gcloud-help/gcloud/alpha/ai-platform/versions/create

NAME
    gcloud alpha ai-platform versions create - create a new AI Platform version

SYNOPSIS
    gcloud alpha ai-platform versions create VERSION --model=MODEL
        [--accelerator=[count=COUNT],[type=TYPE]] [--async] [--config=CONFIG]
        [--description=DESCRIPTION] [--explanation-method=EXPLANATION_METHOD]
        [--framework=FRAMEWORK] [--labels=[KEY=VALUE,...]]
        [--machine-type=MACHINE_TYPE]
        [--num-integral-steps=NUM_INTEGRAL_STEPS; default=50]
        [--num-paths=NUM_PATHS; default=50] [--origin=ORIGIN]
        [--python-version=PYTHON_VERSION] [--region=REGION]
        [--runtime-version=RUNTIME_VERSION] [--service-account=SERVICE_ACCOUNT]
        [--staging-bucket=STAGING_BUCKET]
        [--args=[ARG,...] --command=[COMMAND,...]
          --env-vars=[KEY=VALUE,...] --image=IMAGE --ports=[ARG,...]]
        [--health-route=HEALTH_ROUTE --predict-route=PREDICT_ROUTE]
        [--max-nodes=MAX_NODES
          --metric-targets=[METRIC-NAME=TARGET,...] --min-nodes=MIN_NODES]
        [--package-uris=[PACKAGE_URI,...] --prediction-class=PREDICTION_CLASS]
        [GCLOUD_WIDE_FLAG ...]

DESCRIPTION
    (ALPHA) 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 alpha ai-platform 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.

     --explanation-method=EXPLANATION_METHOD
        Enable explanations and select the explanation method to use.

        The valid options are: integrated-gradients: Use Integrated Gradients.
        sampled-shapley: Use Sampled Shapley. xrai: Use XRAI.

        EXPLANATION_METHOD must be one of: integrated-gradients,
        sampled-shapley, xrai.

     --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.

     --num-integral-steps=NUM_INTEGRAL_STEPS; default=50
        Number of integral steps for Integrated Gradients. Only valid when
        --explanation-method=integrated-gradients or --explanation-method=xrai
        is specified.

     --num-paths=NUM_PATHS; default=50
        Number of paths for Sampled Shapley. Only valid when
        --explanation-method=sampled-shapley is specified.

     --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

     --service-account=SERVICE_ACCOUNT
        Specifies the service account for resource access control.

     --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 container to be deployed.

       --args=[ARG,...]
          Comma-separated arguments passed to the command run by the container
          image. If not specified and no '--command' is provided, the container
          image's default Cmd is used.

       --command=[COMMAND,...]
          Entrypoint for the container image. If not specified, the container
          image's default Entrypoint is run.

       --env-vars=[KEY=VALUE,...]
          List of key-value pairs to set as environment variables.

       --image=IMAGE
          Name of the container image to deploy (e.g.
          gcr.io/myproject/server:latest).

       --ports=[ARG,...]
          Container ports to receive requests at. Must be a number between 1
          and 65535, inclusive.

     Flags to control the paths that requests and health checks are sent to.

       --health-route=HEALTH_ROUTE
          HTTP path to send health checks to inside the container.

       --predict-route=PREDICT_ROUTE
          HTTP path to send prediction requests to inside the container.

     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.

     Configure user code in prediction.

         AI Platform allows a model to have user-provided prediction
         code; these options configure that code.

       --package-uris=[PACKAGE_URI,...]
          Comma-separated list of Cloud Storage URIs ('gs://...') for
          user-supplied Python packages to use.

       --prediction-class=PREDICTION_CLASS
          Fully-qualified name of the custom prediction class in the package
          provided for custom prediction.

          For example, --prediction-class=my_package.SequenceModel.

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-platform versions create

        $ gcloud beta ai-platform versions create