gcloud-help/gcloud/ai/models/upload

NAME
    gcloud ai models upload - upload a new model

SYNOPSIS
    gcloud ai models upload --container-image-uri=CONTAINER_IMAGE_URI
        --display-name=DISPLAY_NAME [--artifact-uri=ARTIFACT_URI]
        [--container-args=[ARG,...]] [--container-command=[COMMAND,...]]
        [--container-env-vars=[KEY=VALUE,...]]
        [--container-health-route=CONTAINER_HEALTH_ROUTE]
        [--container-ports=[PORT,...]]
        [--container-predict-route=CONTAINER_PREDICT_ROUTE]
        [--description=DESCRIPTION]
        [--explanation-metadata-file=EXPLANATION_METADATA_FILE]
        [--explanation-method=EXPLANATION_METHOD]
        [--explanation-path-count=EXPLANATION_PATH_COUNT]
        [--explanation-step-count=EXPLANATION_STEP_COUNT] [--region=REGION]
        [--smooth-grad-noise-sigma=SMOOTH_GRAD_NOISE_SIGMA]
        [--smooth-grad-noise-sigma-by-feature=[KEY=VALUE,...]]
        [--smooth-grad-noisy-sample-count=SMOOTH_GRAD_NOISY_SAMPLE_COUNT]
        [GCLOUD_WIDE_FLAG ...]

EXAMPLES
    To upload a model under project example in region us-central1, run:

        $ gcloud ai models upload \
            --container-image-uri="gcr.io/example/my-image" \
            --description=example-model --display-name=my-model \
            --artifact-uri='gs://bucket/path' --project=example \
            --region=us-central1

REQUIRED FLAGS
     --container-image-uri=CONTAINER_IMAGE_URI
        URI of the Model serving container file in the Container Registry (e.g.
        gcr.io/myproject/server:latest).

     --display-name=DISPLAY_NAME
        Display name of the model.

OPTIONAL FLAGS
     --artifact-uri=ARTIFACT_URI
        Path to the directory containing the Model artifact and any of its
        supporting files.

     --container-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 command is used.

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

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

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

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

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

     --description=DESCRIPTION
        Description of the model.

     --explanation-metadata-file=EXPLANATION_METADATA_FILE
        Path to a local JSON file that contains the metadata describing the
        Model's input and output for explanation.

     --explanation-method=EXPLANATION_METHOD
        Method used for explanation. Accepted values are integrated-gradients,
        xrai and sampled-shapley.

     --explanation-path-count=EXPLANATION_PATH_COUNT
        Number of feature permutations to consider when approximating the
        Shapley values for explanation.

     --explanation-step-count=EXPLANATION_STEP_COUNT
        Number of steps to approximate the path integral for explanation.

     Region resource - Cloud region to upload model. 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.

     --smooth-grad-noise-sigma=SMOOTH_GRAD_NOISE_SIGMA
        Single float value used to add noise to all the features for
        explanation. Only applicable to explanation method integrated-gradients
        or xrai.

     --smooth-grad-noise-sigma-by-feature=[KEY=VALUE,...]
        Noise sigma by features for explanation. Noise sigma represents the
        standard deviation of the gaussian kernel that will be used to add
        noise to interpolated inputs prior to computing gradients. Only
        applicable to explanation method integrated-gradients or xrai.

     --smooth-grad-noisy-sample-count=SMOOTH_GRAD_NOISY_SAMPLE_COUNT
        Number of gradient samples used for approximation at explanation. Only
        applicable to explanation method integrated-gradients or xrai.

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 models upload

        $ gcloud beta ai models upload