FAfastino-ai
training-jobs

Sync Training Job Status

Changed on

Sync training job status from the provider to the database.

post/v1/training/jobs/{job_id}/sync

Request

Path parameters

job_idstring required

Response

Successful Response

artifact_readyboolean nullable

Whether an artifact location is recorded, so there is something to serve.

base_modelstring required

The catalog base model the adapter was trained on (for an interactive_checkpoint model, the base of the interactive job it came from).

batch_sizeinteger nullable required

Training batch size. Null for an interactive_checkpoint model.

completed_atstring nullable
created_atstring required
current_epochinteger nullable

Epoch currently in progress (1-indexed). Updated live during training.

deployability_reasonstring nullable

Why the job is not deployable (e.g. 'job_incomplete', 'missing_artifact', 'provider_incompatible', 'adapter_inference_unavailable'). Null when deployable.

deployment_statusstring nullable required

Deprecated. Always returns None -- deployment_status no longer exists.

Kept for backward compat with clients that read this field.

error_messagestring nullable
examplestring nullable

Sample text to pre-load into inference input

experiment_idstring nullable

Experiment associated with this training job, when available. Null means no associated experiment was recorded.

hub_model_idstring nullable

HuggingFace repo id (e.g. 'username/model-name') set only after a successful push_training_job_to_hub call. This is the sole source of truth for whether a checkpoint has been pushed to the Hub -- it does not mean the repo is reachable by anyone other than the pusher: see hub_model_private for that. trained_model_path is an internal storage key and must never be parsed to infer a Hub repo id.

hub_model_privateboolean nullable

Whether the pushed Hub repository is private, as recorded at push time. Null means the job was not pushed or its repository visibility was not recorded.

idstring required
instance_typestring nullable
is_deployableboolean nullable

Whether this job passes server-side deployability validation for its own project. Authoritative -- the same check the deployment endpoints enforce.

is_terminal_statusboolean nullable

Whether this status is terminal for polling loops

job_referencestring nullable
labelsstring[] nullable

Merged labels from training datasets (entity types for NER, class labels for classification)

learning_ratenumber nullable required

Learning rate. Null for an interactive_checkpoint model.

metricsobject nullable

Training and evaluation metrics dictionary. Contains final_training_loss, final_validation_loss, best_validation_loss from training logs, and optional evaluation metrics (f1_score, precision_score, recall_score, accuracy) if an evaluation has been run.

model_auto_selectedboolean nullable
model_kind'lora' | 'full' nullable

Normalized fine-tune kind: 'lora' for adapters (lora/qlora) or 'full' for merged weights. Null when the persisted training type is unrecognised -- clients must not claim a kind in that case.

model_namestring nullable
model_selection_reasonstring nullable
normalized_statusstring nullable

Canonical status alias for compatibility handling (requested, running, complete, deployed, failed, cancelled)

nr_epochsinteger nullable required

Training epochs. Null for an interactive_checkpoint model.

progress_percentinteger nullable

Overall training completion percentage (0-100). Updated live during training.

project_idstring nullable

Project ID this training job is associated with

provider_deploymentsobject nullable

Provider-specific deployment metadata written by the training monitor, keyed by provider: {"modal": {...}}.

provider_namestring nullable

Training provider that handled this job (e.g. 'modal'). Jobs predating a provider removal carry an 'archived_<provider>' label.

provider_readyboolean nullable

Whether a provider is already serving this artifact. False is not a deployment blocker: promotion provisions or re-warms a provider.

resolved_recipeobject nullable

Immutable snapshot of what this job actually trained with, taken at dispatch: LoRA rank/alpha/dropout, learning rate, warmup ratio, gradient accumulation, packing, precision, attention backend, reasoning parser, container image, runtime profile, max sequence length, and seed, for every seed-capable provider/model. Null for jobs dispatched before the snapshot existed, and for providers that resolve no catalog recipe -- except Modal encoder and Modal RL strategies, which have no dense-LoRA catalog recipe but still return a non-null (seed) snapshot, since seed has no other persisted column. Read this rather than re-deriving from the catalog: the catalog reports what a job dispatched today would get, which is a different question.

