chariot.bulk_inference package

Submodules

chariot.bulk_inference.job module

chariot.bulk_inference.job.create_bulk_inference_job(job_spec: NewBulkInferenceJobRequest) → str | None

Create a bulk inference job.

Parameters:

job_spec (models.NewBulkInferenceJobRequest) – The bulk inference job specification.

Returns:

The bulk inference job id.

Return type:

Optional[str]

chariot.bulk_inference.job.get_bulk_inference_job(job_id: str) → BulkInferenceJob | None

Get a bulk inference job.

Parameters:

job_id (str) – The bulk inference job id.

Returns:

The bulk inference job.

Return type:

Optional[BulkInferenceJob]

chariot.bulk_inference.job.get_bulk_inference_jobs(model_id: str, dataset_version_id: str | None = None, dataset_snapshot_id: str | None = None, dataset_version_split: str | None = None, inference_method: str | None = None, limit: int | None = None, offset: int | None = None) → list[BulkInferenceJob]

Get bulk inference jobs.

Parameters:

• model_id (str) – The model id.

• dataset_version_id (Optional[str]) – Optional dataset version ID. Defaults to None.

• dataset_snapshot_id (Optional[str]) – Optional dataset snapshot ID. Defaults to None.

• dataset_version_split (Optional[str]) – Optional dataset snapshot split. Defaults to None.

• inference_method (Optional[str]) – Optional model inference method. Defaults to None.

• limit (Optional[int]) – Optional pagination limit. Defaults to None.

• offset (Optional[int]) – Optional pagination offset. Defaults to None.

Returns:

.

Return type:

List[BulkInferenceJob]

chariot.bulk_inference.models module

class chariot.bulk_inference.models.BulkInferenceJob(*, execution_id: str, execution_status: str, num_computed: int | None = None, expected_total: int | None = None)

Bases: BaseModel

execution_id: str

execution_status: str

expected_total: int | None

property job_id

property job_status: ProgressState

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

num_computed: int | None

class chariot.bulk_inference.models.NewBulkInferenceJobRequest(*, model_project_id: str, model_id: str, dataset_project_id: str, dataset_id: str, dataset_snapshot_id: str, dataset_snapshot_split: str, inference_method: str = '', evaluate_metrics: bool = False, batch_size: int = 1)

Bases: BaseModel

batch_size: int

dataset_id: str

dataset_project_id: str

dataset_snapshot_id: str

dataset_snapshot_split: str

evaluate_metrics: bool

inference_method: str

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_id: str

model_project_id: str

class chariot.bulk_inference.models.ProgressState(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: StrEnum

ABORTED = 'aborted'

FAILED = 'failed'

INITIALIZED = 'initialized'

IN_PROGRESS = 'in_progress'

RUNNING = 'running'

RUNNING_FAILED = 'running_failed'

SUCCEEDED = 'succeeded'

Module contents