Package aiplatform (1.62.0)

API documentation for aiplatform package.

Classes

Artifact

Metadata Artifact resource for Vertex AI

AutoMLForecastingTrainingJob

Class to train AutoML forecasting models.

The AutoMLForecastingTrainingJob class uses the AutoML training method to train and run a forecasting model. The AutoML training method is a good choice for most forecasting use cases. If your use case doesn't benefit from the Seq2seq or the Temporal fusion transformer training method offered by the SequenceToSequencePlusForecastingTrainingJob and [TemporalFusionTransformerForecastingTrainingJob]https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform.TemporalFusionTransformerForecastingTrainingJob) classes respectively, then AutoML is likely the best training method for your forecasting predictions.

For sample code that shows you how to use AutoMLForecastingTrainingJob see the Create a training pipeline forecasting sample on GitHub.

AutoMLImageTrainingJob

Creates an AutoML image training job.

Use the AutoMLImageTrainingJob class to create, train, and return an image model. For more information about working with image data models in Vertex AI, see Image data.

For an example of how to use the AutoMLImageTrainingJob class, see the tutorial in the AutoML image classification notebook on GitHub.

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.

Use the CustomPythonPackageTrainingJob class to use a Python package to launch a custom training pipeline in Vertex AI. For an example of how to use the CustomPythonPackageTrainingJob class, see the tutorial in the Custom training using Python package, managed text dataset, and TensorFlow serving container notebook.

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.

DeploymentResourcePool

Retrieves a DeploymentResourcePool.

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

A managed image dataset resource for Vertex AI.

Use this class to work with a managed image dataset. To create a managed image dataset, you need a datasource file in CSV format and a schema file in YAML format. A schema is optional for a custom model. You put the CSV file and the schema into Cloud Storage buckets.

Use image data for the following objectives:

The following code shows you how to create an image dataset by importing data from a CSV datasource file and a YAML schema file. The schema file you use depends on whether your image dataset is used for single-label classification, multi-label classification, or object detection.

my_dataset = aiplatform.ImageDataset.create(
    display_name="my-image-dataset",
    gcs_source=['gs://path/to/my/image-dataset.csv'],
    import_schema_uri=['gs://path/to/my/schema.yaml']
)

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.

The SequenceToSequencePlusForecastingTrainingJob class uses the Seq2seq+ training method to train and run a forecasting model. The Seq2seq+ training method is a good choice for experimentation. Its algorithm is simpler and uses a smaller search space than the AutoML option. Seq2seq+ is a good option if you want fast results and your datasets are smaller than 1 GB.

For sample code that shows you how to use SequenceToSequencePlusForecastingTrainingJob, see the Create a training pipeline forecasting Seq2seq sample on GitHub.

TabularDataset

A managed tabular dataset resource for Vertex AI.

Use this class to work with tabular datasets. You can use a CSV file, BigQuery, or a pandas DataFrame to create a tabular dataset. For more information about paging through BigQuery data, see Read data with BigQuery API using pagination. For more information about tabular data, see Tabular data.

The following code shows you how to create and import a tabular dataset with a CSV file.

my_dataset = aiplatform.TabularDataset.create(
    display_name="my-dataset", gcs_source=['gs://path/to/my/dataset.csv'])

The following code shows you how to create and import a tabular dataset in two distinct steps.

my_dataset = aiplatform.TextDataset.create(
    display_name="my-dataset")

my_dataset.import(
    gcs_source=['gs://path/to/my/dataset.csv']
    import_schema_uri=aiplatform.schema.dataset.ioformat.text.multi_label_classification
)

If you create a tabular dataset with a pandas DataFrame, you need to use a BigQuery table to stage the data for Vertex AI:

my_dataset = aiplatform.TabularDataset.create_from_dataframe(
    df_source=my_pandas_dataframe,
    staging_path=f"bq://{bq_dataset_id}.table-unique"
)

TemporalFusionTransformerForecastingTrainingJob

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

The TemporalFusionTransformerForecastingTrainingJob class uses the Temporal Fusion Transformer (TFT) training method to train and run a forecasting model. The TFT training method implements an attention-based deep neural network (DNN) model that uses a multi-horizon forecasting task to produce predictions.

