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+<h1><a href="ml_v1.html">Google 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>
+<h2>Instance Methods</h2>
+<p class="toc_element">
+ <code><a href="#create">create(parent=None, body, x__xgafv=None)</a></code></p>
+<p class="firstline">Creates a new version of a model from a trained TensorFlow model.</p>
+<p class="toc_element">
+ <code><a href="#delete">delete(name=None, x__xgafv=None)</a></code></p>
+<p class="firstline">Deletes a model version.</p>
+<p class="toc_element">
+ <code><a href="#get">get(name=None, x__xgafv=None)</a></code></p>
+<p class="firstline">Gets information about a model version.</p>
+<p class="toc_element">
+ <code><a href="#list">list(parent=None, pageToken=None, x__xgafv=None, pageSize=None)</a></code></p>
+<p class="firstline">Gets basic information about all the versions of a model.</p>
+<p class="toc_element">
+ <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
+<p class="firstline">Retrieves the next page of results.</p>
+<p class="toc_element">
+ <code><a href="#setDefault">setDefault(name=None, body, x__xgafv=None)</a></code></p>
+<p class="firstline">Designates a version to be the default for the model.</p>
+<h3>Method Details</h3>
+<div class="method">
+ <code class="details" id="create">create(parent=None, body, x__xgafv=None)</code>
+ <pre>Creates a new version of a model from a trained TensorFlow model.
+
+If the version created in the cloud by this call is the first deployed
+version of the specified model, it will be made the default version of the
+model. When you add a version to a model that already has one or more
+versions, the default version does not automatically change. If you want a
+new version to be the default, you must call
+[projects.models.versions.setDefault](/ml/reference/rest/v1/projects.models.versions/setDefault).
+
+Args:
+ parent: string, Required. The name of the model.
+
+Authorization: requires `Editor` role on the parent project. (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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the
+ # model. If unset (i.e., by default), the number of nodes used to serve
+ # the model automatically scales with traffic. However, care should be
+ # taken to ramp up traffic according to the model's ability to scale. If
+ # your model needs to handle bursts of traffic beyond it's ability to
+ # scale, it is recommended you set this field appropriately.
+ "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
+ # deployment.
+ },
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "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/docs/concepts/deployment-overview) for
+ # more informaiton.
+ #
+ # When passing Version to
+ # [projects.models.versions.create](/ml/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.
+ "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/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 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` which 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 purpose.
+ #
+ # - 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="delete">delete(name=None, 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/reference/rest/v1/projects.models.versions/list).
+
+Authorization: requires `Editor` 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:
+
+ { # 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 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` which 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 purpose.
+ #
+ # - 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=None, x__xgafv=None)</code>
+ <pre>Gets information about a model version.
+
+Models can have multiple versions. You can call
+[projects.models.versions.list](/ml/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.
+
+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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the
+ # model. If unset (i.e., by default), the number of nodes used to serve
+ # the model automatically scales with traffic. However, care should be
+ # taken to ramp up traffic according to the model's ability to scale. If
+ # your model needs to handle bursts of traffic beyond it's ability to
+ # scale, it is recommended you set this field appropriately.
+ "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
+ # deployment.
+ },
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "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/docs/concepts/deployment-overview) for
+ # more informaiton.
+ #
+ # When passing Version to
+ # [projects.models.versions.create](/ml/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.
+ "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/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=None, pageToken=None, x__xgafv=None, pageSize=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)
+ 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.
+
+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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the
+ # model. If unset (i.e., by default), the number of nodes used to serve
+ # the model automatically scales with traffic. However, care should be
+ # taken to ramp up traffic according to the model's ability to scale. If
+ # your model needs to handle bursts of traffic beyond it's ability to
+ # scale, it is recommended you set this field appropriately.
+ "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
+ # deployment.
+ },
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "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/docs/concepts/deployment-overview) for
+ # more informaiton.
+ #
+ # When passing Version to
+ # [projects.models.versions.create](/ml/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.
+ "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/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="setDefault">setDefault(name=None, 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/reference/rest/v1/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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the
+ # model. If unset (i.e., by default), the number of nodes used to serve
+ # the model automatically scales with traffic. However, care should be
+ # taken to ramp up traffic according to the model's ability to scale. If
+ # your model needs to handle bursts of traffic beyond it's ability to
+ # scale, it is recommended you set this field appropriately.
+ "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
+ # deployment.
+ },
+ "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
+ "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/docs/concepts/deployment-overview) for
+ # more informaiton.
+ #
+ # When passing Version to
+ # [projects.models.versions.create](/ml/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.
+ "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/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>
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