Package aiplatform (1.18.2)

API documentation for aiplatform package.

Classes

Artifact

Metadata Artifact resource for Vertex AI

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.

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.

PrivateEndpoint

Represents a Vertex AI PrivateEndpoint resource.

Read more about private endpoints in the documentation.

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.

TensorboardTimeSeries

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

end_run

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

Ends the the current experiment run.

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

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})

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_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,
    experiment_tensorboard: Optional[
        Union[google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, 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
NameDescription
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"

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

log(
    *, pipeline_job: 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
NameDescription
pipeline_job

Optional. Vertex PipelineJob to associate to this Experiment Run.

log_classification_metrics

log_classification_metrics(
    *,
    labels: Optional[List[str]] = None,
    matrix: Optional[List[List[int]]] = None,
    fpr: Optional[List[float]] = None,
    tpr: Optional[List[float]] = None,
    threshold: Optional[List[float]] = None,
    display_name: Optional[str] = None
)

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')
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
NameDescription
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: Dict[str, 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
NameDescription
metrics

Required. Metrics key/value pairs.

log_params

log_params(params: Dict[str, 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
NameDescription
params

Required. Parameter key/value pairs.

log_time_series_metrics

log_time_series_metrics(
    metrics: Dict[str, float],
    step: Optional[int] = None,
    wall_time: 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
NameDescription
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()

start_execution

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

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
NameDescription
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: Optional[
        Union[google.cloud.aiplatform.tensorboard.tensorboard_resource.Tensorboard, str]
    ] = None,
    resume=False
)

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') as my_run:
    my_run.log_params({'learning_rate':0.1})
Parameters
NameDescription
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.