For sample code that shows you how to use `TemporalFusionTransformerForecastingTrainingJob, see the Create a training pipeline forecasting temporal fusion transformer sample on GitHub.

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

A managed text dataset resource for Vertex AI.

Use this class to work with a managed text dataset. To create a managed text dataset, you need a datasource file in CSV format and a schema file in YAML format. A schema is optional for a custom model. The CSV file and the schema are accessed in Cloud Storage buckets.

Use text data for the following objectives:

The following code shows you how to create and import a text dataset with a CSV datasource file and a YAML schema file. The schema file you use depends on whether your text dataset is used for single-label classification, multi-label classification, or object detection.

my_dataset = aiplatform.TextDataset.create(
    display_name="my-text-dataset",
    gcs_source=['gs://path/to/my/text-dataset.csv'],
    import_schema_uri=['gs://path/to/my/schema.yaml'],
)

TimeSeriesDataset

A managed time series dataset resource for Vertex AI.

Use this class to work with time series datasets. A time series is a dataset that contains data recorded at different time intervals. The dataset includes time and at least one variable that's dependent on time. You use a time series dataset for forecasting predictions. For more information, see Forecasting overview.

You can create a managed time series dataset from CSV files in a Cloud Storage bucket or from a BigQuery table.

The following code shows you how to create a TimeSeriesDataset with a CSV file that has the time series dataset:

my_dataset = aiplatform.TimeSeriesDataset.create(
    display_name="my-dataset",
    gcs_source=['gs://path/to/my/dataset.csv'],
)

The following code shows you how to create with a TimeSeriesDataset with a BigQuery table file that has the time series dataset:

my_dataset = aiplatform.TimeSeriesDataset.create(
    display_name="my-dataset",
    bq_source=['bq://path/to/my/bigquerydataset.train'],
)

TimeSeriesDenseEncoderForecastingTrainingJob

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

The TimeSeriesDenseEncoderForecastingTrainingJob class uses the Time-series Dense Encoder (TiDE) training method to train and run a forecasting model. TiDE uses a multi-layer perceptron (MLP) to provide the speed of forecasting linear models with covariates and non-linear dependencies. For more information about TiDE, see Recent advances in deep long-horizon forecasting and this TiDE blog post.

VideoDataset

A managed video dataset resource for Vertex AI.

Use this class to work with a managed video dataset. To create a video dataset, you need a datasource in CSV format and a schema in YAML format. The CSV file and the schema are accessed in Cloud Storage buckets.

Use video data for the following objectives:

Classification. For more information, see Classification schema files. Action recognition. For more information, see Action recognition schema files. Object tracking. For more information, see Object tracking schema files. The following code shows you how to create and import a dataset to train a video classification model. The schema file you use depends on whether you use your video dataset for action classification, recognition, or object tracking.

my_dataset = aiplatform.VideoDataset.create(
    gcs_source=['gs://path/to/my/dataset.csv'],
    import_schema_uri=['gs://aip.schema.dataset.ioformat.video.classification.yaml']
)

Packages Functions

autolog

autolog(disable=False)

Enables autologging of parameters and metrics to Vertex Experiments.

After calling aiplatform.autolog(), any metrics and parameters from model training calls with supported ML frameworks will be automatically logged to Vertex Experiments.

Using autologging requires setting an experiment and experiment_tensorboard.

Parameter
Name Description
disable

Optional. Whether to disable autologging. Defaults to False. If set to True, this resets the MLFlow tracking URI to its previous state before autologging was called and remove logging filters.

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()

end_upload_tb_log

end_upload_tb_log()

Ends the current TensorBoard uploader

aiplatform.start_upload_tb_log(...)
...
aiplatform.end_upload_tb_log()

get_experiment_df

get_experiment_df(
    experiment: typing.Optional[str] = None, *, include_time_series: bool = True
) -> 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_experiment_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
Parameters
Name Description
experiment

Name of the Experiment to filter results. If not set, return results of current active experiment.
include_time_series

Optional. Whether or not to include time series metrics in df. Default is True. Setting to False will largely improve execution time and reduce quota contributing calls. Recommended when time series metrics are not needed or number of runs in Experiment is large. For time series metrics consider querying a specific run using get_time_series_data_frame.

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.

Parameter
Name Description
pipeline

Name of the Pipeline to filter results.

