Overview
FireworksClient provides Fireworks platform operations that are independent of any running trainer job: checkpoint promotion, training shape resolution, and model validation. It is the base class for TrainerJobManager, which adds trainer job lifecycle methods.
Use FireworksClient directly when you don’t need to create or manage trainer jobs — for example, promoting a checkpoint after the trainer has already been deleted, or resolving training shape configuration before deciding whether to launch a job.
from fireworks.training.sdk import FireworksClient
Constructor
client = FireworksClient(
api_key="<FIREWORKS_API_KEY>",
base_url="https://api.fireworks.ai", # optional
)
| Parameter | Type | Default | Description |
|---|
api_key | str | — | Fireworks API key |
base_url | str | "https://api.fireworks.ai" | Control-plane URL |
additional_headers | dict | None | None | Extra HTTP headers |
verify_ssl | bool | None | None | SSL verification override |
Methods
Promote a sampler checkpoint to a deployable Fireworks model. The trainer job does not need to be running — the job_id is used only to resolve the GCS bucket where the checkpoint files reside. The base model for metadata inheritance is automatically resolved from the trainer job’s training config.
model = client.promote_checkpoint(
job_id="<job-id>",
checkpoint_id="<snapshot-name>",
output_model_id="my-fine-tuned-model",
)
print(f"Model state: {model['state']}, kind: {model['kind']}")
| Parameter | Type | Description |
|---|
job_id | str | RLOR trainer job ID that produced the checkpoint |
checkpoint_id | str | The snapshot_name from save_weights_for_sampler_ext |
output_model_id | str | Desired model ID (1-63 chars, lowercase a-z, 0-9, hyphen only) |
state, kind, peftDetails). See Saving and Loading for details.
The trainer job can be in any state (running, failed, cancelled, or deleted) as long as the checkpoint files still exist in GCS. Promotion is a file copy — it does not interact with the trainer process.
resolve_training_profile(shape_id)
Resolve a training shape ID into a full configuration profile:
shape_id = "accounts/fireworks/trainingShapes/ts-qwen3-8b-policy"
profile = client.resolve_training_profile(shape_id)
print(profile.accelerator_type) # e.g. "NVIDIA_B200_192GB"
print(profile.trainer_image_tag) # e.g. "0.0.0-dev-..."
print(profile.node_count) # e.g. 1
print(profile.pipeline_parallelism) # e.g. 1
validate_output_model_id(output_model_id)
Client-side validation helper for promote_checkpoint(..., output_model_id=...):
from fireworks.training.sdk import validate_output_model_id
errors = validate_output_model_id("my-fine-tuned-model")
if errors:
raise ValueError("\n".join(errors))
Relationship to TrainerJobManager
TrainerJobManager inherits from FireworksClient and adds trainer job lifecycle methods (create, wait_for_ready, delete, etc.). All FireworksClient methods are also available on TrainerJobManager.
Use FireworksClient when you only need platform-level operations. Use TrainerJobManager when you also need to create and manage trainer jobs.
from fireworks.training.sdk import FireworksClient, TrainerJobManager
# Trainer-free: promote a checkpoint from a completed experiment
client = FireworksClient(api_key=api_key)
client.promote_checkpoint(job_id, checkpoint_id, "my-model")
# Full lifecycle: create trainer, train, promote
mgr = TrainerJobManager(api_key=api_key)
endpoint = mgr.create_and_wait(config)
# ... train ...
mgr.promote_checkpoint(job_id, checkpoint_id, "my-model")
mgr.delete(job_id)
TrainingShapeProfile
Returned by resolve_training_profile:
| Field | Type | Description |
|---|
training_shape_version | str | Resolved shape version |
trainer_image_tag | str | Docker image tag for the trainer |
max_supported_context_length | int | Maximum supported context length |
node_count | int | Number of trainer nodes |
deployment_shape_version | str | Linked deployment shape |
deployment_image_tag | str | Docker image tag for deployment |
accelerator_type | str | GPU type |
accelerator_count | int | Number of GPUs per node |
base_model_weight_precision | str | Model weight precision |
pipeline_parallelism | int | Pipeline parallelism degree |
training_shape | str | Training shape name (without /versions/... suffix) |
deployment_shape | str | Deployment shape name (without /versions/... suffix) |
