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| <h1><a href="ml_v1.html">AI Platform Training & Prediction API</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, body=None, 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, x__xgafv=None)</a></code></p> |
| <p class="firstline">Deletes a model version.</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 version.</p> |
| <p class="toc_element"> |
| <code><a href="#list">list(parent, filter=None, pageToken=None, pageSize=None, x__xgafv=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="#patch">patch(name, body=None, updateMask=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Updates the specified Version resource.</p> |
| <p class="toc_element"> |
| <code><a href="#setDefault">setDefault(name, body=None, 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, body=None, 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. |
| |
| Args: |
| parent: string, Required. The name of the model. (required) |
| body: object, The request body. |
| 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. |
| "labels": { # Optional. One or more labels that you can add, to organize your model |
| # versions. Each label is a key-value pair, where both the key and the value |
| # are arbitrary strings that you supply. |
| # For more information, see the documentation on |
| # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>. |
| "a_key": "A String", |
| }, |
| "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only |
| # applies to online prediction service. If this field is not specified, it |
| # defaults to `mls1-c1-m2`. |
| # |
| # Online prediction supports the following machine types: |
| # |
| # * `mls1-c1-m2` |
| # * `mls1-c4-m2` |
| # * `n1-standard-2` |
| # * `n1-standard-4` |
| # * `n1-standard-8` |
| # * `n1-standard-16` |
| # * `n1-standard-32` |
| # * `n1-highmem-2` |
| # * `n1-highmem-4` |
| # * `n1-highmem-8` |
| # * `n1-highmem-16` |
| # * `n1-highmem-32` |
| # * `n1-highcpu-2` |
| # * `n1-highcpu-4` |
| # * `n1-highcpu-8` |
| # * `n1-highcpu-16` |
| # * `n1-highcpu-32` |
| # |
| # `mls1-c1-m2` is generally available. All other machine types are available |
| # in beta. Learn more about the [differences between machine |
| # types](/ml-engine/docs/machine-types-online-prediction). |
| "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom |
| # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) |
| # or [scikit-learn pipelines with custom |
| # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). |
| # |
| # For a custom prediction routine, one of these packages must contain your |
| # Predictor class (see |
| # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, |
| # include any dependencies used by your Predictor or scikit-learn pipeline |
| # uses that are not already included in your selected [runtime |
| # version](/ml-engine/docs/tensorflow/runtime-version-list). |
| # |
| # If you specify this field, you must also set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater. |
| "A String", |
| ], |
| "acceleratorConfig": { # Represents a hardware accelerator request config. # Optional. Accelerator config for using GPUs for online prediction (beta). |
| # Only specify this field if you have specified a Compute Engine (N1) machine |
| # type in the `machineType` field. Learn more about [using GPUs for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| # Note that the AcceleratorConfig can be used in both Jobs and Versions. |
| # Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and |
| # [accelerators for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| "type": "A String", # The type of accelerator to use. |
| "count": "A String", # The number of accelerators to attach to each machine running the job. |
| }, |
| "state": "A String", # Output only. The state of a version. |
| "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. |
| "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in |
| # response to increases and decreases in traffic. Care should be |
| # taken to ramp up traffic according to the model's ability to scale |
| # or you will start seeing increases in latency and 429 response codes. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify |
| # `manual_scaling`. |
| "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These |
| # nodes are always up, starting from the time the model is deployed. |
| # Therefore, the cost of operating this model will be at least |
| # `rate` * `min_nodes` * number of hours since last billing cycle, |
| # where `rate` is the cost per node-hour as documented in the |
| # [pricing guide](/ml-engine/docs/pricing), |
| # even if no predictions are performed. There is additional cost for each |
| # prediction performed. |
| # |
| # Unlike manual scaling, if the load gets too heavy for the nodes |
| # that are up, the service will automatically add nodes to handle the |
| # increased load as well as scale back as traffic drops, always maintaining |
| # at least `min_nodes`. You will be charged for the time in which additional |
| # nodes are used. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [legacy |
| # (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `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. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [Compute |
| # Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a |
| # Compute Engine machine type. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use |
| # ManualScaling. |
| # |
| # You can set `min_nodes` when creating the model version, and you can also |
| # update `min_nodes` for an existing version: |
| # <pre> |
| # update_body.json: |
| # { |
| # 'autoScaling': { |
| # 'minNodes': 5 |
| # } |
| # } |
| # </pre> |
| # HTTP request: |
| # <pre style="max-width: 626px;"> |
| # PATCH |
| # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes |
| # -d @./update_body.json |
| # </pre> |
| }, |
| "explanationConfig": { # Message holding configuration options for explaining model predictions. # Optional. Configures explainability features on the model's version. |
| # Some explanation features require additional metadata to be loaded |
| # as part of the model payload. |
| # There are two feature attribution methods supported for TensorFlow models: |
| # integrated gradients and sampled Shapley. |
| # [Learn more about feature |
| # attributions.](/ai-platform/prediction/docs/ai-explanations/overview) |
| "integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage # Attributes credit by computing the Aumann-Shapley value taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: http://proceedings.mlr.press/v70/sundararajan17a.html |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1703.01365 |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "xraiAttribution": { # Attributes credit by computing the XRAI taking advantage # Attributes credit by computing the XRAI taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that # An attribution method that approximates Shapley values for features that |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| "numPaths": 42, # The number of feature permutations to consider when approximating the |
| # Shapley values. |
| }, |
| }, |
| "pythonVersion": "A String", # Required. The version of Python used in prediction. |
| # |
| # The following Python versions are available: |
| # |
| # * Python '3.7' is available when `runtime_version` is set to '1.15' or |
| # later. |
| # * Python '3.5' is available when `runtime_version` is set to a version |
| # from '1.4' to '1.14'. |
| # * Python '2.7' is available when `runtime_version` is set to '1.15' or |
| # earlier. |
| # |
| # Read more about the Python versions available for [each runtime |
| # version](/ml-engine/docs/runtime-version-list). |
| "requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. # Optional. *Only* specify this field in a |
| # projects.models.versions.patch |
| # request. Specifying it in a |
| # projects.models.versions.create |
| # request has no effect. |
| # |
| # Configures the request-response pair logging on predictions from this |
| # Version. |
| # Online prediction requests to a model version and the responses to these |
| # requests are converted to raw strings and saved to the specified BigQuery |
| # table. Logging is constrained by [BigQuery quotas and |
| # limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, |
| # AI Platform Prediction does not log request-response pairs, but it continues |
| # to serve predictions. |
| # |
| # If you are using [continuous |
| # evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to |
| # specify this configuration manually. Setting up continuous evaluation |
| # automatically enables logging of request-response pairs. |
| "samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. |
| # For example, if you want to log 10% of requests, enter `0.1`. The sampling |
| # window is the lifetime of the model version. Defaults to 0. |
| "bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: |
| # "<var>project_id</var>.<var>dataset_name</var>.<var>table_name</var>" |
| # |
| # The specified table must already exist, and the "Cloud ML Service Agent" |
| # for your project must have permission to write to it. The table must have |
| # the following [schema](/bigquery/docs/schemas): |
| # |
| # <table> |
| # <tr><th>Field name</th><th style="display: table-cell">Type</th> |
| # <th style="display: table-cell">Mode</th></tr> |
| # <tr><td>model</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>model_version</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>time</td><td>TIMESTAMP</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_data</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_prediction</td><td>STRING</td><td>NULLABLE</td></tr> |
| # <tr><td>groundtruth</td><td>STRING</td><td>NULLABLE</td></tr> |
| # </table> |
| }, |
| "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the |
| # model. You should generally use `auto_scaling` with an appropriate |
| # `min_nodes` instead, but this option is available if you want more |
| # predictable billing. Beware that latency and error rates will increase |
| # if the traffic exceeds that capability of the system to serve it based |
| # on the selected number of nodes. |
| "nodes": 42, # The number of nodes to allocate for this model. These nodes are always up, |
| # starting from the time the model is deployed, so the cost of operating |
| # this model will be proportional to `nodes` * number of hours since |
| # last billing cycle plus the cost for each prediction performed. |
| }, |
| "createTime": "A String", # Output only. The time the version was created. |
| "lastUseTime": "A String", # Output only. The time the version was last used for prediction. |
| "framework": "A String", # Optional. The machine learning framework AI Platform uses to train |
| # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, |
| # `XGBOOST`. If you do not specify a framework, AI Platform |
| # will analyze files in the deployment_uri to determine a framework. If you |
| # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version |
| # of the model to 1.4 or greater. |
| # |
| # Do **not** specify a framework if you're deploying a [custom |
| # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # |
| # If you specify a [Compute Engine (N1) machine |
| # type](/ml-engine/docs/machine-types-online-prediction) in the |
| # `machineType` field, you must specify `TENSORFLOW` |
| # for the framework. |
| "predictionClass": "A String", # Optional. The fully qualified name |
| # (<var>module_name</var>.<var>class_name</var>) of a class that implements |
| # the Predictor interface described in this reference field. The module |
| # containing this class should be included in a package provided to the |
| # [`packageUris` field](#Version.FIELDS.package_uris). |
| # |
| # Specify this field if and only if you are deploying a [custom prediction |
| # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # If you specify this field, you must set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and |
| # you must set `machineType` to a [legacy (MLS1) |
| # machine type](/ml-engine/docs/machine-types-online-prediction). |
| # |
| # The following code sample provides the Predictor interface: |
| # |
| # <pre style="max-width: 626px;"> |
| # class Predictor(object): |
| # """Interface for constructing custom predictors.""" |
| # |
| # def predict(self, instances, **kwargs): |
| # """Performs custom prediction. |
| # |
| # Instances are the decoded values from the request. They have already |
| # been deserialized from JSON. |
| # |
| # Args: |
| # instances: A list of prediction input instances. |
| # **kwargs: A dictionary of keyword args provided as additional |
| # fields on the predict request body. |
| # |
| # Returns: |
| # A list of outputs containing the prediction results. This list must |
| # be JSON serializable. |
| # """ |
| # raise NotImplementedError() |
| # |
| # @classmethod |
| # def from_path(cls, model_dir): |
| # """Creates an instance of Predictor using the given path. |
| # |
| # Loading of the predictor should be done in this method. |
| # |
| # Args: |
| # model_dir: The local directory that contains the exported model |
| # file along with any additional files uploaded when creating the |
| # version resource. |
| # |
| # Returns: |
| # An instance implementing this Predictor class. |
| # """ |
| # raise NotImplementedError() |
| # </pre> |
| # |
| # Learn more about [the Predictor interface and custom prediction |
| # routines](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| "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. |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a model from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform model updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `GetVersion`, and |
| # systems are expected to put that etag in the request to `UpdateVersion` to |
| # ensure that their change will be applied to the model as intended. |
| "serviceAccount": "A String", # Optional. Specifies the service account for resource access control. |
| "errorMessage": "A String", # Output only. The details of a failure or a cancellation. |
| "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to |
| # create the version. See the |
| # [guide to model |
| # deployment](/ml-engine/docs/tensorflow/deploying-models) for more |
| # information. |
| # |
| # When passing Version to |
| # projects.models.versions.create |
| # 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. |
| "runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. |
| # |
| # For more information, see the |
| # [runtime version list](/ml-engine/docs/runtime-version-list) and |
| # [how to manage runtime versions](/ml-engine/docs/versioning). |
| "description": "A String", # Optional. The description specified for the version 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: |
| |
| { # This resource represents a long-running operation that is the result of a |
| # network API call. |
| "error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation. |
| # different programming environments, including REST APIs and RPC APIs. It is |
| # used by [gRPC](https://github.com/grpc). Each `Status` message contains |
| # three pieces of data: error code, error message, and error details. |
| # |
| # You can find out more about this error model and how to work with it in the |
| # [API Design Guide](https://cloud.google.com/apis/design/errors). |
| "details": [ # A list of messages that carry the error details. There is a common set of |
| # message types for APIs to use. |
| { |
| "a_key": "", # Properties of the object. Contains field @type with type URL. |
| }, |
| ], |
| "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. |
| }, |
| "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. |
| }, |
| "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. |
| }, |
| "name": "A String", # The server-assigned name, which is only unique within the same service that |
| # originally returns it. If you use the default HTTP mapping, the |
| # `name` should be a resource name ending with `operations/{unique_id}`. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="delete">delete(name, x__xgafv=None)</code> |
| <pre>Deletes a model version. |
| |
| Each model can have multiple versions deployed and in use at any given |
| time. Use this method to remove a single version. |
| |
| Note: You cannot delete the version that is set as the default version |
| of the model unless it is the only remaining version. |
| |
| Args: |
| name: string, Required. The name of the version. You can get the names of all the |
| versions of a model by calling |
| projects.models.versions.list. (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. |
| "error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation. |
| # different programming environments, including REST APIs and RPC APIs. It is |
| # used by [gRPC](https://github.com/grpc). Each `Status` message contains |
| # three pieces of data: error code, error message, and error details. |
| # |
| # You can find out more about this error model and how to work with it in the |
| # [API Design Guide](https://cloud.google.com/apis/design/errors). |
| "details": [ # A list of messages that carry the error details. There is a common set of |
| # message types for APIs to use. |
| { |
| "a_key": "", # Properties of the object. Contains field @type with type URL. |
| }, |
| ], |
| "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. |
| }, |
| "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. |
| }, |
| "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. |
| }, |
| "name": "A String", # The server-assigned name, which is only unique within the same service that |
| # originally returns it. If you use the default HTTP mapping, the |
| # `name` should be a resource name ending with `operations/{unique_id}`. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="get">get(name, x__xgafv=None)</code> |
| <pre>Gets information about a model version. |
| |
| Models can have multiple versions. You can call |
| projects.models.versions.list |
| to get the same information that this method returns for all of the |
| versions of a model. |
| |
| Args: |
| name: string, Required. The name of the version. (required) |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # Represents a version of the model. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "labels": { # Optional. One or more labels that you can add, to organize your model |
| # versions. Each label is a key-value pair, where both the key and the value |
| # are arbitrary strings that you supply. |
| # For more information, see the documentation on |
| # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>. |
| "a_key": "A String", |
| }, |
| "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only |
| # applies to online prediction service. If this field is not specified, it |
| # defaults to `mls1-c1-m2`. |
| # |
| # Online prediction supports the following machine types: |
| # |
| # * `mls1-c1-m2` |
| # * `mls1-c4-m2` |
| # * `n1-standard-2` |
| # * `n1-standard-4` |
| # * `n1-standard-8` |
| # * `n1-standard-16` |
| # * `n1-standard-32` |
| # * `n1-highmem-2` |
| # * `n1-highmem-4` |
| # * `n1-highmem-8` |
| # * `n1-highmem-16` |
| # * `n1-highmem-32` |
| # * `n1-highcpu-2` |
| # * `n1-highcpu-4` |
| # * `n1-highcpu-8` |
| # * `n1-highcpu-16` |
| # * `n1-highcpu-32` |
| # |
| # `mls1-c1-m2` is generally available. All other machine types are available |
| # in beta. Learn more about the [differences between machine |
| # types](/ml-engine/docs/machine-types-online-prediction). |
| "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom |
| # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) |
| # or [scikit-learn pipelines with custom |
| # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). |
| # |
| # For a custom prediction routine, one of these packages must contain your |
| # Predictor class (see |
| # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, |
| # include any dependencies used by your Predictor or scikit-learn pipeline |
| # uses that are not already included in your selected [runtime |
