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<h1><a href="ml_v1beta1.html">Google Cloud Machine Learning Engine</a> . <a href="ml_v1beta1.projects.html">projects</a> . <a href="ml_v1beta1.projects.models.html">models</a> . <a href="ml_v1beta1.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, pageSize=None, pageToken=None, x__xgafv=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="#setDefault">setDefault(name, body, 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/v1beta1/projects.models.versions/setDefault).
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Args:
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parent: string, Required. The name of the model.
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Authorization: requires `Editor` role on the parent project. (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/v1beta1/projects.models.versions/list).
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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 Google Cloud ML runtime version to use for this deployment.
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# If not set, Google Cloud ML will choose a version.
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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 `automatic_scaling` with an appropriate
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# `min_nodes` instead, but this option is available if you want predictable
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# billing. Beware that latency and error rates will increase if the
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# traffic exceeds that capability of the system to serve it based on
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# 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.
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},
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"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
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# create the version. See the
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# [overview of model
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# deployment](/ml-engine/docs/concepts/deployment-overview) for more
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# informaiton.
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#
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# When passing Version to
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# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.models.versions/create)
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# the model service uses the specified location as the source of the model.
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# Once deployed, the model version is hosted by the prediction service, so
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# this location is useful only as a historical record.
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# The total number of model files can't exceed 1000.
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"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
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"automaticScaling": { # 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
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# 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
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# nodes are always up, starting from the time the model is deployed, so the
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# cost of operating this model will be at least
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# `rate` * `min_nodes` * number of hours since last billing cycle,
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# where `rate` is the cost per node-hour as documented in
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# [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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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"createTime": "A String", # Output only. The time the version was created.
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"isDefault": True or False, # Output only. If true, this version will be used to handle prediction
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# requests that do not specify a version.
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#
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# You can change the default version by calling
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# [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/projects.models.versions/setDefault).
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"name": "A String", # Required.The name specified for the version when it was created.
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#
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# The version name must be unique within the model it is created in.
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}
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x__xgafv: string, V1 error format.
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Allowed values
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1 - v1 error format
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2 - v2 error format
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Returns:
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An object of the form:
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{ # This resource represents a long-running operation that is the result of a
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# network API call.
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"metadata": { # Service-specific metadata associated with the operation. It typically
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# contains progress information and common metadata such as create time.
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# Some services might not provide such metadata. Any method that returns a
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# long-running operation should document the metadata type, if any.
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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"error": { # The `Status` type defines a logical error model that is suitable for different # The error result of the operation in case of failure or cancellation.
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# programming environments, including REST APIs and RPC APIs. It is used by
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# [gRPC](https://github.com/grpc). The error model is designed to be:
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#
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# - Simple to use and understand for most users
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# - Flexible enough to meet unexpected needs
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#
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# # Overview
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#
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# The `Status` message contains three pieces of data: error code, error message,
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# and error details. The error code should be an enum value of
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# google.rpc.Code, but it may accept additional error codes if needed. The
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# error message should be a developer-facing English message that helps
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# developers *understand* and *resolve* the error. If a localized user-facing
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# error message is needed, put the localized message in the error details or
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# localize it in the client. The optional error details may contain arbitrary
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# information about the error. There is a predefined set of error detail types
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# in the package `google.rpc` that can be used for common error conditions.
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#
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# # Language mapping
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#
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# The `Status` message is the logical representation of the error model, but it
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# is not necessarily the actual wire format. When the `Status` message is
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# exposed in different client libraries and different wire protocols, it can be
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# mapped differently. For example, it will likely be mapped to some exceptions
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# in Java, but more likely mapped to some error codes in C.
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#
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# # Other uses
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#
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# The error model and the `Status` message can be used in a variety of
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# environments, either with or without APIs, to provide a
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# consistent developer experience across different environments.
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#
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# Example uses of this error model include:
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#
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# - Partial errors. If a service needs to return partial errors to the client,
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# it may embed the `Status` in the normal response to indicate the partial
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# errors.
