Package preview (1.53.0)

Version 1.53.0

• 1.73.0 (latest)
• 1.72.0
• 1.71.1
• 1.70.0
• 1.69.0
• 1.68.0
• 1.67.1
• 1.66.0
• 1.65.0
• 1.63.0
• 1.62.0
• 1.60.0
• 1.59.0
• 1.58.0
• 1.57.0
• 1.56.0
• 1.55.0
• 1.54.1
• 1.53.0
• 1.52.0
• 1.51.0
• 1.50.0
• 1.49.0
• 1.48.0
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

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, *, 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.

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.