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<h1><a href="ml_v1.html">Cloud Machine Learning Engine</a> . <a href="ml_v1.projects.html">projects</a> . <a href="ml_v1.projects.models.html">models</a> . <a href="ml_v1.projects.models.versions.html">versions</a></h1>
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<h2>Instance Methods</h2>
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<p class="toc_element">
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<code><a href="#create">create(parent, body, x__xgafv=None)</a></code></p>
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<p class="firstline">Creates a new version of a model from a trained TensorFlow model.</p>
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<p class="toc_element">
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<code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
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<p class="firstline">Deletes a model version.</p>
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<p class="toc_element">
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<code><a href="#get">get(name, x__xgafv=None)</a></code></p>
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<p class="firstline">Gets information about a model version.</p>
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<p class="toc_element">
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<code><a href="#list">list(parent, pageToken=None, x__xgafv=None, pageSize=None, filter=None)</a></code></p>
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<p class="firstline">Gets basic information about all the versions of a model.</p>
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<p class="toc_element">
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<code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
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<p class="firstline">Retrieves the next page of results.</p>
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<p class="toc_element">
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<code><a href="#patch">patch(name, body, updateMask=None, x__xgafv=None)</a></code></p>
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<p class="firstline">Updates the specified Version resource.</p>
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<p class="toc_element">
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<code><a href="#setDefault">setDefault(name, body=None, x__xgafv=None)</a></code></p>
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<p class="firstline">Designates a version to be the default for the model.</p>
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<h3>Method Details</h3>
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<div class="method">
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<code class="details" id="create">create(parent, body, x__xgafv=None)</code>
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<pre>Creates a new version of a model from a trained TensorFlow model.
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If the version created in the cloud by this call is the first deployed
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version of the specified model, it will be made the default version of the
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model. When you add a version to a model that already has one or more
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versions, the default version does not automatically change. If you want a
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new version to be the default, you must call
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[projects.models.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
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Args:
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parent: string, Required. The name of the model. (required)
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body: object, The request body. (required)
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The object takes the form of:
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{ # Represents a version of the model.
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#
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# Each version is a trained model deployed in the cloud, ready to handle
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# prediction requests. A model can have multiple versions. You can get
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# information about all of the versions of a given model by calling
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# [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list).
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"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
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"labels": { # Optional. One or more labels that you can add, to organize your model
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# versions. Each label is a key-value pair, where both the key and the value
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# are arbitrary strings that you supply.
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# For more information, see the documentation on
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# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
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"a_key": "A String",
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},
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"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
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# applies to online prediction service.
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# <dl>
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# <dt>mls1-c1-m2</dt>
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# <dd>
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# The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
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# name for this machine type is "mls1-highmem-1".
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# </dd>
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# <dt>mls1-c4-m2</dt>
|
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# <dd>
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# In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
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# deprecated name for this machine type is "mls1-highcpu-4".
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# </dd>
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# </dl>
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"description": "A String", # Optional. The description specified for the version when it was created.
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"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
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# If not set, AI Platform uses the default stable version, 1.0. For more
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# information, see the
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# [runtime version list](/ml-engine/docs/runtime-version-list) and
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# [how to manage runtime versions](/ml-engine/docs/versioning).
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"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
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# model. You should generally use `auto_scaling` with an appropriate
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# `min_nodes` instead, but this option is available if you want more
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# predictable billing. Beware that latency and error rates will increase
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# if the traffic exceeds that capability of the system to serve it based
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# on the selected number of nodes.
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"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
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# starting from the time the model is deployed, so the cost of operating
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# this model will be proportional to `nodes` * number of hours since
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# last billing cycle plus the cost for each prediction performed.
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},
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"predictionClass": "A String", # Optional. The fully qualified name
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# (<var>module_name</var>.<var>class_name</var>) of a class that implements
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# the Predictor interface described in this reference field. The module
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# containing this class should be included in a package provided to the
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# [`packageUris` field](#Version.FIELDS.package_uris).
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#
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# Specify this field if and only if you are deploying a [custom prediction
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# routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
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# If you specify this field, you must set
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# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
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#
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|
# The following code sample provides the Predictor interface:
|
|
#
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|
# ```py
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# class Predictor(object):
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# """Interface for constructing custom predictors."""
|
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#
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# def predict(self, instances, **kwargs):
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|
# """Performs custom prediction.
|
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#
|
|
# Instances are the decoded values from the request. They have already
|
|
# been deserialized from JSON.
|
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#
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|
# Args:
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# instances: A list of prediction input instances.
|
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# **kwargs: A dictionary of keyword args provided as additional
|
|
# fields on the predict request body.
|
|
#
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|
# Returns:
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|
# A list of outputs containing the prediction results. This list must
|
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# be JSON serializable.
