4"; +@fa-var-behance-square: "\f1b5"; +@fa-var-bell: "\f0f3"; +@fa-var-bell-o: "\f0a2"; +@fa-var-bell-slash: "\f1f6"; +@fa-var-bell-slash-o: "\f1f7"; +@fa-var-bicycle: "\f206"; +@fa-var-binoculars: "\f1e5"; +@fa-var-birthday-cake: "\f1fd"; +@fa-var-bitbucket: "\f171"; +@fa-var-bitbucket-square: "\f172"; +@fa-var-bitcoin: "\f15a"; +@fa-var-black-tie: "\f27e"; +@fa-var-blind: "\f29d"; +@fa-var-bluetooth: "\f293"; +@fa-var-bluetooth-b: "\f294"; +@fa-var-bold: "\f032"; +@fa-var-bolt: "\f0e7"; +@fa-var-bomb: "\f1e2"; +@fa-var-book: "\f02d"; +@fa-var-bookmark: "\f02e"; +@fa-var-bookmark-o: "\f097"; +@fa-var-braille: "\f2a1"; +@fa-var-briefcase: "\f0b1"; +@fa-var-btc: "\f15a"; +@fa-var-bug: "\f188"; +@fa-var-building: "\f1ad"; +@fa-var-building-o: "\f0f7"; +@fa-var-bullhorn: "\f0a1"; +@fa-var-bullseye: "\f140"; +@fa-var-bus: "\f207"; +@fa-var-buysellads: "\f20d"; +@fa-var-cab: "\f1ba"; +@fa-var-calculator: "\f1ec"; +@fa-var-calendar: "\f073"; +@fa-var-calendar-check-o: "\f274"; +@fa-var-calendar-minus-o: "\f272"; +@fa-var-calendar-o: "\f133"; +@fa-var-calendar-plus-o: "\f271"; +@fa-var-calendar-times-o: "\f273"; +@fa-var-camera: "\f030"; +@fa-var-camera-retro: "\f083"; +@fa-var-car: "\f1b9"; +@fa-var-caret-down: "\f0d7"; +@fa-var-caret-left: "\f0d9"; +@fa-var-caret-right: "\f0da"; +@fa-var-caret-square-o-down: "\f150"; +@fa-var-caret-square-o-left: "\f191"; +@fa-var-caret-square-o-right: "\f152"; +@fa-var-caret-square-o-up: "\f151"; +@fa-var-caret-up: "\f0d8"; +@fa-var-cart-arrow-down: "\f218"; +@fa-var-cart-plus: "\f217"; +@fa-var-cc: "\f20a"; +@fa-var-cc-amex: "\f1f3"; +@fa-var-cc-diners-club: "\f24c"; +@fa-var-cc-discover: "\f1f2"; +@fa-var-cc-jcb: "\f24b"; +@fa-var-cc-mastercard: "\f1f1"; +@fa-var-cc-paypal: "\f1f4"; +@fa-var-cc-stripe: "\f1f5"; +@fa-var-cc-visa: "\f1f0"; +@fa-var-certificate: "\f0a3"; +@fa-var-chain: "\f0c1"; +@fa-var-chain-broken: "\f127"; +@fa-var-check: "\f00c"; +@fa-var-check-circle: "\f058"; +@fa-var-check-circle-o: "\f05d"; +@fa-var-check-square: "\f14a"; +@fa-var-check-square-o: "\f046"; +@fa-var-chevron-circle-down: "\f13a"; +@fa-var-chevron-circle-left: "\f137"; +@fa-var-chevron-circle-right: "\f138"; +@fa-var-chevron-circle-up: "\f139"; +@fa-var-chevron-down: "\f078"; +@fa-var-chevron-left: "\f053"; +@fa-var-chevron-right: "\f054"; +@fa-var-chevron-up: "\f077"; +@fa-var-child: "\f1ae"; +@fa-var-chrome: "\f268"; +@fa-var-circle: "\f111"; +@fa-var-circle-o: "\f10c"; +@fa-var-circle-o-notch: "\f1ce"; +@fa-var-circle-thin: "\f1db"; +@fa-var-clipboard: "\f0ea"; +@fa-var-clock-o: "\f017"; +@fa-var-clone: "\f24d"; +@fa-var-close: "\f00d"; +@fa-var-cloud: "\f0c2"; +@fa-var-cloud-download: "\f0ed"; +@fa-var-cloud-upload: "\f0ee"; +@fa-var-cny: "\f157"; +@fa-var-code: "\f121"; +@fa-var-code-fork: "\f126"; +@fa-var-codepen: "\f1cb"; +@fa-var-codiepie: "\f284"; +@fa-var-coffee: "\f0f4"; +@fa-var-cog: "\f013"; +@fa-var-cogs: "\f085"; +@fa-var-columns: "\f0db"; +@fa-var-comment: "\f075"; +@fa-var-comment-o: "\f0e5"; +@fa-var-commenting: "\f27a"; +@fa-var-commenting-o: "\f27b"; +@fa-var-comments: "\f086"; +@fa-var-comments-o: "\f0e6"; +@fa-var-compass: "\f14e"; +@fa-var-compress: "\f066"; +@fa-var-connectdevelop: "\f20e"; +@fa-var-contao: "\f26d"; +@fa-var-copy: "\f0c5"; +@fa-var-copyright: "\f1f9"; +@fa-var-creative-commons: "\f25e"; +@fa-var-credit-card: "\f09d"; +@fa-var-credit-card-alt: "\f283"; +@fa-var-crop: "\f125"; +@fa-var-crosshairs: "\f05b"; +@fa-var-css3: "\f13c"; +@fa-var-cube: "\f1b2"; +@fa-var-cubes: "\f1b3"; +@fa-var-cut: "\f0c4"; +@fa-var-cutlery: "\f0f5"; +@fa-var-dashboard: "\f0e4"; +@fa-var-dashcube: "\f210"; +@fa-var-database: "\f1c0"; +@fa-var-deaf: "\f2a4"; +@fa-var-deafness: "\f2a4"; +@fa-var-dedent: "\f03b"; +@fa-var-delicious: "\f1a5"; +@fa-var-desktop: "\f108"; +@fa-var-deviantart: "\f1bd"; +@fa-var-diamond: "\f219"; +@fa-var-digg: "\f1a6"; +@fa-var-dollar: "\f155"; +@fa-var-dot-circle-o: "\f192"; +@fa-var-download: "\f019"; +@fa-var-dribbble: "\f17d"; +@fa-var-drivers-license: "\f2c2"; +@fa-var-drivers-license-o: "\f2c3"; +@fa-var-dropbox: "\f16b"; +@fa-var-drupal: "\f1a9"; +@fa-var-edge: "\f282"; +@fa-var-edit: "\f044"; +@fa-var-eercast: "\f2da"; +@fa-var-eject: "\f052"; +@fa-var-ellipsis-h: "\f141"; +@fa-var-ellipsis-v: "\f142"; +@fa-var-empire: "\f1d1"; +@fa-var-envelope: "\f0e0"; +@fa-var-envelope-o: "\f003"; +@fa-var-envelope-open: "\f2b6"; +@fa-var-envelope-open-o: "\f2b7"; +@fa-var-envelope-square: "\f199"; +@fa-var-envira: "\f299"; +@fa-var-eraser: "\f12d"; +@fa-var-etsy: "\f2d7"; +@fa-var-eur: "\f153"; +@fa-var-euro: "\f153"; +@fa-var-exchange: "\f0ec"; +@fa-var-exclamation: "\f12a"; +@fa-var-exclamation-circle: "\f06a"; +@fa-var-exclamation-triangle: "\f071"; +@fa-var-expand: "\f065"; +@fa-var-expeditedssl: "\f23e"; +@fa-var-external-link: "\f08e"; +@fa-var-external-link-square: "\f14c"; +@fa-var-eye: "\f06e"; +@fa-var-eye-slash: "\f070"; +@fa-var-eyedropper: "\f1fb"; +@fa-var-fa: "\f2b4"; +@fa-var-facebook: "\f09a"; +@fa-var-facebook-f: "\f09a"; +@fa-var-facebook-official: "\f230"; +@fa-var-facebook-square: "\f082"; +@fa-var-fast-backward: "\f049"; +@fa-var-fast-forward: "\f050"; +@fa-var-fax: "\f1ac"; +@fa-var-feed: "\f09e"; +@fa-var-female: "\f182"; +@fa-var-fighter-jet: "\f0fb"; +@fa-var-file: "\f15b"; +@fa-var-file-archive-o: "\f1c6"; +@fa-var-file-audio-o: "\f1c7"; +@fa-var-file-code-o: "\f1c9"; +@fa-var-file-excel-o: "\f1c3"; +@fa-var-file-image-o: "\f1c5"; +@fa-var-file-movie-o: "\f1c8"; +@fa-var-file-o: "\f016"; +@fa-var-file-pdf-o: "\f1c1"; +@fa-var-file-photo-o: "\f1c5"; +@fa-var-file-picture-o: "\f1c5"; +@fa-var-file-powerpoint-o: "\f1c4"; +@fa-var-file-sound-o: "\f1c7"; +@fa-var-file-text: "\f15c"; +@fa-var-file-text-o: "\f0f6"; +@fa-var-file-video-o: "\f1c8"; +@fa-var-file-word-o: "\f1c2"; +@fa-var-file-zip-o: "\f1c6"; +@fa-var-files-o: "\f0c5"; +@fa-var-film: "\f008"; +@fa-var-filter: "\f0b0"; +@fa-var-fire: "\f06d"; +@fa-var-fire-extinguisher: "\f134"; +@fa-var-firefox: "\f269"; +@fa-var-first-order: "\f2b0"; +@fa-var-flag: "\f024"; +@fa-var-flag-checkered: "\f11e"; +@fa-var-flag-o: "\f11d"; +@fa-var-flash: "\f0e7"; +@fa-var-flask: "\f0c3"; +@fa-var-flickr: "\f16e"; +@fa-var-floppy-o: "\f0c7"; +@fa-var-folder: "\f07b"; +@fa-var-folder-o: "\f114"; +@fa-var-folder-open: "\f07c"; +@fa-var-folder-open-o: "\f115"; +@fa-var-font: "\f031"; +@fa-var-font-awesome: "\f2b4"; +@fa-var-fonticons: "\f280"; +@fa-var-fort-awesome: "\f286"; +@fa-var-forumbee: "\f211"; +@fa-var-forward: "\f04e"; +@fa-var-foursquare: "\f180"; +@fa-var-free-code-camp: "\f2c5"; +@fa-var-frown-o: "\f119"; +@fa-var-futbol-o: "\f1e3"; +@fa-var-gamepad: "\f11b"; +@fa-var-gavel: "\f0e3"; +@fa-var-gbp: "\f154"; +@fa-var-ge: "\f1d1"; +@fa-var-gear: "\f013"; +@fa-var-gears: "\f085"; +@fa-var-genderless: "\f22d"; +@fa-var-get-pocket: "\f265"; +@fa-var-gg: "\f260"; +@fa-var-gg-circle: "\f261"; +@fa-var-gift: "\f06b"; +@fa-var-git: "\f1d3"; +@fa-var-git-square: "\f1d2"; +@fa-var-github: "\f09b"; +@fa-var-github-alt: "\f113"; +@fa-var-github-square: "\f092"; +@fa-var-gitlab: "\f296"; +@fa-var-gittip: "\f184"; +@fa-var-glass: "\f000"; +@fa-var-glide: "\f2a5"; +@fa-var-glide-g: "\f2a6"; +@fa-var-globe: "\f0ac"; +@fa-var-google: "\f1a0"; +@fa-var-google-plus: "\f0d5"; +@fa-var-google-plus-circle: "\f2b3"; +@fa-var-google-plus-official: "\f2b3"; +@fa-var-google-plus-square: "\f0d4"; +@fa-var-google-wallet: "\f1ee"; +@fa-var-graduation-cap: "\f19d"; +@fa-var-gratipay: "\f184"; +@fa-var-grav: "\f2d6"; +@fa-var-group: "\f0c0"; +@fa-var-h-square: "\f0fd"; +@fa-var-hacker-news: "\f1d4"; +@fa-var-hand-grab-o: "\f255"; +@fa-var-hand-lizard-o: "\f258"; +@fa-var-hand-o-down: "\f0a7"; +@fa-var-hand-o-left: "\f0a5"; +@fa-var-hand-o-right: "\f0a4"; +@fa-var-hand-o-up: "\f0a6"; +@fa-var-hand-paper-o: "\f256"; +@fa-var-hand-peace-o: "\f25b"; +@fa-var-hand-pointer-o: "\f25a"; +@fa-var-hand-rock-o: "\f255"; +@fa-var-hand-scissors-o: "\f257"; +@fa-var-hand-spock-o: "\f259"; +@fa-var-hand-stop-o: "\f256"; +@fa-var-handshake-o: "\f2b5"; +@fa-var-hard-of-hearing: "\f2a4"; +@fa-var-hashtag: "\f292"; +@fa-var-hdd-o: "\f0a0"; +@fa-var-header: "\f1dc"; +@fa-var-headphones: "\f025"; +@fa-var-heart: "\f004"; +@fa-var-heart-o: "\f08a"; +@fa-var-heartbeat: "\f21e"; +@fa-var-history: "\f1da"; +@fa-var-home: "\f015"; +@fa-var-hospital-o: "\f0f8"; +@fa-var-hotel: "\f236"; +@fa-var-hourglass: "\f254"; +@fa-var-hourglass-1: "\f251"; +@fa-var-hourglass-2: "\f252"; +@fa-var-hourglass-3: "\f253"; +@fa-var-hourglass-end: "\f253"; +@fa-var-hourglass-half: "\f252"; +@fa-var-hourglass-o: "\f250"; +@fa-var-hourglass-start: "\f251"; +@fa-var-houzz: "\f27c"; +@fa-var-html5: "\f13b"; +@fa-var-i-cursor: "\f246"; +@fa-var-id-badge: "\f2c1"; +@fa-var-id-card: "\f2c2"; +@fa-var-id-card-o: "\f2c3"; +@fa-var-ils: "\f20b"; +@fa-var-image: "\f03e"; +@fa-var-imdb: "\f2d8"; +@fa-var-inbox: "\f01c"; +@fa-var-indent: "\f03c"; +@fa-var-industry: "\f275"; +@fa-var-info: "\f129"; +@fa-var-info-circle: "\f05a"; +@fa-var-inr: "\f156"; +@fa-var-instagram: "\f16d"; +@fa-var-institution: "\f19c"; +@fa-var-internet-explorer: "\f26b"; +@fa-var-intersex: "\f224"; +@fa-var-ioxhost: "\f208"; +@fa-var-italic: "\f033"; +@fa-var-joomla: "\f1aa"; +@fa-var-jpy: "\f157"; +@fa-var-jsfiddle: "\f1cc"; +@fa-var-key: "\f084"; +@fa-var-keyboard-o: "\f11c"; +@fa-var-krw: "\f159"; +@fa-var-language: "\f1ab"; +@fa-var-laptop: "\f109"; +@fa-var-lastfm: "\f202"; +@fa-var-lastfm-square: "\f203"; +@fa-var-leaf: "\f06c"; +@fa-var-leanpub: "\f212"; +@fa-var-legal: "\f0e3"; +@fa-var-lemon-o: "\f094"; +@fa-var-level-down: "\f149"; +@fa-var-level-up: "\f148"; +@fa-var-life-bouy: "\f1cd"; +@fa-var-life-buoy: "\f1cd"; +@fa-var-life-ring: "\f1cd"; +@fa-var-life-saver: "\f1cd"; +@fa-var-lightbulb-o: "\f0eb"; +@fa-var-line-chart: "\f201"; +@fa-var-link: "\f0c1"; +@fa-var-linkedin: "\f0e1"; +@fa-var-linkedin-square: "\f08c"; +@fa-var-linode: "\f2b8"; +@fa-var-linux: "\f17c"; +@fa-var-list: "\f03a"; +@fa-var-list-alt: "\f022"; +@fa-var-list-ol: "\f0cb"; +@fa-var-list-ul: "\f0ca"; +@fa-var-location-arrow: "\f124"; +@fa-var-lock: "\f023"; +@fa-var-long-arrow-down: "\f175"; +@fa-var-long-arrow-left: "\f177"; +@fa-var-long-arrow-right: "\f178"; +@fa-var-long-arrow-up: "\f176"; +@fa-var-low-vision: "\f2a8"; +@fa-var-magic: "\f0d0"; +@fa-var-magnet: "\f076"; +@fa-var-mail-forward: "\f064"; +@fa-var-mail-reply: "\f112"; +@fa-var-mail-reply-all: "\f122"; +@fa-var-male: "\f183"; +@fa-var-map: "\f279"; +@fa-var-map-marker: "\f041"; +@fa-var-map-o: "\f278"; +@fa-var-map-pin: "\f276"; +@fa-var-map-signs: "\f277"; +@fa-var-mars: "\f222"; +@fa-var-mars-double: "\f227"; +@fa-var-mars-stroke: "\f229"; +@fa-var-mars-stroke-h: "\f22b"; +@fa-var-mars-stroke-v: "\f22a"; +@fa-var-maxcdn: "\f136"; +@fa-var-meanpath: "\f20c"; +@fa-var-medium: "\f23a"; +@fa-var-medkit: "\f0fa"; +@fa-var-meetup: "\f2e0"; +@fa-var-meh-o: "\f11a"; +@fa-var-mercury: "\f223"; +@fa-var-microchip: "\f2db"; +@fa-var-microphone: "\f130"; +@fa-var-microphone-slash: "\f131"; +@fa-var-minus: "\f068"; +@fa-var-minus-circle: "\f056"; +@fa-var-minus-square: "\f146"; +@fa-var-minus-square-o: "\f147"; +@fa-var-mixcloud: "\f289"; +@fa-var-mobile: "\f10b"; +@fa-var-mobile-phone: "\f10b"; +@fa-var-modx: "\f285"; +@fa-var-money: "\f0d6"; +@fa-var-moon-o: "\f186"; +@fa-var-mortar-board: "\f19d"; +@fa-var-motorcycle: "\f21c"; +@fa-var-mouse-pointer: "\f245"; +@fa-var-music: "\f001"; +@fa-var-navicon: "\f0c9"; +@fa-var-neuter: "\f22c"; +@fa-var-newspaper-o: "\f1ea"; +@fa-var-object-group: "\f247"; +@fa-var-object-ungroup: "\f248"; +@fa-var-odnoklassniki: "\f263"; +@fa-var-odnoklassniki-square: "\f264"; +@fa-var-opencart: "\f23d"; +@fa-var-openid: "\f19b"; +@fa-var-opera: "\f26a"; +@fa-var-optin-monster: "\f23c"; +@fa-var-outdent: "\f03b"; +@fa-var-pagelines: "\f18c"; +@fa-var-paint-brush: "\f1fc"; +@fa-var-paper-plane: "\f1d8"; +@fa-var-paper-plane-o: "\f1d9"; +@fa-var-paperclip: "\f0c6"; +@fa-var-paragraph: "\f1dd"; +@fa-var-paste: "\f0ea"; +@fa-var-pause: "\f04c"; +@fa-var-pause-circle: "\f28b"; +@fa-var-pause-circle-o: "\f28c"; +@fa-var-paw: "\f1b0"; +@fa-var-paypal: "\f1ed"; +@fa-var-pencil: "\f040"; +@fa-var-pencil-square: "\f14b"; +@fa-var-pencil-square-o: "\f044"; +@fa-var-percent: "\f295"; +@fa-var-phone: "\f095"; +@fa-var-phone-square: "\f098"; +@fa-var-photo: "\f03e"; +@fa-var-picture-o: "\f03e"; +@fa-var-pie-chart: "\f200"; +@fa-var-pied-piper: "\f2ae"; +@fa-var-pied-piper-alt: "\f1a8"; +@fa-var-pied-piper-pp: "\f1a7"; +@fa-var-pinterest: "\f0d2"; +@fa-var-pinterest-p: "\f231"; +@fa-var-pinterest-square: "\f0d3"; +@fa-var-plane: "\f072"; +@fa-var-play: "\f04b"; +@fa-var-play-circle: "\f144"; +@fa-var-play-circle-o: "\f01d"; +@fa-var-plug: "\f1e6"; +@fa-var-plus: "\f067"; +@fa-var-plus-circle: "\f055"; +@fa-var-plus-square: "\f0fe"; +@fa-var-plus-square-o: "\f196"; +@fa-var-podcast: "\f2ce"; +@fa-var-power-off: "\f011"; +@fa-var-print: "\f02f"; +@fa-var-product-hunt: "\f288"; +@fa-var-puzzle-piece: "\f12e"; +@fa-var-qq: "\f1d6"; +@fa-var-qrcode: "\f029"; +@fa-var-question: "\f128"; +@fa-var-question-circle: "\f059"; +@fa-var-question-circle-o: "\f29c"; +@fa-var-quora: "\f2c4"; +@fa-var-quote-left: "\f10d"; +@fa-var-quote-right: "\f10e"; +@fa-var-ra: "\f1d0"; +@fa-var-random: "\f074"; +@fa-var-ravelry: "\f2d9"; +@fa-var-rebel: "\f1d0"; +@fa-var-recycle: "\f1b8"; +@fa-var-reddit: "\f1a1"; +@fa-var-reddit-alien: "\f281"; +@fa-var-reddit-square: "\f1a2"; +@fa-var-refresh: "\f021"; +@fa-var-registered: "\f25d"; +@fa-var-remove: "\f00d"; +@fa-var-renren: "\f18b"; +@fa-var-reorder: "\f0c9"; +@fa-var-repeat: "\f01e"; +@fa-var-reply: "\f112"; +@fa-var-reply-all: "\f122"; +@fa-var-resistance: "\f1d0"; +@fa-var-retweet: "\f079"; +@fa-var-rmb: "\f157"; +@fa-var-road: "\f018"; +@fa-var-rocket: "\f135"; +@fa-var-rotate-left: "\f0e2"; +@fa-var-rotate-right: "\f01e"; +@fa-var-rouble: "\f158"; +@fa-var-rss: "\f09e"; +@fa-var-rss-square: "\f143"; +@fa-var-rub: "\f158"; +@fa-var-ruble: "\f158"; +@fa-var-rupee: "\f156"; +@fa-var-s15: "\f2cd"; +@fa-var-safari: "\f267"; +@fa-var-save: "\f0c7"; +@fa-var-scissors: "\f0c4"; +@fa-var-scribd: "\f28a"; +@fa-var-search: "\f002"; +@fa-var-search-minus: "\f010"; +@fa-var-search-plus: "\f00e"; +@fa-var-sellsy: "\f213"; +@fa-var-send: "\f1d8"; +@fa-var-send-o: "\f1d9"; +@fa-var-server: "\f233"; +@fa-var-share: "\f064"; +@fa-var-share-alt: "\f1e0"; +@fa-var-share-alt-square: "\f1e1"; +@fa-var-share-square: "\f14d"; +@fa-var-share-square-o: "\f045"; +@fa-var-shekel: "\f20b"; +@fa-var-sheqel: "\f20b"; +@fa-var-shield: "\f132"; +@fa-var-ship: "\f21a"; +@fa-var-shirtsinbulk: "\f214"; +@fa-var-shopping-bag: "\f290"; +@fa-var-shopping-basket: "\f291"; +@fa-var-shopping-cart: "\f07a"; +@fa-var-shower: "\f2cc"; +@fa-var-sign-in: "\f090"; +@fa-var-sign-language: "\f2a7"; +@fa-var-sign-out: "\f08b"; +@fa-var-signal: "\f012"; +@fa-var-signing: "\f2a7"; +@fa-var-simplybuilt: "\f215"; +@fa-var-sitemap: "\f0e8"; +@fa-var-skyatlas: "\f216"; +@fa-var-skype: "\f17e"; +@fa-var-slack: "\f198"; +@fa-var-sliders: "\f1de"; +@fa-var-slideshare: "\f1e7"; +@fa-var-smile-o: "\f118"; +@fa-var-snapchat: "\f2ab"; +@fa-var-snapchat-ghost: "\f2ac"; +@fa-var-snapchat-square: "\f2ad"; +@fa-var-snowflake-o: "\f2dc"; +@fa-var-soccer-ball-o: "\f1e3"; +@fa-var-sort: "\f0dc"; +@fa-var-sort-alpha-asc: "\f15d"; +@fa-var-sort-alpha-desc: "\f15e"; +@fa-var-sort-amount-asc: "\f160"; +@fa-var-sort-amount-desc: "\f161"; +@fa-var-sort-asc: "\f0de"; +@fa-var-sort-desc: "\f0dd"; +@fa-var-sort-down: "\f0dd"; +@fa-var-sort-numeric-asc: "\f162"; +@fa-var-sort-numeric-desc: "\f163"; +@fa-var-sort-up: "\f0de"; +@fa-var-soundcloud: "\f1be"; +@fa-var-space-shuttle: "\f197"; +@fa-var-spinner: "\f110"; +@fa-var-spoon: "\f1b1"; +@fa-var-spotify: "\f1bc"; +@fa-var-square: "\f0c8"; +@fa-var-square-o: "\f096"; +@fa-var-stack-exchange: "\f18d"; +@fa-var-stack-overflow: "\f16c"; +@fa-var-star: "\f005"; +@fa-var-star-half: "\f089"; +@fa-var-star-half-empty: "\f123"; +@fa-var-star-half-full: "\f123"; +@fa-var-star-half-o: "\f123"; +@fa-var-star-o: "\f006"; +@fa-var-steam: "\f1b6"; +@fa-var-steam-square: "\f1b7"; +@fa-var-step-backward: "\f048"; +@fa-var-step-forward: "\f051"; +@fa-var-stethoscope: "\f0f1"; +@fa-var-sticky-note: "\f249"; +@fa-var-sticky-note-o: "\f24a"; +@fa-var-stop: "\f04d"; +@fa-var-stop-circle: "\f28d"; +@fa-var-stop-circle-o: "\f28e"; +@fa-var-street-view: "\f21d"; +@fa-var-strikethrough: "\f0cc"; +@fa-var-stumbleupon: "\f1a4"; +@fa-var-stumbleupon-circle: "\f1a3"; +@fa-var-subscript: "\f12c"; +@fa-var-subway: "\f239"; +@fa-var-suitcase: "\f0f2"; +@fa-var-sun-o: "\f185"; +@fa-var-superpowers: "\f2dd"; +@fa-var-superscript: "\f12b"; +@fa-var-support: "\f1cd"; +@fa-var-table: "\f0ce"; +@fa-var-tablet: "\f10a"; +@fa-var-tachometer: "\f0e4"; +@fa-var-tag: "\f02b"; +@fa-var-tags: "\f02c"; +@fa-var-tasks: "\f0ae"; +@fa-var-taxi: "\f1ba"; +@fa-var-telegram: "\f2c6"; +@fa-var-television: "\f26c"; +@fa-var-tencent-weibo: "\f1d5"; +@fa-var-terminal: "\f120"; +@fa-var-text-height: "\f034"; +@fa-var-text-width: "\f035"; +@fa-var-th: "\f00a"; +@fa-var-th-large: "\f009"; +@fa-var-th-list: "\f00b"; +@fa-var-themeisle: "\f2b2"; +@fa-var-thermometer: "\f2c7"; +@fa-var-thermometer-0: "\f2cb"; +@fa-var-thermometer-1: "\f2ca"; +@fa-var-thermometer-2: "\f2c9"; +@fa-var-thermometer-3: "\f2c8"; +@fa-var-thermometer-4: "\f2c7"; +@fa-var-thermometer-empty: "\f2cb"; +@fa-var-thermometer-full: "\f2c7"; +@fa-var-thermometer-half: "\f2c9"; +@fa-var-thermometer-quarter: "\f2ca"; +@fa-var-thermometer-three-quarters: "\f2c8"; +@fa-var-thumb-tack: "\f08d"; +@fa-var-thumbs-down: "\f165"; +@fa-var-thumbs-o-down: "\f088"; +@fa-var-thumbs-o-up: "\f087"; +@fa-var-thumbs-up: "\f164"; +@fa-var-ticket: "\f145"; +@fa-var-times: "\f00d"; +@fa-var-times-circle: "\f057"; +@fa-var-times-circle-o: "\f05c"; +@fa-var-times-rectangle: "\f2d3"; +@fa-var-times-rectangle-o: "\f2d4"; +@fa-var-tint: "\f043"; +@fa-var-toggle-down: "\f150"; +@fa-var-toggle-left: "\f191"; +@fa-var-toggle-off: "\f204"; +@fa-var-toggle-on: "\f205"; +@fa-var-toggle-right: "\f152"; +@fa-var-toggle-up: "\f151"; +@fa-var-trademark: "\f25c"; +@fa-var-train: "\f238"; +@fa-var-transgender: "\f224"; +@fa-var-transgender-alt: "\f225"; +@fa-var-trash: "\f1f8"; +@fa-var-trash-o: "\f014"; +@fa-var-tree: "\f1bb"; +@fa-var-trello: "\f181"; +@fa-var-tripadvisor: "\f262"; +@fa-var-trophy: "\f091"; +@fa-var-truck: "\f0d1"; +@fa-var-try: "\f195"; +@fa-var-tty: "\f1e4"; +@fa-var-tumblr: "\f173"; +@fa-var-tumblr-square: "\f174"; +@fa-var-turkish-lira: "\f195"; +@fa-var-tv: "\f26c"; +@fa-var-twitch: "\f1e8"; +@fa-var-twitter: "\f099"; +@fa-var-twitter-square: "\f081"; +@fa-var-umbrella: "\f0e9"; +@fa-var-underline: "\f0cd"; +@fa-var-undo: "\f0e2"; +@fa-var-universal-access: "\f29a"; +@fa-var-university: "\f19c"; +@fa-var-unlink: "\f127"; +@fa-var-unlock: "\f09c"; +@fa-var-unlock-alt: "\f13e"; +@fa-var-unsorted: "\f0dc"; +@fa-var-upload: "\f093"; +@fa-var-usb: "\f287"; +@fa-var-usd: "\f155"; +@fa-var-user: "\f007"; +@fa-var-user-circle: "\f2bd"; +@fa-var-user-circle-o: "\f2be"; +@fa-var-user-md: "\f0f0"; +@fa-var-user-o: "\f2c0"; +@fa-var-user-plus: "\f234"; +@fa-var-user-secret: "\f21b"; +@fa-var-user-times: "\f235"; +@fa-var-users: "\f0c0"; +@fa-var-vcard: "\f2bb"; +@fa-var-vcard-o: "\f2bc"; +@fa-var-venus: "\f221"; +@fa-var-venus-double: "\f226"; +@fa-var-venus-mars: "\f228"; +@fa-var-viacoin: "\f237"; +@fa-var-viadeo: "\f2a9"; +@fa-var-viadeo-square: "\f2aa"; +@fa-var-video-camera: "\f03d"; +@fa-var-vimeo: "\f27d"; +@fa-var-vimeo-square: "\f194"; +@fa-var-vine: "\f1ca"; +@fa-var-vk: "\f189"; +@fa-var-volume-control-phone: "\f2a0"; +@fa-var-volume-down: "\f027"; +@fa-var-volume-off: "\f026"; +@fa-var-volume-up: "\f028"; +@fa-var-warning: "\f071"; +@fa-var-wechat: "\f1d7"; +@fa-var-weibo: "\f18a"; +@fa-var-weixin: "\f1d7"; +@fa-var-whatsapp: "\f232"; +@fa-var-wheelchair: "\f193"; +@fa-var-wheelchair-alt: "\f29b"; +@fa-var-wifi: "\f1eb"; +@fa-var-wikipedia-w: "\f266"; +@fa-var-window-close: "\f2d3"; +@fa-var-window-close-o: "\f2d4"; +@fa-var-window-maximize: "\f2d0"; +@fa-var-window-minimize: "\f2d1"; +@fa-var-window-restore: "\f2d2"; +@fa-var-windows: "\f17a"; +@fa-var-won: "\f159"; +@fa-var-wordpress: "\f19a"; +@fa-var-wpbeginner: "\f297"; +@fa-var-wpexplorer: "\f2de"; +@fa-var-wpforms: "\f298"; +@fa-var-wrench: "\f0ad"; +@fa-var-xing: "\f168"; +@fa-var-xing-square: "\f169"; +@fa-var-y-combinator: "\f23b"; +@fa-var-y-combinator-square: "\f1d4"; +@fa-var-yahoo: "\f19e"; +@fa-var-yc: "\f23b"; +@fa-var-yc-square: "\f1d4"; +@fa-var-yelp: "\f1e9"; +@fa-var-yen: "\f157"; +@fa-var-yoast: "\f2b1"; +@fa-var-youtube: "\f167"; +@fa-var-youtube-play: "\f16a"; +@fa-var-youtube-square: "\f166"; + diff --git a/dashboard/src/assets/font-awesome/scss/_animated.scss b/dashboard/src/assets/font-awesome/scss/_animated.scss new file mode 100644 index 00000000..8a020dbf --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_animated.scss @@ -0,0 +1,34 @@ +// Spinning Icons +// -------------------------- + +.#{$fa-css-prefix}-spin { + -webkit-animation: fa-spin 2s infinite linear; + animation: fa-spin 2s infinite linear; +} + +.#{$fa-css-prefix}-pulse { + -webkit-animation: fa-spin 1s infinite steps(8); + animation: fa-spin 1s infinite steps(8); +} + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} diff --git a/dashboard/src/assets/font-awesome/scss/_bordered-pulled.scss b/dashboard/src/assets/font-awesome/scss/_bordered-pulled.scss new file mode 100644 index 00000000..d4b85a02 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_bordered-pulled.scss @@ -0,0 +1,25 @@ +// Bordered & Pulled +// ------------------------- + +.#{$fa-css-prefix}-border { + padding: .2em .25em .15em; + border: solid .08em $fa-border-color; + border-radius: .1em; +} + +.#{$fa-css-prefix}-pull-left { float: left; } +.#{$fa-css-prefix}-pull-right { float: right; } + +.#{$fa-css-prefix} { + &.#{$fa-css-prefix}-pull-left { margin-right: .3em; } + &.#{$fa-css-prefix}-pull-right { margin-left: .3em; } +} + +/* Deprecated as of 4.4.0 */ +.pull-right { float: right; } +.pull-left { float: left; } + +.#{$fa-css-prefix} { + &.pull-left { margin-right: .3em; } + &.pull-right { margin-left: .3em; } +} diff --git a/dashboard/src/assets/font-awesome/scss/_core.scss b/dashboard/src/assets/font-awesome/scss/_core.scss new file mode 100644 index 00000000..7425ef85 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_core.scss @@ -0,0 +1,12 @@ +// Base Class Definition +// ------------------------- + +.#{$fa-css-prefix} { + display: inline-block; + font: normal normal normal #{$fa-font-size-base}/#{$fa-line-height-base} FontAwesome; // shortening font declaration + font-size: inherit; // can't have font-size inherit on line above, so need to override + text-rendering: auto; // optimizelegibility throws things off #1094 + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + +} diff --git a/dashboard/src/assets/font-awesome/scss/_fixed-width.scss b/dashboard/src/assets/font-awesome/scss/_fixed-width.scss new file mode 100644 index 00000000..b221c981 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_fixed-width.scss @@ -0,0 +1,6 @@ +// Fixed Width Icons +// ------------------------- +.#{$fa-css-prefix}-fw { + width: (18em / 14); + text-align: center; +} diff --git a/dashboard/src/assets/font-awesome/scss/_icons.scss b/dashboard/src/assets/font-awesome/scss/_icons.scss new file mode 100644 index 00000000..e63e702c --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_icons.scss @@ -0,0 +1,789 @@ +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen + readers do not read off random characters that represent icons */ + +.