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[
            str,
            google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard,
            bool,
        ]
    ] = 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,
    api_endpoint: typing.Optional[str] = None,
    api_key: typing.Optional[str] = None,
    api_transport: typing.Optional[str] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, 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. If experiment_tensorboard is ommitted or set to True or None the global Tensorboard will be assigned to the experiment. If a global Tensorboard is not set, the default Tensorboard instance will be used, and created if it does not exist. To disable creating and using Tensorboard with experiment, set experiment_tensorboard to False. Any subsequent calls to aiplatform.init() should include this setting as well.
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.
api_endpoint

Optional. The desired API endpoint, e.g., us-central1-aiplatform.googleapis.com
api_key

Optional. The API key to use for service calls. NOTE: Not all services support API keys.
api_transport

Optional. The transport method which is either 'grpc' or 'rest'. NOTE: "rest" transport functionality is currently in a beta state (preview).

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.

start_upload_tb_log

start_upload_tb_log(
    tensorboard_experiment_name: str,
    logdir: str,
    tensorboard_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    experiment_display_name: typing.Optional[str] = None,
    run_name_prefix: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    allowed_plugins: typing.Optional[typing.FrozenSet[str]] = None,
)

Continues to listen for new data in the logdir and uploads when it appears.

Note that after calling start_upload_tb_log() your thread will kept alive even if an exception is thrown. To ensure the thread gets shut down, put any code after start_upload_tb_log() and before end_upload_tb_log() in a try statement, and call end_upload_tb_log() in finally.

Sample usage:
aiplatform.init(location='us-central1', project='my-project')
aiplatform.start_upload_tb_log(tensorboard_id='123',tensorboard_experiment_name='my-experiment',logdir='my-logdir')

try:
  # your code here
finally:
  aiplatform.end_upload_tb_log()
Parameters
Name Description
tensorboard_experiment_name

Required. Name of this tensorboard experiment. Unique to the given projects/{project}/locations/{location}/tensorboards/{tensorboard_id}.
logdir

Required. path of the log directory to upload
tensorboard_id

Optional. TensorBoard ID. If not set, tensorboard_id in aiplatform.init will be used.
project

Optional. Project the TensorBoard is in. If not set, project set in aiplatform.init will be used.
location

Optional. Location the TensorBoard is in. If not set, location set in aiplatform.init will be used.
experiment_display_name

Optional. The display name of the experiment.
run_name_prefix

Optional. If present, all runs created by this invocation will have their name prefixed by this value.
description

Optional. String description to assign to the experiment.
allowed_plugins

Optional. List of additional allowed plugin names.

upload_tb_log

upload_tb_log(
    tensorboard_experiment_name: str,
    logdir: str,
    tensorboard_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    experiment_display_name: typing.Optional[str] = None,
    run_name_prefix: typing.Optional[str] = None,
    description: typing.Optional[str] = None,
    verbosity: typing.Optional[int] = 1,
    allowed_plugins: typing.Optional[typing.FrozenSet[str]] = None,
)

upload only the existing data in the logdir and then return immediately

Sample usage:
aiplatform.init(location='us-central1', project='my-project')
aiplatform.upload_tb_log(tensorboard_id='123',tensorboard_experiment_name='my-experiment',logdir='my-logdir')
Parameters
Name Description
tensorboard_experiment_name

Required. Name of this tensorboard experiment. Unique to the given projects/{project}/locations/{location}/tensorboards/{tensorboard_id}
logdir

Required. The location of the TensorBoard logs that resides either in the local file system or Cloud Storage
tensorboard_id

Optional. TensorBoard ID. If not set, tensorboard_id in aiplatform.init will be used.
project

Optional. Project the TensorBoard is in. If not set, project set in aiplatform.init will be used.
location

Optional. Location the TensorBoard is in. If not set, location set in aiplatform.init will be used.
experiment_display_name

Optional. The display name of the experiment.
run_name_prefix

Optional. If present, all runs created by this invocation will have their name prefixed by this value.
description

Optional. String description to assign to the experiment.
verbosity

Optional. Level of verbosity, an integer. Supported value: 0 - No upload statistics is printed. 1 - Print upload statistics while uploading data (default).
allowed_plugins

Optional. List of additional allowed plugin names.