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# """
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|
# raise NotImplementedError()
|
|
#
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# @classmethod
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|
# def from_path(cls, model_dir):
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# """Creates an instance of Predictor using the given path.
|
|
#
|
|
# Loading of the predictor should be done in this method.
|
|
#
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# Args:
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# model_dir: The local directory that contains the exported model
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# file along with any additional files uploaded when creating the
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|
# version resource.
|
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#
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# Returns:
|
|
# An instance implementing this Predictor class.
|
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# """
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# raise NotImplementedError()
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|
# ```
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|
#
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# Learn more about [the Predictor interface and custom prediction
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|
# routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
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"autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
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|
# response to increases and decreases in traffic. Care should be
|
|
# taken to ramp up traffic according to the model's ability to scale
|
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# or you will start seeing increases in latency and 429 response codes.
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"minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
|
|
# nodes are always up, starting from the time the model is deployed.
|
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# Therefore, the cost of operating this model will be at least
|
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# `rate` * `min_nodes` * number of hours since last billing cycle,
|
|
# where `rate` is the cost per node-hour as documented in the
|
|
# [pricing guide](/ml-engine/docs/pricing),
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|
# even if no predictions are performed. There is additional cost for each
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|
# prediction performed.
|
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#
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# Unlike manual scaling, if the load gets too heavy for the nodes
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|
# that are up, the service will automatically add nodes to handle the
|
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# increased load as well as scale back as traffic drops, always maintaining
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|
# at least `min_nodes`. You will be charged for the time in which additional
|
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# nodes are used.
|
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#
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|
# If not specified, `min_nodes` defaults to 0, in which case, when traffic
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# to a model stops (and after a cool-down period), nodes will be shut down
|
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# and no charges will be incurred until traffic to the model resumes.
|
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#
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# You can set `min_nodes` when creating the model version, and you can also
|
|
# update `min_nodes` for an existing version:
|
|
# <pre>
|
|
# update_body.json:
|
|
# {
|
|
# 'autoScaling': {
|
|
# 'minNodes': 5
|
|
# }
|
|
# }
|
|
# </pre>
|
|
# HTTP request:
|
|
# <pre>
|
|
# PATCH
|
|
# https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
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# -d @./update_body.json
|
|
# </pre>
|
|
},
|
|
"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
|
|
"state": "A String", # Output only. The state of a version.
|
|
"pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
|
|
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
|
|
# to '1.4' and above. Python '2.7' works with all supported runtime versions.
|
|
"framework": "A 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.
|
|
#
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|
# Do **not** specify a framework if you're deploying a [custom
|
|
# prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
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"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
|
|
# prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
|
|
# or [scikit-learn pipelines with custom
|
|
# code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
|
|
#
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|
# For a custom prediction routine, one of these packages must contain your
|
|
# Predictor class (see
|
|
# [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
|
|
# include any dependencies used by your Predictor or scikit-learn pipeline
|
|
# uses that are not already included in your selected [runtime
|
|
# version](/ml-engine/docs/tensorflow/runtime-version-list).
|
|
#
|
|
# If you specify this field, you must also set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
"A String",
|
|
],
|
|
"etag": "A 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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [guide to model
|
|
# deployment](/ml-engine/docs/tensorflow/deploying-models) for more
|
|
# information.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create)
|
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# 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.
|
|
"createTime": "A String", # Output only. The time the version was created.
|
|
"isDefault": True or False, # 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](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
|
|
"name": "A 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.
|
|
}
|
|
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # This resource represents a long-running operation that is the result of a
|
|
# network API call.
|
|
"metadata": { # Service-specific metadata associated with the operation. It typically
|
|
# contains progress information and common metadata such as create time.
|
|
# Some services might not provide such metadata. Any method that returns a
|
|
# long-running operation should document the metadata type, if any.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
"error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation.
|
|
# different programming environments, including REST APIs and RPC APIs. It is
|
|
# used by [gRPC](https://github.com/grpc). Each `Status` message contains
|
|
# three pieces of data: error code, error message, and error details.
|
|
#
|
|
# You can find out more about this error model and how to work with it in the
|
|
# [API Design Guide](https://cloud.google.com/apis/design/errors).
|
|
"message": "A String", # A developer-facing error message, which should be in English. Any
|
|
# user-facing error message should be localized and sent in the
|
|
# google.rpc.Status.details field, or localized by the client.
|
|
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
|
|
"details": [ # A list of messages that carry the error details. There is a common set of
|
|
# message types for APIs to use.
|
|
{
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
],
|
|
},
|
|
"done": True or False, # If the value is `false`, it means the operation is still in progress.
