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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,
labels: Optional[Dict[str, str]] = 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 > builtins.object > abc.ABC > google.cloud.aiplatform.base.DoneMixin > google.cloud.aiplatform.base.StatefulResource > google.cloud.aiplatform.base.VertexAiStatefulResource > google.cloud.aiplatform.jobs._Job > google.cloud.aiplatform.jobs._RunnableJob > HyperparameterTuningJobProperties
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,
labels: Optional[Dict[str, str]] = 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,
labels={'my_key': 'my_value'},
)
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,
labels={'my_key': 'my_value'},
)
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
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 argument, 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
Required. 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: |
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. |
labels |
Dict[str, str]
Optional. The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
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,
enable_web_access: bool = False,
tensorboard: Optional[str] = None,
sync: bool = True,
create_request_timeout: Optional[float] = None,
)
Run this configured CustomJob.
Name | Description |
service_account |
str
Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. |
network |
str
Optional. The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. |
timeout |
int
Optional. The maximum job running time in seconds. The default is 7 days. |
restart_job_on_worker_restart |
bool
Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job. |
enable_web_access |
bool
Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell |
tensorboard |
str
Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: |
sync |
bool
Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |