Package aiplatform (1.32.0)

API documentation for aiplatform package.

Classes

Artifact

Metadata Artifact resource for Vertex AI

AutoMLForecastingTrainingJob

Class to train AutoML forecasting models.

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

Public managed EntityType resource for Vertex AI.

Execution

Metadata Execution resource for Vertex AI

Experiment

Represents a Vertex AI Experiment resource.

ExperimentRun

A Vertex AI Experiment run.

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.

ModelDeploymentMonitoringJob

Vertex AI Model Deployment Monitoring Job.

This class should be used in conjunction with the Endpoint class in order to configure model monitoring for deployed models.

ModelEvaluation

Retrieves the ModelEvaluation resource and instantiates its representation.

PipelineJob

Retrieves a PipelineJob resource and instantiates its representation.

PipelineJobSchedule

Retrieves a PipelineJobSchedule resource and instantiates its representation.

PrivateEndpoint

Represents a Vertex AI PrivateEndpoint resource.

Read more about private endpoints in the documentation.

SequenceToSequencePlusForecastingTrainingJob

Class to train Sequence to Sequence (Seq2Seq) forecasting models.

TabularDataset

Managed tabular dataset resource for Vertex AI.

TemporalFusionTransformerForecastingTrainingJob

Class to train Temporal Fusion Transformer (TFT) forecasting models.

Tensorboard

Managed tensorboard resource for Vertex AI.

TensorboardExperiment

Managed tensorboard resource for Vertex AI.

TensorboardRun

Managed tensorboard resource for Vertex AI.

TensorboardTimeSeries

Managed tensorboard resource for Vertex AI.

TextDataset

Managed text dataset resource for Vertex AI.

TimeSeriesDataset

Managed time series dataset resource for Vertex AI

TimeSeriesDenseEncoderForecastingTrainingJob

Class to train Time series Dense Encoder (TiDE) forecasting models.

VideoDataset

Managed video dataset resource for Vertex AI.

Packages Functions

end_run

end_run(
    state: google.cloud.aiplatform_v1.types.execution.Execution.State = State.COMPLETE,
)

Ends the the current experiment run.

aiplatform.start_run('my-run')
...
aiplatform.end_run()

get_experiment_df

get_experiment_df(experiment: typing.Optional[str] = None) -> pd.DataFrame

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})

aiplatform.get_experiments_df()

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_experiment_model

get_experiment_model(
    artifact_id: str,
    *,
    metadata_store_id: str = "default",
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None
) -> google.cloud.aiplatform.metadata.schema.google.artifact_schema.ExperimentModel

Retrieves an existing ExperimentModel artifact given an artifact id.

Parameters
Name Description
artifact_id

Required. An artifact id of the ExperimentModel artifact.
metadata_store_id

Optional. MetadataStore to retrieve Artifact from. If not set, metadata_store_id is set to "default". If artifact_id is a fully-qualified resource name, its metadata_store_id overrides this one.
project

Optional. Project to retrieve the artifact from. If not set, project set in aiplatform.init will be used.
location

Optional. Location to retrieve the Artifact from. If not set, location set in aiplatform.init will be used.
credentials

Optional. Custom credentials to use to retrieve this Artifact. Overrides credentials set in aiplatform.init.

get_pipeline_df

get_pipeline_df(pipeline: str) -> pd.DataFrame

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

init

init(
    *,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    experiment: typing.Optional[str] = None,
    experiment_description: typing.Optional[str] = None,
    experiment_tensorboard: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str
        ]
    ] = None,
    staging_bucket: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: typing.Optional[str] = None,
    network: typing.Optional[str] = None,
    service_account: typing.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

Optional. The experiment name.
experiment_description

Optional. The description of the experiment.
experiment_tensorboard

Optional. The Vertex AI TensorBoard instance, Tensorboard resource name, or Tensorboard resource ID to use as a backing Tensorboard for the provided experiment. Example tensorboard resource name format: "projects/123/locations/us-central1/tensorboards/456" If experiment_tensorboard is provided and experiment is not, the provided experiment_tensorboard will be set as the global Tensorboard. Any subsequent calls to aiplatform.init() with experiment and without experiment_tensorboard will automatically assign the global Tensorboard to 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.
network

Optional. The full name of the Compute Engine network to which jobs and resources should be peered. E.g. "projects/12345/global/networks/myVPC". Private services access must already be configured for the network. If specified, all eligible jobs and resources created will be peered with this VPC.
service_account

Optional. The service account used to launch jobs and deploy models. Jobs that use service_account: BatchPredictionJob, CustomJob, PipelineJob, HyperparameterTuningJob, CustomTrainingJob, CustomPythonPackageTrainingJob, CustomContainerTrainingJob, ModelEvaluationJob.

log

log(
    *,
    pipeline_job: typing.Optional[
        google.cloud.aiplatform.pipeline_jobs.PipelineJob
    ] = None
)

Log Vertex AI Resources to the current experiment run.

aiplatform.start_run('my-run')
my_job = aiplatform.PipelineJob(...)
my_job.submit()
aiplatform.log(my_job)
Parameter
Name Description
pipeline_job