|
|
# If `true`, the operation is completed, and either `error` or `response` is
|
|
# available.
|
|
"response": { # The normal response of the operation in case of success. If the original
|
|
# method returns no data on success, such as `Delete`, the response is
|
|
# `google.protobuf.Empty`. If the original method is standard
|
|
# `Get`/`Create`/`Update`, the response should be the resource. For other
|
|
# methods, the response should have the type `XxxResponse`, where `Xxx`
|
|
# is the original method name. For example, if the original method name
|
|
# is `TakeSnapshot()`, the inferred response type is
|
|
# `TakeSnapshotResponse`.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
"name": "A String", # The server-assigned name, which is only unique within the same service that
|
|
# originally returns it. If you use the default HTTP mapping, the
|
|
# `name` should be a resource name ending with `operations/{unique_id}`.
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="delete">delete(name, x__xgafv=None)</code>
|
|
<pre>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.
|
|
|
|
Args:
|
|
name: string, Required. The name of the version. You can get the names of all the
|
|
versions of a model by calling
|
|
[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list). (required)
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # This resource represents a long-running operation that is the result of a
|
|
# network API call.
|
|
"metadata": { # Service-specific metadata associated with the operation. It typically
|
|
# contains progress information and common metadata such as create time.
|
|
# Some services might not provide such metadata. Any method that returns a
|
|
# long-running operation should document the metadata type, if any.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
"error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation.
|
|
# different programming environments, including REST APIs and RPC APIs. It is
|
|
# used by [gRPC](https://github.com/grpc). Each `Status` message contains
|
|
# three pieces of data: error code, error message, and error details.
|
|
#
|
|
# You can find out more about this error model and how to work with it in the
|
|
# [API Design Guide](https://cloud.google.com/apis/design/errors).
|
|
"message": "A String", # A developer-facing error message, which should be in English. Any
|
|
# user-facing error message should be localized and sent in the
|
|
# google.rpc.Status.details field, or localized by the client.
|
|
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
|
|
"details": [ # A list of messages that carry the error details. There is a common set of
|
|
# message types for APIs to use.
|
|
{
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
],
|
|
},
|
|
"done": True or False, # If the value is `false`, it means the operation is still in progress.
|
|
# If `true`, the operation is completed, and either `error` or `response` is
|
|
# available.
|
|
"response": { # The normal response of the operation in case of success. If the original
|
|
# method returns no data on success, such as `Delete`, the response is
|
|
# `google.protobuf.Empty`. If the original method is standard
|
|
# `Get`/`Create`/`Update`, the response should be the resource. For other
|
|
# methods, the response should have the type `XxxResponse`, where `Xxx`
|
|
# is the original method name. For example, if the original method name
|
|
# is `TakeSnapshot()`, the inferred response type is
|
|
# `TakeSnapshotResponse`.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
"name": "A String", # The server-assigned name, which is only unique within the same service that
|
|
# originally returns it. If you use the default HTTP mapping, the
|
|
# `name` should be a resource name ending with `operations/{unique_id}`.
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="get">get(name, x__xgafv=None)</code>
|
|
<pre>Gets information about a model version.
|
|
|
|
Models can have multiple versions. You can call
|
|
[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list)
|
|
to get the same information that this method returns for all of the
|
|
versions of a model.
|
|
|
|
Args:
|
|
name: string, Required. The name of the version. (required)
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # 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](/ml-engine/reference/rest/v1/projects.models.versions/list).
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"labels": { # 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
|
|
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
|
|
"a_key": "A String",
|
|
},
|
|
"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
|
|
# applies to online prediction service.
|
|
# <dl>
|
|
# <dt>mls1-c1-m2</dt>
|
|
# <dd>
|
|
# The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
|
|
# name for this machine type is "mls1-highmem-1".
|
|
# </dd>
|
|
# <dt>mls1-c4-m2</dt>
|
|
# <dd>
|
|
# In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
|
|
# deprecated name for this machine type is "mls1-highcpu-4".
|
|
# </dd>
|
|
# </dl>
|
|
"description": "A String", # Optional. The description specified for the version when it was created.
