Package aiplatform (1.14.0)

API documentation for aiplatform package.

Classes

AutoMLForecastingTrainingJob

Constructs a Forecasting Training Job.

AutoMLImageTrainingJob

Constructs a AutoML Image Training Job.

AutoMLTabularTrainingJob

Constructs a AutoML Tabular Training Job.

Example usage:

job = training_jobs.AutoMLTabularTrainingJob( display_name="my_display_name", optimization_prediction_type="classification", optimization_objective="minimize-log-loss", column_specs={"column_1": "auto", "column_2": "numeric"}, labels={'key': 'value'}, )

AutoMLTextTrainingJob

Constructs a AutoML Text Training Job.

AutoMLVideoTrainingJob

Constructs a AutoML Video Training Job.

BatchPredictionJob

Retrieves a BatchPredictionJob resource and instantiates its representation.

CustomContainerTrainingJob

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

CustomJob

Vertex AI Custom Job.

CustomPythonPackageTrainingJob

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.

CustomTrainingJob

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.

Endpoint

Retrieves an endpoint resource.

EntityType

Managed entityType resource for Vertex AI.

Feature

Managed feature resource for Vertex AI.

Featurestore

Managed featurestore resource for Vertex AI.

HyperparameterTuningJob

Vertex AI Hyperparameter Tuning Job.

ImageDataset

Managed image dataset resource for Vertex AI.

MatchingEngineIndex

Matching Engine index resource for Vertex AI.

MatchingEngineIndexEndpoint

Matching Engine index endpoint resource for Vertex AI.

Model

Retrieves the model resource and instantiates its representation.

ModelEvaluation

Retrieves the ModelEvaluation resource and instantiates its representation.

PipelineJob

Retrieves a PipelineJob resource and instantiates its representation.

SequenceToSequencePlusForecastingTrainingJob

Constructs a Forecasting Training Job.

TabularDataset

Managed tabular dataset resource for Vertex AI.

Tensorboard

Managed tensorboard resource for Vertex AI.

TensorboardExperiment

Managed tensorboard resource for Vertex AI.

TensorboardRun

Managed tensorboard resource for Vertex AI.

TextDataset

Managed text dataset resource for Vertex AI.

TimeSeriesDataset

Managed time series dataset resource for Vertex AI

VideoDataset

Managed video dataset resource for Vertex AI.

Packages Functions

get_experiment_df

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

| experiment_name | run_name | param.learning_rate | metric.accuracy |

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

| exp-1 | run-2 | 0.2 | 0.95 |

get_pipeline_df

get_pipeline_df(pipeline: str)

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

init

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.

Parameters
Name Description
project

The default project to use when making API calls.
location

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

The experiment name.
experiment_description

The description of the experiment.
staging_bucket

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

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

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

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

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

Parameter
Name Description
metrics

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

log_params

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

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

Parameter
Name Description
params

Required. Parameter key/value pairs.

start_run

start_run(run: str)

Setup a run to current session.

Parameter
Name Description
run

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