Package preview (1.49.0)

API documentation for preview package.

Packages

evaluation

API documentation for evaluation package.

reasoning_engines

API documentation for reasoning_engines package.

Classes

VertexModel

mixin class that can be used to add Vertex AI remote execution to a custom model.

Modules

generative_models

Classes for working with the Gemini models.

language_models

Classes for working with language models.

vision_models

Classes for working with vision models.

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()

from_pretrained

from_pretrained(
    *,
    model_name: typing.Optional[str] = None,
    custom_job_name: typing.Optional[str] = None,
    foundation_model_name: typing.Optional[str] = None
) -> typing.Union[sklearn.base.BaseEstimator, tf.Module, torch.nn.Module]

Pulls a model from Model Registry or from a CustomJob ID for retraining.

The returned model is wrapped with a Vertex wrapper for running remote jobs on Vertex, unless an unwrapped model was registered to Model Registry.

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_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

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

init

init(
    *,
    remote: typing.Optional[bool] = None,
    autolog: typing.Optional[bool] = None,
    cluster: typing.Optional[
        vertexai.preview._workflow.shared.configs.PersistentResourceConfig
    ] = None
)

Updates preview global parameters for Vertex remote execution.

Parameters
Name	Description
`remote`	Optional. A global flag to indicate whether or not a method will be executed remotely. Default is Flase. The method level remote flag has higher priority than this global flag.
`autolog`	Optional. Whether or not to turn on autologging feature for remote execution. To learn more about the autologging feature, see https://cloud.google.com/vertex-ai/docs/experiments/autolog-data.
`cluster`	Optional. If passed, check if the cluster exists. If not, create a default one (single node, "n1-standard-4", no GPU) with the given name. Then use the cluster to run CustomJobs. Default is None. Example usage: from vertexai.preview.shared.configs import PersistentResourceConfig cluster = PersistentResourceConfig( name="my-cluster-1", resource_pools=[ ResourcePool(replica_count=1,), ResourcePool( machine_type="n1-standard-8", replica_count=2, accelerator_type="NVIDIA_TESLA_P100", accelerator_count=1, ), ] )

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_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()

register

register(
    model: typing.Union[sklearn.base.BaseEstimator, tf.Module, torch.nn.Module],
    use_gpu: bool = False,
) -> google.cloud.aiplatform.models.Model

Registers a model and returns a Model representing the registered Model resource.

Parameters
Name	Description
`model`	Required. An OSS model. Supported frameworks: sklearn, tensorflow, pytorch.
`use_gpu`	Optional. Whether to use GPU for model serving. Default to False.

remote

remote(cls_or_method: typing.Any) -> typing.Any

Takes a class or method and add Vertex remote execution support.

ex:


LogisticRegression = vertexai.preview.remote(LogisticRegression)
model = LogisticRegression()
model.fit.vertex.remote_config.staging_bucket = REMOTE_JOB_BUCKET
model.fit.vertex.remote=True
model.fit(X_train, y_train)

Parameter
Name	Description
`cls_or_method`	Required. A class or method that will be added Vertex remote execution support.

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.