This release is a pre-release and may not be stable for production use.
The torch-ort packages uses the PyTorch APIs to accelerate PyTorch models using ONNX Runtime.
Dependencies
The torch-ort package depends on the onnxruntime-training package, which depends on specific versions of GPU libraries such as NVIDIA CUDA.
The default command pip install torch-ort installs the onnxruntime-training version that depends on CUDA 10.2.
If you have a different version of CUDA installed, you can install a different version of onnxruntime-training explicitly:
- CUDA 11.1
pip install onnxruntime-training -f https://download.onnxruntime.ai/onnxruntime_stable_cu111.html
Post-installation step
Once torch-ort is installed, there is a post-installation step:
python -m torch_ort.configure
If this step fails, it is likely due to GPU library version mismatch between onnxruntime-training and your installation. You can check the version of onnxruntime-training by running pip list. For example:
onnxruntime-training 1.9.0+cu111
Releases
-
1.9.0
Release Notes : https://github.com/pytorch/ort/releases/tag/v1.9.0
-
1.8.1
Release Notes : https://github.com/pytorch/ort/releases/tag/v1.8.1
1 maintainer
Metadata
1 maintainer
Release files for torch-ort 1.13.0.dev20221022
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| File | Interpreter | ABI | Platform | Actions Reset |
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| torch_ort-1.13.0.dev20221022-py3-none-any.whl | Python 3 | none | any | Details for torch_ort-1.13.0.dev20221022-py3-none-any.whl |
Release files / torch_ort-1.13.0.dev20221022-py3-none-any.whl
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