#{$fa-css-prefix}-glass:before { content: $fa-var-glass; } +.#{$fa-css-prefix}-music:before { content: $fa-var-music; } +.#{$fa-css-prefix}-search:before { content: $fa-var-search; } +.#{$fa-css-prefix}-envelope-o:before { content: $fa-var-envelope-o; } +.#{$fa-css-prefix}-heart:before { content: $fa-var-heart; } +.#{$fa-css-prefix}-star:before { content: $fa-var-star; } +.#{$fa-css-prefix}-star-o:before { content: $fa-var-star-o; } +.#{$fa-css-prefix}-user:before { content: $fa-var-user; } +.#{$fa-css-prefix}-film:before { content: $fa-var-film; } +.#{$fa-css-prefix}-th-large:before { content: $fa-var-th-large; } +.#{$fa-css-prefix}-th:before { content: $fa-var-th; } +.#{$fa-css-prefix}-th-list:before { content: $fa-var-th-list; } +.#{$fa-css-prefix}-check:before { content: $fa-var-check; } +.#{$fa-css-prefix}-remove:before, +.#{$fa-css-prefix}-close:before, +.#{$fa-css-prefix}-times:before { content: $fa-var-times; } +.#{$fa-css-prefix}-search-plus:before { content: $fa-var-search-plus; } +.#{$fa-css-prefix}-search-minus:before { content: $fa-var-search-minus; } +.#{$fa-css-prefix}-power-off:before { content: $fa-var-power-off; } +.#{$fa-css-prefix}-signal:before { content: $fa-var-signal; } +.#{$fa-css-prefix}-gear:before, +.#{$fa-css-prefix}-cog:before { content: $fa-var-cog; } +.#{$fa-css-prefix}-trash-o:before { content: $fa-var-trash-o; } +.#{$fa-css-prefix}-home:before { content: $fa-var-home; } +.#{$fa-css-prefix}-file-o:before { content: $fa-var-file-o; } +.#{$fa-css-prefix}-clock-o:before { content: $fa-var-clock-o; } +.#{$fa-css-prefix}-road:before { content: $fa-var-road; } +.#{$fa-css-prefix}-download:before { content: $fa-var-download; } +.#{$fa-css-prefix}-arrow-circle-o-down:before { content: $fa-var-arrow-circle-o-down; } +.#{$fa-css-prefix}-arrow-circle-o-up:before { content: $fa-var-arrow-circle-o-up; } +.#{$fa-css-prefix}-inbox:before { content: $fa-var-inbox; } +.#{$fa-css-prefix}-play-circle-o:before { content: $fa-var-play-circle-o; } +.#{$fa-css-prefix}-rotate-right:before, +.#{$fa-css-prefix}-repeat:before { content: $fa-var-repeat; } +.#{$fa-css-prefix}-refresh:before { content: $fa-var-refresh; } +.#{$fa-css-prefix}-list-alt:before { content: $fa-var-list-alt; } +.#{$fa-css-prefix}-lock:before { content: $fa-var-lock; } +.#{$fa-css-prefix}-flag:before { content: $fa-var-flag; } +.#{$fa-css-prefix}-headphones:before { content: $fa-var-headphones; } +.#{$fa-css-prefix}-volume-off:before { content: $fa-var-volume-off; } +.#{$fa-css-prefix}-volume-down:before { content: $fa-var-volume-down; } +.#{$fa-css-prefix}-volume-up:before { content: $fa-var-volume-up; } +.#{$fa-css-prefix}-qrcode:before { content: $fa-var-qrcode; } +.#{$fa-css-prefix}-barcode:before { content: $fa-var-barcode; } +.#{$fa-css-prefix}-tag:before { content: $fa-var-tag; } +.#{$fa-css-prefix}-tags:before { content: $fa-var-tags; } +.#{$fa-css-prefix}-book:before { content: $fa-var-book; } +.#{$fa-css-prefix}-bookmark:before { content: $fa-var-bookmark; } +.#{$fa-css-prefix}-print:before { content: $fa-var-print; } +.#{$fa-css-prefix}-camera:before { content: $fa-var-camera; } +.#{$fa-css-prefix}-font:before { content: $fa-var-font; } +.#{$fa-css-prefix}-bold:before { content: $fa-var-bold; } +.#{$fa-css-prefix}-italic:before { content: $fa-var-italic; } +.#{$fa-css-prefix}-text-height:before { content: $fa-var-text-height; } +.#{$fa-css-prefix}-text-width:before { content: $fa-var-text-width; } +.#{$fa-css-prefix}-align-left:before { content: $fa-var-align-left; } +.#{$fa-css-prefix}-align-center:before { content: $fa-var-align-center; } +.#{$fa-css-prefix}-align-right:before { content: $fa-var-align-right; } +.#{$fa-css-prefix}-align-justify:before { content: $fa-var-align-justify; } +.#{$fa-css-prefix}-list:before { content: $fa-var-list; } +.#{$fa-css-prefix}-dedent:before, +.#{$fa-css-prefix}-outdent:before { content: $fa-var-outdent; } +.#{$fa-css-prefix}-indent:before { content: $fa-var-indent; } +.#{$fa-css-prefix}-video-camera:before { content: $fa-var-video-camera; } +.#{$fa-css-prefix}-photo:before, +.#{$fa-css-prefix}-image:before, +.#{$fa-css-prefix}-picture-o:before { content: $fa-var-picture-o; } +.#{$fa-css-prefix}-pencil:before { content: $fa-var-pencil; } +.#{$fa-css-prefix}-map-marker:before { content: $fa-var-map-marker; } +.#{$fa-css-prefix}-adjust:before { content: $fa-var-adjust; } +.#{$fa-css-prefix}-tint:before { content: $fa-var-tint; } +.#{$fa-css-prefix}-edit:before, +.#{$fa-css-prefix}-pencil-square-o:before { content: $fa-var-pencil-square-o; } +.#{$fa-css-prefix}-share-square-o:before { content: $fa-var-share-square-o; } +.#{$fa-css-prefix}-check-square-o:before { content: $fa-var-check-square-o; } +.#{$fa-css-prefix}-arrows:before { content: $fa-var-arrows; 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} +.#{$fa-css-prefix}-hand-peace-o:before { content: $fa-var-hand-peace-o; } +.#{$fa-css-prefix}-trademark:before { content: $fa-var-trademark; } +.#{$fa-css-prefix}-registered:before { content: $fa-var-registered; } +.#{$fa-css-prefix}-creative-commons:before { content: $fa-var-creative-commons; } +.#{$fa-css-prefix}-gg:before { content: $fa-var-gg; } +.#{$fa-css-prefix}-gg-circle:before { content: $fa-var-gg-circle; } +.#{$fa-css-prefix}-tripadvisor:before { content: $fa-var-tripadvisor; } +.#{$fa-css-prefix}-odnoklassniki:before { content: $fa-var-odnoklassniki; } +.#{$fa-css-prefix}-odnoklassniki-square:before { content: $fa-var-odnoklassniki-square; } +.#{$fa-css-prefix}-get-pocket:before { content: $fa-var-get-pocket; } +.#{$fa-css-prefix}-wikipedia-w:before { content: $fa-var-wikipedia-w; } +.#{$fa-css-prefix}-safari:before { content: $fa-var-safari; } +.#{$fa-css-prefix}-chrome:before { content: $fa-var-chrome; } +.#{$fa-css-prefix}-firefox:before { content: $fa-var-firefox; } +.#{$fa-css-prefix}-opera:before { content: $fa-var-opera; } +.#{$fa-css-prefix}-internet-explorer:before { content: $fa-var-internet-explorer; } +.#{$fa-css-prefix}-tv:before, +.#{$fa-css-prefix}-television:before { content: $fa-var-television; } +.#{$fa-css-prefix}-contao:before { content: $fa-var-contao; } +.#{$fa-css-prefix}-500px:before { content: $fa-var-500px; } +.#{$fa-css-prefix}-amazon:before { content: $fa-var-amazon; } +.#{$fa-css-prefix}-calendar-plus-o:before { content: $fa-var-calendar-plus-o; } +.#{$fa-css-prefix}-calendar-minus-o:before { content: $fa-var-calendar-minus-o; } +.#{$fa-css-prefix}-calendar-times-o:before { content: $fa-var-calendar-times-o; } +.#{$fa-css-prefix}-calendar-check-o:before { content: $fa-var-calendar-check-o; } +.#{$fa-css-prefix}-industry:before { content: $fa-var-industry; } +.#{$fa-css-prefix}-map-pin:before { content: $fa-var-map-pin; } +.#{$fa-css-prefix}-map-signs:before { content: $fa-var-map-signs; } +.#{$fa-css-prefix}-map-o:before { content: $fa-var-map-o; } +.#{$fa-css-prefix}-map:before { content: $fa-var-map; } +.#{$fa-css-prefix}-commenting:before { content: $fa-var-commenting; } +.#{$fa-css-prefix}-commenting-o:before { content: $fa-var-commenting-o; } +.#{$fa-css-prefix}-houzz:before { content: $fa-var-houzz; } +.#{$fa-css-prefix}-vimeo:before { content: $fa-var-vimeo; } +.#{$fa-css-prefix}-black-tie:before { content: $fa-var-black-tie; } +.#{$fa-css-prefix}-fonticons:before { content: $fa-var-fonticons; } +.#{$fa-css-prefix}-reddit-alien:before { content: $fa-var-reddit-alien; } +.#{$fa-css-prefix}-edge:before { content: $fa-var-edge; } +.#{$fa-css-prefix}-credit-card-alt:before { content: $fa-var-credit-card-alt; } +.#{$fa-css-prefix}-codiepie:before { content: $fa-var-codiepie; } +.#{$fa-css-prefix}-modx:before { content: $fa-var-modx; } +.#{$fa-css-prefix}-fort-awesome:before { content: $fa-var-fort-awesome; } +.#{$fa-css-prefix}-usb:before { content: $fa-var-usb; } +.#{$fa-css-prefix}-product-hunt:before { content: $fa-var-product-hunt; } +.#{$fa-css-prefix}-mixcloud:before { content: $fa-var-mixcloud; } +.#{$fa-css-prefix}-scribd:before { content: $fa-var-scribd; } +.#{$fa-css-prefix}-pause-circle:before { content: $fa-var-pause-circle; } +.#{$fa-css-prefix}-pause-circle-o:before { content: $fa-var-pause-circle-o; } +.#{$fa-css-prefix}-stop-circle:before { content: $fa-var-stop-circle; } +.#{$fa-css-prefix}-stop-circle-o:before { content: $fa-var-stop-circle-o; } +.#{$fa-css-prefix}-shopping-bag:before { content: $fa-var-shopping-bag; } +.#{$fa-css-prefix}-shopping-basket:before { content: $fa-var-shopping-basket; } +.#{$fa-css-prefix}-hashtag:before { content: $fa-var-hashtag; } +.#{$fa-css-prefix}-bluetooth:before { content: $fa-var-bluetooth; } +.#{$fa-css-prefix}-bluetooth-b:before { content: $fa-var-bluetooth-b; } +.#{$fa-css-prefix}-percent:before { content: $fa-var-percent; } +.#{$fa-css-prefix}-gitlab:before { content: $fa-var-gitlab; } +.#{$fa-css-prefix}-wpbeginner:before { content: $fa-var-wpbeginner; } +.#{$fa-css-prefix}-wpforms:before { content: $fa-var-wpforms; } +.#{$fa-css-prefix}-envira:before { content: $fa-var-envira; } +.#{$fa-css-prefix}-universal-access:before { content: $fa-var-universal-access; } +.#{$fa-css-prefix}-wheelchair-alt:before { content: $fa-var-wheelchair-alt; } +.#{$fa-css-prefix}-question-circle-o:before { content: $fa-var-question-circle-o; } +.#{$fa-css-prefix}-blind:before { content: $fa-var-blind; } +.#{$fa-css-prefix}-audio-description:before { content: $fa-var-audio-description; } +.#{$fa-css-prefix}-volume-control-phone:before { content: $fa-var-volume-control-phone; } +.#{$fa-css-prefix}-braille:before { content: $fa-var-braille; } +.#{$fa-css-prefix}-assistive-listening-systems:before { content: $fa-var-assistive-listening-systems; } +.#{$fa-css-prefix}-asl-interpreting:before, +.#{$fa-css-prefix}-american-sign-language-interpreting:before { content: $fa-var-american-sign-language-interpreting; } +.#{$fa-css-prefix}-deafness:before, +.#{$fa-css-prefix}-hard-of-hearing:before, +.#{$fa-css-prefix}-deaf:before { content: $fa-var-deaf; } +.#{$fa-css-prefix}-glide:before { content: $fa-var-glide; } +.#{$fa-css-prefix}-glide-g:before { content: $fa-var-glide-g; } +.#{$fa-css-prefix}-signing:before, +.#{$fa-css-prefix}-sign-language:before { content: $fa-var-sign-language; } +.#{$fa-css-prefix}-low-vision:before { content: $fa-var-low-vision; } +.#{$fa-css-prefix}-viadeo:before { content: $fa-var-viadeo; } +.#{$fa-css-prefix}-viadeo-square:before { content: $fa-var-viadeo-square; } +.#{$fa-css-prefix}-snapchat:before { content: $fa-var-snapchat; } +.#{$fa-css-prefix}-snapchat-ghost:before { content: $fa-var-snapchat-ghost; } +.#{$fa-css-prefix}-snapchat-square:before { content: $fa-var-snapchat-square; } +.#{$fa-css-prefix}-pied-piper:before { content: $fa-var-pied-piper; } +.#{$fa-css-prefix}-first-order:before { content: $fa-var-first-order; } +.#{$fa-css-prefix}-yoast:before { content: $fa-var-yoast; } +.#{$fa-css-prefix}-themeisle:before { content: $fa-var-themeisle; } +.#{$fa-css-prefix}-google-plus-circle:before, +.#{$fa-css-prefix}-google-plus-official:before { content: $fa-var-google-plus-official; } +.#{$fa-css-prefix}-fa:before, +.#{$fa-css-prefix}-font-awesome:before { content: $fa-var-font-awesome; } +.#{$fa-css-prefix}-handshake-o:before { content: $fa-var-handshake-o; } +.#{$fa-css-prefix}-envelope-open:before { content: $fa-var-envelope-open; } +.#{$fa-css-prefix}-envelope-open-o:before { content: $fa-var-envelope-open-o; } +.#{$fa-css-prefix}-linode:before { content: $fa-var-linode; } +.#{$fa-css-prefix}-address-book:before { content: $fa-var-address-book; } +.#{$fa-css-prefix}-address-book-o:before { content: $fa-var-address-book-o; } +.#{$fa-css-prefix}-vcard:before, +.#{$fa-css-prefix}-address-card:before { content: $fa-var-address-card; } +.#{$fa-css-prefix}-vcard-o:before, +.#{$fa-css-prefix}-address-card-o:before { content: $fa-var-address-card-o; } +.#{$fa-css-prefix}-user-circle:before { content: $fa-var-user-circle; } +.#{$fa-css-prefix}-user-circle-o:before { content: $fa-var-user-circle-o; } +.#{$fa-css-prefix}-user-o:before { content: $fa-var-user-o; } +.#{$fa-css-prefix}-id-badge:before { content: $fa-var-id-badge; } +.#{$fa-css-prefix}-drivers-license:before, +.#{$fa-css-prefix}-id-card:before { content: $fa-var-id-card; } +.#{$fa-css-prefix}-drivers-license-o:before, +.#{$fa-css-prefix}-id-card-o:before { content: $fa-var-id-card-o; } +.#{$fa-css-prefix}-quora:before { content: $fa-var-quora; } +.#{$fa-css-prefix}-free-code-camp:before { content: $fa-var-free-code-camp; } +.#{$fa-css-prefix}-telegram:before { content: $fa-var-telegram; } +.#{$fa-css-prefix}-thermometer-4:before, +.#{$fa-css-prefix}-thermometer:before, +.#{$fa-css-prefix}-thermometer-full:before { content: $fa-var-thermometer-full; } +.#{$fa-css-prefix}-thermometer-3:before, +.#{$fa-css-prefix}-thermometer-three-quarters:before { content: $fa-var-thermometer-three-quarters; } +.#{$fa-css-prefix}-thermometer-2:before, +.#{$fa-css-prefix}-thermometer-half:before { content: $fa-var-thermometer-half; } +.#{$fa-css-prefix}-thermometer-1:before, +.#{$fa-css-prefix}-thermometer-quarter:before { content: $fa-var-thermometer-quarter; } +.#{$fa-css-prefix}-thermometer-0:before, +.#{$fa-css-prefix}-thermometer-empty:before { content: $fa-var-thermometer-empty; } +.#{$fa-css-prefix}-shower:before { content: $fa-var-shower; } +.#{$fa-css-prefix}-bathtub:before, +.#{$fa-css-prefix}-s15:before, +.#{$fa-css-prefix}-bath:before { content: $fa-var-bath; } +.#{$fa-css-prefix}-podcast:before { content: $fa-var-podcast; } +.#{$fa-css-prefix}-window-maximize:before { content: $fa-var-window-maximize; } +.#{$fa-css-prefix}-window-minimize:before { content: $fa-var-window-minimize; } +.#{$fa-css-prefix}-window-restore:before { content: $fa-var-window-restore; } +.#{$fa-css-prefix}-times-rectangle:before, +.#{$fa-css-prefix}-window-close:before { content: $fa-var-window-close; } +.#{$fa-css-prefix}-times-rectangle-o:before, +.#{$fa-css-prefix}-window-close-o:before { content: $fa-var-window-close-o; } +.#{$fa-css-prefix}-bandcamp:before { content: $fa-var-bandcamp; } +.#{$fa-css-prefix}-grav:before { content: $fa-var-grav; } +.#{$fa-css-prefix}-etsy:before { content: $fa-var-etsy; } +.#{$fa-css-prefix}-imdb:before { content: $fa-var-imdb; } +.#{$fa-css-prefix}-ravelry:before { content: $fa-var-ravelry; } +.#{$fa-css-prefix}-eercast:before { content: $fa-var-eercast; } +.#{$fa-css-prefix}-microchip:before { content: $fa-var-microchip; } +.#{$fa-css-prefix}-snowflake-o:before { content: $fa-var-snowflake-o; } +.#{$fa-css-prefix}-superpowers:before { content: $fa-var-superpowers; } +.#{$fa-css-prefix}-wpexplorer:before { content: $fa-var-wpexplorer; } +.#{$fa-css-prefix}-meetup:before { content: $fa-var-meetup; } diff --git a/dashboard/src/assets/font-awesome/scss/_larger.scss b/dashboard/src/assets/font-awesome/scss/_larger.scss new file mode 100644 index 00000000..41e9a818 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_larger.scss @@ -0,0 +1,13 @@ +// Icon Sizes +// ------------------------- + +/* makes the font 33% larger relative to the icon container */ +.#{$fa-css-prefix}-lg { + font-size: (4em / 3); + line-height: (3em / 4); + vertical-align: -15%; +} +.#{$fa-css-prefix}-2x { font-size: 2em; } +.#{$fa-css-prefix}-3x { font-size: 3em; } +.#{$fa-css-prefix}-4x { font-size: 4em; } +.#{$fa-css-prefix}-5x { font-size: 5em; } diff --git a/dashboard/src/assets/font-awesome/scss/_list.scss b/dashboard/src/assets/font-awesome/scss/_list.scss new file mode 100644 index 00000000..7d1e4d54 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_list.scss @@ -0,0 +1,19 @@ +// List Icons +// ------------------------- + +.#{$fa-css-prefix}-ul { + padding-left: 0; + margin-left: $fa-li-width; + list-style-type: none; + > li { position: relative; } +} +.#{$fa-css-prefix}-li { + position: absolute; + left: -$fa-li-width; + width: $fa-li-width; + top: (2em / 14); + text-align: center; + &.#{$fa-css-prefix}-lg { + left: -$fa-li-width + (4em / 14); + } +} diff --git a/dashboard/src/assets/font-awesome/scss/_mixins.scss b/dashboard/src/assets/font-awesome/scss/_mixins.scss new file mode 100644 index 00000000..c3bbd574 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_mixins.scss @@ -0,0 +1,60 @@ +// Mixins +// -------------------------- + +@mixin fa-icon() { + display: inline-block; + font: normal normal normal #{$fa-font-size-base}/#{$fa-line-height-base} FontAwesome; // shortening font declaration + font-size: inherit; // can't have font-size inherit on line above, so need to override + text-rendering: auto; // optimizelegibility throws things off #1094 + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + +} + +@mixin fa-icon-rotate($degrees, $rotation) { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=#{$rotation})"; + -webkit-transform: rotate($degrees); + -ms-transform: rotate($degrees); + transform: rotate($degrees); +} + +@mixin fa-icon-flip($horiz, $vert, $rotation) { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=#{$rotation}, mirror=1)"; + -webkit-transform: scale($horiz, $vert); + -ms-transform: scale($horiz, $vert); + transform: scale($horiz, $vert); +} + + +// Only display content to screen readers. A la Bootstrap 4. +// +// See: http://a11yproject.com/posts/how-to-hide-content/ + +@mixin sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0,0,0,0); + border: 0; +} + +// Use in conjunction with .sr-only to only display content when it's focused. +// +// Useful for "Skip to main content" links; see http://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1 +// +// Credit: HTML5 Boilerplate + +@mixin sr-only-focusable { + &:active, + &:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; + } +} diff --git a/dashboard/src/assets/font-awesome/scss/_path.scss b/dashboard/src/assets/font-awesome/scss/_path.scss new file mode 100644 index 00000000..bb457c23 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_path.scss @@ -0,0 +1,15 @@ +/* FONT PATH + * -------------------------- */ + +@font-face { + font-family: 'FontAwesome'; + src: url('#{$fa-font-path}/fontawesome-webfont.eot?v=#{$fa-version}'); + src: url('#{$fa-font-path}/fontawesome-webfont.eot?#iefix&v=#{$fa-version}') format('embedded-opentype'), + url('#{$fa-font-path}/fontawesome-webfont.woff2?v=#{$fa-version}') format('woff2'), + url('#{$fa-font-path}/fontawesome-webfont.woff?v=#{$fa-version}') format('woff'), + url('#{$fa-font-path}/fontawesome-webfont.ttf?v=#{$fa-version}') format('truetype'), + url('#{$fa-font-path}/fontawesome-webfont.svg?v=#{$fa-version}#fontawesomeregular') format('svg'); +// src: url('#{$fa-font-path}/FontAwesome.otf') format('opentype'); // used when developing fonts + font-weight: normal; + font-style: normal; +} diff --git a/dashboard/src/assets/font-awesome/scss/_rotated-flipped.scss b/dashboard/src/assets/font-awesome/scss/_rotated-flipped.scss new file mode 100644 index 00000000..a3558fd0 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_rotated-flipped.scss @@ -0,0 +1,20 @@ +// Rotated & Flipped Icons +// ------------------------- + +.#{$fa-css-prefix}-rotate-90 { @include fa-icon-rotate(90deg, 1); } +.#{$fa-css-prefix}-rotate-180 { @include fa-icon-rotate(180deg, 2); } +.#{$fa-css-prefix}-rotate-270 { @include fa-icon-rotate(270deg, 3); } + +.#{$fa-css-prefix}-flip-horizontal { @include fa-icon-flip(-1, 1, 0); } +.#{$fa-css-prefix}-flip-vertical { @include fa-icon-flip(1, -1, 2); } + +// Hook for IE8-9 +// ------------------------- + +:root .#{$fa-css-prefix}-rotate-90, +:root .#{$fa-css-prefix}-rotate-180, +:root .#{$fa-css-prefix}-rotate-270, +:root .#{$fa-css-prefix}-flip-horizontal, +:root .#{$fa-css-prefix}-flip-vertical { + filter: none; +} diff --git a/dashboard/src/assets/font-awesome/scss/_screen-reader.scss b/dashboard/src/assets/font-awesome/scss/_screen-reader.scss new file mode 100644 index 00000000..637426f0 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_screen-reader.scss @@ -0,0 +1,5 @@ +// Screen Readers +// ------------------------- + +.sr-only { @include sr-only(); } +.sr-only-focusable { @include sr-only-focusable(); } diff --git a/dashboard/src/assets/font-awesome/scss/_stacked.scss b/dashboard/src/assets/font-awesome/scss/_stacked.scss new file mode 100644 index 00000000..aef74036 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_stacked.scss @@ -0,0 +1,20 @@ +// Stacked Icons +// ------------------------- + +.#{$fa-css-prefix}-stack { + position: relative; + display: inline-block; + width: 2em; + height: 2em; + line-height: 2em; + vertical-align: middle; +} +.#{$fa-css-prefix}-stack-1x, .#{$fa-css-prefix}-stack-2x { + position: absolute; + left: 0; + width: 100%; + text-align: center; +} +.#{$fa-css-prefix}-stack-1x { line-height: inherit; } +.#{$fa-css-prefix}-stack-2x { font-size: 2em; } +.