> ## Documentation Index
> Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Installation

> Install Miles on NVIDIA or AMD GPUs. Docker is the recommended path.

There are three ways to install Miles. Docker is recommended because Miles pins patched
versions of SGLang, Megatron-LM, and a few CUDA kernels.

## Method 1: Docker (recommended)

<Tabs>
  <Tab title="NVIDIA">
    ```bash theme={null}
    docker pull radixark/miles:latest

    docker run --rm \
      --gpus all --ipc=host --shm-size=32g \
      --ulimit memlock=-1 --ulimit stack=67108864 \
      --network=host \
      -it radixark/miles:latest /bin/bash
    ```
  </Tab>

  <Tab title="AMD MI300X / MI350X">
    ```bash theme={null}
    docker pull rlsys/miles:MI350-355-latest    # or MI300-latest

    docker run --rm \
      --device /dev/dri --device /dev/kfd \
      --group-add video --ipc=host --shm-size=32g \
      --cap-add SYS_PTRACE --security-opt seccomp=unconfined \
      --privileged \
      -it rlsys/miles:MI350-355-latest /bin/bash
    ```
  </Tab>
</Tabs>

The image ships with:

* PyTorch (matching the container's CUDA / ROCm version)
* Megatron-LM, SGLang, FlashAttention-3, DeepGEMM, Apex
* Ray, uv, and Miles installed editable at `/root/miles`

See [Platforms](/docs/platforms/index) for platform-specific notes.

## Method 2: From source

Clone and install in an existing environment.

```bash theme={null}
git clone https://github.com/radixark/miles.git
cd miles
pip install -r requirements.txt
pip install -e . --no-deps
```

<Warning>
  **Patched dependencies.** Miles pins patched versions of SGLang and Megatron-LM. Installing them yourself at
  the wrong commit is the most common source of bug reports — use Docker if you can.
</Warning>

## Method 3: Update an existing container

If you already run a Miles image and want the latest code:

```bash theme={null}
cd /root/miles
git pull --rebase
pip install -e . --no-deps
ray stop && ray start --head --port=6379
```

## Verify

Confirm Miles imports and the GPUs are visible:

```bash theme={null}
python -c "import miles; print('Miles import OK')"
nvidia-smi
```

If either command fails, see [Debugging](/docs/developer/debug) or the [FAQ](/docs/faq).

## Hardware requirements

| Hardware                          | Status                            |
| --------------------------------- | --------------------------------- |
| NVIDIA H100 / H200                | Production (CI guarded)           |
| NVIDIA B100 / B200                | Production                        |
| NVIDIA A100                       | Supported — FP8 features disabled |
| AMD MI300X, MI325, MI350X, MI355X | Supported via ROCm                |

For multi-node training you also need a high-bandwidth interconnect — InfiniBand, RoCEv2,
or Slingshot — and 200+ GB/s per node. Single-node jobs run fine over NVLink only.

## Next steps

* [Quick Start](/docs/getting-started/quick-start) — run your first training job.
* [Core concepts](/docs/user-guide/concepts) — the mental model behind Miles.
* [Training backends](/docs/user-guide/usage) — Megatron vs FSDP.