gcloud_ml-engine_versions_create (1)
NAME
- gcloud ml-engine versions create - create a new Cloud ML Engine version
SYNOPSIS
-
gcloud ml-engine 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 ...]
DESCRIPTION
For more details on managing ML Engine models and versions see cloud.google.com/ml-engine/docs/how-tos/managing-models-jobs
POSITIONAL ARGUMENTS
-
- VERSION
-
Name of the model version.
REQUIRED FLAGS
-
- --model=MODEL
-
Name of the model.
OPTIONAL FLAGS
-
- --async
-
Display information about the operation in progress, without waiting for the
operation to complete.
- --config=CONFIG
-
Path to a YAML configuration file containing configuration parameters for the
Version (cloud.google.com/ml/reference/rest/v1/projects.models.versions
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'
manualScaling:
nodes: 10 # The number of nodes to allocate for this model. autoScaling:
minNodes: 0 # The minimum number of nodes to allocate for this model. labels:
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 must 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.
-
description: A free-form description of the version.
deploymentUri: gs://path/to/source
runtimeVersion: '1.0'
manualScaling:
- --description=DESCRIPTION
-
The description of the version.
- --framework=FRAMEWORK
-
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.
- --labels=[KEY=VALUE,...]
-
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.
- --origin=ORIGIN
-
Location of model/ "directory" (as output by
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.
- --python-version=PYTHON_VERSION
-
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.
- --runtime-version=RUNTIME_VERSION
-
Google Cloud ML Engine runtime version for this job. Defaults to a stable
version, which is defined in documentation along with the list of supported
versions:
cloud.google.com/ml-engine/docs/tensorflow/runtime-version-list
- --staging-bucket=STAGING_BUCKET
-
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.
GCLOUD WIDE FLAGS
These flags are available to all commands: --account, --configuration, --flags-file, --flatten, --format, --help, --log-http, --project, --quiet, --trace-token, --user-output-enabled, --verbosity. Run $ gcloud help for details.
NOTES
These variants are also available:
- $ gcloud alpha ml-engine versions create $ gcloud beta ml-engine versions create