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On Windows CUDA, Linux ROCm (6.x+), and Linux XPU, this also installs the appropriate Triton package to enable `torch.compile` (`triton-windows`, `pytorch-triton-rocm`, or `pytorch-triton-xpu`).
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## Test torch
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Run:
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`python -m torchruntime test`
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To specifically verify `torch.compile` / Triton:
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`python -m torchruntime test compile`
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## Get device info
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You can use the device database built into `torchruntime` for your projects:
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This will install `torch`, `torchvision`, and `torchaudio`, and will decide the variant based on the user's OS, GPU manufacturer and GPU model number. See [customizing packages](#customizing-packages) for more options.
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On Windows CUDA, Linux ROCm (6.x+), and Linux XPU, this also installs the appropriate Triton package to enable `torch.compile` (`triton-windows`, `pytorch-triton-rocm`, or `pytorch-triton-xpu`).
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**Tip:** You can also add the `--uv` flag to install packages using [uv](https://docs.astral.sh/uv/) (instead of `pip`). For e.g. `python -m torchruntime install --uv`
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### Step 2. Configure torch
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```
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### (Optional) Step 3. Test torch
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Run `python -m torchruntime test` to run a set of tests to check whether the installed version of torch is working correctly.
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Run `python -m torchruntime test` to run a set of tests to check whether the installed version of torch is working correctly (including a `torch.compile` / Triton check on CUDA/XPU systems). You can also run `python -m torchruntime test compile` to run only the compile check.
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## Customizing packages
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By default, `python -m torchruntime install` will install the latest available `torch`, `torchvision` and `torchaudio` suitable on the user's platform.
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