gcloud beta ai hp-tuning-jobs create

NAME

gcloud beta ai hp-tuning-jobs create - create a hyperparameter tuning job

SYNOPSIS

gcloud beta ai hp-tuning-jobs create --config=CONFIG --display-name=DISPLAY_NAME [--algorithm=ALGORITHM] [--enable-dashboard-access] [--enable-web-access] [--labels=[KEY=VALUE,…]] [--max-trial-count=MAX_TRIAL_COUNT] [--network=NETWORK] [--parallel-trial-count=PARALLEL_TRIAL_COUNT] [--region=REGION] [--service-account=SERVICE_ACCOUNT] [--kms-key=KMS_KEY : --kms-keyring=KMS_KEYRING --kms-location=KMS_LOCATION --kms-project=KMS_PROJECT] [GCLOUD_WIDE_FLAG …]

DESCRIPTION

(BETA) Create a hyperparameter tuning job.

EXAMPLES

To create a job named test under project example in region us-central1, run:

gcloud beta ai hp-tuning-jobs create --region=us-central1 --project=example --config=config.yaml --display-name=test

REQUIRED FLAGS

--config=CONFIG

Path to the job configuration file. This file should be a YAML document containing a HyperparameterTuningSpec. If an option is specified both in the configuration file **and** via command line arguments, the command line arguments override the configuration file.

Example(YAML):

displayName: TestHpTuningJob
maxTrialCount: 1
parallelTrialCount: 1
studySpec:
  metrics:
  - metricId: x
    goal: MINIMIZE
  parameters:
  - parameterId: z
    integerValueSpec:
      minValue: 1
      maxValue: 100
  algorithm: RANDOM_SEARCH
trialJobSpec:
  workerPoolSpecs:
  - machineSpec:
      machineType: n1-standard-4
    replicaCount: 1
    containerSpec:
      imageUri: gcr.io/ucaip-test/ucaip-training-test

--display-name=DISPLAY_NAME

Display name of the hyperparameter tuning job to create.

OPTIONAL FLAGS

--algorithm=ALGORITHM

Search algorithm specified for the given study. ALGORITHM must be one of: algorithm-unspecified, grid-search, random-search.

--enable-dashboard-access

Whether you want Vertex AI to enable dashboard built on the training containers. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

--enable-web-access

Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

--labels=[KEY=VALUE,…]

List of label KEY=VALUE pairs to add.

Keys must start with a lowercase character and contain only hyphens (-), underscores (_), lowercase characters, and numbers. Values must contain only hyphens (-), underscores (_), lowercase characters, and numbers.

--max-trial-count=MAX_TRIAL_COUNT

Desired total number of trials. The default value is 1.

--network=NETWORK

Full name of the Google Compute Engine network to which the Job is peered with. Private services access must already have been configured. If unspecified, the Job is not peered with any network.

--parallel-trial-count=PARALLEL_TRIAL_COUNT

Desired number of Trials to run in parallel. The default value is 1.

Region resource - Cloud region to create a hyperparameter tuning job. This represents a Cloud resource. (NOTE) Some attributes are not given arguments in this group but can be set in other ways.

To set the project attribute:

• provide the argument --region on the command line with a fully specified name;
• set the property ai/region with a fully specified name;
• choose one from the prompted list of available regions with a fully specified name;
• provide the argument --project on the command line;
• set the property core/project.

--region=REGION

ID of the region or fully qualified identifier for the region.

To set the region attribute:

• provide the argument --region on the command line;
• set the property ai/region;
• choose one from the prompted list of available regions.

--service-account=SERVICE_ACCOUNT

The email address of a service account to use when running the training appplication. You must have the iam.serviceAccounts.actAs permission for the specified service account.

Key resource - The Cloud KMS (Key Management Service) cryptokey that will be used to protect the hyperparameter tuning job. The 'Vertex AI Service Agent' service account must hold permission 'Cloud KMS CryptoKey Encrypter/Decrypter'. The arguments in this group can be used to specify the attributes of this resource.

--kms-key=KMS_KEY

ID of the key or fully qualified identifier for the key.

To set the kms-key attribute:

• provide the argument --kms-key on the command line.

This flag argument must be specified if any of the other arguments in this group are specified.

--kms-keyring=KMS_KEYRING

The KMS keyring of the key.

To set the kms-keyring attribute:

• provide the argument --kms-key on the command line with a fully specified name;
• provide the argument --kms-keyring on the command line.

--kms-location=KMS_LOCATION

The Google Cloud location for the key.

To set the kms-location attribute:

• provide the argument --kms-key on the command line with a fully specified name;
• provide the argument --kms-location on the command line.

--kms-project=KMS_PROJECT

The Google Cloud project for the key.

To set the kms-project attribute:

• provide the argument --kms-key on the command line with a fully specified name;
• provide the argument --kms-project on the command line;
• set the property core/project.

GCLOUD WIDE FLAGS

These flags are available to all commands: --access-token-file, --account, --billing-project, --configuration, --flags-file, --flatten, --format, --help, --impersonate-service-account, --log-http, --project, --quiet, --trace-token, --user-output-enabled, --verbosity.

Run $ gcloud help for details.

NOTES

This command is currently in beta and might change without notice. These variants are also available:

gcloud ai hp-tuning-jobs create

gcloud alpha ai hp-tuning-jobs create