|
|
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
|
|
# If not set, AI Platform uses the default stable version, 1.0. For more
|
|
# information, see the
|
|
# [runtime version list](/ml-engine/docs/runtime-version-list) and
|
|
# [how to manage runtime versions](/ml-engine/docs/versioning).
|
|
"manualScaling": { # Options for manually scaling a model. # 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.
|
|
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
|
|
# starting from the time the model is deployed, so the cost of operating
|
|
# this model will be proportional to `nodes` * number of hours since
|
|
# last billing cycle plus the cost for each prediction performed.
|
|
},
|
|
"predictionClass": "A String", # Optional. The fully qualified name
|
|
# (<var>module_name</var>.<var>class_name</var>) 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](#Version.FIELDS.package_uris).
|
|
#
|
|
# Specify this field if and only if you are deploying a [custom prediction
|
|
# routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
# If you specify this field, you must set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
#
|
|
# The following code sample provides the Predictor interface:
|
|
#
|
|
# ```py
|
|
# 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"autoScaling": { # Options for automatically scaling a model. # 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.
|
|
"minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
|
|
# nodes are always up, starting from the time the model is deployed.
|
|
# Therefore, the cost of operating this model will be at least
|
|
# `rate` * `min_nodes` * number of hours since last billing cycle,
|
|
# where `rate` is the cost per node-hour as documented in the
|
|
# [pricing guide](/ml-engine/docs/pricing),
|
|
# even if no predictions are performed. There is additional cost for each
|
|
# prediction performed.
|
|
#
|
|
# Unlike manual scaling, if the load gets too heavy for the nodes
|
|
# that are up, the service will automatically add nodes to handle the
|
|
# increased load as well as scale back as traffic drops, always maintaining
|
|
# at least `min_nodes`. You will be charged for the time in which additional
|
|
# nodes are used.
|
|
#
|
|
# If not specified, `min_nodes` defaults to 0, in which case, when traffic
|
|
# to a model stops (and after a cool-down period), nodes will be shut down
|
|
# and no charges will be incurred until traffic to the model resumes.
|
|
#
|
|
# You can set `min_nodes` when creating the model version, and you can also
|
|
# update `min_nodes` for an existing version:
|
|
# <pre>
|
|
# update_body.json:
|
|
# {
|
|
# 'autoScaling': {
|
|
# 'minNodes': 5
|
|
# }
|
|
# }
|
|
# </pre>
|
|
# HTTP request:
|
|
# <pre>
|
|
# PATCH
|
|
# https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
|
|
# -d @./update_body.json
|
|
# </pre>
|
|
},
|
|
"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
|
|
"state": "A String", # Output only. The state of a version.
|
|
"pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
|
|
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
|
|
# to '1.4' and above. Python '2.7' works with all supported runtime versions.
|
|
"framework": "A 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
|
|
# prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
|
|
# or [scikit-learn pipelines with custom
|
|
# code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
|
|
#
|
|
# For a custom prediction routine, one of these packages must contain your
|
|
# Predictor class (see
|
|
# [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
|
|
# include any dependencies used by your Predictor or scikit-learn pipeline
|
|
# uses that are not already included in your selected [runtime
|
|
# version](/ml-engine/docs/tensorflow/runtime-version-list).
|
|
#
|
|
# If you specify this field, you must also set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
"A String",
|
|
],
|
|
"etag": "A 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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [guide to model
|
|
# deployment](/ml-engine/docs/tensorflow/deploying-models) for more
|
|
# information.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1/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.
|
|
"createTime": "A String", # Output only. The time the version was created.
|
|
"isDefault": True or False, # 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](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
|
|
"name": "A 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.
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="list">list(parent, pageToken=None, x__xgafv=None, pageSize=None, filter=None)</code>
|
|
<pre>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: {}.
|
|
|
|
Args:
|
|
parent: string, Required. The name of the model for which to list the version. (required)
|
|
pageToken: string, Optional. A page token to request the next page of results.
|
|
|
|
You get the token from the `next_page_token` field of the response from
|
|
the previous call.
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
pageSize: integer, Optional. The number of versions to retrieve per "page" of results. If
|
|
there are more remaining results than this number, the response message
|
|
will contain a valid value in the `next_page_token` field.
|
|
|
|
The default value is 20, and the maximum page size is 100.
|
|
filter: string, Optional. Specifies the subset of versions to retrieve.
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # Response message for the ListVersions method.
|
|
"nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
|
|
# subsequent call.
|
|
"versions": [ # The list of versions.
|
|
{ # 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](/ml-engine/reference/rest/v1/projects.models.versions/list).
