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gcloud-help/gcloud/beta/ml-engine/versions/create
Automated 1456dab6c7 gcloud: Tue Mar 1 04:29:52 UTC 2022
2022-03-01 04:29:52 +00:00

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NAME
gcloud beta ml-engine versions create - create a new AI Platform version
SYNOPSIS
gcloud beta ml-engine versions create VERSION --model=MODEL
[--accelerator=[count=COUNT],[type=TYPE]] [--async] [--config=CONFIG]
[--description=DESCRIPTION] [--explanation-method=EXPLANATION_METHOD]
[--framework=FRAMEWORK] [--labels=[KEY=VALUE,...]]
[--machine-type=MACHINE_TYPE]
[--num-integral-steps=NUM_INTEGRAL_STEPS; default=50]
[--num-paths=NUM_PATHS; default=50] [--origin=ORIGIN]
[--python-version=PYTHON_VERSION] [--region=REGION]
[--runtime-version=RUNTIME_VERSION] [--service-account=SERVICE_ACCOUNT]
[--staging-bucket=STAGING_BUCKET]
[--args=[ARG,...] --command=[COMMAND,...]
--env-vars=[KEY=VALUE,...] --image=IMAGE --ports=[ARG,...]]
[--health-route=HEALTH_ROUTE --predict-route=PREDICT_ROUTE]
[--max-nodes=MAX_NODES
--metric-targets=[METRIC-NAME=TARGET,...] --min-nodes=MIN_NODES]
[--package-uris=[PACKAGE_URI,...] --prediction-class=PREDICTION_CLASS]
[GCLOUD_WIDE_FLAG ...]
DESCRIPTION
(BETA) Creates a new version of an AI Platform model.
For more details on managing AI Platform models and versions see
https://cloud.google.com/ai-platform/prediction/docs/managing-models-jobs
EXAMPLES
To create an AI Platform version model with the version ID 'versionId' and
with the name 'model-name', run:
$ gcloud beta ml-engine versions create versionId --model=model-name
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 Compute Engine Machine Types, a GPU accelerator may also be
selected.
type
The type of the accelerator. Choices are 'nvidia-tesla-a100',
'nvidia-tesla-k80', 'nvidia-tesla-p100', 'nvidia-tesla-p4',
'nvidia-tesla-t4', 'nvidia-tesla-v100'.
count
The number of accelerators to attach to each machine running the
job. If not specified, the default value is 1. Your model must be
specially designed to accommodate more than 1 accelerator per
machine. To configure how many replicas your model has, set the
manualScaling or autoScaling parameters.
--async
Return immediately, without waiting for the operation in progress to
complete.
--config=CONFIG
Path to a YAML configuration file containing configuration parameters
for the Version
(https://cloud.google.com/ai-platform/prediction/docs/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: '2.1'
# Set only one of either manualScaling or autoScaling.
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 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.
--description=DESCRIPTION
Description of the version.
--explanation-method=EXPLANATION_METHOD
Enable explanations and select the explanation method to use.
The valid options are: integrated-gradients: Use Integrated Gradients.
sampled-shapley: Use Sampled Shapley. xrai: Use XRAI.
EXPLANATION_METHOD must be one of: integrated-gradients,
sampled-shapley, xrai.
--framework=FRAMEWORK
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. For available machine types, see
https://cloud.google.com/ai-platform/prediction/docs/machine-types-online-prediction#available_machine_types.
--num-integral-steps=NUM_INTEGRAL_STEPS; default=50
Number of integral steps for Integrated Gradients. Only valid when
--explanation-method=integrated-gradients or --explanation-method=xrai
is specified.
--num-paths=NUM_PATHS; default=50
Number of paths for Sampled Shapley. Only valid when
--explanation-method=sampled-shapley is specified.
--origin=ORIGIN
Location of model/ "directory" (see
https://cloud.google.com/ai-platform/prediction/docs/deploying-models#upload-model).
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 Cloud Storage (gs://) path or local file path (no prefix). In
the latter case the files will be uploaded to Cloud Storage and a
--staging-bucket argument is required.
--python-version=PYTHON_VERSION
Version of Python used when creating the version. Choices are 3.7, 3.5,
and 2.7. However, this value must be compatible with the chosen runtime
version for the job.
Must be used with a compatible runtime version:
◆ 3.7 is compatible with runtime versions 1.15 and later.
◆ 3.5 is compatible with runtime versions 1.4 through 1.14.
◆ 2.7 is compatible with runtime versions 1.15 and earlier.
--region=REGION
Google Cloud region of the regional endpoint to use for this command.
For the global endpoint, the region needs to be specified as global.
Learn more about regional endpoints and see a list of available
regions:
https://cloud.google.com/ai-platform/prediction/docs/regional-endpoints
REGION must be one of: global, asia-east1, asia-northeast1,
asia-southeast1, australia-southeast1, europe-west1, europe-west2,
europe-west3, europe-west4, northamerica-northeast1, us-central1,
us-east1, us-east4, us-west1.
--runtime-version=RUNTIME_VERSION
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/ai-platform/prediction/docs/runtime-version-list
--service-account=SERVICE_ACCOUNT
Specifies the service account for resource access control.
--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 the container to be deployed.
--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 Cmd is used.
--command=[COMMAND,...]
Entrypoint for the container image. If not specified, the container
image's default Entrypoint is run.
--env-vars=[KEY=VALUE,...]
List of key-value pairs to set as environment variables.
--image=IMAGE
Name of the container image to deploy (e.g.
gcr.io/myproject/server:latest).
--ports=[ARG,...]
Container ports to receive requests at. Must be a number between 1
and 65535, inclusive.
Flags to control the paths that requests and health checks are sent to.
--health-route=HEALTH_ROUTE
HTTP path to send health checks to inside the container.
--predict-route=PREDICT_ROUTE
HTTP path to send prediction requests to inside the container.
Configure the autoscaling settings to be deployed.
--max-nodes=MAX_NODES
The maximum number of nodes to scale this model under load.
--metric-targets=[METRIC-NAME=TARGET,...]
List of key-value pairs to set as metrics' target for autoscaling.
Autoscaling could be based on CPU usage or GPU duty cycle, valid key
could be cpu-usage or gpu-duty-cycle.
--min-nodes=MIN_NODES
The minimum number of nodes to scale this model under load.
Configure user code in prediction.
AI Platform allows a model to have user-provided prediction
code; these options configure that code.
--package-uris=[PACKAGE_URI,...]
Comma-separated list of Cloud Storage URIs ('gs://...') for
user-supplied Python packages to use.
--prediction-class=PREDICTION_CLASS
Fully-qualified name of the custom prediction class in the package
provided for custom prediction.
For example, --prediction-class=my_package.SequenceModel.
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
This command is currently in beta and might change without notice. These
variants are also available:
$ gcloud ml-engine versions create
$ gcloud alpha ml-engine versions create
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