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[chore] Update openYuanrong related expression #96

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0oshowero0 merged 1 commit into from
May 13, 2026
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[chore] Update openYuanrong related expression #96

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -38,7 +38,7 @@ TransferQueue offers **fine-grained, sub-sample-level** data management and **lo
- **Dec 30, 2025**: **TransferQueue x verl** integration has been tested with the DAPO algorithm at scale **(64 nodes, 1024 cards)**. It significantly optimizes host memory utilization and accelerates data transfers. Stay tuned for more details!
- **Dec 20, 2025**: 🔥 The official [tutorial](https://github.com/Ascend/TransferQueue/tree/main/tutorial) is released! Feel free to check it out.
- **Nov 10, 2025**: We disentangled the data retrieval logic from TransferQueueController [PR#101](https://github.com/TransferQueue/TransferQueue/pull/101). Now you can implement your own `Sampler` to customize data consumption.
- **Nov 5, 2025**: We provide a `KVStorageManager` that simplifies the integration with KV-based storage backends [PR#96](https://github.com/TransferQueue/TransferQueue/pull/96). The first available KV-based backend is [Yuanrong](https://gitcode.com/openeuler/yuanrong-datasystem).
- **Nov 5, 2025**: We provide a `KVStorageManager` that simplifies the integration with KV-based storage backends [PR#96](https://github.com/TransferQueue/TransferQueue/pull/96). The first available KV-based backend is [openYuanrong](https://gitcode.com/openeuler/yuanrong-datasystem).
- **Nov 4, 2025**: Data partitioning capability is available in [PR#98](https://github.com/TransferQueue/TransferQueue/pull/98). Now you can define logical data partitions to manage your train/val/test datasets.
- **Oct 25, 2025**: Storage backends are now pluggable in [PR#66](https://github.com/TransferQueue/TransferQueue/pull/66). You can try to integrate your own storage backend with TransferQueue now!
- **Oct 21, 2025**: Early integration with verl is ready [verl/pull/3649](https://github.com/volcengine/verl/pull/3649). Following PRs will optimize the single controller architecture by fully decoupling data & control flows.
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4 changes: 2 additions & 2 deletions scripts/performance_test/draw_figure.py
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Expand Up @@ -32,7 +32,7 @@
# All normalization lives here so the shell script keeps simple lowercase names.
BACKEND_DISPLAY_NAMES = {
"simplestorage": "SimpleStorage",
"yuanrong": "Yuanrong",
"yuanrong": "openYuanrong",
"mooncakestore": "MooncakeStore",
"ray_baseline": "Ray",
}
Expand Down Expand Up @@ -96,7 +96,7 @@ def make_xlabel(size_label: str) -> str:
df["Scenario"] = df["backend_parsed"]

# Set backend display order: only include backends that actually exist in the data
preferred_backend_order = ["Ray", "SimpleStorage", "Yuanrong", "MooncakeStore"]
preferred_backend_order = ["Ray", "SimpleStorage", "openYuanrong", "MooncakeStore"]

# Get actual backends present in the data, maintaining preferred order
actual_backends = df["Scenario"].unique().tolist()
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