gcloud ai models upload - upload a new model
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] [--labels=[KEY=VALUE,...]] [--model-id=MODEL_ID] [--parent-model=PARENT_MODEL] [--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] [--version-aliases=[VERSION_ALIASES,...]] [--version-description=VERSION_DESCRIPTION] [GCLOUD_WIDE_FLAG ...]
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
- --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.
- --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.
- --labels=[KEY=VALUE,...]
Labels with user-defined metadata to organize your Models.
Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.
See https://goo.gl/xmQnxf for more information and examples of labels.
- --model-id=MODEL_ID
ID to use for the uploaded Model, which will become the final component of the model resource name.
- --parent-model=PARENT_MODEL
Resource name of the model into which to upload the version. Only specify this field when uploading a new version.
Value should be provided in format: projects/PROJECT_ID/locations/REGION/models/PARENT_MODEL_ID
- 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.
- --version-aliases=[VERSION_ALIASES,...]
Aliases used to reference a model version instead of auto-generated version ID. The aliases mentioned in the flag will replace the aliases set in the model.
- --version-description=VERSION_DESCRIPTION
Description of the model version.
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.
These variants are also available:
$ gcloud alpha ai models upload
$ gcloud beta ai models upload