From 52d9237a489c7ac8e24b6a7ea6410599d9caa71f Mon Sep 17 00:00:00 2001 From: nsd9696 Date: Sat, 8 Mar 2025 02:22:51 -0800 Subject: [PATCH 1/2] VESSL llm finetuning agent --- .../vessl-llm-finetuning-agent/README.md | 0 .../vessl-llm-finetuning-agent/pyproject.toml | 23 + .../vessl-llm-finetuning-agent/settings.toml | 0 .../vessl-llm-finetuning-agent/uv.lock | 2220 +++++++++++++++++ .../vessl_llm_finetuning_agent/__init__.py | 0 .../vessl_llm_finetuning_agent/agent.py | 330 +++ .../vessl_llm_finetuning_agent/util.py | 28 + libs/docks/hyperdock-container/pyproject.toml | 2 +- libs/docks/hyperdock-container/uv.lock | 18 +- libs/hyperpocket/pyproject.toml | 2 +- libs/hyperpocket/uv.lock | 6 +- 11 files changed, 2621 insertions(+), 8 deletions(-) create mode 100644 examples/langchain/vessl-llm-finetuning-agent/README.md create mode 100644 examples/langchain/vessl-llm-finetuning-agent/pyproject.toml create mode 100644 examples/langchain/vessl-llm-finetuning-agent/settings.toml create mode 100644 examples/langchain/vessl-llm-finetuning-agent/uv.lock create mode 100644 examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/__init__.py create mode 100644 examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py create mode 100644 examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/util.py diff --git a/examples/langchain/vessl-llm-finetuning-agent/README.md b/examples/langchain/vessl-llm-finetuning-agent/README.md new file mode 100644 index 00000000..e69de29b diff --git a/examples/langchain/vessl-llm-finetuning-agent/pyproject.toml b/examples/langchain/vessl-llm-finetuning-agent/pyproject.toml new file mode 100644 index 00000000..c92db2d3 --- /dev/null +++ b/examples/langchain/vessl-llm-finetuning-agent/pyproject.toml @@ -0,0 +1,23 @@ +[project] +name = "vessl-llm-finetuning-agent" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + 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b/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py b/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py new file mode 100644 index 00000000..58ef36f4 --- /dev/null +++ b/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py @@ -0,0 +1,330 @@ +import json +import os +import subprocess +from typing import Dict + +import requests +import yaml +from huggingface_hub import hf_hub_download +from langchain.agents import AgentExecutor, create_tool_calling_agent +from langchain.memory import ConversationBufferMemory +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_openai import ChatOpenAI +from openai import OpenAI +from pydantic import Field + +from hyperpocket.tool import function_tool +from hyperpocket_langchain import PocketLangchain +from util import vessl_configure +import vessl + +# VESSL configuration +VESSL_CONFIG = { + "organization_name": "Your Organization Name", + "project_name": "Your Project Name", + "storage_name": "Your Storage Name", + "volume_name": "Your Volume Name" +} + +# GPU configuration +GPU_CONFIG = { + "cluster_name": "Your gpu cluster name", + "preset": "Your gpu preset name", + "memory": 84557184, + "number": 2 +} + +access_token = vessl_configure( + organization_name=VESSL_CONFIG["organization_name"], + project_name=VESSL_CONFIG["project_name"], + force_update_access_token=False +) + +@function_tool +def get_model_config( + base_model_id: str = Field(default="", description="The Hugging Face model ID to get configuration for") +) -> Dict: + """Get the model configuration from Hugging Face.""" + model_safetensors_index_json = 'model.safetensors.index.json' + model_config_json = 'config.json' + + config_path = hf_hub_download(repo_id=base_model_id, filename=model_config_json) + with open(config_path, 'r', encoding='utf-8') as file: + config_content = json.loads(file.read()) + + model_safetensors_path = hf_hub_download(repo_id=base_model_id, filename=model_safetensors_index_json) + with open(model_safetensors_path, 'r', encoding='utf-8') as file: + model_safetensors_content = json.loads(file.read()) + + config_content['total_model_size'] = model_safetensors_content['metadata']['total_size'] + + os.remove(config_path) + os.remove(model_safetensors_path) + + return config_content + +@function_tool +def get_dataset_info( + dataset_name: str = Field(default="", description="The Hugging Face dataset name to get information for") +) -> Dict: + """Get dataset information from Hugging Face.""" + response = requests.get( + "https://datasets-server.huggingface.co/rows", + params={ + "dataset": dataset_name, + "config": "default", + "split": "train", + "offset": "0", + "length": "1", + }, + timeout=30 + ) + features = response.json()['features'] + + api_url = f"https://datasets-server.huggingface.co/info?dataset={dataset_name}" + response = requests.get(api_url, timeout=30) + info = response.json() + info['features'] = features + + return info + +@function_tool +def construct_finetuning_yaml( + training_yaml_file_name: str = Field(default="", description="Name of the YAML file to create"), + dataset_name: str = Field(default="", description="Name of the dataset to use for training"), + base_model_id: str = Field(default="", description="ID of the base model to fine-tune"), + finetuning_output_dir: str = Field(default="", description="Directory to store fine-tuning outputs"), + base_model_config: str = Field(default="", description="Base Model configuration as a string"), + dataset_info: str = Field(default="", description="Dataset information as a string"), + gpu_memory: int = Field(default=0, description="Available GPU memory in MB"), + gpu_number: int = Field(default=1, description="Number of GPUs to use"), +) -> str: + """Construct the fine-tuning YAML configuration file.""" + prompt = f""" +Generate a YAML file for fine-tuning an LLM using Hugging Face's Autotrain. +The YAML should be formatted correctly and contain only the YAML content without additional text. +Ensure the configuration follows the GPU memory optimization guidelines and dynamically adjusts parameters as needed: + +[Guidelines for GPU memory optimization] +- block_size: Should be adjusted based on available GPU memory and model requirements +- model_max_length: Match max_position_embeddings (131072) to prevent truncation +- batch_size: Should be determined by available GPU memory and adjusted dynamically +- gradient_accumulation: Adjust to balance memory usage and training efficiency +- mixed_precision: Use bf16 for NVIDIA GPUs that support it, otherwise consider fp16 +- deepspeed: Use stage2_offload or other optimizations to manage large model training + +[GPU Memory] +- GPU Number: {gpu_number} +- GPU Memory Size: {gpu_memory} + +[Dataset Info] +{dataset_info} + +[Model Configuration] +{base_model_config} + +[Autotrain Template] +task: vessl-llm-finetuning +base_model: {base_model_id} +project_name: {finetuning_output_dir} +log: tensorboard +backend: local + +data: + path: {dataset_name} + train_split: train + valid_split: null + chat_template: chatml + column_mapping: + text_column: model_answer + rejected_text_column: reference_answer + prompt_text_column: question + +params: + block_size: 8192 + model_max_length: 131072 + epochs: 2 + batch_size: 4 + lr: 1e-5 + peft: true + quantization: null + target_modules: all-linear + padding: right + optimizer: paged_adamw_8bit + scheduler: linear + gradient_accumulation: 8 + mixed_precision: bf16 + deepspeed: stage2_offload + fp16: false + bf16: true + seed: 42 + save_total_limit: 2 + save_steps: 1000 + eval_steps: 500 + logging_steps: 50 + warmup_ratio: 0.03 + weight_decay: 0.01 + max_grad_norm: 1.0 + +Only return valid YAML output, nothing else. +Do not change fields of base_model, project_name and path in YAML. +Do not include any other fields in the YAML output. +""" + if not os.getenv("OPENAI_API_KEY"): + raise ValueError("OPENAI_API_KEY environment variable is not set") + + client = OpenAI() + response = client.chat.completions.create( + model="o3-mini", + messages=[ + {"role": "system", "content": "You are an AI assistant that outputs only YAML files."