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"labels": { # 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
|
|
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
|
|
"a_key": "A String",
|
|
},
|
|
"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
|
|
# applies to online prediction service.
|
|
# <dl>
|
|
# <dt>mls1-c1-m2</dt>
|
|
# <dd>
|
|
# The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
|
|
# name for this machine type is "mls1-highmem-1".
|
|
# </dd>
|
|
# <dt>mls1-c4-m2</dt>
|
|
# <dd>
|
|
# In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
|
|
# deprecated name for this machine type is "mls1-highcpu-4".
|
|
# </dd>
|
|
# </dl>
|
|
"description": "A String", # Optional. The description specified for the version when it was created.
|
|
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
|
|
# If not set, AI Platform uses the default stable version, 1.0. For more
|
|
# information, see the
|
|
# [runtime version list](/ml-engine/docs/runtime-version-list) and
|
|
# [how to manage runtime versions](/ml-engine/docs/versioning).
|
|
"manualScaling": { # Options for manually scaling a model. # 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.
|
|
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
|
|
# starting from the time the model is deployed, so the cost of operating
|
|
# this model will be proportional to `nodes` * number of hours since
|
|
# last billing cycle plus the cost for each prediction performed.
|
|
},
|
|
"predictionClass": "A String", # Optional. The fully qualified name
|
|
# (<var>module_name</var>.<var>class_name</var>) 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](#Version.FIELDS.package_uris).
|
|
#
|
|
# Specify this field if and only if you are deploying a [custom prediction
|
|
# routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
# If you specify this field, you must set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
#
|
|
# The following code sample provides the Predictor interface:
|
|
#
|
|
# ```py
|
|
# 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"autoScaling": { # Options for automatically scaling a model. # 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.
|
|
"minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
|
|
# nodes are always up, starting from the time the model is deployed.
|
|
# Therefore, the cost of operating this model will be at least
|
|
# `rate` * `min_nodes` * number of hours since last billing cycle,
|
|
# where `rate` is the cost per node-hour as documented in the
|
|
# [pricing guide](/ml-engine/docs/pricing),
|
|
# even if no predictions are performed. There is additional cost for each
|
|
# prediction performed.
|
|
#
|
|
# Unlike manual scaling, if the load gets too heavy for the nodes
|
|
# that are up, the service will automatically add nodes to handle the
|
|
# increased load as well as scale back as traffic drops, always maintaining
|
|
# at least `min_nodes`. You will be charged for the time in which additional
|
|
# nodes are used.
|
|
#
|
|
# If not specified, `min_nodes` defaults to 0, in which case, when traffic
|
|
# to a model stops (and after a cool-down period), nodes will be shut down
|
|
# and no charges will be incurred until traffic to the model resumes.
|
|
#
|
|
# You can set `min_nodes` when creating the model version, and you can also
|
|
# update `min_nodes` for an existing version:
|
|
# <pre>
|
|
# update_body.json:
|
|
# {
|
|
# 'autoScaling': {
|
|
# 'minNodes': 5
|
|
# }
|
|
# }
|
|
# </pre>
|
|
# HTTP request:
|
|
# <pre>
|
|
# PATCH
|
|
# https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
|
|
# -d @./update_body.json
|
|
# </pre>
|
|
},
|
|
"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
|
|
"state": "A String", # Output only. The state of a version.
|
|
"pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
|
|
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
|
|
# to '1.4' and above. Python '2.7' works with all supported runtime versions.
|
|
"framework": "A 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
|
|
# prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
|
|
# or [scikit-learn pipelines with custom
|
|
# code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
|
|
#
|
|
# For a custom prediction routine, one of these packages must contain your
|
|
# Predictor class (see
|
|
# [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
|
|
# include any dependencies used by your Predictor or scikit-learn pipeline
|
|
# uses that are not already included in your selected [runtime
|
|
# version](/ml-engine/docs/tensorflow/runtime-version-list).
|
|
#
|
|
# If you specify this field, you must also set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
"A String",
|
|
],
|
|
"etag": "A 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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [guide to model
|
|
# deployment](/ml-engine/docs/tensorflow/deploying-models) for more
|
|
# information.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1/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.
|
|
"createTime": "A String", # Output only. The time the version was created.
|
|
"isDefault": True or False, # 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](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
|
|
"name": "A 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.
|
|
},
|
|
],
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="list_next">list_next(previous_request, previous_response)</code>
|
|
<pre>Retrieves the next page of results.
|
|
|
|
Args:
|
|
previous_request: The request for the previous page. (required)
|
|
previous_response: The response from the request for the previous page. (required)
|
|
|
|
Returns:
|
|
A request object that you can call 'execute()' on to request the next
|
|
page. Returns None if there are no more items in the collection.
|
|
</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="patch">patch(name, body, updateMask=None, x__xgafv=None)</code>
|
|
<pre>Updates the specified Version resource.
|
|
|
|
Currently the only update-able fields are `description` and
|
|
`autoScaling.minNodes`.
|
|
|
|
Args:
|
|
name: string, Required. The name of the model. (required)
|
|
body: object, The request body. (required)
|
|
The object takes the form of:
|
|
|
|
{ # 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](/ml-engine/reference/rest/v1/projects.models.versions/list).
