gcloud-help/gcloud/alpha/ai/endpoints/deploy-model

NAME
    gcloud alpha ai endpoints deploy-model - deploy a model to an existing
        Vertex AI endpoint

SYNOPSIS
    gcloud alpha ai endpoints deploy-model (ENDPOINT : --region=REGION)
        --display-name=DISPLAY_NAME --model=MODEL
        [--accelerator=[count=COUNT],[type=TYPE]]
        [--autoscaling-metric-specs=[METRIC-NAME=TARGET,...]]
        [--deployed-model-id=DEPLOYED_MODEL_ID] [--enable-access-logging]
        [--enable-container-logging] [--machine-type=MACHINE_TYPE]
        [--max-replica-count=MAX_REPLICA_COUNT]
        [--min-replica-count=MIN_REPLICA_COUNT]
        [--service-account=SERVICE_ACCOUNT] [--tpu-topology=TPU_TOPOLOGY]
        [--traffic-split=[DEPLOYED_MODEL_ID=VALUE,...]]
        [--shared-resources=SHARED_RESOURCES
          : --shared-resources-region=SHARED_RESOURCES_REGION]
        [GCLOUD_WIDE_FLAG ...]

EXAMPLES
    To deploy a model 456 to an endpoint 123 under project example in region
    us-central1, run:

        $ gcloud alpha ai endpoints deploy-model 123 --project=example \
            --region=us-central1 --model=456 \
            --display-name=my_deployed_model

POSITIONAL ARGUMENTS
     Endpoint resource - The endpoint to deploy a model to. The arguments in
     this group can be used to specify the attributes of this 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 endpoint on the command line with a fully
        specified name;
      ◆ provide the argument --project on the command line;
      ◆ set the property core/project.

     This must be specified.

       ENDPOINT
          ID of the endpoint or fully qualified identifier for the endpoint.

          To set the name attribute:
          ▸ provide the argument endpoint on the command line.

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

       --region=REGION
          Cloud region for the endpoint.

          To set the region attribute:
          ▸ provide the argument endpoint on the command line with a fully
            specified name;
          ▸ provide the argument --region on the command line;
          ▸ set the property ai/region;
          ▸ choose one from the prompted list of available regions.

REQUIRED FLAGS
     --display-name=DISPLAY_NAME
        Display name of the deployed model.

     --model=MODEL
        Id of the uploaded 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-a100-80gb',
            'nvidia-h100-80gb', 'nvidia-h100-mega-80gb', 'nvidia-l4',
            '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. This is usually 1. If not specified, the default value is 1.

            For example: --accelerator=type=nvidia-tesla-k80,count=1

     --autoscaling-metric-specs=[METRIC-NAME=TARGET,...]
        Metric specifications that overrides a resource utilization metric's
        target value. At most one entry is allowed per metric.

         METRIC-NAME
            Resource metric name. Choices are 'cpu-usage', 'gpu-duty-cycle'.

         TARGET
            Target resource utilization in percentage (1% - 100%) for the given
            metric. If the value is set to 60, the target resource utilization
            is 60%.

            For example: --autoscaling-metric-specs=cpu-usage=70

     --deployed-model-id=DEPLOYED_MODEL_ID
        User-specified ID of the deployed-model.

     --enable-access-logging
        If true, online prediction access logs are sent to Cloud Logging.

        These logs are standard server access logs, containing information like
        timestamp and latency for each prediction request.

     --enable-container-logging
        If true, the container of the deployed model instances will send stderr
        and stdout streams to Cloud Logging.

        Currently, only supported for custom-trained Models and AutoML Tabular
        Models.

     --machine-type=MACHINE_TYPE
        The machine resources to be used for each node of this deployment. For
        available machine types, see
        https://cloud.google.com/ai-platform-unified/docs/predictions/machine-types.

     --max-replica-count=MAX_REPLICA_COUNT
        Maximum number of machine replicas for the deployment resources the
        model will be deployed on.

     --min-replica-count=MIN_REPLICA_COUNT
        Minimum number of machine replicas for the deployment resources the
        model will be deployed on. If specified, the value must be equal to or
        larger than 1.

        If not specified and the uploaded models use dedicated resources, the
        default value is 1.

     --service-account=SERVICE_ACCOUNT
        Service account that the deployed model's container runs as. Specify
        the email address of the service account. If this service account is
        not specified, the container runs as a service account that doesn't
        have access to the resource project.

     --tpu-topology=TPU_TOPOLOGY
        CloudTPU topology to use for this deployment. Required for multihost
        CloudTPU deployments:
        https://cloud.google.com/kubernetes-engine/docs/concepts/tpus#topology.

     --traffic-split=[DEPLOYED_MODEL_ID=VALUE,...]
        List of pairs of deployed model id and value to set as traffic split.

     Deployment resource pool resource - The deployment resource pool to
     co-host a model on. The arguments in this group can be used to specify the
     attributes of this 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 --shared-resources on the command line with a
        fully specified name;
      ◆ provide the argument --project on the command line;
      ◆ set the property core/project.

       --shared-resources=SHARED_RESOURCES
          ID of the deployment_resource_pool or fully qualified identifier for
          the deployment_resource_pool.

          To set the name attribute:
          ▸ provide the argument --shared-resources on the command line.

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

       --shared-resources-region=SHARED_RESOURCES_REGION
          Cloud region for the deployment_resource_pool.

          To set the region attribute:
          ▸ provide the argument --shared-resources on the command line with
            a fully specified name;
          ▸ provide the argument --shared-resources-region on the command
            line;
          ▸ provide the argument --region on the command line;
          ▸ set the property ai/region;
          ▸ choose one from the prompted list of available regions.

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 endpoints deploy-model

        $ gcloud beta ai endpoints deploy-model