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

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

SYNOPSIS
    gcloud ai endpoints deploy-model (ENDPOINT : --region=REGION)
        --display-name=DISPLAY_NAME --model=MODEL
        [--accelerator=[count=COUNT],[type=TYPE]]
        [--deployed-model-id=DEPLOYED_MODEL_ID] [--disable-container-logging]
        [--enable-access-logging] [--machine-type=MACHINE_TYPE]
        [--max-replica-count=MAX_REPLICA_COUNT]
        [--min-replica-count=MIN_REPLICA_COUNT]
        [--service-account=SERVICE_ACCOUNT]
        [--traffic-split=[DEPLOYED_MODEL_ID=VALUE,...]] [GCLOUD_WIDE_FLAG ...]

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

        $ gcloud 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 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-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

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

     --disable-container-logging
        For custom-trained Models and AutoML Tabular Models, the container of
        the deployed model instances will send stderr and stdout streams to
        Cloud Logging by default. Please note that the logs incur cost, which
        are subject to Cloud Logging pricing
        (https://cloud.google.com/stackdriver/pricing).

        User can disable container logging by setting this flag to true.

     --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.

     --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 the deployed model will be always
        deployed on.

     --min-replica-count=MIN_REPLICA_COUNT
        Minimum number of machine replicas the deployed model will be always
        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.

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

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 ai endpoints deploy-model

        $ gcloud beta ai endpoints deploy-model