fit

MultiModalCloudPredictor.fit(train_data: str | Path | DataFrame | None = None, *, tuning_data: str | Path | DataFrame | None = None, predictor_init_args: dict[str, Any], predictor_fit_args: dict[str, Any] | None = None, image_column: str | None = None, leaderboard: bool = True, framework_version: str = '1.6', job_name: str | None = None, instance_type: str = 'ml.m5.2xlarge', instance_count: int | str = 'auto', volume_size: int = 100, custom_image_uri: str | None = None, timeout: int = 86400, wait: bool = True, backend_overrides: dict[str, dict[str, Any]] | None = None, **kwargs) → CloudPredictor

Fit the predictor in a SageMaker training job.

Parameters:

  • train_data (str | pathlib.Path | pd.DataFrame) – Training data, as a pd.DataFrame or local/S3 path to a data file.

  • tuning_data (str | pathlib.Path | pd.DataFrame | None, default = None) – Optional tuning data.

  • predictor_init_args (dict) – Init args for the predictor.

  • predictor_fit_args (dict | None, default = None) – Additional fit args forwarded to the underlying predictor’s fit(). Must NOT contain train_data or tuning_data — pass those as explicit arguments above.

  • leaderboard (bool, default = True) – Whether to include the leaderboard in the output artifact

  • framework_version (str, optional) – AutoGluon version, e.g. “1.6”. Training uses the official AutoGluon DLC image for this version. If custom_image_uri is set, this argument will be ignored.

  • job_name (str, default = None) – Name of the launched training job. If None, CloudPredictor creates one with a predictor-specific prefix.

  • instance_type (str, default = 'ml.m5.2xlarge') – Instance type the predictor will be trained on with SageMaker.

  • instance_count (int | str, default = "auto") – Number of instances used to fit the predictor. If “auto”, the backend decides the instance count.

  • volume_size (int, default = 100) – Size in GB of the EBS volume to use for storing input data during training. Must be large enough to store training data if File Mode is used (which is the default).

  • timeout (int, default = 24*60*60) – Timeout in seconds for training. This timeout doesn’t include time for pre-processing or launching up the training job.

  • wait (bool, default = True) – Whether the call should wait until the job completes To be noticed, the function won’t return immediately because there are some preparations needed prior fit. Use get_fit_job_status to get job status.

  • backend_overrides (dict[str, dict[str, Any]] | None, default = None) –

    Raw SageMaker request fields for settings without a dedicated argument.

    • Keys: request names from the SageMaker API section below.

    • Values: request fields in PascalCase, as in the SageMaker API and boto3. Deep-merged over the request built by AutoGluon-Cloud; lists and other non-dict values replace the generated ones.

    • Example: {"CreateTrainingJob": {"RetryStrategy": {"MaximumRetryAttempts": 2}}}

Returns:

CloudPredictor – The fitted predictor (self).

SageMaker API

  • CreateTrainingJob: trains the predictor on instance_count x instance_type and writes the artifact to cloud_output_path.