GoogleCloudMlV1Version class

Represents a version of the model. Each version is a trained model deployed in the cloud, ready to handle prediction requests. A model can have multiple versions. You can get information about all of the versions of a given model by calling projects.models.versions.list.

Constructors

GoogleCloudMlV1Version()
GoogleCloudMlV1Version.fromJson(Map _json)

Properties

acceleratorConfig GoogleCloudMlV1AcceleratorConfig
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.
read / write
autoScaling GoogleCloudMlV1AutoScaling
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. Note that you cannot use AutoScaling if your version uses GPUs. Instead, you must use specify manual_scaling.
read / write
container GoogleCloudMlV1ContainerSpec
read / write
createTime String
Output only. The time the version was created.
read / write
deploymentUri String
Required. The Cloud Storage location of the trained model used to create the version. See the guide to model deployment for more information. When passing Version to projects.models.versions.create the model service uses the specified location as the source of the model. Once deployed, the model version is hosted by the prediction service, so this location is useful only as a historical record. The total number of model files can't exceed 1000.
read / write
description String
Optional. The description specified for the version when it was created.
read / write
errorMessage String
Output only. The details of a failure or a cancellation.
read / write
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.
read / write
etagAsBytes List<int>
read / write
explanationConfig GoogleCloudMlV1ExplanationConfig
Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
read / write
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. If you specify a Compute Engine (N1) machine type in the machineType field, you must specify TENSORFLOW for the framework. Possible string values are: [...]
read / write
hashCode int
The hash code for this object. [...]
read-only, inherited
isDefault bool
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.
read / write
labels 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.
read / write
lastUseTime String
Output only. The time the version was last used for prediction.
read / write
machineType String
Optional. The type of machine on which to serve the model. Currently only applies to online prediction service. If this field is not specified, it defaults to mls1-c1-m2. Online prediction supports the following machine types: * mls1-c1-m2 * mls1-c4-m2 * n1-standard-2 * n1-standard-4 * n1-standard-8 * n1-standard-16 * n1-standard-32 * n1-highmem-2 * n1-highmem-4 * n1-highmem-8 * n1-highmem-16 * n1-highmem-32 * n1-highcpu-2 * n1-highcpu-4 * n1-highcpu-8 * n1-highcpu-16 * n1-highcpu-32 mls1-c1-m2 is generally available. All other machine types are available in beta. Learn more about the differences between machine types.
read / write
manualScaling GoogleCloudMlV1ManualScaling
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.
read / write
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.
read / write
packageUris 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.
read / write
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.
read / write
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.
read / write
requestLoggingConfig GoogleCloudMlV1RequestLoggingConfig
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.
read / write
routes GoogleCloudMlV1RouteMap
read / write
runtimeType Type
A representation of the runtime type of the object.
read-only, inherited
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.
read / write
serviceAccount String
Optional. Specifies the service account for resource access control.
read / write
state String
Output only. The state of a version. Possible string values are: [...]
read / write

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a non-existent method or property is accessed. [...]
inherited
toJson() Map<String, Object>
toString() String
Returns a string representation of this object.
inherited

Operators

operator ==(Object other) bool
The equality operator. [...]
inherited