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"labels": { # 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
|
|
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
|
|
"a_key": "A String",
|
|
},
|
|
"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
|
|
# applies to online prediction service.
|
|
# <dl>
|
|
# <dt>mls1-c1-m2</dt>
|
|
# <dd>
|
|
# The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
|
|
# name for this machine type is "mls1-highmem-1".
|
|
# </dd>
|
|
# <dt>mls1-c4-m2</dt>
|
|
# <dd>
|
|
# In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
|
|
# deprecated name for this machine type is "mls1-highcpu-4".
|
|
# </dd>
|
|
# </dl>
|
|
"description": "A String", # Optional. The description specified for the version when it was created.
|
|
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
|
|
# If not set, AI Platform uses the default stable version, 1.0. For more
|
|
# information, see the
|
|
# [runtime version list](/ml-engine/docs/runtime-version-list) and
|
|
# [how to manage runtime versions](/ml-engine/docs/versioning).
|
|
"manualScaling": { # Options for manually scaling a model. # 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.
|
|
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
|
|
# starting from the time the model is deployed, so the cost of operating
|
|
# this model will be proportional to `nodes` * number of hours since
|
|
# last billing cycle plus the cost for each prediction performed.
|
|
},
|
|
"predictionClass": "A String", # Optional. The fully qualified name
|
|
# (<var>module_name</var>.<var>class_name</var>) 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](#Version.FIELDS.package_uris).
|
|
#
|
|
# Specify this field if and only if you are deploying a [custom prediction
|
|
# routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
# If you specify this field, you must set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
#
|
|
# The following code sample provides the Predictor interface:
|
|
#
|
|
# ```py
|
|
# 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"autoScaling": { # Options for automatically scaling a model. # 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.
|
|
"minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
|
|
# nodes are always up, starting from the time the model is deployed.
|
|
# Therefore, the cost of operating this model will be at least
|
|
# `rate` * `min_nodes` * number of hours since last billing cycle,
|
|
# where `rate` is the cost per node-hour as documented in the
|
|
# [pricing guide](/ml-engine/docs/pricing),
|
|
# even if no predictions are performed. There is additional cost for each
|
|
# prediction performed.
|
|
#
|
|
# Unlike manual scaling, if the load gets too heavy for the nodes
|
|
# that are up, the service will automatically add nodes to handle the
|
|
# increased load as well as scale back as traffic drops, always maintaining
|
|
# at least `min_nodes`. You will be charged for the time in which additional
|
|
# nodes are used.
|
|
#
|
|
# If not specified, `min_nodes` defaults to 0, in which case, when traffic
|
|
# to a model stops (and after a cool-down period), nodes will be shut down
|
|
# and no charges will be incurred until traffic to the model resumes.
|
|
#
|
|
# You can set `min_nodes` when creating the model version, and you can also
|
|
# update `min_nodes` for an existing version:
|
|
# <pre>
|
|
# update_body.json:
|
|
# {
|
|
# 'autoScaling': {
|
|
# 'minNodes': 5
|
|
# }
|
|
# }
|
|
# </pre>
|
|
# HTTP request:
|
|
# <pre>
|
|
# PATCH
|
|
# https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
|
|
# -d @./update_body.json
|
|
# </pre>
|
|
},
|
|
"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
|
|
"state": "A String", # Output only. The state of a version.
|
|
"pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
|
|
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
|
|
# to '1.4' and above. Python '2.7' works with all supported runtime versions.
|
|
"framework": "A 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
|
|
# prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
|
|
# or [scikit-learn pipelines with custom
|
|
# code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
|
|
#
|
|
# For a custom prediction routine, one of these packages must contain your
|
|
# Predictor class (see
|
|
# [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
|
|
# include any dependencies used by your Predictor or scikit-learn pipeline
|
|
# uses that are not already included in your selected [runtime
|
|
# version](/ml-engine/docs/tensorflow/runtime-version-list).
|
|
#
|
|
# If you specify this field, you must also set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
"A String",
|
|
],
|
|
"etag": "A 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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [guide to model
|
|
# deployment](/ml-engine/docs/tensorflow/deploying-models) for more
|
|
# information.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1/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.
