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Showing content from https://cloud.google.com/python/docs/reference/aiplatform/1.1.1/google.cloud.aiplatform below:

Package aiplatform (1.1.1) | Python client library

API documentation for aiplatform package.

Constructs a AutoML Forecasting Training Job.

Constructs a AutoML Image Training Job.

Constructs a AutoML Tabular Training Job.

Constructs a AutoML Text Training Job.

Constructs a AutoML Video Training Job.

Retrieves a BatchPredictionJob resource and instantiates its representation.

Class to launch a Custom Training Job in Vertex AI using a Container.

Vertex AI Custom Job.

Class to launch a Custom Training Job in Vertex AI using a Python Package.

Takes a training implementation as a python package and executes that package in Cloud Vertex AI Training.

Class to launch a Custom Training Job in Vertex AI using a script.

Takes a training implementation as a python script and executes that script in Cloud Vertex AI Training.

Retrieves an endpoint resource.

Vertex AI Hyperparameter Tuning Job.

Managed image dataset resource for Vertex AI.

Retrieves the model resource and instantiates its representation.

Retrieves a PipelineJob resource and instantiates its representation.

Managed tabular dataset resource for Vertex AI.

Managed text dataset resource for Vertex AI.

Managed time series dataset resource for Vertex AI

Managed video dataset resource for Vertex AI.

get_experiment_df(experiment: Optional[str] = None)

Returns a Pandas DataFrame of the parameters and metrics associated with one experiment.

Example:

aiplatform.init(experiment='exp-1') aiplatform.start_run(run='run-1') aiplatform.log_params({'learning_rate': 0.1}) aiplatform.log_metrics({'accuracy': 0.9})

aiplatform.start_run(run='run-2') aiplatform.log_params({'learning_rate': 0.2}) aiplatform.log_metrics({'accuracy': 0.95})

Will result in the following DataFrame

| exp-1 | run-1 | 0.1 | 0.9 |

get_pipeline_df(pipeline: str)

Returns a Pandas DataFrame of the parameters and metrics associated with one pipeline.

init(
    *,
    project: Optional[str] = None,
    location: Optional[str] = None,
    experiment: Optional[str] = None,
    experiment_description: Optional[str] = None,
    staging_bucket: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: Optional[str] = None
)

Updates common initialization parameters with provided options.

The default project to use when making API calls.

The default location to use when making API calls. If not set defaults to us-central-1.

The experiment name.

The description of the experiment.

The default staging bucket to use to stage artifacts when making API calls. In the form gs://...

The default custom credentials to use when making API calls. If not provided credentials will be ascertained from the environment.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created. If set, this resource and all sub-resources will be secured by this key.

log_metrics(metrics: Dict[str, Union[float, int]])

Log single or multiple Metrics with specified key and value pairs.

Required. Metrics key/value pairs. Only flot and int are supported format for value.

log_params(params: Dict[str, Union[float, int, str]])

Log single or multiple parameters with specified key and value pairs.

Required. Parameter key/value pairs.

Setup a run to current session.

Required. Name of the run to assign current session with.

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