Records

class gretel_client.projects.records.RecordHandler(model: Model, *, record_id: str = None, data_source: DataSourceTypes | None = None, params: dict | None = None, ref_data: RefDataTypes | None = None)

Manages a model’s record handler. After a model has been created and trained, a record handler may be used to “run” the model.

Parameters:

model – The model to generate a record handler for
record_id – The id of an existing record handler.

property artifact_types: List[str]: Returns a list of valid artifacts for the record handler.

property billing_details: dict: Get billing details for the current job.

cancel(): Cancels the active job.

property container_image: str: Return the container image for the job.

property data_source: str | _DataFrameT | None

Returns the data source with which the record handler was configured, if any.

If the record handler has been submitted, returns the resolved artifact ID. Otherwise, returns the originally-supplied data_source argument.

delete(): Deletes the record handler.

download_artifacts(target_dir: str | Path)

Given a target directory, either as a string or a Path object, attempt to enumerate and download all artifacts associated with this Job

Parameters:: target_dir – The target directory to store artifacts in. If the directory does not exist, it will be created for you.

property errors: Return any errors associated with the model.

property external_data_source: bool: Returns True if the data source is external to Gretel Cloud. If the data source is a Gretel Artifact, returns False.

property external_ref_data: bool: Returns True if the data refs are external to Gretel Cloud. If the data refs are Gretel Artifacts, returns False.

get_artifact_handle(artifact_key: str) → BinaryIO

Returns a reference to a remote artifact that can be used to read binary data within a context manager

>>> with job.get_artifact_handle("report_json") as file:
...   print(file.read())

Parameters:: artifact_key – Artifact type to download.
Returns:: a file like object

get_artifact_link(artifact_key: str) → str

Retrieves a signed S3 link that will download the specified artifact type.

Parameters:: artifact_key – Artifact type to download.

get_artifacts() → Iterator[Tuple[str, str]]: List artifact links for all known artifact types.

get_artifacts_by_artifact_types(artifact_types: List[str]) → Iterator[Tuple[str, str]]: List artifact links for all known artifact types.

get_report_summary(report_path: str | None = None) → dict | None: Return a summary of the job results :param report_path: If a report_path is passed, that report

will be used for the summary. If no report path is passed, the function will check for a cloud report artifact.

property instance_type: str: Return CPU or GPU based on the record handler’s run requirements.

property logs: Returns run logs for the job.

property model_type: str: Returns the parent model type of the record handler.

property params: dict | None: Returns the params with which the record handler was configured, if any.

peek_report(report_path: str | None = None) → dict | None

Return a summary of the job results.

Parameters:: report_path – If a report_path is passed, that report will be used for the summary. If no report path is passed, the function will check for a cloud based artifact.

poll_logs_status(wait: int = -1, callback: Callable | None = None) → Iterator[LogStatus]

Returns an iterator that may be used to tail the logs of a running Model.

Parameters:

wait – The time in seconds to wait before closing the iterator. If wait is -1 (WAIT_UNTIL_DONE), the iterator will run until the model has reached a “completed” or “error” state.
callback – This function will be executed on every polling loop. A callback is useful for checking some external state that is working on a Job.

property print_obj: dict: Returns a printable object representation of the job.

project: Project: Project associated with the job.

property ref_data: str | Dict[str, str] | List[str] | Tuple[str] | _DataFrameT | List[_DataFrameT] | None: Returns the ref_data with which the record handler was configured, if any.

refresh(): Update internal state of the job by making an API call to Gretel Cloud.

property runner_mode: str: Returns the runner_mode of the job. May be one of hybrid, manual or cloud.

property status: Status: The status of the job. Is one of gretel_client.projects.jobs.Status.

submit(runner_mode: str | RunnerMode | None = None, dry_run: bool = False) → Job

Submit this Job to the Gretel Cloud API.

Parameters:

runner_mode – Determines where to run the model. If not specified, the runner mode of the project (if configured) is used, otherwise the default runner mode of the session is used.
dry_run – If set to True the model config will be submitted for validation, but won’t be run. Ignored for record handlers.

submit_cloud(dry_run: bool = False) → Job

Submit this Job to the Gretel Cloud API be scheduled for running in Gretel Cloud.

Returns:: The response from the Gretel API.

submit_hybrid(dry_run: bool = False) → Job

Submit this Job to the Gretel Cloud API to be scheduled for running in a hybrid deployment.

Returns:: The response from the Gretel API.

submit_local(dry_run: bool = False) → Job

Submit this Job to the Gretel Cloud API to be scheduled for running in a local container.

Returns:: The response from the Gretel API.

submit_manual(dry_run: bool = False) → Job

Submit this Job to the Gretel Cloud API, which will create the job metadata but no runner will be started. The Model instance can now be passed into a dedicated runner.

Returns:: The response from the Gretel API.

property traceback: str | None: Returns the traceback associated with any job errors.

upload_data_source(_validate: bool = True, _artifacts_handler: CloudArtifactsHandler | HybridArtifactsHandler | None = None) → str | None

Resolves and uploads the data source specified in the model config.

If the data source is already a Gretel artifact, the artifact will not be uploaded.

Returns:: A Gretel artifact key.

upload_ref_data(_validate: bool = True, _artifacts_handler: ArtifactsHandler | None = None) → RefData

Resolves and uploads ref data sources specificed in the model config.

If the ref data are already Gretel artifacts, we’ll return the ref data as-is.

Returns:: A RefData instance that contains the new Gretel artifact values.

worker_key: str | None: Worker key used to launch the job.