Update xpu-max1100.yml with new config and add some tests #5668
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loadams
reviewed
Jun 17, 2024
| python -c "import torch; import intel_extension_for_pytorch; print('XPU available:', torch.xpu.is_available())" | ||
| apt-get update | ||
| apt-get install clinfo libaio-dev python3-pip -y | ||
| pip install torch==2.1.0.post2 -f https://developer.intel.com/ipex-whl-stable-xpu |
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Why do we need the versions from this index?
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Verisions from this index is closer to the release of intel_extension_for_pytorch and wheels can be downloaded stable and fast.
loadams
reviewed
Jun 17, 2024
.github/workflows/xpu-max1100.yml
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| pip list | ||
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| - name: Unit tests | ||
| shell: bash |
loadams
reviewed
Jun 17, 2024
.github/workflows/xpu-max1100.yml
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| pip install oneccl_bind_pt==2.1.300+xpu -f https://developer.intel.com/ipex-whl-stable-xpu | ||
| pip install torchvision==0.16.0.post2 -f https://developer.intel.com/ipex-whl-stable-xpu | ||
| pip install py-cpuinfo pytest pytest-timeout tabulate tensorboard wandb transformers accelerate comet_ml mup numpy==1.26 | ||
| python3 setup.py bdist_wheel |
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Can we replace these with pip install .[dev,autotuning] rather than installing these packages and the whl? That way we ensure we have the correct requirements file?
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Done. Previously we install from the whl to avoid some issue. Now we can install as standard CI way. Thanks for reviewing, @loadams.
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This PR:
1.Change the container
2.Update the software version (align with docker compiler)
3. Add some tests