ServiceClient - Baseten

ServiceClient is the entry point for every session. It calls the Baseten control plane to create a TrainerSession, then provisions trainer and sampling servers within that session on demand. It returns the TrainingClient and SamplingClient you drive for the rest of a run. Provision a trainer and get a TrainingClient:

from baseten.loops import ServiceClient

service_client = ServiceClient()
training_client = service_client.create_lora_training_client(
    base_model="Qwen/Qwen3.5-2B", rank=16,
)

ServiceClient(training_project_id=None, *, api_key=None, base_url=None, reuse_from_session_id=None)

ServiceClient

Construct a ServiceClient and create a new TrainerSession on the Baseten control plane. Omit training_project_id to use the default project for the org, or pass one to target a specific training project. api_key defaults to the BASETEN_API_KEY environment variable.Pass reuse_from_session_id to reuse a prior session’s trainer and sampler for create_lora_training_client and create_sampling_client calls instead of provisioning fresh. The named session must belong to the same team. ServiceClient reads the LOOPS_REUSE_FROM_SESSION_ID environment variable when no kwarg is passed; the kwarg wins when both are set. Reuse is best-effort: if the prior deployment is stopped, failed, or unhealthy, a fresh one is provisioned and the call still succeeds. See Reusing infrastructure across sessions.

ServiceClient.local(*, trainer_url, sampler_url)

ServiceClient

Bind to already-running local trainer and sampler processes without contacting the control plane. Pass trainer_url and sampler_url as the base URLs of local server processes. Useful for end-to-end testing.

create_lora_training_client(base_model, rank=32, seed=None, timeout=600.0, ready_timeout=3600.0, wandb_config=None)

TrainingClient

Provision a TrainerServer for the given Hugging Face base_model and return a connected TrainingClient. The control plane also provisions a paired sampling server in the same call; save_weights_and_get_sampling_client uses that paired URL to gate on version readiness. Pass a WandbConfig instance to stream training metrics to a Weights & Biases run.

create_sampling_client(base_model, timeout=300.0, ready_timeout=3600.0, model_path=None)

SamplingClient

Provision a standalone SamplingServer for base_model and return a connected SamplingClient. Use this when you want to sample from a base model independently of a training run. The model_path argument is reserved and not yet implemented; passing it raises NotImplementedError. To sample from a specific checkpoint, use TrainingClient.create_sampling_client(model_path=...) on a live run instead.

get_server_capabilities()

ServerCapabilities

Return the control plane’s view of supported base models and the GPU classes it can provision them on. Useful for confirming a base model is available before calling create_lora_training_client. Returns ServerCapabilities.

list_checkpoints(run_id)

list[Checkpoint]

List checkpoints saved by the run identified by run_id. Calls the list checkpoints API, not the trainer server directly. Returns a list of Checkpoint.

get_checkpoint_archive_url(checkpoint_id, page_size=1000, page_token=0)

CheckpointFilesResponse

Return presigned URLs for every file in the specified checkpoint folder. Checkpoint IDs are globally unique, so no run scoping is required. The Loops stack writes checkpoints as unzipped directories rather than archives, so this returns a file list instead of a single archive URL. Wraps the get checkpoint files API.

session_id

str

Property. The session ID assigned by the control plane. Available after construction.