Optional. Vertex PipelineJob to associate to this Experiment Run.

log_classification_metrics

log_classification_metrics(
    *,
    labels: typing.Optional[typing.List[str]] = None,
    matrix: typing.Optional[typing.List[typing.List[int]]] = None,
    fpr: typing.Optional[typing.List[float]] = None,
    tpr: typing.Optional[typing.List[float]] = None,
    threshold: typing.Optional[typing.List[float]] = None,
    display_name: typing.Optional[str] = None
) -> (
    google.cloud.aiplatform.metadata.schema.google.artifact_schema.ClassificationMetrics
)

Create an artifact for classification metrics and log to ExperimentRun. Currently support confusion matrix and ROC curve.

my_run = aiplatform.ExperimentRun('my-run', experiment='my-experiment')
classification_metrics = my_run.log_classification_metrics(
    display_name='my-classification-metrics',
    labels=['cat', 'dog'],
    matrix=[[9, 1], [1, 9]],
    fpr=[0.1, 0.5, 0.9],
    tpr=[0.1, 0.7, 0.9],
    threshold=[0.9, 0.5, 0.1],
)
Parameters
Name Description
labels

Optional. List of label names for the confusion matrix. Must be set if 'matrix' is set.
matrix

Optional. Values for the confusion matrix. Must be set if 'labels' is set.
fpr

Optional. List of false positive rates for the ROC curve. Must be set if 'tpr' or 'thresholds' is set.
tpr

Optional. List of true positive rates for the ROC curve. Must be set if 'fpr' or 'thresholds' is set.
threshold

Optional. List of thresholds for the ROC curve. Must be set if 'fpr' or 'tpr' is set.
display_name

Optional. The user-defined name for the classification metric artifact.

log_metrics

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

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

Metrics with the same key will be overwritten.

aiplatform.start_run('my-run', experiment='my-experiment')
aiplatform.log_metrics({'accuracy': 0.9, 'recall': 0.8})
Parameter
Name Description
metrics

Required. Metrics key/value pairs.

log_model

log_model(
    model: typing.Union[sklearn.base.BaseEstimator, xgb.Booster, tf.Module],
    artifact_id: typing.Optional[str] = None,
    *,
    uri: typing.Optional[str] = None,
    input_example: typing.Union[list, dict, pd.DataFrame, np.ndarray] = None,
    display_name: typing.Optional[str] = None,
    metadata_store_id: typing.Optional[str] = "default",
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None
) -> google.cloud.aiplatform.metadata.schema.google.artifact_schema.ExperimentModel

Saves a ML model into a MLMD artifact and log it to this ExperimentRun.

Supported model frameworks: sklearn, xgboost, tensorflow.

Example usage: model = LinearRegression() model.fit(X, y) aiplatform.init( project="my-project", location="my-location", staging_bucket="gs://my-bucket", experiment="my-exp" ) with aiplatform.start_run("my-run"): aiplatform.log_model(model, "my-sklearn-model")

Parameters
Name Description
model

Required. A machine learning model.
artifact_id

Optional. The resource id of the artifact. This id must be globally unique in a metadataStore. It may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
uri

Optional. A gcs directory to save the model file. If not provided, gs://default-bucket/timestamp-uuid-frameworkName-model will be used. If default staging bucket is not set, a new bucket will be created.
input_example

Optional. An example of a valid model input. Will be stored as a yaml file in the gcs uri. Accepts list, dict, pd.DataFrame, and np.ndarray The value inside a list must be a scalar or list. The value inside a dict must be a scalar, list, or np.ndarray.
display_name

Optional. The display name of the artifact.
metadata_store_id

Optional. The <metadata_store_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id> If not provided, the MetadataStore's ID will be set to "default".
project

Optional. Project used to create this Artifact. Overrides project set in aiplatform.init.
location

Optional. Location used to create this Artifact. Overrides location set in aiplatform.init.
credentials

Optional. Custom credentials used to create this Artifact. Overrides credentials set in aiplatform.init.

log_params

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

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

Parameters with the same key will be overwritten.

aiplatform.start_run('my-run')
aiplatform.log_params({'learning_rate': 0.1, 'dropout_rate': 0.2})
Parameter
Name Description
params

Required. Parameter key/value pairs.

log_time_series_metrics

log_time_series_metrics(
    metrics: typing.Dict[str, float],
    step: typing.Optional[int] = None,
    wall_time: typing.Optional[google.protobuf.timestamp_pb2.Timestamp] = None,
)

Logs time series metrics to to this Experiment Run.

