- NAME
-
- gcloud beta ai-platform versions create - create a new AI Platform version
- SYNOPSIS
-
-
gcloud beta ai-platform 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 ai-platform 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
orautoScaling
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
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 ai-platform versions create
gcloud alpha ai-platform versions create
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-02-06 UTC.
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