#{$fa-css-prefix}-inverse { color: $fa-inverse; } diff --git a/dashboard/src/assets/font-awesome/scss/_variables.scss b/dashboard/src/assets/font-awesome/scss/_variables.scss new file mode 100644 index 00000000..498fc4a0 --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/_variables.scss @@ -0,0 +1,800 @@ +// Variables +// -------------------------- + +$fa-font-path: "../fonts" !default; +$fa-font-size-base: 14px !default; +$fa-line-height-base: 1 !default; +//$fa-font-path: "//netdna.bootstrapcdn.com/font-awesome/4.7.0/fonts" !default; // for referencing Bootstrap CDN font files directly +$fa-css-prefix: fa !default; +$fa-version: "4.7.0" !default; +$fa-border-color: #eee !default; +$fa-inverse: #fff !default; +$fa-li-width: (30em / 14) !default; + +$fa-var-500px: "\f26e"; +$fa-var-address-book: "\f2b9"; +$fa-var-address-book-o: "\f2ba"; +$fa-var-address-card: "\f2bb"; +$fa-var-address-card-o: "\f2bc"; +$fa-var-adjust: "\f042"; +$fa-var-adn: "\f170"; +$fa-var-align-center: "\f037"; +$fa-var-align-justify: "\f039"; +$fa-var-align-left: "\f036"; +$fa-var-align-right: "\f038"; +$fa-var-amazon: "\f270"; +$fa-var-ambulance: "\f0f9"; +$fa-var-american-sign-language-interpreting: "\f2a3"; +$fa-var-anchor: "\f13d"; +$fa-var-android: "\f17b"; +$fa-var-angellist: "\f209"; +$fa-var-angle-double-down: "\f103"; +$fa-var-angle-double-left: "\f100"; +$fa-var-angle-double-right: "\f101"; +$fa-var-angle-double-up: "\f102"; +$fa-var-angle-down: "\f107"; +$fa-var-angle-left: "\f104"; +$fa-var-angle-right: "\f105"; +$fa-var-angle-up: "\f106"; +$fa-var-apple: "\f179"; +$fa-var-archive: "\f187"; +$fa-var-area-chart: "\f1fe"; +$fa-var-arrow-circle-down: "\f0ab"; +$fa-var-arrow-circle-left: "\f0a8"; +$fa-var-arrow-circle-o-down: "\f01a"; +$fa-var-arrow-circle-o-left: "\f190"; +$fa-var-arrow-circle-o-right: "\f18e"; +$fa-var-arrow-circle-o-up: "\f01b"; +$fa-var-arrow-circle-right: "\f0a9"; +$fa-var-arrow-circle-up: "\f0aa"; +$fa-var-arrow-down: "\f063"; +$fa-var-arrow-left: "\f060"; +$fa-var-arrow-right: "\f061"; +$fa-var-arrow-up: "\f062"; +$fa-var-arrows: "\f047"; +$fa-var-arrows-alt: "\f0b2"; +$fa-var-arrows-h: "\f07e"; +$fa-var-arrows-v: "\f07d"; +$fa-var-asl-interpreting: "\f2a3"; +$fa-var-assistive-listening-systems: "\f2a2"; +$fa-var-asterisk: "\f069"; +$fa-var-at: "\f1fa"; +$fa-var-audio-description: "\f29e"; +$fa-var-automobile: "\f1b9"; +$fa-var-backward: "\f04a"; +$fa-var-balance-scale: "\f24e"; +$fa-var-ban: "\f05e"; +$fa-var-bandcamp: "\f2d5"; +$fa-var-bank: "\f19c"; +$fa-var-bar-chart: "\f080"; +$fa-var-bar-chart-o: "\f080"; +$fa-var-barcode: "\f02a"; +$fa-var-bars: "\f0c9"; +$fa-var-bath: "\f2cd"; +$fa-var-bathtub: "\f2cd"; +$fa-var-battery: "\f240"; +$fa-var-battery-0: "\f244"; +$fa-var-battery-1: "\f243"; +$fa-var-battery-2: "\f242"; +$fa-var-battery-3: "\f241"; +$fa-var-battery-4: "\f240"; +$fa-var-battery-empty: "\f244"; +$fa-var-battery-full: "\f240"; +$fa-var-battery-half: "\f242"; +$fa-var-battery-quarter: "\f243"; +$fa-var-battery-three-quarters: "\f241"; +$fa-var-bed: "\f236"; +$fa-var-beer: "\f0fc"; +$fa-var-behance: "\f1b4"; +$fa-var-behance-square: "\f1b5"; +$fa-var-bell: "\f0f3"; +$fa-var-bell-o: "\f0a2"; +$fa-var-bell-slash: "\f1f6"; +$fa-var-bell-slash-o: "\f1f7"; +$fa-var-bicycle: "\f206"; +$fa-var-binoculars: "\f1e5"; +$fa-var-birthday-cake: "\f1fd"; +$fa-var-bitbucket: "\f171"; +$fa-var-bitbucket-square: "\f172"; +$fa-var-bitcoin: "\f15a"; +$fa-var-black-tie: "\f27e"; +$fa-var-blind: "\f29d"; +$fa-var-bluetooth: "\f293"; +$fa-var-bluetooth-b: "\f294"; +$fa-var-bold: "\f032"; +$fa-var-bolt: "\f0e7"; +$fa-var-bomb: "\f1e2"; +$fa-var-book: "\f02d"; +$fa-var-bookmark: "\f02e"; +$fa-var-bookmark-o: "\f097"; +$fa-var-braille: "\f2a1"; +$fa-var-briefcase: "\f0b1"; +$fa-var-btc: "\f15a"; +$fa-var-bug: "\f188"; +$fa-var-building: "\f1ad"; +$fa-var-building-o: "\f0f7"; +$fa-var-bullhorn: "\f0a1"; +$fa-var-bullseye: "\f140"; +$fa-var-bus: "\f207"; +$fa-var-buysellads: "\f20d"; +$fa-var-cab: "\f1ba"; +$fa-var-calculator: "\f1ec"; +$fa-var-calendar: "\f073"; +$fa-var-calendar-check-o: "\f274"; +$fa-var-calendar-minus-o: "\f272"; +$fa-var-calendar-o: "\f133"; +$fa-var-calendar-plus-o: "\f271"; +$fa-var-calendar-times-o: "\f273"; +$fa-var-camera: "\f030"; +$fa-var-camera-retro: "\f083"; +$fa-var-car: "\f1b9"; +$fa-var-caret-down: "\f0d7"; +$fa-var-caret-left: "\f0d9"; +$fa-var-caret-right: "\f0da"; +$fa-var-caret-square-o-down: "\f150"; +$fa-var-caret-square-o-left: "\f191"; +$fa-var-caret-square-o-right: "\f152"; +$fa-var-caret-square-o-up: "\f151"; +$fa-var-caret-up: "\f0d8"; +$fa-var-cart-arrow-down: "\f218"; +$fa-var-cart-plus: "\f217"; +$fa-var-cc: "\f20a"; +$fa-var-cc-amex: "\f1f3"; +$fa-var-cc-diners-club: "\f24c"; +$fa-var-cc-discover: "\f1f2"; +$fa-var-cc-jcb: "\f24b"; +$fa-var-cc-mastercard: "\f1f1"; +$fa-var-cc-paypal: "\f1f4"; +$fa-var-cc-stripe: "\f1f5"; +$fa-var-cc-visa: "\f1f0"; +$fa-var-certificate: "\f0a3"; +$fa-var-chain: "\f0c1"; +$fa-var-chain-broken: "\f127"; +$fa-var-check: "\f00c"; +$fa-var-check-circle: "\f058"; +$fa-var-check-circle-o: "\f05d"; +$fa-var-check-square: "\f14a"; +$fa-var-check-square-o: "\f046"; +$fa-var-chevron-circle-down: "\f13a"; +$fa-var-chevron-circle-left: "\f137"; +$fa-var-chevron-circle-right: "\f138"; +$fa-var-chevron-circle-up: "\f139"; +$fa-var-chevron-down: "\f078"; +$fa-var-chevron-left: "\f053"; +$fa-var-chevron-right: "\f054"; +$fa-var-chevron-up: "\f077"; +$fa-var-child: "\f1ae"; +$fa-var-chrome: "\f268"; +$fa-var-circle: "\f111"; +$fa-var-circle-o: "\f10c"; +$fa-var-circle-o-notch: "\f1ce"; +$fa-var-circle-thin: "\f1db"; +$fa-var-clipboard: "\f0ea"; +$fa-var-clock-o: "\f017"; +$fa-var-clone: "\f24d"; +$fa-var-close: "\f00d"; +$fa-var-cloud: "\f0c2"; +$fa-var-cloud-download: "\f0ed"; +$fa-var-cloud-upload: "\f0ee"; +$fa-var-cny: "\f157"; +$fa-var-code: "\f121"; +$fa-var-code-fork: "\f126"; +$fa-var-codepen: "\f1cb"; +$fa-var-codiepie: "\f284"; +$fa-var-coffee: "\f0f4"; +$fa-var-cog: "\f013"; +$fa-var-cogs: "\f085"; +$fa-var-columns: "\f0db"; +$fa-var-comment: "\f075"; +$fa-var-comment-o: "\f0e5"; +$fa-var-commenting: "\f27a"; +$fa-var-commenting-o: "\f27b"; +$fa-var-comments: "\f086"; +$fa-var-comments-o: "\f0e6"; +$fa-var-compass: "\f14e"; +$fa-var-compress: "\f066"; +$fa-var-connectdevelop: "\f20e"; +$fa-var-contao: "\f26d"; +$fa-var-copy: "\f0c5"; +$fa-var-copyright: "\f1f9"; +$fa-var-creative-commons: "\f25e"; +$fa-var-credit-card: "\f09d"; +$fa-var-credit-card-alt: "\f283"; +$fa-var-crop: "\f125"; +$fa-var-crosshairs: "\f05b"; +$fa-var-css3: "\f13c"; +$fa-var-cube: "\f1b2"; +$fa-var-cubes: "\f1b3"; +$fa-var-cut: "\f0c4"; +$fa-var-cutlery: "\f0f5"; +$fa-var-dashboard: "\f0e4"; +$fa-var-dashcube: "\f210"; +$fa-var-database: "\f1c0"; +$fa-var-deaf: "\f2a4"; +$fa-var-deafness: "\f2a4"; +$fa-var-dedent: "\f03b"; +$fa-var-delicious: "\f1a5"; +$fa-var-desktop: "\f108"; +$fa-var-deviantart: "\f1bd"; +$fa-var-diamond: "\f219"; +$fa-var-digg: "\f1a6"; +$fa-var-dollar: "\f155"; +$fa-var-dot-circle-o: "\f192"; +$fa-var-download: "\f019"; +$fa-var-dribbble: "\f17d"; +$fa-var-drivers-license: "\f2c2"; +$fa-var-drivers-license-o: "\f2c3"; +$fa-var-dropbox: "\f16b"; +$fa-var-drupal: "\f1a9"; +$fa-var-edge: "\f282"; +$fa-var-edit: "\f044"; +$fa-var-eercast: "\f2da"; +$fa-var-eject: "\f052"; +$fa-var-ellipsis-h: "\f141"; +$fa-var-ellipsis-v: "\f142"; +$fa-var-empire: "\f1d1"; +$fa-var-envelope: "\f0e0"; +$fa-var-envelope-o: "\f003"; +$fa-var-envelope-open: "\f2b6"; +$fa-var-envelope-open-o: "\f2b7"; +$fa-var-envelope-square: "\f199"; +$fa-var-envira: "\f299"; +$fa-var-eraser: "\f12d"; +$fa-var-etsy: "\f2d7"; +$fa-var-eur: "\f153"; +$fa-var-euro: "\f153"; +$fa-var-exchange: "\f0ec"; +$fa-var-exclamation: "\f12a"; +$fa-var-exclamation-circle: "\f06a"; +$fa-var-exclamation-triangle: "\f071"; +$fa-var-expand: "\f065"; +$fa-var-expeditedssl: "\f23e"; +$fa-var-external-link: "\f08e"; +$fa-var-external-link-square: "\f14c"; +$fa-var-eye: "\f06e"; +$fa-var-eye-slash: "\f070"; +$fa-var-eyedropper: "\f1fb"; +$fa-var-fa: "\f2b4"; +$fa-var-facebook: "\f09a"; +$fa-var-facebook-f: "\f09a"; +$fa-var-facebook-official: "\f230"; +$fa-var-facebook-square: "\f082"; +$fa-var-fast-backward: "\f049"; +$fa-var-fast-forward: "\f050"; +$fa-var-fax: "\f1ac"; +$fa-var-feed: "\f09e"; +$fa-var-female: "\f182"; +$fa-var-fighter-jet: "\f0fb"; +$fa-var-file: "\f15b"; +$fa-var-file-archive-o: "\f1c6"; +$fa-var-file-audio-o: "\f1c7"; +$fa-var-file-code-o: "\f1c9"; +$fa-var-file-excel-o: "\f1c3"; +$fa-var-file-image-o: "\f1c5"; +$fa-var-file-movie-o: "\f1c8"; +$fa-var-file-o: "\f016"; +$fa-var-file-pdf-o: "\f1c1"; +$fa-var-file-photo-o: "\f1c5"; +$fa-var-file-picture-o: "\f1c5"; +$fa-var-file-powerpoint-o: "\f1c4"; +$fa-var-file-sound-o: "\f1c7"; +$fa-var-file-text: "\f15c"; +$fa-var-file-text-o: "\f0f6"; +$fa-var-file-video-o: "\f1c8"; +$fa-var-file-word-o: "\f1c2"; +$fa-var-file-zip-o: "\f1c6"; +$fa-var-files-o: "\f0c5"; +$fa-var-film: "\f008"; +$fa-var-filter: "\f0b0"; +$fa-var-fire: "\f06d"; +$fa-var-fire-extinguisher: "\f134"; +$fa-var-firefox: "\f269"; +$fa-var-first-order: "\f2b0"; +$fa-var-flag: "\f024"; +$fa-var-flag-checkered: "\f11e"; +$fa-var-flag-o: "\f11d"; +$fa-var-flash: "\f0e7"; +$fa-var-flask: "\f0c3"; +$fa-var-flickr: "\f16e"; +$fa-var-floppy-o: "\f0c7"; +$fa-var-folder: "\f07b"; +$fa-var-folder-o: "\f114"; +$fa-var-folder-open: "\f07c"; +$fa-var-folder-open-o: "\f115"; +$fa-var-font: "\f031"; +$fa-var-font-awesome: "\f2b4"; +$fa-var-fonticons: "\f280"; +$fa-var-fort-awesome: "\f286"; +$fa-var-forumbee: "\f211"; +$fa-var-forward: "\f04e"; +$fa-var-foursquare: "\f180"; +$fa-var-free-code-camp: "\f2c5"; +$fa-var-frown-o: "\f119"; +$fa-var-futbol-o: "\f1e3"; +$fa-var-gamepad: "\f11b"; +$fa-var-gavel: "\f0e3"; +$fa-var-gbp: "\f154"; +$fa-var-ge: "\f1d1"; +$fa-var-gear: "\f013"; +$fa-var-gears: "\f085"; +$fa-var-genderless: "\f22d"; +$fa-var-get-pocket: "\f265"; +$fa-var-gg: "\f260"; +$fa-var-gg-circle: "\f261"; +$fa-var-gift: "\f06b"; +$fa-var-git: "\f1d3"; +$fa-var-git-square: "\f1d2"; +$fa-var-github: "\f09b"; +$fa-var-github-alt: "\f113"; +$fa-var-github-square: "\f092"; +$fa-var-gitlab: "\f296"; +$fa-var-gittip: "\f184"; +$fa-var-glass: "\f000"; +$fa-var-glide: "\f2a5"; +$fa-var-glide-g: "\f2a6"; +$fa-var-globe: "\f0ac"; +$fa-var-google: "\f1a0"; +$fa-var-google-plus: "\f0d5"; +$fa-var-google-plus-circle: "\f2b3"; +$fa-var-google-plus-official: "\f2b3"; +$fa-var-google-plus-square: "\f0d4"; +$fa-var-google-wallet: "\f1ee"; +$fa-var-graduation-cap: "\f19d"; +$fa-var-gratipay: "\f184"; +$fa-var-grav: "\f2d6"; +$fa-var-group: "\f0c0"; +$fa-var-h-square: "\f0fd"; +$fa-var-hacker-news: "\f1d4"; +$fa-var-hand-grab-o: "\f255"; +$fa-var-hand-lizard-o: "\f258"; +$fa-var-hand-o-down: "\f0a7"; +$fa-var-hand-o-left: "\f0a5"; +$fa-var-hand-o-right: "\f0a4"; +$fa-var-hand-o-up: "\f0a6"; +$fa-var-hand-paper-o: "\f256"; +$fa-var-hand-peace-o: "\f25b"; +$fa-var-hand-pointer-o: "\f25a"; +$fa-var-hand-rock-o: "\f255"; +$fa-var-hand-scissors-o: "\f257"; +$fa-var-hand-spock-o: "\f259"; +$fa-var-hand-stop-o: "\f256"; +$fa-var-handshake-o: "\f2b5"; +$fa-var-hard-of-hearing: "\f2a4"; +$fa-var-hashtag: "\f292"; +$fa-var-hdd-o: "\f0a0"; +$fa-var-header: "\f1dc"; +$fa-var-headphones: "\f025"; +$fa-var-heart: "\f004"; +$fa-var-heart-o: "\f08a"; +$fa-var-heartbeat: "\f21e"; +$fa-var-history: "\f1da"; +$fa-var-home: "\f015"; +$fa-var-hospital-o: "\f0f8"; +$fa-var-hotel: "\f236"; +$fa-var-hourglass: "\f254"; +$fa-var-hourglass-1: "\f251"; +$fa-var-hourglass-2: "\f252"; +$fa-var-hourglass-3: "\f253"; +$fa-var-hourglass-end: "\f253"; +$fa-var-hourglass-half: "\f252"; +$fa-var-hourglass-o: "\f250"; +$fa-var-hourglass-start: "\f251"; +$fa-var-houzz: "\f27c"; +$fa-var-html5: "\f13b"; +$fa-var-i-cursor: "\f246"; +$fa-var-id-badge: "\f2c1"; +$fa-var-id-card: "\f2c2"; +$fa-var-id-card-o: "\f2c3"; +$fa-var-ils: "\f20b"; +$fa-var-image: "\f03e"; +$fa-var-imdb: "\f2d8"; +$fa-var-inbox: "\f01c"; +$fa-var-indent: "\f03c"; +$fa-var-industry: "\f275"; +$fa-var-info: "\f129"; +$fa-var-info-circle: "\f05a"; +$fa-var-inr: "\f156"; +$fa-var-instagram: "\f16d"; +$fa-var-institution: "\f19c"; +$fa-var-internet-explorer: "\f26b"; +$fa-var-intersex: "\f224"; +$fa-var-ioxhost: "\f208"; +$fa-var-italic: "\f033"; +$fa-var-joomla: "\f1aa"; +$fa-var-jpy: "\f157"; +$fa-var-jsfiddle: "\f1cc"; +$fa-var-key: "\f084"; +$fa-var-keyboard-o: "\f11c"; +$fa-var-krw: "\f159"; +$fa-var-language: "\f1ab"; +$fa-var-laptop: "\f109"; +$fa-var-lastfm: "\f202"; +$fa-var-lastfm-square: "\f203"; +$fa-var-leaf: "\f06c"; +$fa-var-leanpub: "\f212"; +$fa-var-legal: "\f0e3"; +$fa-var-lemon-o: "\f094"; +$fa-var-level-down: "\f149"; +$fa-var-level-up: "\f148"; +$fa-var-life-bouy: "\f1cd"; +$fa-var-life-buoy: "\f1cd"; +$fa-var-life-ring: "\f1cd"; +$fa-var-life-saver: "\f1cd"; +$fa-var-lightbulb-o: "\f0eb"; +$fa-var-line-chart: "\f201"; +$fa-var-link: "\f0c1"; +$fa-var-linkedin: "\f0e1"; +$fa-var-linkedin-square: "\f08c"; +$fa-var-linode: "\f2b8"; +$fa-var-linux: "\f17c"; +$fa-var-list: "\f03a"; +$fa-var-list-alt: "\f022"; +$fa-var-list-ol: "\f0cb"; +$fa-var-list-ul: "\f0ca"; +$fa-var-location-arrow: "\f124"; +$fa-var-lock: "\f023"; +$fa-var-long-arrow-down: "\f175"; +$fa-var-long-arrow-left: "\f177"; +$fa-var-long-arrow-right: "\f178"; +$fa-var-long-arrow-up: "\f176"; +$fa-var-low-vision: "\f2a8"; +$fa-var-magic: "\f0d0"; +$fa-var-magnet: "\f076"; +$fa-var-mail-forward: "\f064"; +$fa-var-mail-reply: "\f112"; +$fa-var-mail-reply-all: "\f122"; +$fa-var-male: "\f183"; +$fa-var-map: "\f279"; +$fa-var-map-marker: "\f041"; +$fa-var-map-o: "\f278"; +$fa-var-map-pin: "\f276"; +$fa-var-map-signs: "\f277"; +$fa-var-mars: "\f222"; +$fa-var-mars-double: "\f227"; +$fa-var-mars-stroke: "\f229"; +$fa-var-mars-stroke-h: "\f22b"; +$fa-var-mars-stroke-v: "\f22a"; +$fa-var-maxcdn: "\f136"; +$fa-var-meanpath: "\f20c"; +$fa-var-medium: "\f23a"; +$fa-var-medkit: "\f0fa"; +$fa-var-meetup: "\f2e0"; +$fa-var-meh-o: "\f11a"; +$fa-var-mercury: "\f223"; +$fa-var-microchip: "\f2db"; +$fa-var-microphone: "\f130"; +$fa-var-microphone-slash: "\f131"; +$fa-var-minus: "\f068"; +$fa-var-minus-circle: "\f056"; +$fa-var-minus-square: "\f146"; +$fa-var-minus-square-o: "\f147"; +$fa-var-mixcloud: "\f289"; +$fa-var-mobile: "\f10b"; +$fa-var-mobile-phone: "\f10b"; +$fa-var-modx: "\f285"; +$fa-var-money: "\f0d6"; +$fa-var-moon-o: "\f186"; +$fa-var-mortar-board: "\f19d"; +$fa-var-motorcycle: "\f21c"; +$fa-var-mouse-pointer: "\f245"; +$fa-var-music: "\f001"; +$fa-var-navicon: "\f0c9"; +$fa-var-neuter: "\f22c"; +$fa-var-newspaper-o: "\f1ea"; +$fa-var-object-group: "\f247"; +$fa-var-object-ungroup: "\f248"; +$fa-var-odnoklassniki: "\f263"; +$fa-var-odnoklassniki-square: "\f264"; +$fa-var-opencart: "\f23d"; +$fa-var-openid: "\f19b"; +$fa-var-opera: "\f26a"; +$fa-var-optin-monster: "\f23c"; +$fa-var-outdent: "\f03b"; +$fa-var-pagelines: "\f18c"; +$fa-var-paint-brush: "\f1fc"; +$fa-var-paper-plane: "\f1d8"; +$fa-var-paper-plane-o: "\f1d9"; +$fa-var-paperclip: "\f0c6"; +$fa-var-paragraph: "\f1dd"; +$fa-var-paste: "\f0ea"; +$fa-var-pause: "\f04c"; +$fa-var-pause-circle: "\f28b"; +$fa-var-pause-circle-o: "\f28c"; +$fa-var-paw: "\f1b0"; +$fa-var-paypal: "\f1ed"; +$fa-var-pencil: "\f040"; +$fa-var-pencil-square: "\f14b"; +$fa-var-pencil-square-o: "\f044"; +$fa-var-percent: "\f295"; +$fa-var-phone: "\f095"; +$fa-var-phone-square: "\f098"; +$fa-var-photo: "\f03e"; +$fa-var-picture-o: "\f03e"; +$fa-var-pie-chart: "\f200"; +$fa-var-pied-piper: "\f2ae"; +$fa-var-pied-piper-alt: "\f1a8"; +$fa-var-pied-piper-pp: "\f1a7"; +$fa-var-pinterest: "\f0d2"; +$fa-var-pinterest-p: "\f231"; +$fa-var-pinterest-square: "\f0d3"; +$fa-var-plane: "\f072"; +$fa-var-play: "\f04b"; +$fa-var-play-circle: "\f144"; +$fa-var-play-circle-o: "\f01d"; +$fa-var-plug: "\f1e6"; +$fa-var-plus: "\f067"; +$fa-var-plus-circle: "\f055"; +$fa-var-plus-square: "\f0fe"; +$fa-var-plus-square-o: "\f196"; +$fa-var-podcast: "\f2ce"; +$fa-var-power-off: "\f011"; +$fa-var-print: "\f02f"; +$fa-var-product-hunt: "\f288"; +$fa-var-puzzle-piece: "\f12e"; +$fa-var-qq: "\f1d6"; +$fa-var-qrcode: "\f029"; +$fa-var-question: "\f128"; +$fa-var-question-circle: "\f059"; +$fa-var-question-circle-o: "\f29c"; +$fa-var-quora: "\f2c4"; +$fa-var-quote-left: "\f10d"; +$fa-var-quote-right: "\f10e"; +$fa-var-ra: "\f1d0"; +$fa-var-random: "\f074"; +$fa-var-ravelry: "\f2d9"; +$fa-var-rebel: "\f1d0"; +$fa-var-recycle: "\f1b8"; +$fa-var-reddit: "\f1a1"; +$fa-var-reddit-alien: "\f281"; +$fa-var-reddit-square: "\f1a2"; +$fa-var-refresh: "\f021"; +$fa-var-registered: "\f25d"; +$fa-var-remove: "\f00d"; +$fa-var-renren: "\f18b"; +$fa-var-reorder: "\f0c9"; +$fa-var-repeat: "\f01e"; +$fa-var-reply: "\f112"; +$fa-var-reply-all: "\f122"; +$fa-var-resistance: "\f1d0"; +$fa-var-retweet: "\f079"; +$fa-var-rmb: "\f157"; +$fa-var-road: "\f018"; +$fa-var-rocket: "\f135"; +$fa-var-rotate-left: "\f0e2"; +$fa-var-rotate-right: "\f01e"; +$fa-var-rouble: "\f158"; +$fa-var-rss: "\f09e"; +$fa-var-rss-square: "\f143"; +$fa-var-rub: "\f158"; +$fa-var-ruble: "\f158"; +$fa-var-rupee: "\f156"; +$fa-var-s15: "\f2cd"; +$fa-var-safari: "\f267"; +$fa-var-save: "\f0c7"; +$fa-var-scissors: "\f0c4"; +$fa-var-scribd: "\f28a"; +$fa-var-search: "\f002"; +$fa-var-search-minus: "\f010"; +$fa-var-search-plus: "\f00e"; +$fa-var-sellsy: "\f213"; +$fa-var-send: "\f1d8"; +$fa-var-send-o: "\f1d9"; +$fa-var-server: "\f233"; +$fa-var-share: "\f064"; +$fa-var-share-alt: "\f1e0"; +$fa-var-share-alt-square: "\f1e1"; +$fa-var-share-square: "\f14d"; +$fa-var-share-square-o: "\f045"; +$fa-var-shekel: "\f20b"; +$fa-var-sheqel: "\f20b"; +$fa-var-shield: "\f132"; +$fa-var-ship: "\f21a"; +$fa-var-shirtsinbulk: "\f214"; +$fa-var-shopping-bag: "\f290"; +$fa-var-shopping-basket: "\f291"; +$fa-var-shopping-cart: "\f07a"; +$fa-var-shower: "\f2cc"; +$fa-var-sign-in: "\f090"; +$fa-var-sign-language: "\f2a7"; +$fa-var-sign-out: "\f08b"; +$fa-var-signal: "\f012"; +$fa-var-signing: "\f2a7"; +$fa-var-simplybuilt: "\f215"; +$fa-var-sitemap: "\f0e8"; +$fa-var-skyatlas: "\f216"; +$fa-var-skype: "\f17e"; +$fa-var-slack: "\f198"; +$fa-var-sliders: "\f1de"; +$fa-var-slideshare: "\f1e7"; +$fa-var-smile-o: "\f118"; +$fa-var-snapchat: "\f2ab"; +$fa-var-snapchat-ghost: "\f2ac"; +$fa-var-snapchat-square: "\f2ad"; +$fa-var-snowflake-o: "\f2dc"; +$fa-var-soccer-ball-o: "\f1e3"; +$fa-var-sort: "\f0dc"; +$fa-var-sort-alpha-asc: "\f15d"; +$fa-var-sort-alpha-desc: "\f15e"; +$fa-var-sort-amount-asc: "\f160"; +$fa-var-sort-amount-desc: "\f161"; +$fa-var-sort-asc: "\f0de"; +$fa-var-sort-desc: "\f0dd"; +$fa-var-sort-down: "\f0dd"; +$fa-var-sort-numeric-asc: "\f162"; +$fa-var-sort-numeric-desc: "\f163"; +$fa-var-sort-up: "\f0de"; +$fa-var-soundcloud: "\f1be"; +$fa-var-space-shuttle: "\f197"; +$fa-var-spinner: "\f110"; +$fa-var-spoon: "\f1b1"; +$fa-var-spotify: "\f1bc"; +$fa-var-square: "\f0c8"; +$fa-var-square-o: "\f096"; +$fa-var-stack-exchange: "\f18d"; +$fa-var-stack-overflow: "\f16c"; +$fa-var-star: "\f005"; +$fa-var-star-half: "\f089"; +$fa-var-star-half-empty: "\f123"; +$fa-var-star-half-full: "\f123"; +$fa-var-star-half-o: "\f123"; +$fa-var-star-o: "\f006"; +$fa-var-steam: "\f1b6"; +$fa-var-steam-square: "\f1b7"; +$fa-var-step-backward: "\f048"; +$fa-var-step-forward: "\f051"; +$fa-var-stethoscope: "\f0f1"; +$fa-var-sticky-note: "\f249"; +$fa-var-sticky-note-o: "\f24a"; +$fa-var-stop: "\f04d"; +$fa-var-stop-circle: "\f28d"; +$fa-var-stop-circle-o: "\f28e"; +$fa-var-street-view: "\f21d"; +$fa-var-strikethrough: "\f0cc"; +$fa-var-stumbleupon: "\f1a4"; +$fa-var-stumbleupon-circle: "\f1a3"; +$fa-var-subscript: "\f12c"; +$fa-var-subway: "\f239"; +$fa-var-suitcase: "\f0f2"; +$fa-var-sun-o: "\f185"; +$fa-var-superpowers: "\f2dd"; +$fa-var-superscript: "\f12b"; +$fa-var-support: "\f1cd"; +$fa-var-table: "\f0ce"; +$fa-var-tablet: "\f10a"; +$fa-var-tachometer: "\f0e4"; +$fa-var-tag: "\f02b"; +$fa-var-tags: "\f02c"; +$fa-var-tasks: "\f0ae"; +$fa-var-taxi: "\f1ba"; +$fa-var-telegram: "\f2c6"; +$fa-var-television: "\f26c"; +$fa-var-tencent-weibo: "\f1d5"; +$fa-var-terminal: "\f120"; +$fa-var-text-height: "\f034"; +$fa-var-text-width: "\f035"; +$fa-var-th: "\f00a"; +$fa-var-th-large: "\f009"; +$fa-var-th-list: "\f00b"; +$fa-var-themeisle: "\f2b2"; +$fa-var-thermometer: "\f2c7"; +$fa-var-thermometer-0: "\f2cb"; +$fa-var-thermometer-1: "\f2ca"; +$fa-var-thermometer-2: "\f2c9"; +$fa-var-thermometer-3: "\f2c8"; +$fa-var-thermometer-4: "\f2c7"; +$fa-var-thermometer-empty: "\f2cb"; +$fa-var-thermometer-full: "\f2c7"; +$fa-var-thermometer-half: "\f2c9"; +$fa-var-thermometer-quarter: "\f2ca"; +$fa-var-thermometer-three-quarters: "\f2c8"; +$fa-var-thumb-tack: "\f08d"; +$fa-var-thumbs-down: "\f165"; +$fa-var-thumbs-o-down: "\f088"; +$fa-var-thumbs-o-up: "\f087"; +$fa-var-thumbs-up: "\f164"; +$fa-var-ticket: "\f145"; +$fa-var-times: "\f00d"; +$fa-var-times-circle: "\f057"; +$fa-var-times-circle-o: "\f05c"; +$fa-var-times-rectangle: "\f2d3"; +$fa-var-times-rectangle-o: "\f2d4"; +$fa-var-tint: "\f043"; +$fa-var-toggle-down: "\f150"; +$fa-var-toggle-left: "\f191"; +$fa-var-toggle-off: "\f204"; +$fa-var-toggle-on: "\f205"; +$fa-var-toggle-right: "\f152"; +$fa-var-toggle-up: "\f151"; +$fa-var-trademark: "\f25c"; +$fa-var-train: "\f238"; +$fa-var-transgender: "\f224"; +$fa-var-transgender-alt: "\f225"; +$fa-var-trash: "\f1f8"; +$fa-var-trash-o: "\f014"; +$fa-var-tree: "\f1bb"; +$fa-var-trello: "\f181"; +$fa-var-tripadvisor: "\f262"; +$fa-var-trophy: "\f091"; +$fa-var-truck: "\f0d1"; +$fa-var-try: "\f195"; +$fa-var-tty: "\f1e4"; +$fa-var-tumblr: "\f173"; +$fa-var-tumblr-square: "\f174"; +$fa-var-turkish-lira: "\f195"; +$fa-var-tv: "\f26c"; +$fa-var-twitch: "\f1e8"; +$fa-var-twitter: "\f099"; +$fa-var-twitter-square: "\f081"; +$fa-var-umbrella: "\f0e9"; +$fa-var-underline: "\f0cd"; +$fa-var-undo: "\f0e2"; +$fa-var-universal-access: "\f29a"; +$fa-var-university: "\f19c"; +$fa-var-unlink: "\f127"; +$fa-var-unlock: "\f09c"; +$fa-var-unlock-alt: "\f13e"; +$fa-var-unsorted: "\f0dc"; +$fa-var-upload: "\f093"; +$fa-var-usb: "\f287"; +$fa-var-usd: "\f155"; +$fa-var-user: "\f007"; +$fa-var-user-circle: "\f2bd"; +$fa-var-user-circle-o: "\f2be"; +$fa-var-user-md: "\f0f0"; +$fa-var-user-o: "\f2c0"; +$fa-var-user-plus: "\f234"; +$fa-var-user-secret: "\f21b"; +$fa-var-user-times: "\f235"; +$fa-var-users: "\f0c0"; +$fa-var-vcard: "\f2bb"; +$fa-var-vcard-o: "\f2bc"; +$fa-var-venus: "\f221"; +$fa-var-venus-double: "\f226"; +$fa-var-venus-mars: "\f228"; +$fa-var-viacoin: "\f237"; +$fa-var-viadeo: "\f2a9"; +$fa-var-viadeo-square: "\f2aa"; +$fa-var-video-camera: "\f03d"; +$fa-var-vimeo: "\f27d"; +$fa-var-vimeo-square: "\f194"; +$fa-var-vine: "\f1ca"; +$fa-var-vk: "\f189"; +$fa-var-volume-control-phone: "\f2a0"; +$fa-var-volume-down: "\f027"; +$fa-var-volume-off: "\f026"; +$fa-var-volume-up: "\f028"; +$fa-var-warning: "\f071"; +$fa-var-wechat: "\f1d7"; +$fa-var-weibo: "\f18a"; +$fa-var-weixin: "\f1d7"; +$fa-var-whatsapp: "\f232"; +$fa-var-wheelchair: "\f193"; +$fa-var-wheelchair-alt: "\f29b"; +$fa-var-wifi: "\f1eb"; +$fa-var-wikipedia-w: "\f266"; +$fa-var-window-close: "\f2d3"; +$fa-var-window-close-o: "\f2d4"; +$fa-var-window-maximize: "\f2d0"; +$fa-var-window-minimize: "\f2d1"; +$fa-var-window-restore: "\f2d2"; +$fa-var-windows: "\f17a"; +$fa-var-won: "\f159"; +$fa-var-wordpress: "\f19a"; +$fa-var-wpbeginner: "\f297"; +$fa-var-wpexplorer: "\f2de"; +$fa-var-wpforms: "\f298"; +$fa-var-wrench: "\f0ad"; +$fa-var-xing: "\f168"; +$fa-var-xing-square: "\f169"; +$fa-var-y-combinator: "\f23b"; +$fa-var-y-combinator-square: "\f1d4"; +$fa-var-yahoo: "\f19e"; +$fa-var-yc: "\f23b"; +$fa-var-yc-square: "\f1d4"; +$fa-var-yelp: "\f1e9"; +$fa-var-yen: "\f157"; +$fa-var-yoast: "\f2b1"; +$fa-var-youtube: "\f167"; +$fa-var-youtube-play: "\f16a"; +$fa-var-youtube-square: "\f166"; + diff --git a/dashboard/src/assets/font-awesome/scss/font-awesome.scss b/dashboard/src/assets/font-awesome/scss/font-awesome.scss new file mode 100644 index 00000000..f1c83aaa --- /dev/null +++ b/dashboard/src/assets/font-awesome/scss/font-awesome.scss @@ -0,0 +1,18 @@ +/*! + * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome + * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) + */ + +@import "variables"; +@import "mixins"; +@import "path"; +@import "core"; +@import "larger"; +@import "fixed-width"; +@import "list"; +@import "bordered-pulled"; +@import "animated"; +@import "rotated-flipped"; +@import "stacked"; +@import "icons"; +@import "screen-reader"; diff --git a/dashboard/src/index.html b/dashboard/src/index.html index ba66b2e6..64e61b41 100644 --- a/dashboard/src/index.html +++ b/dashboard/src/index.html @@ -9,7 +9,7 @@ - + Loading... From 255213b1334219fd00fa0c73b887cc544b898ffa Mon Sep 17 00:00:00 2001 From: Waldemar Hummer Date: Wed, 14 Mar 2018 13:37:27 -0400 Subject: [PATCH 16/87] add initial list of contributors (#36) --- CONTRIBUTING.md | 4 ---- README.md | 21 +++++++++++++++++++-- 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e94f42f4..fecd304b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -4,10 +4,6 @@ This is an open source project, and we appreciate your help! We use the GitHub issue tracker to discuss new features and non-trivial bugs. -In addition to the issue tracker, [#journeys on -Slack](https://dwopen.slack.com) is the best way to get into contact with the -project's maintainers. - To contribute code, documentation, or tests, please submit a pull request to the GitHub repository. Generally, we expect two maintainers to review your pull request before it is approved for merging. For more details, see the diff --git a/README.md b/README.md index 991f4bea..d026d81d 100644 --- a/README.md +++ b/README.md @@ -405,10 +405,27 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) * To remove FfDL on your Cluster, simply run `make undeploy` -## 9. References +## 9. Contributors + +Below is an (incomplete) list of people (in alphabetical order) who contributed to this project +via code, tests, or documentation: + +* Scott Boag +* Parijat Dube +* Benjamin Herta +* Waldemar Hummer +* Vatche Ishakian +* Michael Kalantar +* Tommy Li +* Vinod Muthusamy +* Florian Rosenberg +* Animesh Singh +* Atin Sood + +## 10. References Based on IBM Research work in Deep Learning. * B. Bhattacharjee et al., "IBM Deep Learning Service," in IBM Journal of Research and Development, vol. 61, no. 4, pp. 10:1-10:11, July-Sept. 1 2017. https://arxiv.org/abs/1709.05871 -* Scott Boag, et al. Scalable Multi-Framework Multi-Tenant Lifecycle Management of Deep Learning Training Jobs, In Workshop on ML Systems at NIPS'17, 2017. http://learningsys.org/nips17/assets/papers/paper_29.pdf +* Scott Boag, et al. Scalable Multi-Framework Multi-Tenant Lifecycle Management of Deep Learning Training Jobs, In Workshop on ML Systems at NIPS'17, 2017. http://learningsys.org/nips17/assets/papers/paper_29.pdf From e083b80ac54227dc354ed08385efae4feb06b5ce Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Wed, 14 Mar 2018 11:14:37 -0700 Subject: [PATCH 17/87] Adding Contributors in CONTRIBUTING.MD (#37) * adding contributors * Update README.md --- CONTRIBUTING.md | 19 +++++++++++++++++-- README.md | 19 +------------------ 2 files changed, 18 insertions(+), 20 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index fecd304b..6cd88ef0 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,5 +1,3 @@ -# Contributing - This is an open source project, and we appreciate your help! We use the GitHub issue tracker to discuss new features and non-trivial bugs. @@ -8,3 +6,20 @@ To contribute code, documentation, or tests, please submit a pull request to the GitHub repository. Generally, we expect two maintainers to review your pull request before it is approved for merging. For more details, see the [MAINTAINERS](MAINTAINERS.md) page. + +## Contributors + +Below is an (incomplete) list of people (in alphabetical order) who contributed to this project +via code, tests, or documentation: + +* Scott Boag +* Parijat Dube +* Benjamin Herta +* Waldemar Hummer +* Vatche Ishakian +* Michael Kalantar +* Tommy Li +* Vinod Muthusamy +* Florian Rosenberg +* Animesh Singh +* Atin Sood diff --git a/README.md b/README.md index d026d81d..e4815b17 100644 --- a/README.md +++ b/README.md @@ -405,24 +405,7 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) * To remove FfDL on your Cluster, simply run `make undeploy` -## 9. Contributors - -Below is an (incomplete) list of people (in alphabetical order) who contributed to this project -via code, tests, or documentation: - -* Scott Boag -* Parijat Dube -* Benjamin Herta -* Waldemar Hummer -* Vatche Ishakian -* Michael Kalantar -* Tommy Li -* Vinod Muthusamy -* Florian Rosenberg -* Animesh Singh -* Atin Sood - -## 10. References +## 9. References Based on IBM Research work in Deep Learning. From 4434e2dfe8467f8222f157b4741ea0bffbeba44c Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Wed, 14 Mar 2018 14:06:45 -0700 Subject: [PATCH 18/87] Updating maintainers file (#38) * Updating maintainers file * Update MAINTAINERS.md --- MAINTAINERS.