Package preview (1.44.0)

API documentation for preview 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.

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_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

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.

Package preview (1.44.0) Stay organized with collections Save and categorize content based on your preferences.

Classes

VertexModel

Modules

generative_models

language_models

vision_models

Packages Functions

end_run

from_pretrained

get_experiment_df

init

log_classification_metrics

log_metrics

log_params

log_time_series_metrics

register

remote

start_run

Package preview (1.44.0)