}, + {"role": "user", "content": prompt}, + ], + ) + + yaml_output = response.choices[0].message.content.strip() + + try: + yaml.safe_load(yaml_output) + with open(training_yaml_file_name, "w", encoding='utf-8') as f: + f.write(yaml_output) + except yaml.YAMLError as e: + print("Invalid YAML generated:", e) + + return yaml_output + +def copy_file(source: str, dest: str) -> None: + """Copy a file between local and VESSL storage.""" + try: + subprocess.run(["vessl", "storage", "copy-file", source, dest], check=True, capture_output=True, text=True) + except subprocess.CalledProcessError as e: + raise RuntimeError(f"Failed to copy file: {e.stderr}") + +@function_tool +def copy_file_local_to_storage( + local_file_name: str = Field(default="", description="Local file path to copy from"), + storage_name: str = Field(default="", description="Name of the storage to copy to"), + volume_name: str = Field(default="", description="Name of the volume in storage") +) -> str: + """Copy a file from local to VESSL storage.""" + if not os.path.exists(local_file_name): + raise FileNotFoundError(f"Local file {local_file_name} does not exist") + + copy_file(local_file_name, f"volume://{storage_name}/{volume_name}") + return "success copy file local to storage" + +@function_tool +def create_run( + cluster_name: str = Field(default="", description="Name of the cluster to run on"), + preset: str = Field(default="", description="Preset configuration name"), + training_yaml_file_name: str = Field(default="", description="Name of the training YAML file"), + storage_name: str = Field(default="", description="Name of the storage"), + volume_name: str = Field(default="", description="Name of the volume"), + run_yaml_file_name: str = Field(default="", description="Name of the run YAML file") +) -> str: + """Create and start a VESSL run.""" + if not os.path.exists(training_yaml_file_name): + raise FileNotFoundError(f"Training YAML file {training_yaml_file_name} does not exist") + + run_yaml = f""" +name: vessl-llm-autotrain-finetuning +import: + /root/: volume://{storage_name}/{volume_name} +export: + /root/: volume://{storage_name} +resources: + cluster: {cluster_name} + preset: {preset} +image: quay.io/vessl-ai/torch:2.3.1-cuda12.1-r5 +run: + - | + pip install autotrain-advanced + - autotrain --config {training_yaml_file_name} +""" + + with open(run_yaml_file_name, "w", encoding='utf-8') as f: + f.write(run_yaml) + + with open(run_yaml_file_name, "r", encoding='utf-8') as yaml_file: + vessl.create_run( + yaml_file=yaml_file, + yaml_body="", + yaml_file_name=run_yaml_file_name + ) + + return "success create run" + +def agent(pocket: PocketLangchain) -> None: + """Initialize and run the LangChain agent.""" + tools = pocket.get_tools() + + if not os.getenv("OPENAI_API_KEY"): + raise ValueError("OPENAI_API_KEY environment variable is not set") + + llm = ChatOpenAI(model="gpt-4o") + + prompt = ChatPromptTemplate.from_messages([ + ("placeholder", "{chat_history}"), + ( + "system", + f""" + You are a agent who automatically creates a vessl run for finetuning an LLM. + GPU cluster name: {GPU_CONFIG["cluster_name"]} + GPU preset: {GPU_CONFIG["preset"]} + GPU memory: {GPU_CONFIG["memory"]} + GPU number: {GPU_CONFIG["number"]} + + VESSL ORGANIZATION NAME: {VESSL_CONFIG["organization_name"]} + VESSL PROJECT NAME: {VESSL_CONFIG["project_name"]} + VESSL STORAGE NAME: {VESSL_CONFIG["storage_name"]} + VESSL VOLUME NAME: {VESSL_CONFIG["volume_name"]} + + You will be given base_model_id and dataset_name. + You will first get the base model config and dataset info. + Then you will construct the finetuning yaml file. + You will also copy the finetuning yaml file to the storage. + After that, you will create a vessl run. + """, + ), + ("placeholder", "{chat_history}"), + ("user", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ]) + + memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) + agent = create_tool_calling_agent(llm, tools, prompt) + agent_executor = AgentExecutor( + agent=agent, + tools=tools, + memory=memory, + verbose=True, + handle_parsing_errors=True, + ) + + print("\n\n\n") + print("Hello, this is your VESSL agent.") + while True: + print("user(q to quit) : ", end="") + user_input = input() + if user_input == "q": + print("Good bye!") + break + + response = agent_executor.invoke({"input": user_input}) + print("slack agent : ", response["output"]) + print() + + +if __name__ == "__main__": + # model_repo_id = "HuggingFaceTB/SmolLM2-1.7B-Instruct" + # dataset_name = "HuggingFaceH4/no_robots" + with PocketLangchain( + tools=[ + construct_finetuning_yaml, + copy_file_local_to_storage, + create_run, + get_model_config, + get_dataset_info, + ], + ) as pocket: + agent(pocket) diff --git a/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/util.py b/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/util.py new file mode 100644 index 00000000..78acf016 --- /dev/null +++ b/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/util.py @@ -0,0 +1,28 @@ +import vessl +from vessl.util.config import VesslConfigLoader +from vessl.util.exception import VesslApiException + + +def vessl_configure( + organization_name: str, + project_name: str, + force_update_access_token: bool = False +) -> str: + """Configure VESSL settings and return access token. + + Args: + organization_name: Name of the VESSL organization + project_name: Name of the VESSL project + force_update_access_token: Whether to force update the access token + + Returns: + str: VESSL access token + """ + vessl.configure( + organization_name=organization_name, + project_name=project_name, + force_update_access_token=force_update_access_token, + ) + config = VesslConfigLoader() + return config.access_token + diff --git a/libs/docks/hyperdock-container/pyproject.toml b/libs/docks/hyperdock-container/pyproject.toml index d1316344..b9084a64 100644 --- a/libs/docks/hyperdock-container/pyproject.toml +++ b/libs/docks/hyperdock-container/pyproject.toml @@ -6,7 +6,7 @@ readme = "README.md" requires-python = ">=3.10" dependencies = [ "hyperpocket", - "pydantic>=2.10.6", + "pydantic>=2.8.2", "docker>=7.1.0", ] diff --git a/libs/docks/hyperdock-container/uv.lock b/libs/docks/hyperdock-container/uv.lock index 4b9642b6..1fe97ec7 100644 --- a/libs/docks/hyperdock-container/uv.lock +++ b/libs/docks/hyperdock-container/uv.lock @@ -166,7 +166,7 @@ name = "click" version = "8.1.8" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 } wheels = [ @@ -413,12 +413,12 @@ dependencies = [ requires-dist = [ { name = "docker", specifier = ">=7.1.0" }, { name = "hyperpocket", editable = "../../hyperpocket" }, - { name = "pydantic", specifier = ">=2.10.6" }, + { name = "pydantic", specifier = ">=2.8.2" }, ] [[package]] name = "hyperpocket" -version = "0.3.1" +version = "0.3.6" source = { editable = "../../hyperpocket" } dependencies = [ { name = "click" }, @@ -428,6 +428,7 @@ dependencies = [ { name = "httpx" }, { name = "jinja2" }, { name = "multiprocess" }, + { name = "nest-asyncio" }, { name = "playwright" }, { name = "pydantic" }, { name = "pygithub" }, @@ -445,8 +446,10 @@ requires-dist = [ { name = "fastapi", specifier = ">=0.115.5" }, { name = "gitpython", specifier = ">=3.1.43" }, { name = "httpx", specifier = "==0.27" }, + { name = "hyperdock-container", marker = "extra == 'standard'", editable = "." }, { name = "jinja2", specifier = ">=3.1.4" }, { name = "multiprocess", specifier = ">=0.70.17" }, + { name = "nest-asyncio", specifier = ">=1.6.0" }, { name = "playwright", specifier = ">=1.49.0" }, { name = "pydantic", specifier = ">=2.10.2" }, { name = "pygithub", specifier = ">=2.5.0" }, @@ -563,6 +566,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f9/41/0618ac724b8a56254962c143759e04fa01c73b37aa69dd433f16643bd38b/multiprocess-0.70.17-py39-none-any.whl", hash = "sha256:c3feb874ba574fbccfb335980020c1ac631fbf2a3f7bee4e2042ede62558a021", size = 133359 }, ] +[[package]] +name = "nest-asyncio" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 }, +] + [[package]] name = "playwright" version = "1.50.0" diff --git a/libs/hyperpocket/pyproject.toml b/libs/hyperpocket/pyproject.toml index 8d0a1ae6..a6917ae9 100644 --- a/libs/hyperpocket/pyproject.toml +++ b/libs/hyperpocket/pyproject.toml @@ -6,7 +6,7 @@ authors = [{ name = "Hyperpocket Team", email = "hyperpocket@vessl.ai" }] requires-python = ">=3.10" readme = "README.md" dependencies = [ - "pydantic>=2.10.2", + "pydantic>=2.8.2", "fastapi>=0.115.5", "uvicorn>=0.32.1", "click>=8.1.7", diff --git a/libs/hyperpocket/uv.lock b/libs/hyperpocket/uv.lock index e6b1d430..bb995563 100644 --- a/libs/hyperpocket/uv.lock +++ b/libs/hyperpocket/uv.lock @@ -166,7 +166,7 @@ name = "click" version = "8.1.8" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 } wheels = [ @@ -477,7 +477,7 @@ dependencies = [ requires-dist = [ { name = "docker", specifier = ">=7.1.0" }, { name = "hyperpocket", editable = "." }, - { name = "pydantic", specifier = ">=2.10.6" }, + { name = "pydantic", specifier = ">=2.8.2" }, ] [[package]] @@ -527,7 +527,7 @@ requires-dist = [ { name = "multiprocess", specifier = ">=0.70.17" }, { name = "nest-asyncio", specifier = ">=1.6.0" }, { name = "playwright", specifier = ">=1.49.0" }, - { name = "pydantic", specifier = ">=2.10.2" }, + { name = "pydantic", specifier = ">=2.8.2" }, { name = "pygithub", specifier = ">=2.5.0" }, { name = "python-multipart", specifier = ">=0.0.19" }, { name = "redis", specifier = ">=5.2.1" }, From c7e04fdf58c6e1a9305bdbabd047322ec1222523 Mon Sep 17 00:00:00 2001 From: nsd9696 Date: Sat, 8 Mar 2025 02:33:01 -0800 Subject: [PATCH 2/2] code refactor --- .../vessl_llm_finetuning_agent/agent.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py b/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py index 58ef36f4..202232fe 100644 --- a/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py +++ b/examples/langchain/vessl-llm-finetuning-agent/vessl_llm_finetuning_agent/agent.py @@ -30,8 +30,8 @@ GPU_CONFIG = { "cluster_name": "Your gpu cluster name", "preset": "Your gpu preset name", - "memory": 84557184, - "number": 2 + "memory": 84557184, # Memory size in MB + "number": 2 # Number of GPUs } access_token = vessl_configure( @@ -311,7 +311,7 @@ def agent(pocket: PocketLangchain) -> None: break response = agent_executor.invoke({"input": user_input}) - print("slack agent : ", response["output"]) + print("vessl agent : ", response["output"]) print() @@ -320,11 +320,11 @@ def agent(pocket: PocketLangchain) -> None: # dataset_name = "HuggingFaceH4/no_robots" with PocketLangchain( tools=[ + get_model_config, + get_dataset_info, construct_finetuning_yaml, copy_file_local_to_storage, create_run, - get_model_config, - get_dataset_info, ], ) as pocket: agent(pocket)