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    Package preview (1.95.1)

    API documentation for preview package.

    Packages

    tuning

    API documentation for tuning package.

    reasoning_engines

    API documentation for reasoning_engines package.

    Modules

    generative_models

    Classes for working with the Gemini models.

    prompts

    API documentation for prompts module.

    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()

    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
    str

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

    include_time_series
    bool

    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.

    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
    List[str]

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

    matrix
    List[List[int]

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

    fpr
    List[float]

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

    tpr
    List[float]

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

    threshold
    List[float]

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

    display_name
    str

    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
    Dict[str, Union[float, int, str]]

    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
    Dict[str, Union[float, int, str]]

    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
    Dict[str, Union[str, float]]

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

    step
    int

    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
    timestamp_pb2.Timestamp

    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_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
    str

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

    resume
    bool

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