GoogleCloudMlV1__Version
import type { GoogleCloudMlV1__Version } from "https://googleapis.deno.dev/v1/ml:v1.ts";
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.
§Properties
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.
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.
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
.
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.
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.
Optional. Configures explainability features on the model's version. Some explanation features require additional metadata to be loaded as part of the model payload.
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.
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.
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.
Output only. The AI Platform (Unified)
Model
ID for the last model
migration.
Output only. The last time this version was successfully migrated to AI Platform (Unified).
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
.
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.
Required. The name specified for the version when it was created. The version name must be unique within the model it is created in.
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.
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()
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.
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.
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.
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.
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.