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ART divides the logic for training an agent into two distinct abstractions. The client is responsible for interfacing with the environment in which the agent runs and for sending inference and training requests to the backend. The backend is responsible for generating tokens at inference time, updating the agent’s weights based on past performance, and managing GPU memory as it switches from inference to training mode. This separation of concerns simplifies the process of teaching an agent to improve its performance using RL. While the backend’s training and inference settings are highly configurable, they’re also set up to use intelligent defaults that save beginners time while getting started. However, there are a few important considerations to take before running your first training job.

LocalBackend

SkyPilotBackend

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Remote or local training

ART provides two backend classes, LocalBackend and SkyPilotBackend. If your agent is already running on a machine equipped with an advanced GPU and you want to run training on the same machine, use LocalBackend. If your agent is running on a machine without an advanced GPU (this includes most personal computers and production servers), use SkyPilotBackend instead. Both LocalBackend and SkyPilotBackend implement the art.Backend class, and once initialized, the client interacts with them in the exact same way. Under the hood, SkyPilotBackend configures a remote machine equipped with a GPU to run LocalBackend, and forwards requests from the client to the remote instance.

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LocalBackend

The LocalBackend class runs a vLLM server and either an Unsloth or torchtune instance on whatever machine your agent itself is executing. This is a good fit if you’re already running your agent on a machine with a GPU. To declare a LocalBackend instance, follow the code sample below: 
from art.local import LocalBackend

backend = LocalBackend(
    # set to True if you want your backend to shut down automatically
    # when your client process ends
    in_process: False,
    # local path where the backend will store trajectory logs and model weights
    path: './.art',
)

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SkyPilotBackend

To use SkyPilotBackend, you’ll need to install the optional dependency: 
pip install openpipe-art[skypilot]
When a SkyPilotBackend instance is initialized, it does a few things:
  • Provisions a remote machine with an advanced GPU (by default on RunPod)
  • Installs openpipe-art and its dependencies
  • Initializes a LocalBackend instance with vLLM and a training server (unsloth or torchtune)
  • Registers the LocalBackend instance to forward requests to it over http
To initialize a SkyPilotBackend instance, follow the code sample below: 
from art.skypilot import SkyPilotBackend

backend = await SkyPilotBackend.initialize_cluster(
    # name of the cluster in SkyPilot's registry
    cluster_name="my-cluster",
    # version of openpipe-art that should be installed on the remote cluster
    # default to version installed on the client
    art_version="0.3.12",
    # path to environment variables (e.g. WANDB_API_KEY) to set on the remote cluster
    env_path=".env",
    # the GPU the cluster is equipped with
    gpu="H100"
    # alternatively, more complicated requirements can be specified in
    # the `resources` argument
)
When a training job is finished, you can shut down a cluster either through code or the cli. Code: 
backend = await SkyPilotBackend.initialize_cluster(...)

# ...training code...

backend.down()
CLI: 
uv run sky down my-cluster

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Using a backend

Once initialized, a backend can be used in the same way regardless of whether it runs locally or remotely. 
LOCAL = True

if LOCAL:
    from art.local import LocalBackend
    backend = LocalBackend()
else:
    from art.skypilot import SkyPilotBackend
    backend = await SkyPilotBackend.initialize_cluster(
        cluster_name="my-cluster",
        gpu="H100"
    )

model = art.TrainableModel(...)

await model.register(backend)

# ...training code...

if not LOCAL:
    # optionally shut down the cluster when training finishes
    await backend.down()
To see LocalBackend and SkyPilotBackend in action, try the examples below.

2048 Notebook

Use LocalBackend to train an agent to play 2048.

Summarizer

Use SkyPilotBackend to train a SOTA summarizing agent.