root_job_idstring nullable

ID of the original/root training job this version derives from

seedinteger nullable

Effective reproducibility seed for Modal decoder or GLiNER2 (encoder) training. Null for Fireworks, unknown, and other providers/architectures that cannot honor this contract, and also null for a legacy Modal decoder/encoder job created before seed provenance was recorded, because its seed is unknown, and for an interactive_checkpoint model, which this job did not train. A non-null value is always a genuinely recorded seed. Decoder contract: A pinned seed governs LoRA initialisation and the trainer's own RNG (dataloader shuffle order and dropout). It does not select the train/validation split: that partition uses a dedicated split seed so two runs that differ only in seed are scored on the same held-out rows. It does not make runs bit-identical: GPU reduction order stays non-deterministic, so metrics can differ between otherwise identical runs. Across six observed same-config decoder pairs, final validation loss agreed to within 6.8% relative and two pairs agreed exactly. Treat that as an observed envelope from production history, not a guaranteed bound. Encoder contract: A pinned seed governs dataset shuffle and auto-sizing downsample order, and the trainer's own weight-initialisation and dropout RNG. It does not select the train/validation split on a Brain-dispatched run: that partition is a fixed left-to-right split derived from validation_data_percentage (validation is the tail), independent of seed, so two runs that differ only in seed are scored on the same held-out rows. Runs are seeded but not bit-identical: GPU reduction order stays non-deterministic (cuBLAS GEMM split-k, and the embedding-backward scatter_add/index_add path, which is not forced deterministic), so metrics can still differ between identically-configured runs. Before encoder seed control existed, three identically-configured launches measured classification macro-F1 ranging 0.38–0.63 — treat pinning a seed as removing one real, measured source of that noise, not as a guaranteed bound on the rest. Every GLiNER2 job unconditionally pins cuDNN's own algorithm selection (the same flags GLiNER2's bundled Trainer sets) -- this is a fixed container default, not a per-request knob. The cuDNN pin currently costs nothing and changes nothing on this backbone: cuDNN governs convolution/pooling/RNN kernels, and this encoder's DeBERTa-v2 backbone has none, so pinning it has nothing to pin there; the throughput trade-off only materializes if a future backbone adds conv/pooling/RNN layers. torch deterministic algorithms are not enabled: they would roughly halve training throughput without making runs bit-identical.

source'config_training' | 'interactive_checkpoint'

How the model came to exist: config_training for a job Fastino trained from data and a config, interactive_checkpoint for a sampler checkpoint of an interactive job served as a model (POST /v1/training/jobs/{job_id}/checkpoints/sampler_weights/{name}/deploy). An interactive_checkpoint model ran no training of its own, so it lists no datasets and its training settings are null.

started_atstring nullable
statusstring required
task_typestring nullable

Task type derived from training datasets: 'ner', 'classification', 'custom', or 'decoder'

trained_model_pathstring nullable
training_typestring nullable

Raw training method as persisted: 'lora', 'qlora', or 'full'.

type'config'

A config training job (you give data and a config, Fastino runs the loop). /v1/trained-models also lists interactive jobs (type: interactive).

updated_atstring required
user_idstring required
validation_data_percentagenumber nullable required

Share of the data held out for validation (0-1). Null for an interactive_checkpoint model.

version_numberstring nullable

Version number for this training job (e.g., '1', '2', '3')

Changes

  • 5efbc8b70225Revision changes
    • breaking

      response property batch_size list-of-types was widened by adding types null to media type application/json of response 200

    • breaking

      response property learning_rate list-of-types was widened by adding types null to media type application/json of response 200

    • breaking

      response property nr_epochs list-of-types was widened by adding types null to media type application/json of response 200

    • breaking

      response property validation_data_percentage list-of-types was widened by adding types null to media type application/json of response 200

    • info

      added the optional property to the response with the status

  • 5ebfd9fe8f46Revision changes
    • info

      endpoint added