gcloud alpha ai hp-tuning-jobs create - create a hyperparameter tuning job
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; default=1] [--network=NETWORK] [--parallel-trial-count=PARALLEL_TRIAL_COUNT; default=1] [--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 ...]
(ALPHA) Create a hyperparameter tuning job.
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
- --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.
- --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; default=1
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; default=1
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 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 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.
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.
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