versions

Overview

Name	versions
Type	Resource
Id	google.ml.versions

Fields

Name	Datatype	Description
name	string	Required. The name specified for the version when it was created. The version name must be unique within the model it is created in.
description	string	Optional. The description specified for the version when it was created.
acceleratorConfig	object	Represents a hardware accelerator request config. Note that the AcceleratorConfig can be used in both Jobs and Versions. Learn more about accelerators for training and accelerators for online prediction.
autoScaling	object	Options for automatically scaling a model.
container	object	Specification of a custom container for serving predictions. This message is a subset of the Kubernetes Container v1 core specification.
createTime	string	Output only. The time the version was created.
deploymentUri	string	The Cloud Storage URI of a directory containing trained model artifacts to be used to create the model version. See the guide to deploying models for more information. The total number of files under this directory must not exceed 1000. During projects.models.versions.create, AI Platform Prediction copies all files from the specified directory to a location managed by the service. From then on, AI Platform Prediction uses these copies of the model artifacts to serve predictions, not the original files in Cloud Storage, so this location is useful only as a historical record. If you specify container, then this field is optional. Otherwise, it is required. Learn how to use this field with a custom container.
errorMessage	string	Output only. The details of a failure or a cancellation.
etag	string	etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a model from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform model updates in order to avoid race conditions: An etag is returned in the response to GetVersion, and systems are expected to put that etag in the request to UpdateVersion to ensure that their change will be applied to the model as intended.
explanationConfig	object	Message holding configuration options for explaining model predictions. There are three feature attribution methods supported for TensorFlow models: integrated gradients, sampled Shapley, and XRAI. Learn more about feature attributions.
framework	string	Optional. The machine learning framework AI Platform uses to train this version of the model. Valid values are TENSORFLOW, SCIKIT_LEARN, XGBOOST. If you do not specify a framework, AI Platform will analyze files in the deployment_uri to determine a framework. If you choose SCIKIT_LEARN or XGBOOST, you must also set the runtime version of the model to 1.4 or greater. Do not specify a framework if you're deploying a custom prediction routine or if you're using a custom container.
isDefault	boolean	Output only. If true, this version will be used to handle prediction requests that do not specify a version. You can change the default version by calling projects.methods.versions.setDefault.
labels	object	Optional. One or more labels that you can add, to organize your model versions. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels. Note that this field is not updatable for mls1* models.
lastMigrationModelId	string	Output only. The AI Platform (Unified) Model ID for the last model migration.
lastMigrationTime	string	Output only. The last time this version was successfully migrated to AI Platform (Unified).
lastUseTime	string	Output only. The time the version was last used for prediction.
machineType	string	Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. To learn about valid values for this field, read Choosing a machine type for online prediction. If this field is not specified and you are using a regional endpoint, then the machine type defaults to n1-standard-2. If this field is not specified and you are using the global endpoint (ml.googleapis.com), then the machine type defaults to mls1-c1-m2.
manualScaling	object	Options for manually scaling a model.
packageUris	array	Optional. Cloud Storage paths (gs://…) of packages for custom prediction routines or scikit-learn pipelines with custom code. For a custom prediction routine, one of these packages must contain your Predictor class (see predictionClass). Additionally, include any dependencies used by your Predictor or scikit-learn pipeline uses that are not already included in your selected runtime version. If you specify this field, you must also set runtimeVersion to 1.4 or greater.
predictionClass	string	Optional. The fully qualified name (module_name.class_name) of a class that implements the Predictor interface described in this reference field. The module containing this class should be included in a package provided to the packageUris field. Specify this field if and only if you are deploying a custom prediction routine (beta). If you specify this field, you must set runtimeVersion to 1.4 or greater and you must set machineType to a legacy (MLS1) machine type. The following code sample provides the Predictor interface: class Predictor(object): """Interface for constructing custom predictors.""" def predict(self, instances, kwargs): """Performs custom prediction. Instances are the decoded values from the request. They have already been deserialized from JSON. Args: instances: A list of prediction input instances. kwargs: A dictionary of keyword args provided as additional fields on the predict request body. Returns: A list of outputs containing the prediction results. This list must be JSON serializable. """ raise NotImplementedError() @classmethod def from_path(cls, model_dir): """Creates an instance of Predictor using the given path. Loading of the predictor should be done in this method. Args: model_dir: The local directory that contains the exported model file along with any additional files uploaded when creating the version resource. Returns: An instance implementing this Predictor class. """ raise NotImplementedError() Learn more about the Predictor interface and custom prediction routines.
pythonVersion	string	Required. The version of Python used in prediction. The following Python versions are available: Python '3.7' is available when runtime_version is set to '1.15' or later. Python '3.5' is available when runtime_version is set to a version from '1.4' to '1.14'. * Python '2.7' is available when runtime_version is set to '1.15' or earlier. Read more about the Python versions available for each runtime version.
requestLoggingConfig	object	Configuration for logging request-response pairs to a BigQuery table. Online prediction requests to a model version and the responses to these requests are converted to raw strings and saved to the specified BigQuery table. Logging is constrained by BigQuery quotas and limits. If your project exceeds BigQuery quotas or limits, AI Platform Prediction does not log request-response pairs, but it continues to serve predictions. If you are using continuous evaluation, you do not need to specify this configuration manually. Setting up continuous evaluation automatically enables logging of request-response pairs.
routes	object	Specifies HTTP paths served by a custom container. AI Platform Prediction sends requests to these paths on the container; the custom container must run an HTTP server that responds to these requests with appropriate responses. Read Custom container requirements for details on how to create your container image to meet these requirements.
runtimeVersion	string	Required. The AI Platform runtime version to use for this deployment. For more information, see the runtime version list and how to manage runtime versions.
serviceAccount	string	Optional. Specifies the service account for resource access control. If you specify this field, then you must also specify either the containerSpec or the predictionClass field. Learn more about using a custom service account.
state	string	Output only. The state of a version.

