docs/dyn/ml_v1.projects.models.html

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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></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
  <code><a href="ml_v1.projects.models.versions.html">versions()</a></code>
</p>
<p class="firstline">Returns the versions Resource.</p>

<p class="toc_element">
  <code><a href="#create">create(parent, body, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a model which will later contain one or more versions.</p>
<p class="toc_element">
  <code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
<p class="firstline">Deletes a model.</p>
<p class="toc_element">
  <code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Gets information about a model, including its name, the description (if</p>
<p class="toc_element">
  <code><a href="#list">list(parent, pageToken=None, x__xgafv=None, pageSize=None)</a></code></p>
<p class="firstline">Lists the models in a project.</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>
<h3>Method Details</h3>
<div class="method">
    <code class="details" id="create">create(parent, body, x__xgafv=None)</code>
  <pre>Creates a model which will later contain one or more versions.

You must add at least one version before you can request predictions from
the model. Add versions by calling
[projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create).

Args:
  parent: string, Required. The project name.

Authorization: requires `Editor` role on the specified project. (required)
  body: object, The request body. (required)
    The object takes the form of:

{ # Represents a machine learning solution.
      #
      # A model can have multiple versions, each of which is a deployed, trained
      # model ready to receive prediction requests. The model itself is just a
      # container.
    "regions": [ # Optional. The list of regions where the model is going to be deployed.
        # Currently only one region per model is supported.
        # Defaults to 'us-central1' if nothing is set.
        # Note:
        # *   No matter where a model is deployed, it can always be accessed by
        #     users from anywhere, both for online and batch prediction.
        # *   The region for a batch prediction job is set by the region field when
        #     submitting the batch prediction job and does not take its value from
        #     this field.
      "A String",
    ],
    "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
        #
        # 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).
      "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 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.
      },
      "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/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.
      "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/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.
    },
    "name": "A String", # Required. The name specified for the model when it was created.
        #
        # The model name must be unique within the project it is created in.
    "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
        # Default is false.
    "description": "A String", # Optional. The description specified for the model when it was created.
  }

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Represents a machine learning solution.
        #
        # A model can have multiple versions, each of which is a deployed, trained
        # model ready to receive prediction requests. The model itself is just a
        # container.
      "regions": [ # Optional. The list of regions where the model is going to be deployed.
          # Currently only one region per model is supported.
          # Defaults to 'us-central1' if nothing is set.
          # Note:
          # *   No matter where a model is deployed, it can always be accessed by
          #     users from anywhere, both for online and batch prediction.
          # *   The region for a batch prediction job is set by the region field when
          #     submitting the batch prediction job and does not take its value from
          #     this field.
        "A String",
      ],
      "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
          #
          # 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).
        "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 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.
        },
        "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/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.
        "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/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.
      },
      "name": "A String", # Required. The name specified for the model when it was created.
          #
          # The model name must be unique within the project it is created in.
      "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
          # Default is false.
      "description": "A String", # Optional. The description specified for the model when it was created.
    }</pre>
</div>

<div class="method">
    <code class="details" id="delete">delete(name, x__xgafv=None)</code>
  <pre>Deletes a model.

You can only delete a model if there are no versions in it. You can delete
versions by calling
[projects.models.versions.delete](/ml-engine/reference/rest/v1/projects.models.versions/delete).

Args:
  name: string, Required. The name of the model.

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` 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, including its name, the description (if
set), and the default version (if at least one version of the model has
been deployed).

Args:
  name: string, Required. The name of the model.

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 machine learning solution.
        #
        # A model can have multiple versions, each of which is a deployed, trained
        # model ready to receive prediction requests. The model itself is just a
        # container.
      "regions": [ # Optional. The list of regions where the model is going to be deployed.
          # Currently only one region per model is supported.
          # Defaults to 'us-central1' if nothing is set.
          # Note:
          # *   No matter where a model is deployed, it can always be accessed by
          #     users from anywhere, both for online and batch prediction.
          # *   The region for a batch prediction job is set by the region field when
          #     submitting the batch prediction job and does not take its value from
          #     this field.
        "A String",
      ],
      "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
          #
          # 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).
        "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 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.
        },
        "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/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.
        "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/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.
      },
      "name": "A String", # Required. The name specified for the model when it was created.
          #
          # The model name must be unique within the project it is created in.
      "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
          # Default is false.
      "description": "A String", # Optional. The description specified for the model when it was created.
    }</pre>
</div>

<div class="method">
    <code class="details" id="list">list(parent, pageToken=None, x__xgafv=None, pageSize=None)</code>
  <pre>Lists the models in a project.

Each project can contain multiple models, and each model can have multiple
versions.

Args:
  parent: string, Required. The name of the project whose models are to be listed.

Authorization: requires `Viewer` role on the specified 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 models 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 ListModels method.
    "models": [ # The list of models.
      { # Represents a machine learning solution.
            #
            # A model can have multiple versions, each of which is a deployed, trained
            # model ready to receive prediction requests. The model itself is just a
            # container.
          "regions": [ # Optional. The list of regions where the model is going to be deployed.
              # Currently only one region per model is supported.
              # Defaults to 'us-central1' if nothing is set.
              # Note:
              # *   No matter where a model is deployed, it can always be accessed by
              #     users from anywhere, both for online and batch prediction.
              # *   The region for a batch prediction job is set by the region field when
              #     submitting the batch prediction job and does not take its value from
              #     this field.
            "A String",
          ],
          "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
              #
              # 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).
            "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 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.
            },
            "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/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.
            "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/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.
          },
          "name": "A String", # Required. The name specified for the model when it was created.
              #
              # The model name must be unique within the project it is created in.
          "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
              # Default is false.
          "description": "A String", # Optional. The description specified for the model when it was created.
        },
    ],
    "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
        # subsequent call.
  }</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>

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