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#
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# - Workflow errors. A typical workflow has multiple steps. Each step may
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# have a `Status` message for error reporting.
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#
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# - Batch operations. If a client uses batch request and batch response, the
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# `Status` message should be used directly inside batch response, one for
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# each error sub-response.
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#
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# - Asynchronous operations. If an API call embeds asynchronous operation
|
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# results in its response, the status of those operations should be
|
|
# represented directly using the `Status` message.
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#
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|
# - Logging. If some API errors are stored in logs, the message `Status` could
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# be used directly after any stripping needed for security/privacy reasons.
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"message": "A String", # A developer-facing error message, which should be in English. Any
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# user-facing error message should be localized and sent in the
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# google.rpc.Status.details field, or localized by the client.
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"code": 42, # The status code, which should be an enum value of google.rpc.Code.
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"details": [ # A list of messages that carry the error details. There will be a
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# common set of message types for APIs to use.
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{
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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],
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},
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"done": True or False, # If the value is `false`, it means the operation is still in progress.
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# If true, the operation is completed, and either `error` or `response` is
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# available.
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"response": { # The normal response of the operation in case of success. If the original
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# method returns no data on success, such as `Delete`, the response is
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# `google.protobuf.Empty`. If the original method is standard
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# `Get`/`Create`/`Update`, the response should be the resource. For other
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# methods, the response should have the type `XxxResponse`, where `Xxx`
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# is the original method name. For example, if the original method name
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# is `TakeSnapshot()`, the inferred response type is
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# `TakeSnapshotResponse`.
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"a_key": "", # Properties of the object. Contains field @type with type URL.
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},
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"name": "A String", # The server-assigned name, which is only unique within the same service that
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# originally returns it. If you use the default HTTP mapping, the
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# `name` should have the format of `operations/some/unique/name`.
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}</pre>
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</div>
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<div class="method">
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<code class="details" id="delete">delete(name, x__xgafv=None)</code>
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<pre>Deletes a model version.
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Each model can have multiple versions deployed and in use at any given
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time. Use this method to remove a single version.
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Note: You cannot delete the version that is set as the default version
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of the model unless it is the only remaining version.
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Args:
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name: string, Required. The name of the version. You can get the names of all the
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versions of a model by calling
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[projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.models.versions/list).
|
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Authorization: requires `Editor` role on the parent project. (required)
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x__xgafv: string, V1 error format.
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Allowed values
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1 - v1 error format
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2 - v2 error format
|
|
|
|
Returns:
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|
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.
|
|
},
|
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"error": { # The `Status` type defines a logical error model that is suitable for different # The error result of the operation in case of failure or cancellation.