Methods

Name	Accessible by	Required Params	Description
projects_models_versions_get	SELECT	modelsId, projectsId, versionsId	Gets information about a model version. Models can have multiple versions. You can call projects.models.versions.list to get the same information that this method returns for all of the versions of a model.
projects_models_versions_list	SELECT	modelsId, projectsId	Gets basic information about all the versions of a model. If you expect that a model has many versions, or if you need to handle only a limited number of results at a time, you can request that the list be retrieved in batches (called pages). If there are no versions that match the request parameters, the list request returns an empty response body: {}.
projects_models_versions_create	INSERT	modelsId, projectsId	Creates a new version of a model from a trained TensorFlow model. If the version created in the cloud by this call is the first deployed version of the specified model, it will be made the default version of the model. When you add a version to a model that already has one or more versions, the default version does not automatically change. If you want a new version to be the default, you must call projects.models.versions.setDefault.
projects_models_versions_delete	DELETE	modelsId, projectsId, versionsId	Deletes a model version. Each model can have multiple versions deployed and in use at any given time. Use this method to remove a single version. Note: You cannot delete the version that is set as the default version of the model unless it is the only remaining version.
_projects_models_versions_list	EXEC	modelsId, projectsId	Gets basic information about all the versions of a model. If you expect that a model has many versions, or if you need to handle only a limited number of results at a time, you can request that the list be retrieved in batches (called pages). If there are no versions that match the request parameters, the list request returns an empty response body: {}.
projects_models_versions_patch	EXEC	modelsId, projectsId, versionsId	Updates the specified Version resource. Currently the only update-able fields are description, requestLoggingConfig, autoScaling.minNodes, and manualScaling.nodes.
projects_models_versions_set_default	EXEC	modelsId, projectsId, versionsId	Designates a version to be the default for the model. The default version is used for prediction requests made against the model that don't specify a version. The first version to be created for a model is automatically set as the default. You must make any subsequent changes to the default version setting manually using this method.