Installing ART
The ART client can be installed into projects designed to run on any machine that runs python.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 thebackend extra:
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: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, installopenpipe-art without any extras and use ServerlessBackend:
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.