POSITIONAL ARGUMENTS

VERSION

Name of the model version.

REQUIRED FLAGS

--model=MODEL

Name of the model.

OPTIONAL FLAGS

--accelerator=[count=COUNT],[type=TYPE]

Manage the accelerator config for GPU serving. When deploying a model with the new Alpha Google Compute Engine Machine Types, a GPU accelerator may also be selected.

type

The type of the accelerator. Choices are 'nvdia-tesla-k80', 'nvdia-tesla-p100', 'nvdia-tesla-v100' and 'nvdia-tesla-p4'.

count

The number of accelerators to attach to each machine running the job.

--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=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.

--machine-type=MACHINE_TYPE

Type of machine on which to serve the model. Currently only applies to online prediction. Currently supported machine_types are:

---

mls1-c1-m2 - A virtual machine with 1 core and 2 Gb RAM (default).

---

mls1-c4-m2 - A virtual machine with 4 core and 2 Gb RAM.

---

mls1-highmem-1 - A virtual machine with 1 core and 2 Gb RAM (will be deprecated soon).

---

mls1-highcpu-4 - A virtual machine with 4 core and 2 Gb RAM (will be deprecated soon).

--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.

Configure user code in prediction. Cloud ML Engine allows a model to have user-provided prediction code; these options configure that code.

--model-class=MODEL_CLASS

The fully-qualified name of the custom Model class in the package provided for custom prediction.
For example, --model-class my_package.SequenceModel.

--package-uris=[PACKAGE_URI,...]

Comma-separated list of Google Cloud Storage URIs (gs://...) for user-supplied Python packages to use.

GCLOUD WIDE FLAGS

NOTES

$ gcloud ml-engine versions create $ gcloud beta ml-engine versions create