Class HyperparameterTuningJob (1.2.0)

HyperparameterTuningJob(
    display_name: str,
    custom_job: google.cloud.aiplatform.jobs.CustomJob,
    metric_spec: Dict[str, str],
    parameter_spec: Dict[
        str, google.cloud.aiplatform.hyperparameter_tuning._ParameterSpec
    ],
    max_trial_count: int,
    parallel_trial_count: int,
    max_failed_trial_count: int = 0,
    search_algorithm: Optional[str] = None,
    measurement_selection: Optional[str] = "best",
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: Optional[str] = None,
)

Vertex AI Hyperparameter Tuning Job.

Inheritance

builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.jobs._Job > google.cloud.aiplatform.jobs._RunnableJob > HyperparameterTuningJob

Properties

network

The full name of the Google Compute Engine network to which this HyperparameterTuningJob should be peered.

Takes the format projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name.

Private services access must already be configured for the network. If left unspecified, the HyperparameterTuningJob is not peered with any network.

Methods

HyperparameterTuningJob

HyperparameterTuningJob(
    display_name: str,
    custom_job: google.cloud.aiplatform.jobs.CustomJob,
    metric_spec: Dict[str, str],
    parameter_spec: Dict[
        str, google.cloud.aiplatform.hyperparameter_tuning._ParameterSpec
    ],
    max_trial_count: int,
    parallel_trial_count: int,
    max_failed_trial_count: int = 0,
    search_algorithm: Optional[str] = None,
    measurement_selection: Optional[str] = "best",
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    encryption_spec_key_name: Optional[str] = None,
)

Configures a HyperparameterTuning Job.

Example usage:

from google.cloud.aiplatform import hyperparameter_tuning as hpt

worker_pool_specs = [
        {
            "machine_spec": {
                "machine_type": "n1-standard-4",
                "accelerator_type": "NVIDIA_TESLA_K80",
                "accelerator_count": 1,
            },
            "replica_count": 1,
            "container_spec": {
                "image_uri": container_image_uri,
                "command": [],
                "args": [],
            },
        }
    ]

custom_job = aiplatform.CustomJob(
    display_name='my_job',
    worker_pool_specs=worker_pool_specs
)


hp_job = aiplatform.HyperparameterTuningJob(
    display_name='hp-test',
    custom_job=job,
    metric_spec={
        'loss': 'minimize',
    },
    parameter_spec={
        'lr': hpt.DoubleParameterSpec(min=0.001, max=0.1, scale='log'),
        'units': hpt.IntegerParameterSpec(min=4, max=128, scale='linear'),
        'activation': hpt.CategoricalParameterSpec(values=['relu', 'selu']),
        'batch_size': hpt.DiscreteParameterSpec(values=[128, 256], scale='linear')
    },
    max_trial_count=128,
    parallel_trial_count=8,
    )

hp_job.run()

print(hp_job.trials)

For more information on using hyperparameter tuning please visit: https://cloud.google.com/ai-platform-unified/docs/training/using-hyperparameter-tuning

Parameters

Name	Description
display_name	`str` Required. The user-defined name of the HyperparameterTuningJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters.
custom_job	`aiplatform.CustomJob` Required. Configured CustomJob. The worker pool spec from this custom job applies to the CustomJobs created in all the trials.
parameter_spec	`Dict[str, hyperparameter_tuning._ParameterSpec]` Required. Dictionary representing parameters to optimize. The dictionary key is the metric_id, which is passed into your training job as a command line key word arguemnt, and the dictionary value is the parameter specification of the metric. from google.cloud.aiplatform import hyperparameter_tuning as hpt parameter_spec={ 'decay': hpt.DoubleParameterSpec(min=1e-7, max=1, scale='linear'), 'learning_rate': hpt.DoubleParameterSpec(min=1e-7, max=1, scale='linear') 'batch_size': hpt.DiscreteParamterSpec(values=[4, 8, 16, 32, 64, 128], scale='linear') } Supported parameter specifications can be found until aiplatform.hyperparameter_tuning. These parameter specification are currently supported: DoubleParameterSpec, IntegerParameterSpec, CategoricalParameterSpace, DiscreteParameterSpec
max_trial_count	`int` Reuired. The desired total number of Trials.
parallel_trial_count	`int` Required. The desired number of Trials to run in parallel.
max_failed_trial_count	`int` Optional. The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
search_algorithm	`str` The search algorithm specified for the Study. Accepts one of the following: `None` - If you do not specify an algorithm, your job uses the default Vertex AI algorithm. The default algorithm applies Bayesian optimization to arrive at the optimal solution with a more effective search over the parameter space. 'grid' - A simple grid search within the feasible space. This option is particularly useful if you want to specify a quantity of trials that is greater than the number of points in the feasible space. In such cases, if you do not specify a grid search, the Vertex AI default algorithm may generate duplicate suggestions. To use grid search, all parameter specs must be of type `IntegerParameterSpec`, `CategoricalParameterSpace`, or `DiscreteParameterSpec`. 'random' - A simple random search within the feasible space.
measurement_selection	`str` This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Accepts: 'best', 'last' Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose 'last'. On the other hand, if you're in a situation where your system can "over-train" and you expect the performance to get better for a while but then start declining, choose 'best'. B) Are your measurements significantly noisy and/or irreproducible? If so, 'best' will tend to be over-optimistic, and it may be better to choose 'last'. If both or neither of (A) and (B) apply, it doesn't matter which selection type is chosen.
project	`str` Optional. Project to run the HyperparameterTuningjob in. Overrides project set in aiplatform.init.
location	`str` Optional. Location to run the HyperparameterTuning in. Overrides location set in aiplatform.init.
credentials	`auth_credentials.Credentials` Optional. Custom credentials to use to run call HyperparameterTuning service. Overrides credentials set in aiplatform.init.
encryption_spec_key_name	`str` Optional. Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.

run

run(
    service_account: Optional[str] = None,
    network: Optional[str] = None,
    timeout: Optional[int] = None,
    restart_job_on_worker_restart: bool = False,
    tensorboard: Optional[str] = None,
    sync: bool = True,
)

Run this configured CustomJob.