mirror of
https://github.com/huggingface/lerobot.git
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Vladislav Sovrasov
43 lines
1.4 KiB
Plaintext
43 lines
1.4 KiB
Plaintext
# PyTorch accelerators
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LeRobot supports multiple hardware acceleration options for both training and inference.
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These options include:
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- **CPU**: CPU executes all computations, no dedicated accelerator is used
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- **CUDA**: acceleration with NVIDIA & AMD GPUs
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- **MPS**: acceleration with Apple Silicon GPUs
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- **XPU**: acceleration with Intel integrated and discrete GPUs
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## Getting Started
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To use particular accelerator, a suitable version of PyTorch should be installed.
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For CPU, CUDA, and MPS backends follow instructions provided on [PyTorch installation page](https://pytorch.org/get-started/locally).
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For XPU backend, follow instructions from [PyTorch documentation](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html).
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### Verifying the installation
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After installation, accelerator availability can be verified by running
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```python
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import torch
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print(torch.<backend_name>.is_available()) # <backend_name> is cuda, mps, or xpu
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```
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## How to run training or evaluation
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To select the desired accelerator, use the `--policy.device` flag when running `lerobot-train` or `lerobot-eval`. For example, to use MPS on Apple Silicon, run:
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```bash
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lerobot-train
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--policy.device=mps ...
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```
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```bash
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lerobot-eval \
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--policy.device=mps ...
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```
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However, in most cases, presence of an accelerator is detected automatically and `policy.device` parameter can be omitted from CLI commands.
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