Add a documentation page with a brief intro to hw backends

docs/source/_toctree.yml

@@ -92,6 +92,10 @@
   - local: phone_teleop
     title: Phone
   title: "Teleoperators"
+- sections:
+  - local: torch_accelerators
+    title: PyTorch accelerators
+  title: "Supported Hardware"
 - sections:
   - local: notebooks
     title: Notebooks

docs/source/torch_accelerators.mdx

@@ -0,0 +1,42 @@
+# PyTorch accelerators
+
+LeRobot supports multiple hardware acceleration options for both training and inference.
+
+These options include:
+
+- **CPU**: CPU executes all computations, no dedicated accelerator is used
+- **CUDA**: acceleration with NVIDIA & AMD GPUs
+- **MPS**: acceleration with Apple Silicon GPUs
+- **XPU**: acceleration with Intel integrated and discrete GPUs
+
+## Getting Started
+
+To use particular accelerator, a suitable version of PyTorch should be installed.
+
+For CPU, CUDA, and MPS backends follow instructions provided on [PyTorch installation page](https://pytorch.org/get-started/locally).
+For XPU backend, follow instructions from [PyTorch documentation](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html).
+
+### Verifying the installation
+
+After installation, accelerator availability can be verified by running
+
+```python
+import torch
+print(torch.<backend_name>.is_available())  # <backend_name> is cuda, mps, or xpu
+```
+
+## How to run training or evaluation
+
+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:
+
+```bash
+lerobot-train
+    --policy.device=mps ...
+```
+
+```bash
+lerobot-eval \
+    --policy.device=mps ...
+```
+
+However, in most cases, presence of an accelerator is detected automatically and `policy.device` parameter can be omitted from CLI commands.