gcloud-help/gcloud/ml-engine/versions/create

NAME
    gcloud ml-engine versions create - create a new AI Platform version

SYNOPSIS
    gcloud ml-engine versions create VERSION --model=MODEL
        [--accelerator=[count=COUNT],[type=TYPE]] [--async] [--config=CONFIG]
        [--description=DESCRIPTION] [--framework=FRAMEWORK]
        [--labels=[KEY=VALUE,...]] [--machine-type=MACHINE_TYPE]
        [--origin=ORIGIN] [--python-version=PYTHON_VERSION] [--region=REGION]
        [--runtime-version=RUNTIME_VERSION] [--staging-bucket=STAGING_BUCKET]
        [--max-nodes=MAX_NODES
          --metric-targets=[METRIC-NAME=TARGET,...] --min-nodes=MIN_NODES]
        [GCLOUD_WIDE_FLAG ...]

DESCRIPTION
    Creates a new version of an AI Platform model.

    For more details on managing AI Platform models and versions see
    https://cloud.google.com/ai-platform/prediction/docs/managing-models-jobs

EXAMPLES
    To create an AI Platform version model with the version ID 'versionId' and
    with the name 'model-name', run:

        $ gcloud ml-engine versions create versionId --model=model-name

POSITIONAL ARGUMENTS
     VERSION
        Name of the model version.

REQUIRED FLAGS
     --model=MODEL
        Name of the model.

OPTIONAL FLAGS
     --accelerator=[count=COUNT],[type=TYPE]
        Manage the accelerator config for GPU serving. When deploying a model
        with Compute Engine Machine Types, a GPU accelerator may also be
        selected.

         type
            The type of the accelerator. Choices are 'nvidia-tesla-a100',
            'nvidia-tesla-k80', 'nvidia-tesla-p100', 'nvidia-tesla-p4',
            'nvidia-tesla-t4', 'nvidia-tesla-v100'.

         count
            The number of accelerators to attach to each machine running the
            job. If not specified, the default value is 1. Your model must be
            specially designed to accommodate more than 1 accelerator per
            machine. To configure how many replicas your model has, set the
            manualScaling or autoScaling parameters.

     --async
        Return immediately, without waiting for the operation in progress to
        complete.

     --config=CONFIG
        Path to a YAML configuration file containing configuration parameters
        for the Version
        (https://cloud.google.com/ai-platform/prediction/docs/reference/rest/v1/projects.models.versions)
        to create.

        The file is in YAML format. Note that not all attributes of a version
        are configurable; available attributes (with example values) are:

            description: A free-form description of the version.
            deploymentUri: gs://path/to/source
            runtimeVersion: '2.1'
            #  Set only one of either manualScaling or autoScaling.
            manualScaling:
              nodes: 10  # The number of nodes to allocate for this model.
            autoScaling:
              minNodes: 0  # The minimum number of nodes to allocate for this model.
            labels:
              user-defined-key: user-defined-value

        The name of the version must always be specified via the required
        VERSION argument.

        Only one of manualScaling or autoScaling can be specified. If both are
        specified in same yaml file an error will be returned.

        If an option is specified both in the configuration file and via
        command-line arguments, the command-line arguments override the
        configuration file.

     --description=DESCRIPTION
        Description of the version.

     --framework=FRAMEWORK
        ML framework used to train this version of the model. If not specified,
        defaults to 'tensorflow'. FRAMEWORK must be one of: scikit-learn,
        tensorflow, xgboost.

     --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.

     --machine-type=MACHINE_TYPE
        Type of machine on which to serve the model. Currently only applies to
        online prediction. For available machine types, see
        https://cloud.google.com/ai-platform/prediction/docs/machine-types-online-prediction#available_machine_types.

     --origin=ORIGIN
        Location of model/ "directory" (see
        https://cloud.google.com/ai-platform/prediction/docs/deploying-models#upload-model).

        This overrides deploymentUri in the --config file. If this flag is not
        passed, deploymentUri must be specified in the file from --config.

        Can be a Cloud Storage (gs://) path or local file path (no prefix). In
        the latter case the files will be uploaded to Cloud Storage and a
        --staging-bucket argument is required.

     --python-version=PYTHON_VERSION
        Version of Python used when creating the version. Choices are 3.7, 3.5,
        and 2.7. However, this value must be compatible with the chosen runtime
        version for the job.

        Must be used with a compatible runtime version:

        ◆ 3.7 is compatible with runtime versions 1.15 and later.
        ◆ 3.5 is compatible with runtime versions 1.4 through 1.14.
        ◆ 2.7 is compatible with runtime versions 1.15 and earlier.

     --region=REGION
        Google Cloud region of the regional endpoint to use for this command.
        For the global endpoint, the region needs to be specified as global.

        Learn more about regional endpoints and see a list of available
        regions:
        https://cloud.google.com/ai-platform/prediction/docs/regional-endpoints

        REGION must be one of: global, asia-east1, asia-northeast1,
        asia-southeast1, australia-southeast1, europe-west1, europe-west2,
        europe-west3, europe-west4, northamerica-northeast1, us-central1,
        us-east1, us-east4, us-west1.

     --runtime-version=RUNTIME_VERSION
        AI Platform runtime version for this job. Must be specified unless
        --master-image-uri is specified instead. It is defined in documentation
        along with the list of supported versions:
        https://cloud.google.com/ai-platform/prediction/docs/runtime-version-list

     --staging-bucket=STAGING_BUCKET
        Bucket in which to stage training archives.

        Required only if a file upload is necessary (that is, other flags
        include local paths) and no other flags implicitly specify an upload
        path.

     Configure the autoscaling settings to be deployed.

       --max-nodes=MAX_NODES
          The maximum number of nodes to scale this model under load.

       --metric-targets=[METRIC-NAME=TARGET,...]
          List of key-value pairs to set as metrics' target for autoscaling.
          Autoscaling could be based on CPU usage or GPU duty cycle, valid key
          could be cpu-usage or gpu-duty-cycle.

       --min-nodes=MIN_NODES
          The minimum number of nodes to scale this model under load.

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
    These variants are also available:

        $ gcloud alpha ml-engine versions create

        $ gcloud beta ml-engine versions create