Import 'optim' only if PyTorch is installed #22072
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Classes in 'onnxruntime.training.optim' depend on 'torch' being installed. This dependency spread to all packages in 'onnxruntime.training' when adding in in 'training/__init__.py'. By ensuring that it is only added if 'torch' is installed, we can avoid this dependency.
xadupre
reviewed
Sep 12, 2024
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/azp run Big Models,Linux Android Emulator QNN CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline,Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline |
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Azure Pipelines successfully started running 8 pipeline(s). |
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/azp run MacOS CI Pipeline,MacOS CI Pipeline,ONNX Runtime Web CI Pipeline,Windows ARM64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU CUDA CI Pipeline,Windows GPU DML CI Pipeline |
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Azure Pipelines successfully started running 6 pipeline(s). |
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/azp run Windows GPU Doc Gen CI Pipeline,Windows GPU TensorRT CI Pipeline,Windows x64 QNN CI Pipeline,onnxruntime-binary-size-checks-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline |
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Azure Pipelines successfully started running 6 pipeline(s). |
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Description
Classes in
onnxruntime.training.optimdepend on 'torch' being installed. This dependency spreads to all packages inonnxruntime.trainingwhen adding it intraining/__init__.py. By ensuring that it is only added if 'torch' is installed, we can avoid this dependency.Motivation and Context
The 'torch' package can be very large and its size can be problematic in some training environments on the edge. As it won't be used if not using
onnxruntime.training.optimit would be great to not depend on it in these environments.See also #22070