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API documentation for aiplatform
package.
Classes
AutoMLForecastingTrainingJob
Constructs a AutoML Forecasting Training Job.
AutoMLImageTrainingJob
Constructs a AutoML Image Training Job.
AutoMLTabularTrainingJob
Constructs a AutoML Tabular Training Job.
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.
HyperparameterTuningJob
Vertex AI Hyperparameter Tuning Job.
ImageDataset
Managed image dataset resource for Vertex AI.
Model
Retrieves the model resource and instantiates its representation.
PipelineJob
Retrieves a PipelineJob resource and instantiates its representation.
TabularDataset
Managed tabular dataset 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
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})
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,
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.
Name | Description |
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 |
The experiment name. |
experiment_description |
The description of the experiment. |
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: |
log_metrics
log_metrics(metrics: Dict[str, Union[float, int]])
Log single or multiple Metrics with specified key and value pairs.
Name | Description |
metrics |
Required. Metrics key/value pairs. Only flot and int are supported format for value. |
log_params
log_params(params: Dict[str, Union[float, int, str]])
Log single or multiple parameters with specified key and value pairs.
Name | Description |
params |
Required. Parameter key/value pairs. |
start_run
start_run(run: str)
Setup a run to current session.
Name | Description |
run |
Required. Name of the run to assign current session with. |