gcloud-help/gcloud/alpha/ai/hp-tuning-jobs/create

NAME
    gcloud alpha ai hp-tuning-jobs create - create a hyperparameter tuning job

SYNOPSIS
    gcloud alpha 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
    (ALPHA) Create a hyperparameter tuning job.

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

        $ gcloud alpha 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
        (https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell)
        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 alpha and might change without notice. If this
    command fails with API permission errors despite specifying the correct
    project, you might be trying to access an API with an invitation-only early
    access allowlist. These variants are also available:

        $ gcloud ai hp-tuning-jobs create

        $ gcloud beta ai hp-tuning-jobs create