Requires the experiment or experiment run has a backing Vertex Tensorboard resource.

my_tensorboard = aiplatform.Tensorboard(...)
aiplatform.init(experiment='my-experiment', experiment_tensorboard=my_tensorboard)
aiplatform.start_run('my-run')

# increments steps as logged
for i in range(10):
    aiplatform.log_time_series_metrics({'loss': loss})

# explicitly log steps
for i in range(10):
    aiplatform.log_time_series_metrics({'loss': loss}, step=i)
Parameters
Name Description
metrics

Required. Dictionary of where keys are metric names and values are metric values.
step

Optional. Step index of this data point within the run. If not provided, the latest step amongst all time series metrics already logged will be used.
wall_time

Optional. Wall clock timestamp when this data point is generated by the end user. If not provided, this will be generated based on the value from time.time()

save_model

save_model(
    model: typing.Union[sklearn.base.BaseEstimator, xgb.Booster, tf.Module],
    artifact_id: typing.Optional[str] = None,
    *,
    uri: typing.Optional[str] = None,
    input_example: typing.Union[list, dict, pd.DataFrame, np.ndarray] = None,
    tf_save_model_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None,
    display_name: typing.Optional[str] = None,
    metadata_store_id: typing.Optional[str] = "default",
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None
) -> google.cloud.aiplatform.metadata.schema.google.artifact_schema.ExperimentModel

Saves a ML model into a MLMD artifact.

Supported model frameworks: sklearn, xgboost, tensorflow.

Example usage: aiplatform.init(project="my-project", location="my-location", staging_bucket="gs://my-bucket") model = LinearRegression() model.fit(X, y) aiplatform.save_model(model, "my-sklearn-model")

Parameters
Name Description
model

Required. A machine learning model.
artifact_id

Optional. The resource id of the artifact. This id must be globally unique in a metadataStore. It may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
uri

Optional. A gcs directory to save the model file. If not provided, gs://default-bucket/timestamp-uuid-frameworkName-model will be used. If default staging bucket is not set, a new bucket will be created.
input_example

Optional. An example of a valid model input. Will be stored as a yaml file in the gcs uri. Accepts list, dict, pd.DataFrame, and np.ndarray The value inside a list must be a scalar or list. The value inside a dict must be a scalar, list, or np.ndarray.
tf_save_model_kwargs

Optional. A dict of kwargs to pass to the model's save method. If saving a tf module, this will pass to "tf.saved_model.save" method. If saving a keras model, this will pass to "tf.keras.Model.save" method.
display_name

Optional. The display name of the artifact.
metadata_store_id

Optional. The <metadata_store_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id> If not provided, the MetadataStore's ID will be set to "default".
project

Optional. Project used to create this Artifact. Overrides project set in aiplatform.init.
location

Optional. Location used to create this Artifact. Overrides location set in aiplatform.init.
credentials

Optional. Custom credentials used to create this Artifact. Overrides credentials set in aiplatform.init.

start_execution

start_execution(
    *,
    schema_title: typing.Optional[str] = None,
    display_name: typing.Optional[str] = None,
    resource_id: typing.Optional[str] = None,
    metadata: typing.Optional[typing.Dict[str, typing.Any]] = None,
    schema_version: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    resume: bool = False,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None
) -> google.cloud.aiplatform.metadata.execution.Execution

Create and starts a new Metadata Execution or resumes a previously created Execution.

To start a new execution:

with aiplatform.start_execution(schema_title='system.ContainerExecution', display_name='trainer) as exc:
  exc.assign_input_artifacts([my_artifact])
  model = aiplatform.Artifact.create(uri='gs://my-uri', schema_title='system.Model')
  exc.assign_output_artifacts([model])

To continue a previously created execution:

with aiplatform.start_execution(resource_id='my-exc', resume=True) as exc:
    ...
Parameters
Name Description
schema_title

Optional. schema_title identifies the schema title used by the Execution. Required if starting a new Execution.
resource_id

Optional. The <resource_id> portion of the Execution name with the format. This is globally unique in a metadataStore: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/executions/<resource_id>.
display_name

Optional. The user-defined name of the Execution.
schema_version

Optional. schema_version specifies the version used by the Execution. If not set, defaults to use the latest version.
metadata

Optional. Contains the metadata information that will be stored in the Execution.
description

Optional. Describes the purpose of the Execution to be created.
metadata_store_id

Optional. The <metadata_store_id> portion of the resource name with the format: projects/123/locations/us-central1/metadataStores/<metadata_store_id>/artifacts/<resource_id> If not provided, the MetadataStore's ID will be set to "default".
project

Optional. Project used to create this Execution. Overrides project set in aiplatform.init.
location

Optional. Location used to create this Execution. Overrides location set in aiplatform.init.
credentials

Optional. Custom credentials used to create this Execution. Overrides credentials set in aiplatform.init.

start_run

start_run(
    run: str,
    *,
    tensorboard: typing.Optional[
        typing.Union[
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str
        ]
    ] = None,
    resume=False
) -> google.cloud.aiplatform.metadata.experiment_run_resource.ExperimentRun

Start a run to current session.

aiplatform.init(experiment='my-experiment')
aiplatform.start_run('my-run')
aiplatform.log_params({'learning_rate':0.1})

Use as context manager. Run will be ended on context exit:

aiplatform.init(experiment='my-experiment')
with aiplatform.start_run('my-run') as my_run:
    my_run.log_params({'learning_rate':0.1})

Resume a previously started run:

aiplatform.init(experiment='my-experiment')
with aiplatform.start_run('my-run', resume=True) as my_run:
    my_run.log_params({'learning_rate':0.1})
Parameters
Name Description
run

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

Whether to resume this run. If False a new run will be created.