gcloud ai-platform versions create

gcloud ai-platform versions create - create a new AI Platform version
gcloud ai-platform versions create VERSION --model=MODEL [--async] [--config=CONFIG] [--description=DESCRIPTION] [--framework=FRAMEWORK] [--labels=[KEY=VALUE,…]] [--origin=ORIGIN] [--python-version=PYTHON_VERSION] [--runtime-version=RUNTIME_VERSION] [--staging-bucket=STAGING_BUCKET] [GCLOUD_WIDE_FLAG]
Creates a new version of an AI Platform model.

For more details on managing AI Platform models and versions see https://cloud.google.com/ml-engine/docs/how-tos/managing-models-jobs

Name of the model version.
Name of the model.
Return immediately, without waiting for the operation in progress to complete.
Path to a YAML configuration file containing configuration parameters for the version 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: '1.0'
    nodes: 10  # The number of nodes to allocate for this model.
    minNodes: 0  # The minimum number of nodes to allocate for this model.
    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.

The description of the version.
The 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.
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.

Location of model/ "directory" (as output by https://www.tensorflow.org/versions/r0.12/api_docs/python/state_ops.html#Saver).

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 Google Cloud Storage (gs://) path or local file path (no prefix). In the latter case the files will be uploaded to Google Cloud Storage and a --staging-bucket argument is required.

Version of Python used when creating the version. If not set, the default version is 2.7. Python 3.5 is available when --runtime-version is set to 1.4 and above. Python 2.7 works with all supported runtime versions.
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/ml-engine/docs/tensorflow/runtime-version-list
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.

These flags are available to all commands: --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.

To create an AI Platform version model with the version ID 'versionId' and with the name 'model-name', run:
gcloud ai-platform versions create versionId --model=model-name
These variants are also available:
gcloud alpha ai-platform versions create
gcloud beta ai-platform versions create