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Installing ART

The ART client can be installed into projects designed to run on any machine that runs python. 
pip install openpipe-art

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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: 
pip install openpipe-art[backend]

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 ...

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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: 
pip install openpipe-art

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 ...

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Running the server on remote dedicated GPUs

The ART client can also be run locally and connected to a remote server, which ART will automatically provision for you. To use SkyPilot, you’ll need to install the optional dependency: 
pip install openpipe-art[skypilot]
Then you can use SkyPilotBackend in your code: 
from art import TrainableModel, gather_trajectory_groups
from art.skypilot.backend import SkyPilotBackend

backend = await SkyPilotBackend.initialize_cluster(
    cluster_name="my-cluster",
    gpu="H100"
)

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.

ART Client

The client is responsible for interfacing between your code and the ART backend.

ART Backend

The backend is responsible for generating tokens and training your models.