|
|
"createTime": "A String", # Output only. The time the version was created.
|
|
"isDefault": True or False, # 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](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
|
|
"name": "A 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.
|
|
}
|
|
|
|
updateMask: string, Required. Specifies the path, relative to `Version`, of the field to
|
|
update. Must be present and non-empty.
|
|
|
|
For example, to change the description of a version to "foo", the
|
|
`update_mask` parameter would be specified as `description`, and the
|
|
`PATCH` request body would specify the new value, as follows:
|
|
{
|
|
"description": "foo"
|
|
}
|
|
|
|
Currently the only supported update mask fields are `description` and
|
|
`autoScaling.minNodes`.
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # This resource represents a long-running operation that is the result of a
|
|
# network API call.
|
|
"metadata": { # Service-specific metadata associated with the operation. It typically
|
|
# contains progress information and common metadata such as create time.
|
|
# Some services might not provide such metadata. Any method that returns a
|
|
# long-running operation should document the metadata type, if any.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
"error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation.
|
|
# different programming environments, including REST APIs and RPC APIs. It is
|
|
# used by [gRPC](https://github.com/grpc). Each `Status` message contains
|
|
# three pieces of data: error code, error message, and error details.
|
|
#
|
|
# You can find out more about this error model and how to work with it in the
|
|
# [API Design Guide](https://cloud.google.com/apis/design/errors).
|
|
"message": "A String", # A developer-facing error message, which should be in English. Any
|
|
# user-facing error message should be localized and sent in the
|
|
# google.rpc.Status.details field, or localized by the client.
|
|
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
|
|
"details": [ # A list of messages that carry the error details. There is a common set of
|
|
# message types for APIs to use.
|
|
{
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
],
|
|
},
|
|
"done": True or False, # If the value is `false`, it means the operation is still in progress.
|
|
# If `true`, the operation is completed, and either `error` or `response` is
|
|
# available.
|
|
"response": { # The normal response of the operation in case of success. If the original
|
|
# method returns no data on success, such as `Delete`, the response is
|
|
# `google.protobuf.Empty`. If the original method is standard
|
|
# `Get`/`Create`/`Update`, the response should be the resource. For other
|
|
# methods, the response should have the type `XxxResponse`, where `Xxx`
|
|
# is the original method name. For example, if the original method name
|
|
# is `TakeSnapshot()`, the inferred response type is
|
|
# `TakeSnapshotResponse`.
|
|
"a_key": "", # Properties of the object. Contains field @type with type URL.
|
|
},
|
|
"name": "A String", # The server-assigned name, which is only unique within the same service that
|
|
# originally returns it. If you use the default HTTP mapping, the
|
|
# `name` should be a resource name ending with `operations/{unique_id}`.
|
|
}</pre>
|
|
</div>
|
|
|
|
<div class="method">
|
|
<code class="details" id="setDefault">setDefault(name, body=None, x__xgafv=None)</code>
|
|
<pre>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.
|
|
|
|
Args:
|
|
name: string, Required. The name of the version to make the default for the model. You
|
|
can get the names of all the versions of a model by calling
|
|
[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list). (required)
|
|
body: object, The request body.
|
|
The object takes the form of:
|
|
|
|
{ # Request message for the SetDefaultVersion request.
|
|
}
|
|
|
|
x__xgafv: string, V1 error format.
|
|
Allowed values
|
|
1 - v1 error format
|
|
2 - v2 error format
|
|
|
|
Returns:
|
|
An object of the form:
|
|
|
|
{ # 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](/ml-engine/reference/rest/v1/projects.models.versions/list).
|
|
"errorMessage": "A String", # Output only. The details of a failure or a cancellation.
|
|
"labels": { # 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
|
|
# <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
|
|
"a_key": "A String",
|
|
},
|
|
"machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
|
|
# applies to online prediction service.
|
|
# <dl>
|
|
# <dt>mls1-c1-m2</dt>
|
|
# <dd>
|
|
# The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
|
|
# name for this machine type is "mls1-highmem-1".
|
|
# </dd>
|
|
# <dt>mls1-c4-m2</dt>
|
|
# <dd>
|
|
# In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
|
|
# deprecated name for this machine type is "mls1-highcpu-4".
|
|
# </dd>
|
|
# </dl>
|
|
"description": "A String", # Optional. The description specified for the version when it was created.
