Version class

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.

Constructors

Version(String name, {VersionArgs? args, CustomResourceOptions? options})
Creates a new Version. name The Pulumi resource name. args Arguments used to configure this Version. The set of arguments for Version. options Resource options controlling this resource's behavior.

Properties

acceleratorConfig ↔ Output<GoogleCloudMlV1AcceleratorConfigResponse>
Optional. Accelerator config for using GPUs for online prediction (beta). Only specify this field if you have specified a Compute Engine (N1) machine type in the machineType field. Learn more about using GPUs for online prediction.
latefinal
autoScaling ↔ Output<GoogleCloudMlV1AutoScalingResponse>
Automatically scale the number of nodes used to serve the model in response to increases and decreases in traffic. Care should be taken to ramp up traffic according to the model's ability to scale or you will start seeing increases in latency and 429 response codes.
latefinal
childResources → Set<Resource>
finalinherited
completionSources ↔ Map<String, IOutputCompletionSource>
latefinalinherited
container ↔ Output<GoogleCloudMlV1ContainerSpecResponse>
Optional. Specifies a custom container to use for serving predictions. If you specify this field, then machineType is required. If you specify this field, then deploymentUri is optional. If you specify this field, then you must not specify runtimeVersion, packageUris, framework, pythonVersion, or predictionClass.
latefinal
createTime ↔ Output<String>
The time the version was created.
latefinal
deploymentUri ↔ Output<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.
latefinal
description ↔ Output<String>
Optional. The description specified for the version when it was created.
latefinal
errorMessage ↔ Output<String>
The details of a failure or a cancellation.
latefinal
etag ↔ Output<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.
latefinal
explanationConfig ↔ Output<GoogleCloudMlV1ExplanationConfigResponse>
Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
latefinal
framework ↔ Output<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.
latefinal
hashCode → int
The hash code for this object.
no setterinherited
id ↔ Output<String>
getter/setter pairinherited
isCustom → bool
Returns whether this resource is provider-managed.
no setterinherited
isDefault ↔ Output<bool>
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.
latefinal
isProtected → bool
Returns whether this resource is protected from deletion.
no setterinherited
isRemote → bool
Whether this resource is registered as remote.
no setterinherited
isResourceReference → bool
Whether this instance represents a resource value returned over RPC.
finalinherited
labels ↔ Output<Map<String, String>>
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.
latefinal
lastMigrationModelId ↔ Output<String>
The AI Platform (Unified) Model ID for the last model migration.
latefinal
lastMigrationTime ↔ Output<String>
The last time this version was successfully migrated to AI Platform (Unified).
latefinal
lastUseTime ↔ Output<String>
The time the version was last used for prediction.
latefinal
machineType ↔ Output<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.
latefinal
manualScaling ↔ Output<GoogleCloudMlV1ManualScalingResponse>
Manually select the number of nodes to use for serving the model. You should generally use auto_scaling with an appropriate min_nodes instead, but this option is available if you want more predictable billing. Beware that latency and error rates will increase if the traffic exceeds that capability of the system to serve it based on the selected number of nodes.
latefinal
modelId ↔ Output<String>
latefinal
name ↔ Output<String>
The name specified for the version when it was created. The version name must be unique within the model it is created in.
latefinal
packageUris ↔ Output<List<String>>
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.
latefinal
predictionClass ↔ Output<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.
latefinal
project ↔ Output<String>
latefinal
pythonVersion ↔ Output<String>
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.
latefinal
requestLoggingConfig ↔ Output<GoogleCloudMlV1RequestLoggingConfigResponse>
Optional. Only specify this field in a projects.models.versions.patch request. Specifying it in a projects.models.versions.create request has no effect. Configures the request-response pair logging on predictions from this Version.
latefinal
resourceTransforms → List<ResourceTransform>
Inherited/explicit async transforms.
no setterinherited
routes ↔ Output<GoogleCloudMlV1RouteMapResponse>
Optional. Specifies paths on a custom container's HTTP server where AI Platform Prediction sends certain requests. If you specify this field, then you must also specify the container field. If you specify the container field and do not specify this field, it defaults to the following: json { "predict": "/v1/models/MODEL/versions/VERSION:predict", "health": "/v1/models/MODEL/versions/VERSION" } See RouteMap for more details about these default values.
latefinal
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
runtimeVersion ↔ Output<String>
The AI Platform runtime version to use for this deployment. For more information, see the runtime version list and how to manage runtime versions.
latefinal
serviceAccount ↔ Output<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.
latefinal
state ↔ Output<String>
The state of a version.
latefinal
transformations → List<ResourceTransformation>
Inherited/explicit legacy transformations.
no setterinherited
urn ↔ Output<String>
latefinalinherited

Methods

failId(Object error) → void
Completes this resource ID with an error when registration fails.
inherited
failOutputs(Object error) → void
Completes all output properties with error.
inherited
failUrn(Object error) → void
Completes this resource URN with an error when registration fails.
inherited
getProvider(String moduleMember) → ProviderResource?
Returns provider for moduleMember's package, if configured.
inherited
getResourceName() → String
Returns this resource's logical name.
inherited
getResourceType() → String
Returns this resource's Pulumi type token.
inherited
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
registerOutput<T>(String propertyName, {Object? decoder(Object?)?, bool isSecret = false}) → Output<T>
Registers a dynamic output property for this resource.
inherited
resolveId(String? value, {required bool isKnown}) → void
Resolves the provider-assigned ID for this resource.
inherited
resolveOutputs(Struct outputs) → void
Resolves all output properties from a monitor response payload.
inherited
resolveUrn(String value) → void
Resolves this resource's URN once assigned by the engine.
inherited
serializeProperties(Map<String, dynamic> properties) → Future<Struct>
Serializes resource properties for RPC transmission.
inherited
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited