Blame - docs/dyn/ml_v1.projects.models.html - platform/external/python/google-api-python-client

<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>

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<h2>Instance Methods</h2>

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<code><a href="ml_v1.projects.models.versions.html">versions()</a></code>

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</p>

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<p class="firstline">Returns the versions Resource.</p>

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<code><a href="#create">create(parent=None, body, x__xgafv=None)</a></code></p>

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<p class="firstline">Creates a model which will later contain one or more versions.</p>

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<code><a href="#delete">delete(name=None, x__xgafv=None)</a></code></p>

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<p class="firstline">Deletes a model.</p>

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<code><a href="#get">get(name=None, x__xgafv=None)</a></code></p>

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<p class="firstline">Gets information about a model, including its name, the description (if</p>

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<code><a href="#list">list(parent=None, pageToken=None, x__xgafv=None, pageSize=None)</a></code></p>

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<p class="firstline">Lists the models in a project.</p>

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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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<h3>Method Details</h3>

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<code class="details" id="create">create(parent=None, body, x__xgafv=None)</code>

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<pre>Creates a model which will later contain one or more versions.

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You must add at least one version before you can request predictions from

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the model. Add versions by calling

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[projects.models.versions.create](/ml/reference/rest/v1/projects.models.versions/create).

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Args:

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parent: string, Required. The project name.

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Authorization: requires `Editor` role on the specified 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 machine learning solution.

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#

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# A model can have multiple versions, each of which is a deployed, trained

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# model ready to receive prediction requests. The model itself is just a

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# container.

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"regions": [ # Optional. The list of regions where the model is going to be deployed.

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# Currently only one region per model is supported.

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# Defaults to 'us-central1' if nothing is set.

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"A String",

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],

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"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to

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# handle prediction 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/reference/rest/v1/projects.models.versions/setDefault).

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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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the

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# model. If unset (i.e., by default), the number of nodes used to serve

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# the model automatically scales with traffic. However, care should be

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# taken to ramp up traffic according to the model's ability to scale. If

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# your model needs to handle bursts of traffic beyond it's ability to

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# scale, it is recommended you set this field appropriately.

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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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# deployment.

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},

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"lastUseTime": "A String", # Output only. The time the version was last used for prediction.

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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 deployment](/ml/docs/concepts/deployment-overview) for

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# more informaiton.

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#

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# When passing Version to

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# [projects.models.versions.create](/ml/reference/rest/v1/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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"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/reference/rest/v1/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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"name": "A String", # Required. The name specified for the model when it was created.

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#

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# The model name must be unique within the project it is created in.

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"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.

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# Default is false.

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"description": "A String", # Optional. The description specified for the model when it was created.

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}

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x__xgafv: string, V1 error format.

Allowed values

1 - v1 error format

2 - v2 error format

Returns:

An object of the form:

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{ # Represents a machine learning solution.

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#

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# A model can have multiple versions, each of which is a deployed, trained

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# model ready to receive prediction requests. The model itself is just a

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# container.

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"regions": [ # Optional. The list of regions where the model is going to be deployed.

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# Currently only one region per model is supported.

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# Defaults to 'us-central1' if nothing is set.

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"A String",

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],

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"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to

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# handle prediction 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/reference/rest/v1/projects.models.versions/setDefault).

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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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the

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# model. If unset (i.e., by default), the number of nodes used to serve

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# the model automatically scales with traffic. However, care should be

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# taken to ramp up traffic according to the model's ability to scale. If

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# your model needs to handle bursts of traffic beyond it's ability to

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# scale, it is recommended you set this field appropriately.

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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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# deployment.

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},

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"lastUseTime": "A String", # Output only. The time the version was last used for prediction.

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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 deployment](/ml/docs/concepts/deployment-overview) for

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# more informaiton.

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#

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# When passing Version to

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# [projects.models.versions.create](/ml/reference/rest/v1/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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"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/reference/rest/v1/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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"name": "A String", # Required. The name specified for the model when it was created.

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#

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# The model name must be unique within the project it is created in.

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"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.

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# Default is false.

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"description": "A String", # Optional. The description specified for the model when it was created.

}</pre>

</div>

<code class="details" id="delete">delete(name=None, x__xgafv=None)</code>

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<pre>Deletes a model.

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You can only delete a model if there are no versions in it. You can delete

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versions by calling

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[projects.models.versions.delete](/ml/reference/rest/v1/projects.models.versions/delete).

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Args:

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

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Authorization: requires `Editor` role on the parent project. (required)

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x__xgafv: string, V1 error format.

Allowed values

1 - v1 error format

2 - v2 error format

Returns:

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

#

# # Overview

#

# 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` which can be used for common error conditions.

#

# # Language mapping

#

# 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.

#

# # Other uses

#

# 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 purpose.

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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

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# 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.

},

],

},

"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`.

}</pre>

</div>

<code class="details" id="get">get(name=None, x__xgafv=None)</code>

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<pre>Gets information about a model, including its name, the description (if

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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.

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Authorization: requires `Viewer` role on the parent project. (required)

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x__xgafv: string, V1 error format.

Allowed values

1 - v1 error format

2 - v2 error format

Returns:

An object of the form:

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{ # Represents a machine learning solution.

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#

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# A model can have multiple versions, each of which is a deployed, trained

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# model ready to receive prediction requests. The model itself is just a

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# container.

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"regions": [ # Optional. The list of regions where the model is going to be deployed.

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# Currently only one region per model is supported.

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# Defaults to 'us-central1' if nothing is set.

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"A String",

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],

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"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to

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# handle prediction 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/reference/rest/v1/projects.models.versions/setDefault).

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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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the

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# model. If unset (i.e., by default), the number of nodes used to serve

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# the model automatically scales with traffic. However, care should be

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# taken to ramp up traffic according to the model's ability to scale. If

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# your model needs to handle bursts of traffic beyond it's ability to

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# scale, it is recommended you set this field appropriately.

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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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# deployment.

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},

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"lastUseTime": "A String", # Output only. The time the version was last used for prediction.

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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 deployment](/ml/docs/concepts/deployment-overview) for

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# more informaiton.

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#

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# When passing Version to

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# [projects.models.versions.create](/ml/reference/rest/v1/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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"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/reference/rest/v1/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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"name": "A String", # Required. The name specified for the model when it was created.

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#

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# The model name must be unique within the project it is created in.

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"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.

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# Default is false.

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"description": "A String", # Optional. The description specified for the model when it was created.

}</pre>

</div>

<code class="details" id="list">list(parent=None, pageToken=None, x__xgafv=None, pageSize=None)</code>

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<pre>Lists the models in a project.

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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.

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Authorization: requires `Viewer` role on the specified project. (required)

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pageToken: string, Optional. A page token to request the next page of results.

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You get the token from the `next_page_token` field of the response from

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the previous call.

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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

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are more remaining results than this number, the response message will

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contain a valid value in the `next_page_token` field.

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The default value is 20, and the maximum page size is 100.

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Returns:

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An object of the form:

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{ # Response message for the ListModels method.

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"nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a

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# subsequent call.

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"models": [ # The list of models.

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{ # Represents a machine learning solution.

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#

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# A model can have multiple versions, each of which is a deployed, trained

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# model ready to receive prediction requests. The model itself is just a

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# container.

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"regions": [ # Optional. The list of regions where the model is going to be deployed.

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# Currently only one region per model is supported.

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# Defaults to 'us-central1' if nothing is set.

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"A String",

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],

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"defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to

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# handle prediction 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/reference/rest/v1/projects.models.versions/setDefault).

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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/reference/rest/v1/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. # Optional. Manually select the number of nodes to use for serving the

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# model. If unset (i.e., by default), the number of nodes used to serve

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# the model automatically scales with traffic. However, care should be

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# taken to ramp up traffic according to the model's ability to scale. If

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# your model needs to handle bursts of traffic beyond it's ability to

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# scale, it is recommended you set this field appropriately.

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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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# deployment.

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},

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"lastUseTime": "A String", # Output only. The time the version was last used for prediction.

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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 deployment](/ml/docs/concepts/deployment-overview) for

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# more informaiton.

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#

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# When passing Version to

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# [projects.models.versions.create](/ml/reference/rest/v1/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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"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/reference/rest/v1/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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"name": "A String", # Required. The name specified for the model when it was created.

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#

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# The model name must be unique within the project it is created in.

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"onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.

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# Default is false.

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"description": "A String", # Optional. The description specified for the model when it was created.

},

],

}</pre>

</div>

<code class="details" id="list_next">list_next(previous_request, previous_response)</code>

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<pre>Retrieves the next page of results.

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Args:

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previous_request: The request for the previous page. (required)

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previous_response: The response from the request for the previous page. (required)

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Returns:

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A request object that you can call 'execute()' on to request the next

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page. Returns None if there are no more items in the collection.

</pre>

</div>

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