|
|
"runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
|
|
# If not set, AI Platform uses the default stable version, 1.0. For more
|
|
# information, see the
|
|
# [runtime version list](/ml-engine/docs/runtime-version-list) and
|
|
# [how to manage runtime versions](/ml-engine/docs/versioning).
|
|
"manualScaling": { # Options for manually scaling a model. # 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.
|
|
"nodes": 42, # The number of nodes to allocate for this model. These nodes are always up,
|
|
# starting from the time the model is deployed, so the cost of operating
|
|
# this model will be proportional to `nodes` * number of hours since
|
|
# last billing cycle plus the cost for each prediction performed.
|
|
},
|
|
"predictionClass": "A String", # Optional. The fully qualified name
|
|
# (<var>module_name</var>.<var>class_name</var>) 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](#Version.FIELDS.package_uris).
|
|
#
|
|
# Specify this field if and only if you are deploying a [custom prediction
|
|
# routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
# If you specify this field, you must set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
#
|
|
# The following code sample provides the Predictor interface:
|
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#
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# ```py
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# class Predictor(object):
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# """Interface for constructing custom predictors."""
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#
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# def predict(self, instances, **kwargs):
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# """Performs custom prediction.
|
|
#
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|
# Instances are the decoded values from the request. They have already
|
|
# been deserialized from JSON.
|
|
#
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|
# Args:
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|
# instances: A list of prediction input instances.
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|
# **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()
|
|
#
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# @classmethod
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|
# def from_path(cls, model_dir):
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|
# """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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"autoScaling": { # Options for automatically scaling a model. # 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.
|
|
"minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
|
|
# nodes are always up, starting from the time the model is deployed.
|
|
# Therefore, the cost of operating this model will be at least
|
|
# `rate` * `min_nodes` * number of hours since last billing cycle,
|
|
# where `rate` is the cost per node-hour as documented in the
|
|
# [pricing guide](/ml-engine/docs/pricing),
|
|
# even if no predictions are performed. There is additional cost for each
|
|
# prediction performed.
|
|
#
|
|
# Unlike manual scaling, if the load gets too heavy for the nodes
|
|
# that are up, the service will automatically add nodes to handle the
|
|
# increased load as well as scale back as traffic drops, always maintaining
|
|
# at least `min_nodes`. You will be charged for the time in which additional
|
|
# nodes are used.
|
|
#
|
|
# If not specified, `min_nodes` defaults to 0, in which case, when traffic
|
|
# to a model stops (and after a cool-down period), nodes will be shut down
|
|
# and no charges will be incurred until traffic to the model resumes.
|
|
#
|
|
# You can set `min_nodes` when creating the model version, and you can also
|
|
# update `min_nodes` for an existing version:
|
|
# <pre>
|
|
# update_body.json:
|
|
# {
|
|
# 'autoScaling': {
|
|
# 'minNodes': 5
|
|
# }
|
|
# }
|
|
# </pre>
|
|
# HTTP request:
|
|
# <pre>
|
|
# PATCH
|
|
# https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
|
|
# -d @./update_body.json
|
|
# </pre>
|
|
},
|
|
"serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
|
|
"state": "A String", # Output only. The state of a version.
|
|
"pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
|
|
# version is '2.7'. Python '3.5' is available when `runtime_version` is set
|
|
# to '1.4' and above. Python '2.7' works with all supported runtime versions.
|
|
"framework": "A 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](/ml-engine/docs/tensorflow/custom-prediction-routines).
|
|
"packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
|
|
# prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
|
|
# or [scikit-learn pipelines with custom
|
|
# code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
|
|
#
|
|
# For a custom prediction routine, one of these packages must contain your
|
|
# Predictor class (see
|
|
# [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
|
|
# include any dependencies used by your Predictor or scikit-learn pipeline
|
|
# uses that are not already included in your selected [runtime
|
|
# version](/ml-engine/docs/tensorflow/runtime-version-list).
|
|
#
|
|
# If you specify this field, you must also set
|
|
# [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
|
|
"A String",
|
|
],
|
|
"etag": "A 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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [guide to model
|
|
# deployment](/ml-engine/docs/tensorflow/deploying-models) for more
|
|
# information.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1/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.
|
|
"createTime": "A String", # Output only. The time the version was created.
|
|
"isDefault": True or False, # 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](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).
|
|
"name": "A 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.
|
|
}</pre>
|
|
</div>
|
|
|
|
</body></html> |