> ## Documentation Index
> Fetch the complete documentation index at: https://openpipe-art-austin-monarch-multinode-training.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Installation + Setup

### Installing ART

The ART client can be installed into projects designed to run on any machine that runs python.

```bash theme={null}
pip install openpipe-art
```

### Running the server locally

The ART server can be run locally on any machine with a GPU. To install the backend dependencies required for training and inference, you can install the `backend` extra:

```bash theme={null}
pip install --extra-index-url https://download.pytorch.org/whl/cu128 \
  "openpipe-art[backend]"
```

```python theme={null}
from art import TrainableModel, gather_trajectory_groups
from art.local.backend import LocalBackend

backend = LocalBackend()

model = TrainableModel(
    name="agent-001",
    project="my-agentic-task",
    base_model="OpenPipe/Qwen3-14B-Instruct",
)

await model.register(backend)

... the rest of your code ...
```

CUDA 13 hosts use `openpipe-art[backend-cu130]` and the PyTorch `cu130` index.

### Running Megatron

On a supported CUDA 12 trainer image, one install command provides ART's
controller, Monarch runtime, and the locked Megatron runtime contract:

```bash theme={null}
pip install --extra-index-url https://download.pytorch.org/whl/cu128 \
  "openpipe-art[megatron]"
```

CUDA 13 hosts use `openpipe-art[megatron-cu130]` with
`https://download.pytorch.org/whl/cu130`. Megatron currently requires Python
3.12. The first trainer launch materializes the exact trainer environment in a
content-addressed node-local cache. No ART checkout or `setup.sh` invocation is
required.

The image remains responsible for the NVIDIA driver and CUDA toolkit. For
cross-host training it must also provide the NCCL network transport, MOFED/RDMA
devices, and the kernel capabilities described in the multi-node deployment
guide. ART validates these before allocating the model.

Tinker users install `openpipe-art[tinker]`; serverless users need only
`openpipe-art`. These profiles do not install Megatron or vLLM dependencies.

### Using a managed autoscaling backend

Instead of managing the GPUs and training processes yourself, you can optionally send inference and training requests to the W\&B Training cluster, which autoscales to match your job's demand. To do so, install `openpipe-art` without any extras and use `ServerlessBackend`:

```bash theme={null}
pip install openpipe-art
```

```python theme={null}
from art import TrainableModel, gather_trajectory_groups
from art.serverless.backend import ServerlessBackend

backend = ServerlessBackend()

model = TrainableModel(
    name="agent-001",
    project="my-agentic-task",
    base_model="OpenPipe/Qwen3-14B-Instruct",
)

await model.register(backend)

... the rest of your code ...
```

To learn more about the ART client and server, see the docs below.

<div className="cards-container">
  <div className="card-wrapper">
    <Card title="ART Client" icon="laptop-code" href="/fundamentals/art-client" horizontal={true} arrow={true}>
      The client is responsible for interfacing between your code and the ART
      backend.
    </Card>
  </div>

  <div className="card-wrapper">
    <Card title="ART Backend" icon="server" href="/fundamentals/art-backend" horizontal={true} arrow={true}>
      The backend is responsible for generating tokens and training your models.
    </Card>
  </div>
</div>