| # version](/ml-engine/docs/tensorflow/runtime-version-list). |
| # |
| # If you specify this field, you must also set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater. |
| "A String", |
| ], |
| "acceleratorConfig": { # Represents a hardware accelerator request config. # Optional. Accelerator config for using GPUs for online prediction (beta). |
| # Only specify this field if you have specified a Compute Engine (N1) machine |
| # type in the `machineType` field. Learn more about [using GPUs for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| # Note that the AcceleratorConfig can be used in both Jobs and Versions. |
| # Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and |
| # [accelerators for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| "type": "A String", # The type of accelerator to use. |
| "count": "A String", # The number of accelerators to attach to each machine running the job. |
| }, |
| "state": "A String", # Output only. The state of a version. |
| "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. |
| "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in |
| # response to increases and decreases in traffic. Care should be |
| # taken to ramp up traffic according to the model's ability to scale |
| # or you will start seeing increases in latency and 429 response codes. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify |
| # `manual_scaling`. |
| "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These |
| # nodes are always up, starting from the time the model is deployed. |
| # Therefore, the cost of operating this model will be at least |
| # `rate` * `min_nodes` * number of hours since last billing cycle, |
| # where `rate` is the cost per node-hour as documented in the |
| # [pricing guide](/ml-engine/docs/pricing), |
| # even if no predictions are performed. There is additional cost for each |
| # prediction performed. |
| # |
| # Unlike manual scaling, if the load gets too heavy for the nodes |
| # that are up, the service will automatically add nodes to handle the |
| # increased load as well as scale back as traffic drops, always maintaining |
| # at least `min_nodes`. You will be charged for the time in which additional |
| # nodes are used. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [legacy |
| # (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `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. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [Compute |
| # Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a |
| # Compute Engine machine type. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use |
| # ManualScaling. |
| # |
| # You can set `min_nodes` when creating the model version, and you can also |
| # update `min_nodes` for an existing version: |
| # <pre> |
| # update_body.json: |
| # { |
| # 'autoScaling': { |
| # 'minNodes': 5 |
| # } |
| # } |
| # </pre> |
| # HTTP request: |
| # <pre style="max-width: 626px;"> |
| # PATCH |
| # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes |
| # -d @./update_body.json |
| # </pre> |
| }, |
| "explanationConfig": { # Message holding configuration options for explaining model predictions. # Optional. Configures explainability features on the model's version. |
| # Some explanation features require additional metadata to be loaded |
| # as part of the model payload. |
| # There are two feature attribution methods supported for TensorFlow models: |
| # integrated gradients and sampled Shapley. |
| # [Learn more about feature |
| # attributions.](/ai-platform/prediction/docs/ai-explanations/overview) |
| "integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage # Attributes credit by computing the Aumann-Shapley value taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: http://proceedings.mlr.press/v70/sundararajan17a.html |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1703.01365 |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "xraiAttribution": { # Attributes credit by computing the XRAI taking advantage # Attributes credit by computing the XRAI taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that # An attribution method that approximates Shapley values for features that |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| "numPaths": 42, # The number of feature permutations to consider when approximating the |
| # Shapley values. |
| }, |
| }, |
| "pythonVersion": "A String", # Required. The version of Python used in prediction. |
| # |
| # The following Python versions are available: |
| # |
| # * Python '3.7' is available when `runtime_version` is set to '1.15' or |
| # later. |
| # * Python '3.5' is available when `runtime_version` is set to a version |
| # from '1.4' to '1.14'. |
| # * Python '2.7' is available when `runtime_version` is set to '1.15' or |
| # earlier. |
| # |
| # Read more about the Python versions available for [each runtime |
| # version](/ml-engine/docs/runtime-version-list). |
| "requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. # Optional. *Only* specify this field in a |
| # projects.models.versions.patch |