|
|
# programming environments, including REST APIs and RPC APIs. It is used by
|
|
# [gRPC](https://github.com/grpc). The error model is designed to be:
|
|
#
|
|
# - Simple to use and understand for most users
|
|
# - Flexible enough to meet unexpected needs
|
|
#
|
|
# # Overview
|
|
#
|
|
# The `Status` message contains three pieces of data: error code, error message,
|
|
# and error details. The error code should be an enum value of
|
|
# google.rpc.Code, but it may accept additional error codes if needed. The
|
|
# error message should be a developer-facing English message that helps
|
|
# developers *understand* and *resolve* the error. If a localized user-facing
|
|
# error message is needed, put the localized message in the error details or
|
|
# localize it in the client. The optional error details may contain arbitrary
|
|
# information about the error. There is a predefined set of error detail types
|
|
# in the package `google.rpc` that can be used for common error conditions.
|
|
#
|
|
# # Language mapping
|
|
#
|
|
# The `Status` message is the logical representation of the error model, but it
|
|
# is not necessarily the actual wire format. When the `Status` message is
|
|
# exposed in different client libraries and different wire protocols, it can be
|
|
# mapped differently. For example, it will likely be mapped to some exceptions
|
|
# in Java, but more likely mapped to some error codes in C.
|
|
#
|
|
# # Other uses
|
|
#
|
|
# The error model and the `Status` message can be used in a variety of
|
|
# environments, either with or without APIs, to provide a
|
|
# consistent developer experience across different environments.
|
|
#
|
|
# Example uses of this error model include:
|
|
#
|
|
# - Partial errors. If a service needs to return partial errors to the client,
|
|
# it may embed the `Status` in the normal response to indicate the partial
|
|
# errors.
|
|
#
|
|
# - Workflow errors. A typical workflow has multiple steps. Each step may
|
|
# have a `Status` message for error reporting.
|
|
#
|
|
# - Batch operations. If a client uses batch request and batch response, the
|
|
# `Status` message should be used directly inside batch response, one for
|
|
# each error sub-response.
|
|
#
|
|
# - Asynchronous operations. If an API call embeds asynchronous operation
|
|
# results in its response, the status of those operations should be
|
|
# represented directly using the `Status` message.
|
|
#
|
|
# - Logging. If some API errors are stored in logs, the message `Status` could
|
|
# be used directly after any stripping needed for security/privacy reasons.
|
|
"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 will be 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 have the format of `operations/some/unique/name`.
|
|
}</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/v1beta1/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.
|
|
|
|
Authorization: requires `Viewer` role on the parent project. (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/v1beta1/projects.models.versions/list).
|
|
"description": "A String", # Optional. The description specified for the version when it was created.
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
|
|
# If not set, Google Cloud ML will choose a version.
|
|
"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
|
|
# model. You should generally use `automatic_scaling` with an appropriate
|
|
# `min_nodes` instead, but this option is available if you want 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.
|
|
},
|
|
"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [overview of model
|
|
# deployment](/ml-engine/docs/concepts/deployment-overview) for more
|
|
# informaiton.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"automaticScaling": { # 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, so 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
|
|
# [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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.
|
|
},
|
|
"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/v1beta1/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, pageSize=None, pageToken=None, x__xgafv=None)</code>
|
|
<pre>Gets basic information about all the versions of a model.
|
|
|
|
If you expect that a model has a lot of 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):
|
|
|
|
Args:
|
|
parent: string, Required. The name of the model for which to list the version.
|
|
|
|
Authorization: requires `Viewer` role on the parent project. (required)
|
|
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.
|
|
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
|
|
|
|
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/v1beta1/projects.models.versions/list).
|
|
"description": "A String", # Optional. The description specified for the version when it was created.
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
|
|
# If not set, Google Cloud ML will choose a version.
|
|
"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
|
|
# model. You should generally use `automatic_scaling` with an appropriate
|
|
# `min_nodes` instead, but this option is available if you want 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.
|
|
},
|
|
"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [overview of model
|
|
# deployment](/ml-engine/docs/concepts/deployment-overview) for more
|
|
# informaiton.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"automaticScaling": { # 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, so 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
|
|
# [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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.
|
|
},
|
|
"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/v1beta1/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="setDefault">setDefault(name, body, 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/v1beta1/projects.models.versions/list).
|
|
|
|
Authorization: requires `Editor` role on the parent project. (required)
|
|
body: object, The request body. (required)
|
|
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/v1beta1/projects.models.versions/list).
|
|
"description": "A String", # Optional. The description specified for the version when it was created.
|
|
"runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
|
|
# If not set, Google Cloud ML will choose a version.
|
|
"manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
|
|
# model. You should generally use `automatic_scaling` with an appropriate
|
|
# `min_nodes` instead, but this option is available if you want 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.
|
|
},
|
|
"deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
|
|
# create the version. See the
|
|
# [overview of model
|
|
# deployment](/ml-engine/docs/concepts/deployment-overview) for more
|
|
# informaiton.
|
|
#
|
|
# When passing Version to
|
|
# [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/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.
|
|
"lastUseTime": "A String", # Output only. The time the version was last used for prediction.
|
|
"automaticScaling": { # 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, so 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
|
|
# [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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.
|
|
},
|
|
"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/v1beta1/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> |