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/MAINTAINERS.md b/MAINTAINERS.md index e3413218..4289ee4b 100644 --- a/MAINTAINERS.md +++ b/MAINTAINERS.md @@ -1,7 +1,13 @@ # Maintainers Guide -This guide is intended for maintainers - anybody with commit access to one or -more Code Pattern repositories. +Following is the current list of maintainers on this project + +The maintainers are listed in alphabetical order. + +- Scott Boag ([sboagibm](https://github.com/sboagibm)) +- Waldemar Hummer ([whummer](https://github.com/whummer)) +- Tommy Li ([Tomcli](https://github.com/Tomcli)) +- Animesh Singh ([animeshsingh](https://github.com/animeshsingh)) ## Methodology From ebba9d1d9c62f3160bb149e7455b74fef9134b81 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Thu, 15 Mar 2018 22:23:25 -0700 Subject: [PATCH 19/87] Add converting script for FfDL to WML and vice versa (#39) * add converting script * update converter readme and update tensorflow version * update troubleshooting * Update README.md * Update gpu-guide.md * Update README.md * Update README.md * Update README.md --- docs/gpu-guide.md | 9 +- docs/user-guide.md | 4 +- etc/converter/README.md | 58 ++++++ etc/converter/convert-to-FfDL.py | 194 ++++++++++++++++++ etc/converter/convert-to-WML.py | 131 ++++++++++++ etc/converter/requirement.txt | 1 + etc/converter/sample-FfDL.yaml | 30 +++ etc/converter/sample-WML.yaml | 35 ++++ etc/examples/tf-model/mnist_with_summaries.py | 63 +++--- templates/services/learner-configmap.yml | 4 + 10 files changed, 493 insertions(+), 36 deletions(-) create mode 100644 etc/converter/README.md create mode 100644 etc/converter/convert-to-FfDL.py create mode 100644 etc/converter/convert-to-WML.py create mode 100644 etc/converter/requirement.txt create mode 100644 etc/converter/sample-FfDL.yaml create mode 100644 etc/converter/sample-WML.yaml diff --git a/docs/gpu-guide.md b/docs/gpu-guide.md index df006e11..b27fe1a0 100644 --- a/docs/gpu-guide.md +++ b/docs/gpu-guide.md @@ -1,14 +1,15 @@ -# Train Models using GPUs +# Train Deep Learning Models using GPUs ***This is still under development, any of the following instructions may have significant changes in the future.*** ### Prerequisites -* You need to have [FfDL](../README.md#5-detailed-installation-instructions) running on your Cluster. +* You need to have a Kubernetes cluster configured to use GPUs. Currently tested with Kubernetes configured using [feature gate `Accelerators`](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/). If you are using device plugin, please modify the +[values.yaml](../values.yaml)'s lcm.version to `device-plugin` and redeploy FfDL. -* You need to have a Kubernetes cluster configured to use GPUs. Currently tested with Kubernetes configured using [feature gate `Accelerators`](https://kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/). +* You need to have [FfDL](../README.md#5-detailed-installation-instructions) running on your Cluster. -* Currently Tensorflow and Caffe tested with GPUs +* Currently Tensorflow and Caffe are tested with GPUs. ## Instructions diff --git a/docs/user-guide.md b/docs/user-guide.md index 5237c16a..1e8517ca 100644 --- a/docs/user-guide.md +++ b/docs/user-guide.md @@ -24,8 +24,8 @@ Currently, Fabric for Deep Learning supports following community frameworks | Framework | Versions | Processing Unit | | ------------- | ------------- | --------------- | -| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0, 1.3.0-py3, 1.4.0, 1.4.0-py3, 1.5.0, 1.5.0-py3, latest, latest-py3 | CPU | -| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0-gpu, 1.3.0-gpu-py3, 1.4.0-gpu, 1.4.0-gpu-py3, 1.5.0-gpu, 1.5.0-gpu-py3, latest-gpu, latest-gpu-py3 | GPU | +| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0, 1.3.0-py3, 1.4.0, 1.4.0-py3, 1.5.0, 1.5.0-py3, 1.6.0, 1.6.0-py3, latest, latest-py3 | CPU | +| [tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/) | 1.3.0-gpu, 1.3.0-gpu-py3, 1.4.0-gpu, 1.4.0-gpu-py3, 1.5.0-gpu, 1.5.0-gpu-py3, 1.6.0-gpu, 1.6.0-gpu-py3, latest-gpu, latest-gpu-py3 | GPU | | [caffe](https://hub.docker.com/r/bvlc/caffe/) | cpu, intel | CPU | | [caffe](https://hub.docker.com/r/bvlc/caffe/) | gpu | GPU | | [pytorch](https://hub.docker.com/r/pytorch/pytorch/) | v0.2, latest | CPU, GPU | diff --git a/etc/converter/README.md b/etc/converter/README.md new file mode 100644 index 00000000..4cb9faed --- /dev/null +++ b/etc/converter/README.md @@ -0,0 +1,58 @@ +# Convert FfDL Model Definition File to Watson Studio Deep Learning Service format, and vice versa + +Since Watson Studio Deep Learning and FfDL use different model definition file i.e. manifest.yml to define their training jobs, please use this simple script to help you convert between the two different version of the manifest.yml. The [convert-to-WML.py](convert-to-WML.py) and [convert-to-FfDL.py](convert-to-FfDL.py) are the conversion scripts for converting your FfDL training job's manifest.yml to Watson Studio Deep Learning format and vice versa. + +## Instructions + +1. Clone and go into this directory + ```bash + git clone https://github.com/IBM/FfDL + cd FfDL/converter + ``` + +2. Use the following commands to install the necessary Python packages and run the Python Job to build your custom Watson Studio Deep Learning/FfDL `manifest.yml`. + +* ```:``` The manifest file you want to convert. +* ```:``` The filename for the converted manifest file. Default is `manifest-WML.yaml`/`manifest-FfDL.yaml`. +* ```:``` The sample manifest format file with all the default values. Default is `sample-WML.yaml`/`sample-FfDL.yaml`. + + ```bash + pip install -r requirement.txt + + # Convert FfDL manifest.yml to Watson Studio Deep Learning format + python convert-to-WML.py -i -o -s + + # Convert Watson Studio Deep Learning manifest.yml to FfDL format + python convert-to-FfDL.py -i -o -s + ``` + Now, a converted file should be created with all the information in your original manifest file. + +4. Copy the new YAML file and use it for your FfDL/Watson Studio Deep Learning training job. + +5. Note that all the T-shirt size in Watson Studio Deep Learning requires GPU, so that will be the default conversion. If you only want to run on CPU, please modify the `gpus` section to 0 along with `cpus` and `memory` based on your need. Also change the framework version with the one enabled in CPU. You can find the list of CPU framework version at [user-guide.md](../../docs/user-guide.md#1-supported-deep-learning-frameworks). Below is the T-shirt size table between Watson Studio Deep Learning and FfDL. + +| T-shirt Tiers | GPUs | RAM (GB) | CPUs | +| ------------- | ------------- | --------------- | --------------- | +| k80 | 1 | 24 | 4 | +| k80x2 | 2 | 48 | 8 | +| k80x4 | 4 | 96 | 16 | +| p100 | 1 | 24 | 8 | +| p100x2 | 2 | 48 | 16 | +| v100 | 1 | 24 | 26 | +| v100x2 | 2 | 48 | 52 | + +## Troubleshooting + +- If you are converting Watson Studio Deep Learning yml to FfDL format, both `training_data_reference` and `training_results_reference` need to be in the same object storage (could be different bucket) because FfDL only takes one object storage connection. + +- In Watson Studio Deep Learning, TensorFlow version is only available up to 1.5 + +- Caffe2 is not available yet in Watson Studio Deep Learning. Thus, the conversion script won't take any caffe2 input. + +- The conversion script won't take `small`, `medium`, and `large` T-shirt size because they will be deprecated soon. + +## Example Manifest.yml + +The example FfDL manifest.yml is the [sample-FfDL.yaml](sample-FfDL.yaml). The description for each field is available at the [user-guide.md](../../docs/user-guide.md#24-creating-manifest-file). + +The example Watson Studio Deep Learning manifest.yml is the [sample-WML.yaml](sample-WML.yaml). The description for each field is available at the [model definition guide](https://dataplatform.ibm.com/docs/content/analyze-data/ml_dlaas_working_with_training_run.html?audience=wdp&linkInPage=true) at Watson Studio. diff --git a/etc/converter/convert-to-FfDL.py b/etc/converter/convert-to-FfDL.py new file mode 100644 index 00000000..4e41446e --- /dev/null +++ b/etc/converter/convert-to-FfDL.py @@ -0,0 +1,194 @@ +import json +from ruamel.yaml import YAML +import sys +import getopt + +#global variables +language = '' +inputfile = '' +outputfile = '' +samplefile = '' +yaml=YAML(typ='safe') + +def getFfDL(): + try: + f = open(inputfile, "r") + response = f.read() + data = yaml.load(response) + f.close() + return data + except: + print("Missing {}".format(inputfile)) + +def getSampleJob(): + try: + if samplefile: + f = open(samplefile,"r") + else: + f = open("sample-FfDL.yaml","r") + response = f.read() + resYaml = yaml.load(response) + f.close() + return resYaml + except: + print("Missing sample-FfDL.yaml") + +def createJob(sample,data): + try: + sample['framework']['name'] = data['model_definition']['framework']['name'] + sample['name'] = data['model_definition']['name'] + sample['description'] = data['model_definition']['description'] + sample['framework']['command'] = data['model_definition']['execution']['command'] + sample['data_stores'][0]['id'] = data['training_data_reference']['name'] + sample['data_stores'][0]['connection']['auth_url'] = data['training_data_reference']['connection']['endpoint_url'] + sample['data_stores'][0]['connection']['user_name'] = data['training_data_reference']['connection']['access_key_id'] + sample['data_stores'][0]['connection']['password'] = data['training_data_reference']['connection']['secret_access_key'] + sample['data_stores'][0]['training_data']['container'] = data['training_data_reference']['source']['bucket'] + sample['data_stores'][0]['training_results']['container'] = data['training_results_reference']['target']['bucket'] + py2 = False + CPU = False + try: + if data['model_definition']['framework']['name'] == 'tensorflow': + if '2.' in data['model_definition']['framework']['runtimes']['version']: + py2 = True + except: + py2 = False + + try: + sample['learners'] = int(data['model_definition']['execution']['compute_configuration']['nodes']) + except: + sample['learners'] = 1 + + # Detect T-shirt requirements + if data['model_definition']['execution']['compute_configuration']['name'] == "k80": + sample['cpus'] = 4 + sample['gpus'] = 1 + sample['memory'] = '24Gb' + elif data['model_definition']['execution']['compute_configuration']['name'] == "p100": + sample['cpus'] = 8 + sample['gpus'] = 1 + sample['memory'] = '24Gb' + elif data['model_definition']['execution']['compute_configuration']['name'] == "v100": + sample['cpus'] = 26 + sample['gpus'] = 1 + sample['memory'] = '24Gb' + elif data['model_definition']['execution']['compute_configuration']['name'] == "k80x2": + sample['cpus'] = 8 + sample['gpus'] = 2 + sample['memory'] = '48Gb' + elif data['model_definition']['execution']['compute_configuration']['name'] == "p100x2": + sample['cpus'] = 16 + sample['gpus'] = 2 + sample['memory'] = '48Gb' + elif data['model_definition']['execution']['compute_configuration']['name'] == "v100x2": + sample['cpus'] = 52 + sample['gpus'] = 2 + sample['memory'] = '48Gb' + elif data['model_definition']['execution']['compute_configuration']['name'] == "k80x4": + sample['cpus'] = 16 + sample['gpus'] = 4 + sample['memory'] = '96Gb' + else: + CPU = True + sample['cpus'] = 1 + sample['gpus'] = 0 + sample['memory'] = '1Gb' + + # Detect Framework version + try: + if data['model_definition']['framework']['name'] == 'tensorflow': + if '1.3' in data['model_definition']['framework']['version']: + if py2: + if CPU: + sample['framework']['version'] = "1.3.0" + else: + sample['framework']['version'] = "1.3.0-gpu" + else: + if CPU: + sample['framework']['version'] = "1.3.0-py3" + else: + sample['framework']['version'] = "1.3.0-gpu-py3" + elif '1.4' in data['model_definition']['framework']['version']: + if py2: + if CPU: + sample['framework']['version'] = "1.4.0" + else: + sample['framework']['version'] = "1.4.0-gpu" + else: + if CPU: + sample['framework']['version'] = "1.4.0-py3" + else: + sample['framework']['version'] = "1.4.0-gpu-py3" + elif '1.5' in data['model_definition']['framework']['version']: + if py2: + if CPU: + sample['framework']['version'] = "1.5.0" + else: + sample['framework']['version'] = "1.5.0-gpu" + else: + if CPU: + sample['framework']['version'] = "1.5.0-py3" + else: + sample['framework']['version'] = "1.5.0-gpu-py3" + else: + if py2: + if CPU: + sample['framework']['version'] = "latest" + else: + sample['framework']['version'] = "latest-gpu" + else: + if CPU: + sample['framework']['version'] = "latest-py3" + else: + sample['framework']['version'] = "latest-gpu-py3" + elif data['model_definition']['framework']['name'] == 'caffe': + if CPU: + sample['framework']['version'] = "cpu" + else: + sample['framework']['version'] = "gpu" + elif data['model_definition']['framework']['name'] == 'pytorch': + sample['framework']['version'] = "latest" + except: + print("Wrong framework.version contents in {}".format(inputfile)) + + if data['model_definition']['framework']['name'] != "tensorflow": + sample.pop('evaluation_metrics', None) + except: + print("Missing contents in {}".format(inputfile)) + try: + if outputfile: + f = open(outputfile, "w") + else: + f = open("manifest-FfDL.yaml", "w") + yaml.default_flow_style = False + yaml.dump(sample, f) + f.close() + except: + if outputfile: + print("Cannot write contents to {}".format(outputfile)) + else: + print("Cannot write contents to manifest-FfDL.yaml.") + + +if __name__ == "__main__": + argv = sys.argv[1:] + try: + opts, args = getopt.getopt(argv,"i:o:s:",["ifile=","ofile=","sfile="]) + except getopt.GetoptError: + print('Format Error: Wrong format.') + print('convert-to-FfDL.py -i -o -s ') + sys.exit(2) + for opt, arg in opts: + if opt in ("-i", "--ifile"): + inputfile = arg + elif opt in ("-o", "--ofile"): + outputfile = arg + elif opt in ("-s", "--sfile"): + samplefile = arg + if not inputfile: + print('Input Error: inputfile cannot be empty.') + print('convert-to-FfDL.py -i -o -s ') + sys.exit(2) + data = getFfDL() + sample = getSampleJob() + createJob(sample,data) diff --git a/etc/converter/convert-to-WML.py b/etc/converter/convert-to-WML.py new file mode 100644 index 00000000..520ef203 --- /dev/null +++ b/etc/converter/convert-to-WML.py @@ -0,0 +1,131 @@ +import json +from ruamel.yaml import YAML +import sys +import getopt + +#global variables +language = '' +inputfile = '' +outputfile = '' +samplefile = '' +yaml=YAML(typ='safe') + +def getFfDL(): + try: + f = open(inputfile, "r") + response = f.read() + data = yaml.load(response) + f.close() + return data + except: + print("Missing {}".format(inputfile)) + +def getSampleJob(): + try: + if samplefile: + f = open(samplefile,"r") + else: + f = open("sample-WML.yaml","r") + response = f.read() + resYaml = yaml.load(response) + f.close() + return resYaml + except: + print("Missing sample-WML.yaml") + +def createJob(sample,data): + try: + sample['model_definition']['framework']['name'] = data['framework']['name'] + sample['model_definition']['name'] = data['name'] + sample['model_definition']['description'] = data['description'] + sample['model_definition']['execution']['command'] = data['framework']['command'] + sample['training_data_reference']['name'] = data['data_stores'][0]['id'] + sample['training_data_reference']['connection']['endpoint_url'] = data['data_stores'][0]['connection']['auth_url'] + sample['training_data_reference']['connection']['access_key_id'] = data['data_stores'][0]['connection']['user_name'] + sample['training_data_reference']['connection']['secret_access_key'] = data['data_stores'][0]['connection']['password'] + sample['training_data_reference']['source']['bucket'] = data['data_stores'][0]['training_data']['container'] + sample['training_results_reference']['name'] = data['data_stores'][0]['id'] + sample['training_results_reference']['connection']['endpoint_url'] = data['data_stores'][0]['connection']['auth_url'] + sample['training_results_reference']['connection']['access_key_id'] = data['data_stores'][0]['connection']['user_name'] + sample['training_results_reference']['connection']['secret_access_key'] = data['data_stores'][0]['connection']['password'] + sample['training_results_reference']['target']['bucket'] = data['data_stores'][0]['training_results']['container'] + if data['framework']['name'] == 'tensorflow': + if 'py3' in data['framework']['version']: + sample['model_definition']['framework']['runtimes']['version'] = "3.5" + else: + sample['model_definition']['framework']['runtimes']['version'] = "2.7" + else: + sample['model_definition']['framework']['runtimes']['version'] = "3.5" + sample['model_definition']['execution']['compute_configuration']['nodes'] = int(data['learners']) if data.get("learners") else 1 + gpus = int(data['gpus']) if data.get("gpus") else 0 + if gpus <= 1: + if int(data['cpus']) <= 4: + sample['model_definition']['execution']['compute_configuration']['name'] = "k80" + elif int(data['cpus']) <= 8: + sample['model_definition']['execution']['compute_configuration']['name'] = "p100" + else: + sample['model_definition']['execution']['compute_configuration']['name'] = "v100" + elif gpus <= 2: + if int(data['cpus']) <= 8: + sample['model_definition']['execution']['compute_configuration']['name'] = "k80x2" + elif int(data['cpus']) <= 16: + sample['model_definition']['execution']['compute_configuration']['name'] = "p100x2" + else: + sample['model_definition']['execution']['compute_configuration']['name'] = "v100x2" + else: + sample['model_definition']['execution']['compute_configuration']['name'] = "k80x4" + except: + print("Missing contents in {}".format(inputfile)) + try: + if data['framework']['name'] == 'tensorflow': + if '1.3' in data['framework']['version']: + sample['model_definition']['framework']['version'] = "1.3" + elif '1.4' in data['framework']['version']: + sample['model_definition']['framework']['version'] = "1.4" + elif '1.5' in data['framework']['version']: + sample['model_definition']['framework']['version'] = "1.5" + else: + sample['model_definition']['framework']['version'] = "1.5" + elif data['framework']['name'] == 'caffe': + sample['model_definition']['framework']['version'] = "1.0" + elif data['framework']['name'] == 'pytorch': + sample['model_definition']['framework']['version'] = "0.3" + except: + print("Wrong framework.version contents in {}".format(inputfile)) + try: + if outputfile: + f = open(outputfile, "w") + else: + f = open("manifest-WML.yaml", "w") + yaml.default_flow_style = False + yaml.dump(sample, f) + f.close() + except: + if outputfile: + print("Cannot write contents to {}".format(outputfile)) + else: + print("Cannot write contents to manifest-WML.yaml.") + + +if __name__ == "__main__": + argv = sys.argv[1:] + try: + opts, args = getopt.getopt(argv,"i:o:s:",["ifile=","ofile=","sfile="]) + except getopt.GetoptError: + print('Format Error: Wrong format.') + print('convert-to-WML.py -i -o -s ') + sys.exit(2) + for opt, arg in opts: + if opt in ("-i", "--ifile"): + inputfile = arg + elif opt in ("-o", "--ofile"): + outputfile = arg + elif opt in ("-s", "--sfile"): + samplefile = arg + if not inputfile: + print('Input Error: inputfile cannot be empty.') + print('convert-to-WML.py -i -o -s ') + sys.exit(2) + data = getFfDL() + sample = getSampleJob() + createJob(sample,data) diff --git a/etc/converter/requirement.txt b/etc/converter/requirement.txt new file mode 100644 index 00000000..dda9b8a9 --- /dev/null +++ b/etc/converter/requirement.txt @@ -0,0 +1 @@ +ruamel.yaml>=0.15.35 diff --git a/etc/converter/sample-FfDL.yaml b/etc/converter/sample-FfDL.yaml new file mode 100644 index 00000000..fa39e66f --- /dev/null +++ b/etc/converter/sample-FfDL.yaml @@ -0,0 +1,30 @@ +name: sample-training-job +description: Simple example model +version: "1.0" +gpus: 0 +cpus: 1 +learners: 1 +memory: 1000MiB + +# Object stores that allow the system to retrieve training data. +data_stores: + - id: example-object-storage + type: s3_datastore + training_data: + container: training-data-bucket-name + training_results: + container: result-data-bucket-name + connection: + type: s3_datastore + auth_url: + user_name: + password: + +framework: + name: tensorflow + version: "latest-gpu-py3" + command: python3 example.py + +evaluation_metrics: + type: tensorboard + in: "$JOB_STATE_DIR/logs/tb" diff --git a/etc/converter/sample-WML.yaml b/etc/converter/sample-WML.yaml new file mode 100644 index 00000000..7a137ea7 --- /dev/null +++ b/etc/converter/sample-WML.yaml @@ -0,0 +1,35 @@ +model_definition: + name: sample-training-job + author: + name: DL Developer + email: dl@example.com + description: Simple example model + framework: + name: tensorflow + version: "1.5" + runtimes: + name: python + version: "3.5" + execution: + command: python3 example.py + compute_configuration: + name: k80 + nodes: 1 +training_data_reference: + connection: + access_key_id: + endpoint_url: + secret_access_key: + name: example-object-storage + source: + bucket: training-data-bucket-name + type: s3 +training_results_reference: + connection: + access_key_id: + endpoint_url: + secret_access_key: + name: example-object-storage + target: + bucket: result-data-bucket-name + type: s3 diff --git a/etc/examples/tf-model/mnist_with_summaries.py b/etc/examples/tf-model/mnist_with_summaries.py index bd99f34f..ec314c37 100644 --- a/etc/examples/tf-model/mnist_with_summaries.py +++ b/etc/examples/tf-model/mnist_with_summaries.py @@ -72,7 +72,7 @@ def restore_from_checkpoint(saver, sess): def save_checkpoint(step, saver, sess, model_path): path = saver.save(sess, model_path, global_step=step) print("Saved checkpoint %s" % path) - + def train(): # Import data @@ -80,15 +80,15 @@ def train(): # one_hot=True, # fake_data=FLAGS.fake_data) mnist = input_data.read_data_sets(FLAGS.train_images_file, - FLAGS.train_labels_file, + FLAGS.train_labels_file, FLAGS.test_images_file, - FLAGS.test_labels_file, + FLAGS.test_labels_file, one_hot=True) sess = tf.InteractiveSession() - + # Create a multilayer model. - + # Input placeholders with tf.name_scope('input'): x = tf.placeholder(tf.float32, [None, 784], name='x-input') @@ -180,7 +180,7 @@ def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu): with tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) tf.summary.scalar('accuracy', accuracy) - + # with tf.name_scope('loss'): # # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=mnist.test.images, labels=mnist.test.labels)) # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_, labels=y)) @@ -206,7 +206,7 @@ def feed_dict(train): xs, ys = mnist.test.images, mnist.test.labels k = 1.0 return {x: xs, y_: ys, keep_prob: k} - + model_path = os.environ["RESULT_DIR"] + "/model.ckpt" print('Calling tf.train.Saver(...)') @@ -223,27 +223,30 @@ def feed_dict(train): test_writer.add_summary(summary, i) print('Accuracy at step %s: %s' % (i, acc)) else: # Record train set summaries, and train - if i % 100 == 99: # Record execution stats - run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) - run_metadata = tf.RunMetadata() - # summary, _, loss_result = sess.run([merged, train_step, loss], - summary, _ = sess.run([merged, train_step], - feed_dict=feed_dict(True), - options=run_options, - run_metadata=run_metadata) - train_writer.add_run_metadata(run_metadata, 'step%03d' % i) - train_writer.add_summary(summary, i) - print('Adding run metadata for', i) - else: # Record a summary - summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True)) - train_writer.add_summary(summary, i) - + # Commnent the below code since CUPTI library is not Available on basic + # TensorFlow images that use CUDA 9. + # + # if i % 100 == 99: # Record execution stats + # run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) + # run_metadata = tf.RunMetadata() + # # summary, _, loss_result = sess.run([merged, train_step, loss], + # summary, _ = sess.run([merged, train_step], + # feed_dict=feed_dict(True), + # options=run_options, + # run_metadata=run_metadata) + # train_writer.add_run_metadata(run_metadata, 'step%03d' % i) + # train_writer.add_summary(summary, i) + # print('Adding run metadata for', i) + # else: # Record a summary + summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True)) + train_writer.add_summary(summary, i) + save_path = saver.save(sess, model_path) - + train_writer.close() test_writer.close() -def main(_): +def main(_): print('train_images_file: ', FLAGS.train_images_file) print('train_labels_file: ', FLAGS.train_labels_file) print('test_images_file: ', FLAGS.test_images_file) @@ -251,7 +254,7 @@ def main(_): print('learning_rate: ', FLAGS.learning_rate) print('tb_log_dir: ', FLAGS.tb_log_dir) print('log_dir: ', FLAGS.log_dir) - + # if tf.gfile.Exists(FLAGS.log_dir): # tf.gfile.DeleteRecursively(FLAGS.log_dir) tf.gfile.MakeDirs(FLAGS.log_dir) @@ -259,7 +262,7 @@ def main(_): # if tf.gfile.Exists(FLAGS.tb_log_dir): # tf.gfile.DeleteRecursively(FLAGS.tb_log_dir) tf.gfile.MakeDirs(FLAGS.tb_log_dir) - + train() @@ -276,7 +279,7 @@ def main(_): help='Keep probability for training dropout.') # parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', # help='Directory for storing input data') - + if 'LC_TEST_ENV' in os.environ: is_test_env = os.environ["LC_TEST_ENV"] == "local_test" else: @@ -288,7 +291,7 @@ def main(_): else: job_directory = os.environ["JOB_STATE_DIR"] # job_directory = "/job" - + if is_test_env: if not os.path.exists(job_directory): os.makedirs(job_directory) @@ -302,7 +305,7 @@ def main(_): help='DLaaS log directory') parser.add_argument('--tb_log_dir', type=str, default=log_directory+'/tb', help='Summaries log directory') - + parser.add_argument('--train_images_file', type=str, default='bad', help='train_images_file') parser.add_argument('--train_labels_file', type=str, default='bad', @@ -311,7 +314,7 @@ def main(_): help='test_images_file') parser.add_argument('--test_labels_file', type=str, default='bad', help='test_labels_file') - + FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) diff --git a/templates/services/learner-configmap.yml b/templates/services/learner-configmap.yml index 5f76478f..4c7ed8d8 100644 --- a/templates/services/learner-configmap.yml +++ b/templates/services/learner-configmap.yml @@ -7,6 +7,10 @@ data: tensorflow_cpu_latest-py3_CURRENT: manual tensorflow_gpu_latest-gpu-py3_CURRENT: manual tensorflow_gpu_latest-gpu_CURRENT: manual + tensorflow_gpu_1.6.0-gpu-py3_CURRENT: manual + tensorflow_gpu_1.6.0-gpu_CURRENT: manual + tensorflow_cpu_1.6.0_CURRENT: manual + tensorflow_cpu_1.6.0-py3_CURRENT: manual tensorflow_gpu_1.5.0-gpu-py3_CURRENT: manual tensorflow_gpu_1.5.0-gpu_CURRENT: manual tensorflow_cpu_1.5.0_CURRENT: manual From 9365a16d8a95f0f14296dba684975f128b7d0310 Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Fri, 16 Mar 2018 22:11:23 -0700 Subject: [PATCH 20/87] Linkage to watson studio (#40) * Adding references to Watson Studio * Update README.md * Rename README.md to ffdl-wml.md * Update README.md * Create train-deploy-wml.md * Update train-deploy-wml.md * Update README.md * Update ffdl-wml.md * Update ffdl-wml.md * Update ffdl-wml.md * Update README.md * Update README.md * update WML instructions * revert tf example * update caffe manifest --- README.md | 13 +- etc/converter/{README.md => ffdl-wml.md} | 26 ++- etc/converter/train-deploy-wml.md | 191 ++++++++++++++++++++++ etc/examples/caffe-model/gpu-manifest.yml | 2 + etc/examples/caffe-model/manifest.yml | 2 + templates/services/lcm-deployment.yml | 2 +- 6 files changed, 226 insertions(+), 10 deletions(-) rename etc/converter/{README.md => ffdl-wml.md} (68%) create mode 100644 etc/converter/train-deploy-wml.md diff --git a/README.md b/README.md index e4815b17..ac28325e 100644 --- a/README.md +++ b/README.md @@ -26,13 +26,22 @@ FfDL is a collaboration platform for: Once installed, use the command `make minikube` to start Minikube and set up local network routes. Alternatively, use the Vagrant based setup to automatically install a local Kubernetes cluster. -* Follow the appropriate instructions for deployment on [IBM Cloud Public](https://github.com/IBM/container-journey-template/blob/master/README.md) or [IBM Cloud Private](https://github.com/IBM/deploy-ibm-cloud-private/blob/master/README.md) +* Follow the appropriate instructions for standing up your Kubernetes cluster using [IBM Cloud Public](https://github.com/IBM/container-journey-template/blob/master/README.md) or [IBM Cloud Private](https://github.com/IBM/deploy-ibm-cloud-private/blob/master/README.md) * The minimum recommended capacity for FfDL is 4GB Memory and 2 CPUs. -* If you already have a FfDL deployment up and running, you can jump to [User Guide](docs/user-guide.md) to use FfDL for training your models. +## Usage Scenarios + +* If you already have a FfDL deployment up and running, you can jump to [FfDL User Guide](docs/user-guide.md) to use FfDL for training your models. + +* If you have FfDL confiugured to use GPUs, and want to train using GPUs, follow steps [here](docs/gpu-guide.md) + +* If you have used FfDL to train your models, and want to use a GPU enabled public cloud hosted service for further training and serving, please follow instructions [here](etc/converter/ffdl-wml.md) to train and serve your models using [Watson Studio Deep Learning](https://www.ibm.com/cloud/machine-learning) service + +* If you are starting and want to setup your own FfDL deployment, please follow the steps below. ## Steps + 1. [Quick Start](#1-quick-start) - 1.1 [Installation using Vagrant](#11-installation-using-vagrant) - 1.2 [Installation using Minikube](#12-installation-using-minikube) diff --git a/etc/converter/README.md b/etc/converter/ffdl-wml.md similarity index 68% rename from etc/converter/README.md rename to etc/converter/ffdl-wml.md index 4cb9faed..c703f813 100644 --- a/etc/converter/README.md +++ b/etc/converter/ffdl-wml.md @@ -1,22 +1,30 @@ -# Convert FfDL Model Definition File to Watson Studio Deep Learning Service format, and vice versa +# Using Watson Studio Deep Learning Service to train and serve your FfDL Models + +## Steps +1. [Convert FfDL Model defition to Watson Studio Deep Learning Service definition](#1-convert-ffdl-model-definition-to-watson-studio-deep-learning-service-definition) +2. [Train and Serve using Watson Studio Deep Learning Service](train-deploy-wml.md) + +## 1. Convert FfDL Model definition to Watson Studio Deep Learning service definition Since Watson Studio Deep Learning and FfDL use different model definition file i.e. manifest.yml to define their training jobs, please use this simple script to help you convert between the two different version of the manifest.yml. The [convert-to-WML.py](convert-to-WML.py) and [convert-to-FfDL.py](convert-to-FfDL.py) are the conversion scripts for converting your FfDL training job's manifest.yml to Watson Studio Deep Learning format and vice versa. -## Instructions +### Instructions 1. Clone and go into this directory - ```bash + ```shell git clone https://github.com/IBM/FfDL - cd FfDL/converter + cd FfDL/etc/converter ``` -2. Use the following commands to install the necessary Python packages and run the Python Job to build your custom Watson Studio Deep Learning/FfDL `manifest.yml`. +2. Since Watson Machine Learning only takes Cloud Object Storage, if you are using a Local S3 Object Storage on your cluster, please refer to the user-guide [Cloud Object Store](../../docs/user-guide.md#32-cloud-object-store) to provision your cloud object storage, upload data to cloud object storage, and configure your FfDL's `manifest.yml` to use the cloud object storage endpoint and buckets. + +3. Use the following commands to install the necessary Python packages and run the Python Job to build your custom Watson Studio Deep Learning/FfDL `manifest.yml`. * ```:``` The manifest file you want to convert. * ```:``` The filename for the converted manifest file. Default is `manifest-WML.yaml`/`manifest-FfDL.yaml`. * ```:``` The sample manifest format file with all the default values. Default is `sample-WML.yaml`/`sample-FfDL.yaml`. - ```bash + ```shell pip install -r requirement.txt # Convert FfDL manifest.yml to Watson Studio Deep Learning format @@ -29,7 +37,7 @@ Since Watson Studio Deep Learning and FfDL use different model definition file i 4. Copy the new YAML file and use it for your FfDL/Watson Studio Deep Learning training job. -5. Note that all the T-shirt size in Watson Studio Deep Learning requires GPU, so that will be the default conversion. If you only want to run on CPU, please modify the `gpus` section to 0 along with `cpus` and `memory` based on your need. Also change the framework version with the one enabled in CPU. You can find the list of CPU framework version at [user-guide.md](../../docs/user-guide.md#1-supported-deep-learning-frameworks). Below is the T-shirt size table between Watson Studio Deep Learning and FfDL. +5. Note that all the T-shirt size in Watson Studio Deep Learning requires GPU, so that will be the default conversion. If you only want to run on CPU, please modify the `gpus` section to 0 along with `cpus` and `memory` based on your need. Then, change the framework version with the one enabled in CPU. You can find the list of CPU framework version at [user-guide.md](../../docs/user-guide.md#1-supported-deep-learning-frameworks). Below is the T-shirt size table between Watson Studio Deep Learning and FfDL. | T-shirt Tiers | GPUs | RAM (GB) | CPUs | | ------------- | ------------- | --------------- | --------------- | @@ -41,6 +49,10 @@ Since Watson Studio Deep Learning and FfDL use different model definition file i | v100 | 1 | 24 | 26 | | v100x2 | 2 | 48 | 52 | +## 2. Train and Serve using Watson Studio Deep Learning Service + +Please follow the [Train and Serve using Watson Studio Deep Learning Service](train-deploy-wml.md) instructions. + ## Troubleshooting - If you are converting Watson Studio Deep Learning yml to FfDL format, both `training_data_reference` and `training_results_reference` need to be in the same object storage (could be different bucket) because FfDL only takes one object storage connection. diff --git a/etc/converter/train-deploy-wml.md b/etc/converter/train-deploy-wml.md new file mode 100644 index 00000000..d34ee2fd --- /dev/null +++ b/etc/converter/train-deploy-wml.md @@ -0,0 +1,191 @@ +# Training and Serving your Models using Watson Machine Learning + +## Prerequisite + +Install [IBM Cloud CLI](https://console.bluemix.net/docs/cli/reference/bluemix_cli/get_started.html#getting-started) and Machine Learning Plugin. In addition setup your [AWS S3 command line](https://aws.amazon.com/cli/) + +``` shell +# Install Machine Learning Plugin using the IBM Cloud CLI +bx plugin repo-add Bluemix https://plugins.ng.bluemix.net +bx plugin install machine-learning -r bluemix +bx target -o ORG -s SPACE +``` + +# Steps + + + + + - [1. Provision your WML instance](#1-provision-your-wml-instance) + - [1.1 Create an instance of WML service and associated key using BX command line](#11-create-an-instance-of-wml-service-and-associated-key-using-bx-command-line) + - [1.2 Get your service credentials](#12-get-your-service-credentials) + - [1.3 Set the Machine Learning plugin it up with your creds obtained in step 2](#13-set-the-machine-learning-plugin-it-up-with-your-creds-obtained-in-step-2) + - [1.4 Test your WML instance](#14-test-your-wml-instance) + - [2. Submit, Monitor and Store a Training Run](#2-submit-monitor-and-store-a-training-run) + - [2.1 Submit](#21-submit) + - [2.2 Monitor](#22-monitor) + - [2.3 Save the Trained Model](#23-save-the-trained-model) + - [3. Deploy and Serve Models](#3-deploy-and-serve-models) + - [3.1 Deploy stored model to WML](#31-deploy-stored-model-to-wml) + - [3.2 Score the deployed model.](#32-score-the-deployed-model) + + + +## 1. Provision your WML instance + + +### 1.1 Create an instance of WML service and associated key using BX command line + +``` shell +bx cf create-service pm-20 lite watson-machine-learning +bx cf create-service-key watson-machine-learning WML-Key +``` + +### 1.2 Get your service credentials +``` shell +bx cf service-key watson-machine-learning WML-Key +``` + +### 1.3 Set the Machine Learning plugin it up with your creds obtained in step 1.2 + +``` shell +export ML_INSTANCE= +export ML_USERNAME= +export ML_PASSWORD= +export ML_ENV= + ``` +### 1.4 Test your WML instance + +``` shell +bx ml list training-runs +# Fetching the list of training runs ... +# SI No Name guid status framework version submitted-at +# +# 0 records found. +# OK +# List all training-runs successful + ``` + +## 2. Submit, Monitor and Store a Training Run + +Please zip all your model definition files in a zip file before you proceed to the next step. +```shell +zip sample-job.zip ... +``` +### 2.1 Submit + +Submit Training Run +``` shell +bx ml train sample-job.zip manifest-WML.yaml +``` + +### 2.2 Monitor + +Monitor Training Run + +``` shell +bx ml list training-runs +bx ml show training-runs +``` +Sample Output +``` shell +Fetching the training run details with MODEL-ID 'training-4tqYllRiR' ... +ModelId training-4tqYllRiR +url /v3/models/training-4tqYllRiR +Name tf_mnist_with_summaries_tutorial +Training definition ID 6f7705bb-85ee-496b-984c-14b5d6bc756e +Command python mnist_with_summaries.py --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --max_steps 400 + +Source bucket tf_training_data +Target bucket tf_trained_model +Framework name tensorflow +Framework version 1.5 +State pending +Submitted_at 2018-03-16T18:51:14Z +OK +Show training-runs details successful +``` +To continously monitor the logs logs of Training Run + +``` shell +bx ml monitor training-runs +``` + +When a training run has completed successfully (or failed) all files written to $RESULT_DIR and the logs from the run should be written to the Cloud Object Storage bucket specified in the setting training_results_reference within the training manifest file, under a folder with the same name as the model id. + +### 2.3 Save the Trained Model + +Once a training run has completed successfully, the trained model can be permanently stored into the repository from from where it can be later deployed for scoring. To do this use the command bx ml store training-runs : + +``` shell +bx ml store training-runs training-DOl4q2LkR +``` +Sample Output: + +``` shell +OK +Model store successful. Model-ID is '19db0ae7-3a9d-44e7-8e9d-fce3f4f8e0eb'. +``` + +You can inspect the trained model and logs in the object store. These appear in the folder in the test_results bucket. + +You can list the files you have in "test_results" + +``` shell +aws --endpoint-url= --profile ibm_cos s3 ls s3://test_data/ +# training-HrlzIHskg/learner-1/load-data.log +# training-HrlzIHskg/learner-1/load-model.log +# training-HrlzIHskg/learner-1/training-log.txt +# training-HrlzIHskg/model/saved_model.pb +# training-HrlzIHskg/model/variables/variables.data-00000-of-00001 +# training-HrlzIHskg/model/variables/variables.index +# training-HrlzIHskg/saved_model.tar.gz + +# You can download the saved model by running the command below +aws --endpoint-url= --profile ibm_cos s3 cp s3://test_data/saved_model.tar.gz saved_model.tar.gz +``` + +## 3. Deploy and Serve Models + +### 3.1 Deploy stored model to WML + + +``` shell +bx ml deploy a8379aaa-ea31-4c22-824d-89a01315dd6d "my_deployment" + +Sample Output: + +Deploying the model with MODEL-ID 'a8379aaa-ea31-4c22-824d-89a01315dd6d'... +DeploymentId 9d6a656c-e9d4-4d89-b335-f9da40e52179 +Scoring endpoint https://2000ab8b-7e81-41b3-ad07-b70f849594f5.wml-fvt.ng.bluemix.net/v3/published_models/a8379aaa-ea31-4c22-824d-89a01315dd6d/deployments/9d6a656c-e9d4-4d89-b335-f9da40e52179/online +Name test34 +Type tensorflow-1.5 +Runtime python-2.7 +Created at 2018-03-16T19:25:15.498Z +OK +Deploy model successful + +``` + +### 3.2 Score the deployed model. + +To score the model, the scoring_payload.json file must use the following format: +``` shell +{"modelId": "a8379aaa-ea31-4c22-824d-89a01315dd6d","deploymentId": "9d6a656c-e9d4-4d89-b335-f9da40e52179","payload":{"inputs":[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0313725508749485,0.48235297203063965,0.6352941393852234,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3294117748737335,0.40392160415649414,0.7921569347381592,0.8823530077934265,0.3333333432674408,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07058823853731155,0.1568627506494522,0.6705882549285889,0.874509871006012,0.9411765336990356,0.9254902601242065,0.29019609093666077,0.22745099663734436,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.19215688109397888,0.250980406999588,0.5254902243614197,0.9411765336990356,1.0,0.9921569228172302,0.6313725709915161,0.22352942824363708,0.05490196496248245,0.1411764770746231,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.38431376218795776,0.8313726186752319,0.8352941870689392,0.9529412388801575,0.9921569228172302,0.9921569228172302,0.9921569228172302,0.7529412508010864,0.2666666805744171,0.08235294371843338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6274510025978088,0.9019608497619629,0.9058824181556702,0.9019608497619629,0.6235294342041016,0.3137255012989044,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1411764770746231,0.3294117748737335,0.4745098352432251,0.10588236153125763,0.10588236153125763,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16862745583057404,0.4941176772117615,0.3686274588108063,0.22352942824363708,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333432674408,0.7921569347381592,0.1882353127002716,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.05490196496248245,0.9294118285179138,0.9529412388801575,0.13725490868091583,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1411764770746231,0.7058823704719543,1.0,0.8235294818878174,0.7568628191947937,0.14509804546833038,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.125490203499794,0.24705883860588074,0.24705883860588074,0.7176470756530762,0.5568627715110779,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5843137502670288,0.8000000715255737,0.03529411926865578,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9960784912109375,0.8823530077934265,0.05490196496248245,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9960784912109375,0.3686274588108063,0.01568627543747425,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8627451658248901,0.04313725605607033,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08627451211214066,0.6784313917160034,0.9960784912109375,0.24705883860588074,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007843137718737125,0.4901961088180542,0.9921569228172302,0.8784314393997192,0.125490203499794,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08627451211214066,0.9921569228172302,0.7843137979507446,0.1411764770746231,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01568627543747425,0.5098039507865906,0.027450982481241226,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0],[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,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+ +``` + +To score, run the following command, which passes the scoring_payload.json file to the scoring processor: + +``` shell +bx ml score scoring_payload.json +``` + +Sample Output: + +``` shelL +Fetching scoring results for the deployment 'e27c1fb7-0560-43df-bc9f-4c64580d67cd' ... +{"classes":[5,4]} +OK +Score request successful +``` diff --git a/etc/examples/caffe-model/gpu-manifest.yml b/etc/examples/caffe-model/gpu-manifest.yml index 65759ba0..83dff094 100644 --- a/etc/examples/caffe-model/gpu-manifest.yml +++ b/etc/examples/caffe-model/gpu-manifest.yml @@ -12,6 +12,8 @@ data_stores: type: s3_datastore training_data: container: mnist_lmdb_data + training_results: + container: dlaas-trained-models connection: type: s3_datastore auth_url: http://s3.default.svc.cluster.local diff --git a/etc/examples/caffe-model/manifest.yml b/etc/examples/caffe-model/manifest.yml index 8b456a94..8d91ef5e 100644 --- a/etc/examples/caffe-model/manifest.yml +++ b/etc/examples/caffe-model/manifest.yml @@ -11,6 +11,8 @@ data_stores: type: s3_datastore training_data: container: mnist_lmdb_data + training_results: + container: dlaas-trained-models connection: type: s3_datastore auth_url: http://s3.default.svc.cluster.local diff --git a/templates/services/lcm-deployment.yml b/templates/services/lcm-deployment.yml index 8209647c..eecff855 100644 --- a/templates/services/lcm-deployment.yml +++ b/templates/services/lcm-deployment.yml @@ -40,7 +40,7 @@ spec: {{ end }} containers: - name: ffdl-lcm-container - image: {{.Values.docker.registry}}/{{.Values.docker.namespace}}/{{.Values.docker.image_prefix}}lcm:{{.Values.trainer.version}} + image: {{.Values.docker.registry}}/{{.Values.docker.namespace}}/{{.Values.docker.image_prefix}}lcm:{{.Values.lcm.version}} {{ if .Values.docker.pullPolicy }} imagePullPolicy: {{.Values.docker.pullPolicy}} {{ end }} From 35e549a5675327d5ffbec00e508a1325da5a5f6b Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Mon, 19 Mar 2018 22:32:46 -0700 Subject: [PATCH 21/87] Remove Vagrant reference (#43) --- README.md | 27 ++++++++------------------- 1 file changed, 8 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index ac28325e..674ea1aa 100644 --- a/README.md +++ b/README.md @@ -43,10 +43,9 @@ FfDL is a collaboration platform for: ## Steps 1. [Quick Start](#1-quick-start) - - 1.1 [Installation using Vagrant](#11-installation-using-vagrant) - - 1.2 [Installation using Minikube](#12-installation-using-minikube) - - 1.3 [Installation using Kubernetes Cluster](#13-installation-using-kubernetes-cluster) - - 1.4 [Installation using IBM Cloud Kubernetes Cluster](#14-installation-using-ibm-cloud-kubernetes-cluster) + - 1.1 [Installation using Minikube](#11-installation-using-minikube) + - 1.2 [Installation using Kubernetes Cluster](#12-installation-using-kubernetes-cluster) + - 1.3 [Installation using IBM Cloud Kubernetes Cluster](#13-installation-using-ibm-cloud-kubernetes-cluster) 2. [Test](#2-test) 3. [Monitoring](#3-monitoring) 4. [Development](#4-development) @@ -65,17 +64,7 @@ into an existing Kubernetes cluster. > Note: If your Kubernetes Cluster version is 1.7 or below, please go to the [values.yaml](values.yaml) and change `k8s_1dot8_or_above` to **false**. -### 1.1 Installation using Vagrant - -This is the simplest and recommended option for local testing. The following commands will automatically -spin up a Vagrant box with Kubernetes and the FfDL platform deployed on top of it: -``` shell -export VM_TYPE=vagrant -vagrant up -make deploy -``` - -### 1.2 Installation using Minikube +### 1.1 Installation using Minikube If you have Minikube installed on your machine, use these commands to deploy the FfDL platform: ``` shell @@ -84,7 +73,7 @@ make minikube make deploy ``` -### 1.3 Installation using Kubernetes Cluster +### 1.2 Installation using Kubernetes Cluster To install FfDL to a proper Kubernetes cluster, make sure `kubectl` points to the right namespace, then deploy the platform services: @@ -96,7 +85,7 @@ export PUBLIC_IP= make deploy ``` -### 1.4 Installation using IBM Cloud Kubernetes Cluster +### 1.3 Installation using IBM Cloud Kubernetes Cluster To install FfDL to a proper IBM Cloud Kubernetes cluster, make sure `kubectl` points to the right namespace and your machine is logged in with `bx login`, then deploy the platform services: @@ -195,8 +184,8 @@ helm status $(helm list | grep ffdl | awk '{print $1}' | head -n 1) | grep STATU > Note: If you are using a IBM Cloud Cluster, make sure you are logged in with `bx login`. ``` shell -# If your Cluster is running on Vagrant or Minikube, replace "ibmcloud" to "vagrant" | "minikube" -# If your Cluster is not running on Vagrant, Minikube, or IBM Cloud, replace "ibmcloud" to "none" +# If your Cluster is running on Minikube, replace "ibmcloud" to "minikube" +# If your Cluster is not running on Minikube or IBM Cloud, replace "ibmcloud" to "none" export VM_TYPE=ibmcloud # Replace with your IBM Cloud Cluster Name if your cluster is on IBM Cloud. From 25bd03147838da284ec6d8eac1aa6d1b1dc3718e Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Tue, 20 Mar 2018 22:40:46 -0700 Subject: [PATCH 22/87] update tensorflow example (#44) --- etc/converter/convert-to-WML.py | 2 +- etc/examples/old-tf-model/gpu-manifest.yml | 39 ++++ etc/examples/old-tf-model/input_data.py | 146 ++++++++++++ .../{tf-model => old-tf-model}/keys.json | 0 etc/examples/old-tf-model/manifest.yml | 39 ++++ .../mnist_with_summaries.py | 0 .../{tf-model => old-tf-model}/runtb.sh | 0 .../tf-model/convolutional_network.py | 216 ++++++++++++++++++ etc/examples/tf-model/gpu-manifest.yml | 21 +- etc/examples/tf-model/input_data.py | 1 + etc/examples/tf-model/manifest.yml | 18 +- 11 files changed, 459 insertions(+), 23 deletions(-) create mode 100644 etc/examples/old-tf-model/gpu-manifest.yml create mode 100644 etc/examples/old-tf-model/input_data.py rename etc/examples/{tf-model => old-tf-model}/keys.json (100%) create mode 100644 etc/examples/old-tf-model/manifest.yml rename etc/examples/{tf-model => old-tf-model}/mnist_with_summaries.py (100%) rename etc/examples/{tf-model => old-tf-model}/runtb.sh (100%) create mode 100644 etc/examples/tf-model/convolutional_network.py diff --git a/etc/converter/convert-to-WML.py b/etc/converter/convert-to-WML.py index 520ef203..da2c37a6 100644 --- a/etc/converter/convert-to-WML.py +++ b/etc/converter/convert-to-WML.py @@ -44,7 +44,7 @@ def createJob(sample,data): sample['training_data_reference']['connection']['access_key_id'] = data['data_stores'][0]['connection']['user_name'] sample['training_data_reference']['connection']['secret_access_key'] = data['data_stores'][0]['connection']['password'] sample['training_data_reference']['source']['bucket'] = data['data_stores'][0]['training_data']['container'] - sample['training_results_reference']['name'] = data['data_stores'][0]['id'] + sample['training_results_reference']['name'] = ''.join([data['data_stores'][0]['id'],"-results"]) sample['training_results_reference']['connection']['endpoint_url'] = data['data_stores'][0]['connection']['auth_url'] sample['training_results_reference']['connection']['access_key_id'] = data['data_stores'][0]['connection']['user_name'] sample['training_results_reference']['connection']['secret_access_key'] = data['data_stores'][0]['connection']['password'] diff --git a/etc/examples/old-tf-model/gpu-manifest.yml b/etc/examples/old-tf-model/gpu-manifest.yml new file mode 100644 index 00000000..eef4a1ff --- /dev/null +++ b/etc/examples/old-tf-model/gpu-manifest.yml @@ -0,0 +1,39 @@ +name: tf_mnist_with_summaries_gpu_tutorial +description: Convolutional network model using tensorflow (GPU) +version: "1.0" +gpus: 1 +cpus: 0.005 +memory: 2000MiB + +# Object stores that allow the system to retrieve training data. +data_stores: + - id: sl-internal-os + type: s3_datastore + training_data: + container: tf_training_data + training_results: + container: tf_trained_model + connection: + type: s3_datastore + auth_url: http://s3.default.svc.cluster.local + user_name: test + password: test + +framework: + name: tensorflow + version: "1.3.0-gpu" + command: > + python mnist_with_summaries.py + --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz + --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz + --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz + --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz + --max_steps 400 +# --learning_rate 0.001 + +evaluation_metrics: + type: tensorboard + in: "$JOB_STATE_DIR/logs/tb" + # (Eventual) Available event types: 'images', 'distributions', 'histograms', 'images' + # 'audio', 'scalars', 'tensors', 'graph', 'meta_graph', 'run_metadata' + # event_types: [scalars] diff --git a/etc/examples/old-tf-model/input_data.py b/etc/examples/old-tf-model/input_data.py new file mode 100644 index 00000000..8e26b6d1 --- /dev/null +++ b/etc/examples/old-tf-model/input_data.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python + +"""Functions for downloading and reading MNIST data.""" +import gzip +import os +from six.moves.urllib.request import urlretrieve +import numpy + +def _read32(bytestream): + dt = numpy.dtype(numpy.uint32).newbyteorder('>') + return numpy.frombuffer(bytestream.read(4), dtype=dt)[0] + + +def extract_images(filename): + """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" + print('Extracting', filename) + with gzip.open(filename) as bytestream: + magic = _read32(bytestream) + if magic != 2051: + raise ValueError( + 'Invalid magic number %d in MNIST image file: %s' % + (magic, filename)) + num_images = _read32(bytestream) + rows = _read32(bytestream) + cols = _read32(bytestream) + buf = bytestream.read(rows * cols * num_images) + data = numpy.frombuffer(buf, dtype=numpy.uint8) + data = data.reshape(num_images, rows, cols, 1) + return data + + +def dense_to_one_hot(labels_dense, num_classes=10): + """Convert class labels from scalars to one-hot vectors.""" + num_labels = labels_dense.shape[0] + index_offset = numpy.arange(num_labels) * num_classes + labels_one_hot = numpy.zeros((num_labels, num_classes)) + labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 + return labels_one_hot + + +def extract_labels(filename, one_hot=False): + """Extract the labels into a 1D uint8 numpy array [index].""" + print('Extracting', filename) + with gzip.open(filename) as bytestream: + magic = _read32(bytestream) + if magic != 2049: + raise ValueError( + 'Invalid magic number %d in MNIST label file: %s' % + (magic, filename)) + num_items = _read32(bytestream) + buf = bytestream.read(num_items) + labels = numpy.frombuffer(buf, dtype=numpy.uint8) + if one_hot: + return dense_to_one_hot(labels) + return labels + + +class DataSet(object): + def __init__(self, images, labels, fake_data=False): + if fake_data: + self._num_examples = 10000 + else: + assert images.shape[0] == labels.shape[0], ( + "images.shape: %s labels.shape: %s" % (images.shape, + labels.shape)) + self._num_examples = images.shape[0] + # Convert shape from [num examples, rows, columns, depth] + # to [num examples, rows*columns] (assuming depth == 1) + assert images.shape[3] == 1 + images = images.reshape(images.shape[0], + images.shape[1] * images.shape[2]) + # Convert from [0, 255] -> [0.0, 1.0]. + images = images.astype(numpy.float32) + images = numpy.multiply(images, 1.0 / 255.0) + self._images = images + self._labels = labels + self._epochs_completed = 0 + self._index_in_epoch = 0 + + @property + def images(self): + return self._images + + @property + def labels(self): + return self._labels + + @property + def num_examples(self): + return self._num_examples + + @property + def epochs_completed(self): + return self._epochs_completed + + def next_batch(self, batch_size, fake_data=False): + """Return the next `batch_size` examples from this data set.""" + if fake_data: + fake_image = [1.0 for _ in xrange(784)] + fake_label = 0 + return [fake_image for _ in xrange(batch_size)], [ + fake_label for _ in xrange(batch_size)] + start = self._index_in_epoch + self._index_in_epoch += batch_size + if self._index_in_epoch > self._num_examples: + # Finished epoch + self._epochs_completed += 1 + # Shuffle the data + perm = numpy.arange(self._num_examples) + numpy.random.shuffle(perm) + self._images = self._images[perm] + self._labels = self._labels[perm] + # Start next epoch + start = 0 + self._index_in_epoch = batch_size + assert batch_size <= self._num_examples + end = self._index_in_epoch + return self._images[start:end], self._labels[start:end] + + +def read_data_sets(train_images_file, train_labels_file, test_images_file, test_labels_file, fake_data=False, one_hot=False): + class DataSets(object): + pass + data_sets = DataSets() + if fake_data: + data_sets.train = DataSet([], [], fake_data=True) + data_sets.validation = DataSet([], [], fake_data=True) + data_sets.test = DataSet([], [], fake_data=True) + return data_sets + TRAIN_IMAGES = train_images_file + TRAIN_LABELS = train_labels_file + TEST_IMAGES = test_images_file + TEST_LABELS = test_labels_file + VALIDATION_SIZE = 5000 + train_images = extract_images(TRAIN_IMAGES) + train_labels = extract_labels(TRAIN_LABELS, one_hot=one_hot) + test_images = extract_images(TEST_IMAGES) + test_labels = extract_labels(TEST_LABELS, one_hot=one_hot) + validation_images = train_images[:VALIDATION_SIZE] + validation_labels = train_labels[:VALIDATION_SIZE] + train_images = train_images[VALIDATION_SIZE:] + train_labels = train_labels[VALIDATION_SIZE:] + data_sets.train = DataSet(train_images, train_labels) + data_sets.validation = DataSet(validation_images, validation_labels) + data_sets.test = DataSet(test_images, test_labels) + return data_sets \ No newline at end of file diff --git a/etc/examples/tf-model/keys.json b/etc/examples/old-tf-model/keys.json similarity index 100% rename from etc/examples/tf-model/keys.json rename to etc/examples/old-tf-model/keys.json diff --git a/etc/examples/old-tf-model/manifest.yml b/etc/examples/old-tf-model/manifest.yml new file mode 100644 index 00000000..98ce7ae7 --- /dev/null +++ b/etc/examples/old-tf-model/manifest.yml @@ -0,0 +1,39 @@ +name: tf_mnist_with_summaries_tutorial +description: Convolutional network model using tensorflow +version: "1.0" +gpus: 0 +cpus: 0.5 +memory: 500MiB + +# Object stores that allow the system to retrieve training data. +data_stores: + - id: sl-internal-os + type: s3_datastore + training_data: + container: tf_training_data + training_results: + container: tf_trained_model + connection: + type: s3_datastore + auth_url: http://s3.default.svc.cluster.local + user_name: test + password: test + +framework: + name: tensorflow + version: "1.5.0" + command: > + python mnist_with_summaries.py + --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz + --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz + --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz + --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz + --max_steps 400 +# --learning_rate 0.001 + +evaluation_metrics: + type: tensorboard + in: "$JOB_STATE_DIR/logs/tb" + # (Eventual) Available event types: 'images', 'distributions', 'histograms', 'images' + # 'audio', 'scalars', 'tensors', 'graph', 'meta_graph', 'run_metadata' + # event_types: [scalars] diff --git a/etc/examples/tf-model/mnist_with_summaries.py b/etc/examples/old-tf-model/mnist_with_summaries.py similarity index 100% rename from etc/examples/tf-model/mnist_with_summaries.py rename to etc/examples/old-tf-model/mnist_with_summaries.py diff --git a/etc/examples/tf-model/runtb.sh b/etc/examples/old-tf-model/runtb.sh similarity index 100% rename from etc/examples/tf-model/runtb.sh rename to etc/examples/old-tf-model/runtb.sh diff --git a/etc/examples/tf-model/convolutional_network.py b/etc/examples/tf-model/convolutional_network.py new file mode 100644 index 00000000..ffa87327 --- /dev/null +++ b/etc/examples/tf-model/convolutional_network.py @@ -0,0 +1,216 @@ +''' +A Convolutional Network implementation example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +import tensorflow as tf +import input_data +import sys +import os +import itertools +import re +import time +from random import randint + +train_images_file = "" +train_labels_file = "" +test_images_file = "" +test_labels_file = "" + +# Parameters +learning_rate = 0.001 +training_iters = 200000 +batch_size = 128 +display_step = 10 + +model_path = os.environ["RESULT_DIR"]+"/model" + +# This helps distinguish instances when the training job is restarted. +instance_id = randint(0,9999) + +def main(argv): + + if len(argv) < 12: + sys.exit("Not enough arguments provided.") + + global train_images_file, train_labels_file, test_images_file, test_labels_file, learning_rate, training_iters + + i = 1 + while i <= 12: + arg = str(argv[i]) + if arg == "--trainImagesFile": + train_images_file = str(argv[i+1]) + elif arg == "--trainLabelsFile": + train_labels_file = str(argv[i+1]) + elif arg == "--testImagesFile": + test_images_file = str(argv[i+1]) + elif arg == "--testLabelsFile": + test_labels_file = str(argv[i+1]) + elif arg == "--learningRate": + learning_rate = float(argv[i+1]) + elif arg =="--trainingIters": + training_iters = int(argv[i+1]) + i += 2 + +if __name__ == "__main__": + main(sys.argv) + +# Import MNIST data +mnist = input_data.read_data_sets(train_images_file, + train_labels_file, test_images_file, test_labels_file, one_hot=True) + +# Network Parameters +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +# tf Graph input +x = tf.placeholder(tf.float32, [None, n_input], name="x_input") +y = tf.placeholder(tf.float32, [None, n_classes]) + +# Create some wrappers for simplicity +def conv2d(x, W, b, strides=1): + # Conv2D wrapper, with bias and relu activation + x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') + x = tf.nn.bias_add(x, b) + return tf.nn.relu(x) + + +def maxpool2d(x, k=2): + # MaxPool2D wrapper + return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], + padding='SAME') + + +# Create model +def conv_net(x, weights, biases, dropout): + # Reshape input picture + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer + conv1 = conv2d(x, weights['wc1'], biases['bc1']) + # Max Pooling (down-sampling) + conv1 = maxpool2d(conv1, k=2) + + # Convolution Layer + conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) + # Max Pooling (down-sampling) + conv2 = maxpool2d(conv2, k=2) + + # Fully connected layer + # Reshape conv2 output to fit fully connected layer input + fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) + fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) + fc1 = tf.nn.relu(fc1) + # Apply Dropout + fc1 = tf.nn.dropout(fc1, dropout) + + # Output, class prediction + out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) + return out + +# Store layers weight & bias +weights = { + # 5x5 conv, 1 input, 32 outputs + 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), + # 5x5 conv, 32 inputs, 64 outputs + 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), + # fully connected, 7*7*64 inputs, 1024 outputs + 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), + # 1024 inputs, 10 outputs (class prediction) + 'out': tf.Variable(tf.random_normal([1024, n_classes])) +} + +biases = { + 'bc1': tf.Variable(tf.random_normal([32])), + 'bc2': tf.Variable(tf.random_normal([64])), + 'bd1': tf.Variable(tf.random_normal([1024])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + +# Construct model +pred = conv_net(x, weights, biases, dropout) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) + +predictor = tf.argmax(pred, 1, name="predictor") + +# Evaluate model +correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initializing the variables +init = tf.global_variables_initializer() + +# Launch the graph +with tf.Session() as sess: + sess.run(init) + + step = 1 + # Keep training until reach max iterations + while step * batch_size < training_iters: + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(optimizer, feed_dict={x: batch_x, y: batch_y}) + if step % display_step == 0: + + # Calculate batch loss and accuracy + loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, + y: batch_y}) + print("Time " + "{:.4f}".format(time.time()) + \ + ", instance " + str(instance_id) + \ + ", Iter " + str(step * batch_size) + \ + ", Minibatch Loss= " + "{:.6f}".format(loss) + \ + ", Training Accuracy= " + "{:.5f}".format(acc)) + sys.stdout.flush() + step += 1 + print("Optimization Finished!") + + classification_inputs = tf.saved_model.utils.build_tensor_info(x) + classification_outputs_classes = tf.saved_model.utils.build_tensor_info(predictor) + + classification_signature = ( + tf.saved_model.signature_def_utils.build_signature_def( + inputs={ + tf.saved_model.signature_constants.CLASSIFY_INPUTS: + classification_inputs + }, + outputs={ + tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES: + classification_outputs_classes + }, + method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME)) + + print("classification_signature content:") + print(classification_signature) + + # Calculate accuracy for 256 mnist test images + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: mnist.test.images[:256], + y: mnist.test.labels[:256]})) + + + builder = tf.saved_model.builder.SavedModelBuilder(model_path) + legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op') + builder.add_meta_graph_and_variables( + sess, [tf.saved_model.tag_constants.SERVING], + signature_def_map={ + 'predict_images': classification_signature, + }, + legacy_init_op=legacy_init_op) + + save_path = str(builder.save()) + + # save_path = saver.save(sess, model_path) + print("Model saved in file: %s" % save_path) + + os.system("(cd $RESULT_DIR/model;tar cvfz ../saved_model.tar.gz .)") + print(str(os.listdir(os.environ["RESULT_DIR"]))) + print(os.environ["RESULT_DIR"]) + sys.stdout.flush() diff --git a/etc/examples/tf-model/gpu-manifest.yml b/etc/examples/tf-model/gpu-manifest.yml index eef4a1ff..f9ec370f 100644 --- a/etc/examples/tf-model/gpu-manifest.yml +++ b/etc/examples/tf-model/gpu-manifest.yml @@ -1,9 +1,9 @@ -name: tf_mnist_with_summaries_gpu_tutorial -description: Convolutional network model using tensorflow (GPU) +name: tf_convolutional_network_tutorial +description: Convolutional network model using tensorflow version: "1.0" gpus: 1 -cpus: 0.005 -memory: 2000MiB +cpus: 2 +memory: 2Gb # Object stores that allow the system to retrieve training data. data_stores: @@ -21,15 +21,12 @@ data_stores: framework: name: tensorflow - version: "1.3.0-gpu" + version: "1.5.0-gpu-py3" command: > - python mnist_with_summaries.py - --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz - --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz - --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz - --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz - --max_steps 400 -# --learning_rate 0.001 + python3 convolutional_network.py --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz + --trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz + --testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --learningRate 0.001 + --trainingIters 20000 evaluation_metrics: type: tensorboard diff --git a/etc/examples/tf-model/input_data.py b/etc/examples/tf-model/input_data.py index 8e26b6d1..fdf46df4 100644 --- a/etc/examples/tf-model/input_data.py +++ b/etc/examples/tf-model/input_data.py @@ -1,3 +1,4 @@ + #!/usr/bin/env python """Functions for downloading and reading MNIST data.""" diff --git a/etc/examples/tf-model/manifest.yml b/etc/examples/tf-model/manifest.yml index cd75c866..500ae9e0 100644 --- a/etc/examples/tf-model/manifest.yml +++ b/etc/examples/tf-model/manifest.yml @@ -1,9 +1,9 @@ -name: tf_mnist_with_summaries_tutorial +name: tf_convolutional_network_tutorial description: Convolutional network model using tensorflow version: "1.0" gpus: 0 cpus: 0.5 -memory: 500MiB +memory: 1Gb # Object stores that allow the system to retrieve training data. data_stores: @@ -21,15 +21,13 @@ data_stores: framework: name: tensorflow - version: "latest" + version: "1.5.0-py3" command: > - python mnist_with_summaries.py - --train_images_file ${DATA_DIR}/train-images-idx3-ubyte.gz - --train_labels_file ${DATA_DIR}/train-labels-idx1-ubyte.gz - --test_images_file ${DATA_DIR}/t10k-images-idx3-ubyte.gz - --test_labels_file ${DATA_DIR}/t10k-labels-idx1-ubyte.gz - --max_steps 400 -# --learning_rate 0.001 + python3 convolutional_network.py --trainImagesFile ${DATA_DIR}/train-images-idx3-ubyte.gz + --trainLabelsFile ${DATA_DIR}/train-labels-idx1-ubyte.gz --testImagesFile ${DATA_DIR}/t10k-images-idx3-ubyte.gz + --testLabelsFile ${DATA_DIR}/t10k-labels-idx1-ubyte.gz --learningRate 0.001 + --trainingIters 2000 + # Change trainingIters to 20000 if you want your model to have over 80% Accuracy rate. evaluation_metrics: type: tensorboard From 5ab24e49878601d93031ae3bf24e3f19e430fdea Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Thu, 22 Mar 2018 16:05:37 -0700 Subject: [PATCH 23/87] Temporary fix for CI with k8s 1.9.4 (#45) * Update feature-gates for k8s 1.9.4 and above * Update troubleshooting * Update README.md --- Makefile | 2 +- README.md | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index b0be21c0..a9185064 100644 --- a/Makefile +++ b/Makefile @@ -233,7 +233,7 @@ install-minikube-in-ci: chmod +x kubectl && sudo mv kubectl /usr/local/bin/ @curl -s -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 && \ chmod +x minikube && sudo mv minikube /usr/local/bin/ - @sudo minikube start --vm-driver=none + @sudo minikube start --vm-driver=none --feature-gates=ReadOnlyAPIDataVolumes=false @minikube update-context @JSONPATH='{range .items[*]}{@.metadata.name}:{range @.status.conditions[*]}{@.type}={@.status};{end}{end}'; \ until kubectl get nodes -o jsonpath="$$JSONPATH" 2>&1 | grep -q "Ready=True"; do sleep 1; done diff --git a/README.md b/README.md index 674ea1aa..b046ee29 100644 --- a/README.md +++ b/README.md @@ -403,6 +403,8 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) * To remove FfDL on your Cluster, simply run `make undeploy` +* Since the current implementation of FfDL needs write access for its mounted volume, if you are using Kubernetes 1.9.4 or above, please modify the feature gate `ReadOnlyAPIDataVolumes=false`. + ## 9. References Based on IBM Research work in Deep Learning. From 609fbce6aea839db969dcad3bf26ee61f519ee02 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Tue, 27 Mar 2018 14:31:17 -0700 Subject: [PATCH 24/87] Update learner entrypoint command to work on k8s 1.9.4 and above (#47) * update learner entrypoint command * update troubleshooting * Update train.sh path to match with DLaaS. --- Makefile | 2 +- README.md | 6 ++---- lcm/service/lcm/container_helper_extensions.go | 2 +- 3 files changed, 4 insertions(+), 6 deletions(-) diff --git a/Makefile b/Makefile index a9185064..b0be21c0 100644 --- a/Makefile +++ b/Makefile @@ -233,7 +233,7 @@ install-minikube-in-ci: chmod +x kubectl && sudo mv kubectl /usr/local/bin/ @curl -s -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 && \ chmod +x minikube && sudo mv minikube /usr/local/bin/ - @sudo minikube start --vm-driver=none --feature-gates=ReadOnlyAPIDataVolumes=false + @sudo minikube start --vm-driver=none @minikube update-context @JSONPATH='{range .items[*]}{@.metadata.name}:{range @.status.conditions[*]}{@.type}={@.status};{end}{end}'; \ until kubectl get nodes -o jsonpath="$$JSONPATH" 2>&1 | grep -q "Ready=True"; do sleep 1; done diff --git a/README.md b/README.md index b046ee29..e7cd5dbc 100644 --- a/README.md +++ b/README.md @@ -32,13 +32,13 @@ FfDL is a collaboration platform for: ## Usage Scenarios -* If you already have a FfDL deployment up and running, you can jump to [FfDL User Guide](docs/user-guide.md) to use FfDL for training your models. +* If you already have a FfDL deployment up and running, you can jump to [FfDL User Guide](docs/user-guide.md) to use FfDL for training your models. * If you have FfDL confiugured to use GPUs, and want to train using GPUs, follow steps [here](docs/gpu-guide.md) * If you have used FfDL to train your models, and want to use a GPU enabled public cloud hosted service for further training and serving, please follow instructions [here](etc/converter/ffdl-wml.md) to train and serve your models using [Watson Studio Deep Learning](https://www.ibm.com/cloud/machine-learning) service -* If you are starting and want to setup your own FfDL deployment, please follow the steps below. +* If you are starting and want to setup your own FfDL deployment, please follow the steps below. ## Steps @@ -403,8 +403,6 @@ helm delete $(helm list | grep ffdl | awk '{print $1}' | head -n 1) * To remove FfDL on your Cluster, simply run `make undeploy` -* Since the current implementation of FfDL needs write access for its mounted volume, if you are using Kubernetes 1.9.4 or above, please modify the feature gate `ReadOnlyAPIDataVolumes=false`. - ## 9. References Based on IBM Research work in Deep Learning. diff --git a/lcm/service/lcm/container_helper_extensions.go b/lcm/service/lcm/container_helper_extensions.go index 04de99fc..cb61a5ab 100644 --- a/lcm/service/lcm/container_helper_extensions.go +++ b/lcm/service/lcm/container_helper_extensions.go @@ -35,7 +35,7 @@ func extendLearnerContainer(learner *v1core.Container, req *service.JobDeploymen // TODO! } - extCmd := "export PATH=" + learnerEntrypointFilesPath + ":$PATH; chmod +x " + learnerEntrypointFilesPath + "/*.sh; " + extCmd := "export PATH=/usr/local/bin/:$PATH; cp " + learnerEntrypointFilesPath + "/*.sh /usr/local/bin/; chmod +x /usr/local/bin/*.sh;" extMount := v1core.VolumeMount{ Name: learnerEntrypointFilesVolume, MountPath: learnerEntrypointFilesPath, From 7abbd1f58229f019ae422e7af2928833bd7090a9 Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Wed, 28 Mar 2018 10:44:19 -0700 Subject: [PATCH 25/87] Removing orphan vagrant references (#50) --- README.md | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index e7cd5dbc..f7a24960 100644 --- a/README.md +++ b/README.md @@ -23,8 +23,7 @@ FfDL is a collaboration platform for: * `S3 CLI`: The [command-line interface](https://aws.amazon.com/cli/) to configure your Object Storage * An existing Kubernetes cluster (e.g., [Minikube](https://github.com/kubernetes/minikube) for local testing). - Once installed, use the command `make minikube` to start Minikube and set up local network routes. Alternatively, - use the Vagrant based setup to automatically install a local Kubernetes cluster. + Once installed, use the command `make minikube` to start Minikube and set up local network routes. * Follow the appropriate instructions for standing up your Kubernetes cluster using [IBM Cloud Public](https://github.com/IBM/container-journey-template/blob/master/README.md) or [IBM Cloud Private](https://github.com/IBM/deploy-ibm-cloud-private/blob/master/README.md) @@ -32,13 +31,13 @@ FfDL is a collaboration platform for: ## Usage Scenarios -* If you already have a FfDL deployment up and running, you can jump to [FfDL User Guide](docs/user-guide.md) to use FfDL for training your models. +* If you already have a FfDL deployment up and running, you can jump to [FfDL User Guide](docs/user-guide.md) to use FfDL for training your deep learning models. * If you have FfDL confiugured to use GPUs, and want to train using GPUs, follow steps [here](docs/gpu-guide.md) -* If you have used FfDL to train your models, and want to use a GPU enabled public cloud hosted service for further training and serving, please follow instructions [here](etc/converter/ffdl-wml.md) to train and serve your models using [Watson Studio Deep Learning](https://www.ibm.com/cloud/machine-learning) service +* If you have used FfDL to train your models, and want to use a GPU enabled public cloud hosted service for further training and serving, please follow instructions [here](etc/converter/ffdl-wml.md) to train and serve your models using [Watson Studio Deep Learning](https://www.ibm.com/cloud/deep-learning) service -* If you are starting and want to setup your own FfDL deployment, please follow the steps below. +* If you are getting started and want to setup your own FfDL deployment, please follow the steps below. ## Steps @@ -59,8 +58,7 @@ FfDL is a collaboration platform for: ## 1. Quick Start -There are multiple installation paths for installing FfDL locally ("1-click-install") or -into an existing Kubernetes cluster. +There are multiple installation paths for installing FfDL locally ("1-click-install") or into an existing Kubernetes cluster. > Note: If your Kubernetes Cluster version is 1.7 or below, please go to the [values.yaml](values.yaml) and change `k8s_1dot8_or_above` to **false**. From 6a41a8407f314e15d537d93b6481ec226be608f0 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Mon, 2 Apr 2018 09:12:45 -0700 Subject: [PATCH 26/87] Add developer guide and some customizable features for development build (#51) * * Add codebase configuration for device plugin and custom learner images * Add developer guide for those who want to do a custom FfDL build * update developer-guide * fix declare type --- README.md | 33 +----------- docs/developer-guide.md | 50 +++++++++++++++++++ lcm/service/lcm/constants.go | 1 + lcm/service/lcm/container_helper.go | 7 ++- .../lcm/container_helper_extensions.go | 2 + lcm/service/lcm/resources.go | 7 ++- trainer/trainer/frameworks.go | 5 ++ 7 files changed, 70 insertions(+), 35 deletions(-) create mode 100644 docs/developer-guide.md diff --git a/README.md b/README.md index f7a24960..8d339e3e 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ FfDL is a collaboration platform for: * `S3 CLI`: The [command-line interface](https://aws.amazon.com/cli/) to configure your Object Storage * An existing Kubernetes cluster (e.g., [Minikube](https://github.com/kubernetes/minikube) for local testing). - Once installed, use the command `make minikube` to start Minikube and set up local network routes. + Once installed, use the command `make minikube` to start Minikube and set up local network routes. * Follow the appropriate instructions for standing up your Kubernetes cluster using [IBM Cloud Public](https://github.com/IBM/container-journey-template/blob/master/README.md) or [IBM Cloud Private](https://github.com/IBM/deploy-ibm-cloud-private/blob/master/README.md) @@ -107,36 +107,7 @@ The platform ships with a simple Grafana monitoring dashboard. The URL is printe ## 4. Development -Use the following instructions if you want to run a full development build, compile the code, and build the -Docker images locally. - -Install: - -* `go`: a working [Go](https://golang.org/) environment is required to build the code - -* `glide`: the glide package manager for Go (https://glide.sh) - -* `npm`: the Node.js package manager (https://www.npmjs.com) for building the Web UI - -* `Go` is very specific about directory layouts. Make sure to set your `$GOPATH` and clone this repo to a directory -`$GOPATH/src/github.com/IBM/FfDL` before proceeding with the next steps. - - -Then, fetch the dependencies via: -``` -glide install -``` -Compile the code and build the Docker images via: -``` -make build -make docker-build -``` - -Make sure `kubectl` points to the right target context/namespace, then deploy the services to your Kubernetes -environment (using `helm`): -``` -make deploy -``` +Please refer to the [developer guide](docs/developer-guide.md) for more details. ## 5. Detailed Installation Instructions diff --git a/docs/developer-guide.md b/docs/developer-guide.md new file mode 100644 index 00000000..86cda084 --- /dev/null +++ b/docs/developer-guide.md @@ -0,0 +1,50 @@ +# Developer Guide + +## Run a full development build + +Use the following instructions if you want to run a full development build, compile the code, and build the +Docker images locally. + +Install: + +* `go`: a working [Go](https://golang.org/) environment is required to build the code + +* `glide`: the glide package manager for Go (https://glide.sh) + +* `npm`: the Node.js package manager (https://www.npmjs.com) for building the Web UI + +* `Go` is very specific about directory layouts. Make sure to set your `$GOPATH` and clone this repo to a directory +`$GOPATH/src/github.com/IBM/FfDL` before proceeding with the next steps. + + +Then, fetch the dependencies via: +``` +glide install +``` +Compile the code and build the Docker images via: +``` +make build +make docker-build +``` + +Make sure `kubectl` points to the right target context/namespace, then deploy the services to your Kubernetes +environment (using `helm`): +``` +make deploy +``` + +## Enable device plugin for GPU workloads with development build + +Please modify the `resourceGPU` under [lcm/service/lcm/container_helper.go](../lcm/service/lcm/container_helper.go#L530) and [lcm/service/lcm/resources.go](../lcm/service/lcm/resources.go#L149) to `"nvidia.com/gpu"` and rebuild the lcm image to enable device plugin for all GPU workloads on your development build. + +## Enable custom learner images with development build + +Please uncomment the following section under [trainer/trainer/frameworks.go](../trainer/trainer/frameworks.go#L39) and rebuild the trainer image to enable custom learner images from any users. Alternatively, you can use the pre-built images `ffdl/ffdl-trainer:customizable` on DockerHub. + +``` go +if fwName == "custom" { + return true, "" +} +``` + +After you deployed `ffdl-trainer` with custom image feature, you can use your custom learner images by changing the `framework.name` to **custom** and `framework.version` to your learner image in your training job's manifest.yml file. If you are using any private registry, you need to enable access to the private registry in your Kubernetes default namespace. diff --git a/lcm/service/lcm/constants.go b/lcm/service/lcm/constants.go index 16fcbe28..375f0fd1 100644 --- a/lcm/service/lcm/constants.go +++ b/lcm/service/lcm/constants.go @@ -56,6 +56,7 @@ const ( torchFrameworkName string = "torch" caffe2FrameworkName string = "caffe2" pytorchFrameworkName string = "pytorch" + customFrameworkName string = "custom" numRetries = 5 maxGPUsPerNode = 4 errCodeNormal = "000" diff --git a/lcm/service/lcm/container_helper.go b/lcm/service/lcm/container_helper.go index b2a4eb28..cbdc231d 100644 --- a/lcm/service/lcm/container_helper.go +++ b/lcm/service/lcm/container_helper.go @@ -526,6 +526,9 @@ func constructLearnerContainer(req *service.JobDeploymentRequest, learnerID int, } cmd := wrapCommand(command, learnerContainerName, PodLevelJobDir) + // Set the resourceGPU to "nvidia.com/gpu" if you want to run your GPU workloads using device plugin. + var resourceGPU v1core.ResourceName = v1core.ResourceNvidiaGPU + learnerContainer := v1core.Container{ Name: learnerContainerName, Image: learnerImage, @@ -540,12 +543,12 @@ func constructLearnerContainer(req *service.JobDeploymentRequest, learnerID int, Requests: v1core.ResourceList{ v1core.ResourceCPU: *cpuCount, v1core.ResourceMemory: *memCount, - v1core.ResourceNvidiaGPU: *gpuCount, + resourceGPU: *gpuCount, }, Limits: v1core.ResourceList{ v1core.ResourceCPU: *cpuCount, v1core.ResourceMemory: *memCount, - v1core.ResourceNvidiaGPU: *gpuCount, + resourceGPU: *gpuCount, }, }, VolumeMounts: volumes, diff --git a/lcm/service/lcm/container_helper_extensions.go b/lcm/service/lcm/container_helper_extensions.go index cb61a5ab..04a23bfc 100644 --- a/lcm/service/lcm/container_helper_extensions.go +++ b/lcm/service/lcm/container_helper_extensions.go @@ -31,6 +31,8 @@ func extendLearnerContainer(learner *v1core.Container, req *service.JobDeploymen learnerImage = "caffe2ai/caffe2:" + req.Version case pytorchFrameworkName: learnerImage = "pytorch/pytorch:" + req.Version + case customFrameworkName: + learnerImage = req.Version default: // TODO! } diff --git a/lcm/service/lcm/resources.go b/lcm/service/lcm/resources.go index 7e5c3bbf..ec414795 100644 --- a/lcm/service/lcm/resources.go +++ b/lcm/service/lcm/resources.go @@ -145,6 +145,9 @@ func getResources(s *lcmService, logr *logger.LocLoggingEntry) (bool, *allocatab } } + // Set the resourceGPU to "nvidia.com/gpu" if you want to run your GPU workloads using device plugin. + var resourceGPU v1core.ResourceName = v1core.ResourceNvidiaGPU + //By querying nodes, determine the number of allocatable resources for _, node := range nodes.Items { availableResources := node.Status.Allocatable @@ -153,7 +156,7 @@ func getResources(s *lcmService, logr *logger.LocLoggingEntry) (bool, *allocatab cpu, _ := strconv.ParseFloat(cpuQty.AsDec().String(), 64) memQty := availableResources[v1core.ResourceMemory] mem, _ := strconv.ParseFloat(memQty.AsDec().String(), 64) - gpuQty := availableResources[v1core.ResourceNvidiaGPU] + gpuQty := availableResources[resourceGPU] gpu, _ := strconv.ParseFloat(gpuQty.AsDec().String(), 64) cpusAllocatable += cpu @@ -171,7 +174,7 @@ func getResources(s *lcmService, logr *logger.LocLoggingEntry) (bool, *allocatab cpu, _ := strconv.ParseFloat(cpuQty.AsDec().String(), 64) memQty := resourcesRequested[v1core.ResourceMemory] mem, _ := strconv.ParseFloat(memQty.AsDec().String(), 64) - gpuQty := resourcesRequested[v1core.ResourceNvidiaGPU] + gpuQty := resourcesRequested[resourceGPU] gpu, _ := strconv.ParseFloat(gpuQty.AsDec().String(), 64) cpusRequested += cpu diff --git a/trainer/trainer/frameworks.go b/trainer/trainer/frameworks.go index 0ab9c21b..96123883 100644 --- a/trainer/trainer/frameworks.go +++ b/trainer/trainer/frameworks.go @@ -35,6 +35,11 @@ func validateFrameworks(fw *grpc_trainer_v2.Framework) (bool, string) { return false, "framework version is required" } + // Uncomment the below condition to enable users to apply any custom learner. + // if fwName == "custom" { + // return true, "" + // } + loc := config.GetCurrentLearnerConfigLocation(fwName, fwVersion) if loc == "" { return false, fmt.Sprintf("%s version %s not supported", fwName, fwVersion) From 0cb99357efdddf5c4ebdc2c38fb9a6f1f5e048e7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dar=C3=ADo=20Here=C3=B1=C3=BA?= Date: Wed, 4 Apr 2018 12:41:21 -0300 Subject: [PATCH 27/87] Minor fix on #273 & #370 (#35) Plus minor fixes --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 8d339e3e..5512d1d3 100644 --- a/README.md +++ b/README.md @@ -237,7 +237,7 @@ done ``` 5. Now you should have all the necessary training data set in your object storage. Let's go ahead to set up your restapi endpoint -and default credentials for Deep Learning as a Service. Once you done that, you can start running jobs using the FfDL CLI(executable +and default credentials for Deep Learning as a Service. Once you done that, you can start running jobs using the FfDL CLI (executable binary). ```shell @@ -251,7 +251,7 @@ $CLI_CMD train etc/examples/tf-model/manifest.yml etc/examples/tf-model Congratulation, you had submitted your first job on FfDL. You can check your FfDL status either from the FfDL UI or simply run `$CLI_CMD list` -> You can learn about how to create your own model definition files and manifest.yaml at [user guild](docs/user-guide.md#2-create-new-models-with-ffdl). +> You can learn about how to create your own model definition files and `manifest.yaml` at [user guild](docs/user-guide.md#2-create-new-models-with-ffdl). 6. Since it's simple and straightforward to submit jobs with different deep learning framework on FfDL, let's try to run a Caffe Job. @@ -262,7 +262,7 @@ $CLI_CMD train etc/examples/caffe-model/manifest.yml etc/examples/caffe-model Congratulation, now you know how to deploy jobs with different deep learning framework. To learn more about your job execution results, you can simply run `$CLI_CMD logs ` -> If you no longer need any of the MNIST dataset we used in this example, you can simply delete the tmp repository. +> If you no longer need any of the MNIST dataset we used in this example, you can simply delete the `tmp` repository. 7. If you want to run your job via the FfDL UI, simply run the below command to create your model zip file. @@ -334,7 +334,7 @@ fi ``` 6. Now you should have all the necessary training data set in your training data bucket. Let's go ahead to set up your restapi endpoint -and default credentials for Deep Learning as a Service. Once you done that, you can start running jobs using the FfDL CLI(executable +and default credentials for Deep Learning as a Service. Once you done that, you can start running jobs using the FfDL CLI (executable binary). ```shell From 6281c1258a04ef062c9003dfdcaaabaeed75896e Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Wed, 4 Apr 2018 11:14:44 -0700 Subject: [PATCH 28/87] Creating CLA (#52) * Creating CLA * Update CLA.md * Update CONTRIBUTING.md --- CLA.md | 31 +++++++++++++++++++++++++++++++ CONTRIBUTING.md | 6 ++++-- 2 files changed, 35 insertions(+), 2 deletions(-) create mode 100644 CLA.md diff --git a/CLA.md b/CLA.md new file mode 100644 index 00000000..c6d7e0bb --- /dev/null +++ b/CLA.md @@ -0,0 +1,31 @@ +To contribute, please submit a Pull Request. As part of that, please a very simple step, include a line modeled after this line: “DCO 1.1 Signed-off-by: Random J Developer " as part of your pull request comments. By doing this one simple thing you are telling the community that you wrote the code you are contributing or have the right to pass on the code that you are contributing. The DCO 1.1 referenced is the Developer Certificate of Origin that is used in the Linux Kernel community, you can find it [here](https://elinux.org/Developer_Certificate_Of_Origin). +Thank you! + +Following is the reproduced text + +Developer's Certificate of Origin 1.1 + + By making a contribution to this project, I certify that: + + (a) The contribution was created in whole or in part by me and I + have the right to submit it under the open source license + indicated in the file; or + + (b) The contribution is based upon previous work that, to the best + of my knowledge, is covered under an appropriate open source + license and I have the right under that license to submit that + work with modifications, whether created in whole or in part + by me, under the same open source license (unless I am + permitted to submit under a different license), as indicated + in the file; or + + (c) The contribution was provided directly to me by some other + person who certified (a), (b) or (c) and I have not modified + it. + + (d) I understand and agree that this project and the contribution + are public and that a record of the contribution (including all + personal information I submit with it, including my sign-off) is + maintained indefinitely and may be redistributed consistent with + this project or the open source license(s) involved. + diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6cd88ef0..11978db3 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -3,9 +3,11 @@ This is an open source project, and we appreciate your help! We use the GitHub issue tracker to discuss new features and non-trivial bugs. To contribute code, documentation, or tests, please submit a pull request to -the GitHub repository. Generally, we expect two maintainers to review your pull +the GitHub repository. Please follow the guidelines specified in [CLA](MAINTAINERS.md) before submitting your pull request. + +Generally, we expect two maintainers to review your pull request before it is approved for merging. For more details, see the -[MAINTAINERS](MAINTAINERS.md) page. +[MAINTAINERS](MAINTAINERS.md) page. ## Contributors From 35450324d56e6d99b82163e192f1bc1c95c410dc Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Fri, 6 Apr 2018 14:13:12 -0700 Subject: [PATCH 29/87] fix logging bug (#53) --- .../{old-tf-model => tf-summary-model}/gpu-manifest.yml | 0 etc/examples/{old-tf-model => tf-summary-model}/input_data.py | 0 etc/examples/{old-tf-model => tf-summary-model}/keys.json | 0 etc/examples/{old-tf-model => tf-summary-model}/manifest.yml | 2 +- .../mnist_with_summaries.py | 0 etc/examples/{old-tf-model => tf-summary-model}/runtb.sh | 0 lcm/service/lcm/constants.go | 4 ++-- templates/services/lcm-deployment.yml | 2 -- templates/services/restapi-deployment.yml | 2 -- templates/services/trainer-deployment.yml | 2 -- templates/services/trainingdata-deployment.yml | 2 -- templates/services/web-ui.yml | 2 -- 12 files changed, 3 insertions(+), 13 deletions(-) rename etc/examples/{old-tf-model => tf-summary-model}/gpu-manifest.yml (100%) rename etc/examples/{old-tf-model => tf-summary-model}/input_data.py (100%) rename etc/examples/{old-tf-model => tf-summary-model}/keys.json (100%) rename etc/examples/{old-tf-model => tf-summary-model}/manifest.yml (98%) rename etc/examples/{old-tf-model => tf-summary-model}/mnist_with_summaries.py (100%) rename etc/examples/{old-tf-model => tf-summary-model}/runtb.sh (100%) diff --git a/etc/examples/old-tf-model/gpu-manifest.yml b/etc/examples/tf-summary-model/gpu-manifest.yml similarity index 100% rename from etc/examples/old-tf-model/gpu-manifest.yml rename to etc/examples/tf-summary-model/gpu-manifest.yml diff --git a/etc/examples/old-tf-model/input_data.py b/etc/examples/tf-summary-model/input_data.py similarity index 100% rename from etc/examples/old-tf-model/input_data.py rename to etc/examples/tf-summary-model/input_data.py diff --git a/etc/examples/old-tf-model/keys.json b/etc/examples/tf-summary-model/keys.json similarity index 100% rename from etc/examples/old-tf-model/keys.json rename to etc/examples/tf-summary-model/keys.json diff --git a/etc/examples/old-tf-model/manifest.yml b/etc/examples/tf-summary-model/manifest.yml similarity index 98% rename from etc/examples/old-tf-model/manifest.yml rename to etc/examples/tf-summary-model/manifest.yml index 98ce7ae7..4f858682 100644 --- a/etc/examples/old-tf-model/manifest.yml +++ b/etc/examples/tf-summary-model/manifest.yml @@ -3,7 +3,7 @@ description: Convolutional network model using tensorflow version: "1.0" gpus: 0 cpus: 0.5 -memory: 500MiB +memory: 1000MiB # Object stores that allow the system to retrieve training data. data_stores: diff --git a/etc/examples/old-tf-model/mnist_with_summaries.py b/etc/examples/tf-summary-model/mnist_with_summaries.py similarity index 100% rename from etc/examples/old-tf-model/mnist_with_summaries.py rename to etc/examples/tf-summary-model/mnist_with_summaries.py diff --git a/etc/examples/old-tf-model/runtb.sh b/etc/examples/tf-summary-model/runtb.sh similarity index 100% rename from etc/examples/old-tf-model/runtb.sh rename to etc/examples/tf-summary-model/runtb.sh diff --git a/lcm/service/lcm/constants.go b/lcm/service/lcm/constants.go index 375f0fd1..9d444d80 100644 --- a/lcm/service/lcm/constants.go +++ b/lcm/service/lcm/constants.go @@ -78,8 +78,8 @@ const ( loadModelMemInMB=50 loadTrainingDataMilliCPU=20 loadTrainingDataMemInMB=300 - logCollectorMilliCPU=20 - logCollectorMemInMB=100 + logCollectorMilliCPU=60 + logCollectorMemInMB=300 controllerMilliCPU=20 controllerMemInMB=100 ) diff --git a/templates/services/lcm-deployment.yml b/templates/services/lcm-deployment.yml index eecff855..f1c5270b 100644 --- a/templates/services/lcm-deployment.yml +++ b/templates/services/lcm-deployment.yml @@ -115,5 +115,3 @@ spec: - "grpc-health-checker --port 8443 --tls" initialDelaySeconds: 15 timeoutSeconds: 2 - imagePullSecrets: - - name: bluemix-cr-ng diff --git a/templates/services/restapi-deployment.yml b/templates/services/restapi-deployment.yml index e9bde634..7d298fac 100644 --- a/templates/services/restapi-deployment.yml +++ b/templates/services/restapi-deployment.yml @@ -53,5 +53,3 @@ spec: port: 8080 initialDelaySeconds: 30 timeoutSeconds: 10 - imagePullSecrets: - - name: bluemix-cr-ng diff --git a/templates/services/trainer-deployment.yml b/templates/services/trainer-deployment.yml index 2a410d89..b0a8fe3f 100644 --- a/templates/services/trainer-deployment.yml +++ b/templates/services/trainer-deployment.yml @@ -112,5 +112,3 @@ spec: - "grpc-health-checker --port 8443 --tls" initialDelaySeconds: 15 timeoutSeconds: 2 - imagePullSecrets: - - name: bluemix-cr-ng diff --git a/templates/services/trainingdata-deployment.yml b/templates/services/trainingdata-deployment.yml index 52033242..28282b2c 100644 --- a/templates/services/trainingdata-deployment.yml +++ b/templates/services/trainingdata-deployment.yml @@ -86,5 +86,3 @@ spec: # - "grpc-health-checker --port 8443 --tls" # initialDelaySeconds: 15 # timeoutSeconds: 2 - imagePullSecrets: - - name: bluemix-cr-ng diff --git a/templates/services/web-ui.yml b/templates/services/web-ui.yml index 9051a7c0..1501b3e7 100644 --- a/templates/services/web-ui.yml +++ b/templates/services/web-ui.yml @@ -27,8 +27,6 @@ spec: limits: cpu: {{.Values.webui.cpus}} memory: {{.Values.webui.memory}} - imagePullSecrets: - - name: bluemix-cr-ng --- From d242892ce52e177ae25be5112e6859f1be43cb03 Mon Sep 17 00:00:00 2001 From: Luciano Resende Date: Sat, 7 Apr 2018 21:39:21 -0700 Subject: [PATCH 30/87] Move DCO signoff as part of the PR Template (#58) --- .github/PULL_REQUEST_TEMPLATE | 28 ++++++++++++++++++++++++++++ CLA.md | 31 ------------------------------- CONTRIBUTING.md | 10 ++++++++-- 3 files changed, 36 insertions(+), 33 deletions(-) create mode 100644 .github/PULL_REQUEST_TEMPLATE delete mode 100644 CLA.md diff --git a/.github/PULL_REQUEST_TEMPLATE b/.github/PULL_REQUEST_TEMPLATE new file mode 100644 index 00000000..e7eb9261 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE @@ -0,0 +1,28 @@ + + + +Developer's Certificate of Origin 1.1 + + By making a contribution to this project, I certify that: + + (a) The contribution was created in whole or in part by me and I + have the right to submit it under the Apache License 2.0; or + + (b) The contribution is based upon previous work that, to the best + of my knowledge, is covered under an appropriate open source + license and I have the right under that license to submit that + work with modifications, whether created in whole or in part + by me, under the same open source license (unless I am + permitted to submit under a different license), as indicated + in the file; or + + (c) The contribution was provided directly to me by some other + person who certified (a), (b) or (c) and I have not modified + it. + + (d) I understand and agree that this project and the contribution + are public and that a record of the contribution (including all + personal information I submit with it, including my sign-off) is + maintained indefinitely and may be redistributed consistent with + this project or the open source license(s) involved. + diff --git a/CLA.md b/CLA.md deleted file mode 100644 index c6d7e0bb..00000000 --- a/CLA.md +++ /dev/null @@ -1,31 +0,0 @@ -To contribute, please submit a Pull Request. As part of that, please a very simple step, include a line modeled after this line: “DCO 1.1 Signed-off-by: Random J Developer " as part of your pull request comments. By doing this one simple thing you are telling the community that you wrote the code you are contributing or have the right to pass on the code that you are contributing. The DCO 1.1 referenced is the Developer Certificate of Origin that is used in the Linux Kernel community, you can find it [here](https://elinux.org/Developer_Certificate_Of_Origin). -Thank you! - -Following is the reproduced text - -Developer's Certificate of Origin 1.1 - - By making a contribution to this project, I certify that: - - (a) The contribution was created in whole or in part by me and I - have the right to submit it under the open source license - indicated in the file; or - - (b) The contribution is based upon previous work that, to the best - of my knowledge, is covered under an appropriate open source - license and I have the right under that license to submit that - work with modifications, whether created in whole or in part - by me, under the same open source license (unless I am - permitted to submit under a different license), as indicated - in the file; or - - (c) The contribution was provided directly to me by some other - person who certified (a), (b) or (c) and I have not modified - it. - - (d) I understand and agree that this project and the contribution - are public and that a record of the contribution (including all - personal information I submit with it, including my sign-off) is - maintained indefinitely and may be redistributed consistent with - this project or the open source license(s) involved. - diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 11978db3..2a90798a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -3,11 +3,17 @@ This is an open source project, and we appreciate your help! We use the GitHub issue tracker to discuss new features and non-trivial bugs. To contribute code, documentation, or tests, please submit a pull request to -the GitHub repository. Please follow the guidelines specified in [CLA](MAINTAINERS.md) before submitting your pull request. +the GitHub repository. The pull request template includes a disclaimer based +on the [Developer's Certificate of Origin 1.1](https://elinux.org/Developer_Certificate_Of_Origin) +and provides your assurance to the community that you wrote the code you are +contributing or have the right to pass on the code that you are contributing. + +Please follow the guidelines specified in [MAINTAINERS](MAINTAINERS.md) before +submitting your pull request. Generally, we expect two maintainers to review your pull request before it is approved for merging. For more details, see the -[MAINTAINERS](MAINTAINERS.md) page. +[MAINTAINERS](MAINTAINERS.md) page. ## Contributors From a7a7ded2b44e8f855c440b4f2c2fb129b9f1ee78 Mon Sep 17 00:00:00 2001 From: Luciano Resende Date: Sat, 7 Apr 2018 23:21:53 -0700 Subject: [PATCH 31/87] Update LICENSE and generate NOTICE file (#59) Some dependencies used in FfDL and included as source are not licensed as AL2 and thus we should add them explicitly in the main LICENSE file. Other dependencies require attribution, and thus mention it on NOTICE file. --- LICENSE | 65 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ NOTICE | 10 +++++++++ 2 files changed, 75 insertions(+) create mode 100644 NOTICE diff --git a/LICENSE b/LICENSE index 261eeb9e..22de1d99 100644 --- a/LICENSE +++ b/LICENSE @@ -199,3 +199,68 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. + + ---------------------------------------------------------------------------- + + File : restapi/middleware/response_writer.go + Source : https://github.com/urfave/negroni/blob/master/response_writer.go + + The MIT License (MIT) + + Copyright (c) 2014 Jeremy Saenz + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. + + ---------------------------------------------------------------------------- + Font Awesome: dashboard/src/assets/font-awesome + + Font Awesome Free License + ------------------------- + + Font Awesome Free is free, open source, and GPL friendly. You can use it for + commercial projects, open source projects, or really almost whatever you want. + Full Font Awesome Free license: https://fontawesome.com/license. + + # Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/) + In the Font Awesome Free download, the CC BY 4.0 license applies to all icons + packaged as SVG and JS file types. + + # Fonts: SIL OFL 1.1 License (https://scripts.sil.org/OFL) + In the Font Awesome Free download, the SIL OLF license applies to all icons + packaged as web and desktop font files. + + # Code: MIT License (https://opensource.org/licenses/MIT) + In the Font Awesome Free download, the MIT license applies to all non-font and + non-icon files. + + # Attribution + Attribution is required by MIT, SIL OLF, and CC BY licenses. Downloaded Font + Awesome Free files already contain embedded comments with sufficient + attribution, so you shouldn't need to do anything additional when using these + files normally. + + We've kept attribution comments terse, so we ask that you do not actively work + to remove them from files, especially code. They're a great way for folks to + learn about Font Awesome. + + # Brand Icons + All brand icons are trademarks of their respective owners. The use of these + trademarks does not indicate endorsement of the trademark holder by Font + Awesome, nor vice versa. **Please do not use brand logos for any purpose except + to represent the company, product, or service to which they refer.** diff --git a/NOTICE b/NOTICE new file mode 100644 index 00000000..cd4d3faf --- /dev/null +++ b/NOTICE @@ -0,0 +1,10 @@ +FfDL - Fabric for Deep Learning +Copyright 2017-2018 IBM Corporation + +This product includes MNIST cafe2-model example +Copyright (c) 2016-present, Facebook, Inc. + +This product includes MNIST tf-summary-model example +Copyright 2015 The TensorFlow Authors. All Rights Reserved. + + From 6c8326804e968ccc92b9506778578a003497c424 Mon Sep 17 00:00:00 2001 From: Tommy Li Date: Wed, 11 Apr 2018 15:47:03 -0700 Subject: [PATCH 32/87] fix dependencies issue (#63) * fix dependencies issue * update prereq --- Makefile | 6 ++++-- README.md | 2 +- templates/infrastructure/storage.yml | 2 +- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index b0be21c0..cabc92cb 100644 --- a/Makefile +++ b/Makefile @@ -22,6 +22,8 @@ UI_REPO = git@github.com:IBM/FfDL-dashboard.git CLI_CMD = $(shell pwd)/cli/bin/ffdl-$(UNAME_SHORT) CLUSTER_NAME ?= mycluster PUBLIC_IP ?= 127.0.0.1 +CI_MINIKUBE_VERSION ?= v0.25.1 +CI_KUBECTL_VERSION ?= v1.9.4 IMAGE_DIR := $(IMAGE_NAME) ifneq ($(filter $(IMAGE_NAME),controller ),) @@ -229,9 +231,9 @@ kubernetes-ip: install-minikube-in-ci: @echo "Starting local kubernetes cluster (minikube)" - @curl -s -Lo kubectl https://storage.googleapis.com/kubernetes-release/release/v1.7.0/bin/linux/amd64/kubectl && \ + @curl -s -Lo kubectl https://storage.googleapis.com/kubernetes-release/release/$(CI_KUBECTL_VERSION)/bin/linux/amd64/kubectl && \ chmod +x kubectl && sudo mv kubectl /usr/local/bin/ - @curl -s -Lo minikube https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64 && \ + @curl -s -Lo minikube https://storage.googleapis.com/minikube/releases/$(CI_MINIKUBE_VERSION)/minikube-linux-amd64 && \ chmod +x minikube && sudo mv minikube /usr/local/bin/ @sudo minikube start --vm-driver=none @minikube update-context diff --git a/README.md b/README.md index 5512d1d3..4d8d6357 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ FfDL is a collaboration platform for: * `S3 CLI`: The [command-line interface](https://aws.amazon.com/cli/) to configure your Object Storage * An existing Kubernetes cluster (e.g., [Minikube](https://github.com/kubernetes/minikube) for local testing). - Once installed, use the command `make minikube` to start Minikube and set up local network routes. + For Minikube, use the command `make minikube` to start Minikube and set up local network routes. Minikube **v0.25.1** is tested with Travis CI. * Follow the appropriate instructions for standing up your Kubernetes cluster using [IBM Cloud Public](https://github.com/IBM/container-journey-template/blob/master/README.md) or [IBM Cloud Private](https://github.com/IBM/deploy-ibm-cloud-private/blob/master/README.md) diff --git a/templates/infrastructure/storage.yml b/templates/infrastructure/storage.yml index a8e88498..bc3b2138 100644 --- a/templates/infrastructure/storage.yml +++ b/templates/infrastructure/storage.yml @@ -49,7 +49,7 @@ spec: spec: containers: - name: storage - image: localstack/localstack + image: localstack/localstack:0.8.6 {{ if .Values.docker.pullPolicy }} imagePullPolicy: {{.Values.docker.pullPolicy}} {{ end }} From 50c4609e3cb797009908d52f8b70735f91e9ed12 Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Thu, 19 Apr 2018 13:10:53 -0700 Subject: [PATCH 33/87] Compiling a list of Models we can train - expect this to grow (#66) * sample models * Update models.md * Update models.md --- samples/models.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 samples/models.md diff --git a/samples/models.md b/samples/models.md new file mode 100644 index 00000000..aa2b05a8 --- /dev/null +++ b/samples/models.md @@ -0,0 +1,35 @@ +# Sample Models + +We will keep this as a running list of models being trained using Deep Learning as a Service within Watson Studio and FfDL, as long as we can point to external open source references. + +To run these experiments using Fabric for Deep Learning (FFDL), you can simply clone the FfDL repository and follow the instructions over [here](https://github.com/IBM/FfDL/blob/master/etc/converter/ffdl-wml.md) to convert your training-runs.yml into FfDL's specification. + +## Model Asset Exchange Models + + +### 1. IBM Code Model Asset Exchange + +#### Adversarial Cryptography Experiment + +This repository contains code to run an Adversarial-Crypto experiment on IBM Watson Machine Learning. This experiment performs adversarial training to learn trivial encryption functions. The experiment is based on the 2016 paper "Learning to Protect Communications with Adversarial Neural Cryptography" by Abadi and Andersen. + +[MAX - Adversarial Cryptography Experiment](https://github.com/IBM/MAX-Adversarial-Cryptography) + +#### Spatial Transformer Network + +This repository contains code to train and score a Spatial Transformer Network/ + +[MAX-Spatial Transformer Network](https://github.com/IBM/MAX-Spatial-Transformer-Network) + + +### 2. CIFAR10 with PyTorch + +The code here is forked from [kuangliu work with PyTorch on the CIFAR10 dataset](https://github.com/kuangliu/pytorch-cifar), and adapted for submitting the model to Watson Studio for training. It is meant to get you quick-started. + +[CIFAR10 with PyTorch](https://github.com/IBM/pytorch-cifar10-in-ibm-cloud) + +### 3. Adversarial Robustness Toolbox (ART v0.1) + +This is a library dedicated to adversarial machine learning. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers. We will be adding support here for running these experiments on FfDL + +[Adversarial Robustness Toolbox (ART v0.1)](https://github.com/IBM/adversarial-robustness-toolbox) From dbb096ff847657d4a9dde2e104d97a783cc8510b Mon Sep 17 00:00:00 2001 From: Animesh Singh Date: Thu, 19 Apr 2018 15:46:38 -0700 Subject: [PATCH 34/87] Design details (#67) * design-details * design-details * design-details --- README.md | 2 ++ design/design_docs.md | 33 +++++++++++++++++++++++++++++++++ docs/images/ffdl-arch-web.png | Bin 0 -> 195699 bytes docs/images/ffdl-overview.png | Bin 0 -> 211200 bytes 4 files changed, 35 insertions(+) create mode 100644 design/design_docs.md create mode 100644 docs/images/ffdl-arch-web.png create mode 100644 docs/images/ffdl-overview.png diff --git a/README.md b/README.md index 4d8d6357..ab4e2ade 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,8 @@ FfDL is a collaboration platform for: ![ffdl-architecture](docs/images/ffdl-architecture.png) +To know more about the architectural details, please read [here](design/design_docs.md) + ## Prerequisites * `kubectl`: The Kubernetes command line interface (https://kubernetes.io/docs/tasks/tools/install-kubectl/) diff --git a/design/design_docs.md b/design/design_docs.md new file mode 100644 index 00000000..d38f16ee --- /dev/null +++ b/design/design_docs.md @@ -0,0 +1,33 @@ +# FfDL Design Considerations + +Training deep neural network models requires a highly tuned system with the right combination of software, drivers, compute, memory, network, and storage resources. To address the challenges around obtaining and managing these resources, we are happy to announce the launch of Fabric for Deep Learning (FfDL). + +FfDL offers a stack that abstracts away these concerns so data scientists can execute training jobs with their choice of deep learning framework at scale in the cloud. It has been built to offer resilience, scalability, multi-tenancy, and security without modifying the deep learning frameworks, and with no or minimal changes to model code. + +![ffdl-overview](../docs/images/ffdl-overview.png) + +## FfDL architecture +The FfDL platform uses a microservices architecture, with a focus on scalability, resiliency, and fault tolerance. According to one IDC survey, by 2021 enterprise apps will shift toward hyper-agile architectures, with 80% of application development on cloud platforms using microservices and functions, and over 95% of new microservices deployed in containers. And what better cloud native platform to build on than Kubernetes? The FfDL control plane microservices are deployed as pods, and we rely on Kubernetes to manage this cluster of GPU- and CPU-enabled machines effectively, to restart microservices when they crash, and to report the health of microservices. + + +![ffdl-arch](../docs/images/ffdl-architecture.png) + + +### REST API +The REST API microservice handles REST-level HTTP requests and acts as proxy to the lower-level gRPC Trainer service. The service also load-balances requests and is responsible for authentication. Load balancing is implemented by registering the REST API service instances dynamically in a service registry. The interface is specified through a Swagger definition file. + +### Trainer +The Trainer service admits training job requests, persisting metadata and model input configuration in a database (MongoDB). It initiates job deployment, halting, and (user-requested) job termination by calling the appropriate gRPC methods on the Lifecycle Manager microservice. The Trainer also assigns a unique identifier to each job, which is used by all other components to track the job. + +### Lifecycle Manager and learner pods +The Lifecycle Manager (LCM) deploys training jobs arriving from the Trainer, halting (pausing) and terminating training jobs. LCM uses the Kubernetes cluster manager to deploy containerized training jobs. A training job is a set of interconnected Kubernetes pods, each containing one or more Docker containers. + +The LCM determines the learner pods, parameter servers, and interconnections among them based on the job configuration, and calls on Kubernetes for deployment. For example, if a user creates a Caffe2 training job with four learners and two CPUs/GPUs per learner, the LCM creates five pods: one for each learner (called the learner pod), and one monitoring pod called the job monitor. + +### Training Data Service +The Training Data Service (TDS) provides short-lived storage and retrieval for logs and evaluation data from a Deep Learning training job. As the training job progresses, information is needed for evaluation of the ongoing success or failure of the learning progress. These metrics normally come in the form of scalar values, and are termed evaluation metrics (or sometimes the term emetrics might be used). Debugging information can also be output through log lines. + +While the learning job is running, a process runs as a sidecar to extract the training data from the learner, and then pushes that data into the TDS, which pushes the data into ElasticSearch. The sidecars used for collecting training data are termed log-collectors. Depending on the framework and desired extraction method, different types of log-collectors can be used. Log-collectors are somewhat misnamed, since their responsibilities include at least both log line collection, and evaluation metrics extraction. + +## FfDL forms the core of Watson Studio Deep Learning Service +FfDL, developed in close collaboration with IBM Research and Watson product development teams, forms the core of our newly announced Deep Learning as a Service within Watson Studio. 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