| # request. Specifying it in a |
| # projects.models.versions.create |
| # request has no effect. |
| # |
| # Configures the request-response pair logging on predictions from this |
| # Version. |
| # Online prediction requests to a model version and the responses to these |
| # requests are converted to raw strings and saved to the specified BigQuery |
| # table. Logging is constrained by [BigQuery quotas and |
| # limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, |
| # AI Platform Prediction does not log request-response pairs, but it continues |
| # to serve predictions. |
| # |
| # If you are using [continuous |
| # evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to |
| # specify this configuration manually. Setting up continuous evaluation |
| # automatically enables logging of request-response pairs. |
| "samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. |
| # For example, if you want to log 10% of requests, enter `0.1`. The sampling |
| # window is the lifetime of the model version. Defaults to 0. |
| "bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: |
| # "<var>project_id</var>.<var>dataset_name</var>.<var>table_name</var>" |
| # |
| # The specified table must already exist, and the "Cloud ML Service Agent" |
| # for your project must have permission to write to it. The table must have |
| # the following [schema](/bigquery/docs/schemas): |
| # |
| # <table> |
| # <tr><th>Field name</th><th style="display: table-cell">Type</th> |
| # <th style="display: table-cell">Mode</th></tr> |
| # <tr><td>model</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>model_version</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>time</td><td>TIMESTAMP</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_data</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_prediction</td><td>STRING</td><td>NULLABLE</td></tr> |
| # <tr><td>groundtruth</td><td>STRING</td><td>NULLABLE</td></tr> |
| # </table> |
| }, |
| "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the |
| # model. You should generally use `auto_scaling` with an appropriate |
| # `min_nodes` instead, but this option is available if you want more |
| # predictable billing. Beware that latency and error rates will increase |
| # if the traffic exceeds that capability of the system to serve it based |
| # on the selected number of nodes. |
| "nodes": 42, # The number of nodes to allocate for this model. These nodes are always up, |
| # starting from the time the model is deployed, so the cost of operating |
| # this model will be proportional to `nodes` * number of hours since |
| # last billing cycle plus the cost for each prediction performed. |
| }, |
| "createTime": "A String", # Output only. The time the version was created. |
| "lastUseTime": "A String", # Output only. The time the version was last used for prediction. |
| "framework": "A String", # Optional. The machine learning framework AI Platform uses to train |
| # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, |
| # `XGBOOST`. If you do not specify a framework, AI Platform |
| # will analyze files in the deployment_uri to determine a framework. If you |
| # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version |
| # of the model to 1.4 or greater. |
| # |
| # Do **not** specify a framework if you're deploying a [custom |
| # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # |
| # If you specify a [Compute Engine (N1) machine |
| # type](/ml-engine/docs/machine-types-online-prediction) in the |
| # `machineType` field, you must specify `TENSORFLOW` |
| # for the framework. |
| "predictionClass": "A String", # Optional. The fully qualified name |
| # (<var>module_name</var>.<var>class_name</var>) of a class that implements |
| # the Predictor interface described in this reference field. The module |
| # containing this class should be included in a package provided to the |
| # [`packageUris` field](#Version.FIELDS.package_uris). |
| # |
| # Specify this field if and only if you are deploying a [custom prediction |
| # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # If you specify this field, you must set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and |
| # you must set `machineType` to a [legacy (MLS1) |
| # machine type](/ml-engine/docs/machine-types-online-prediction). |
| # |
| # The following code sample provides the Predictor interface: |
| # |
| # <pre style="max-width: 626px;"> |
| # class Predictor(object): |
| # """Interface for constructing custom predictors.""" |
| # |
| # def predict(self, instances, **kwargs): |
| # """Performs custom prediction. |
| # |
| # Instances are the decoded values from the request. They have already |
| # been deserialized from JSON. |
| # |
| # Args: |
| # instances: A list of prediction input instances. |
| # **kwargs: A dictionary of keyword args provided as additional |
| # fields on the predict request body. |
| # |
| # Returns: |
| # A list of outputs containing the prediction results. This list must |
| # be JSON serializable. |
| # """ |
| # raise NotImplementedError() |
| # |
| # @classmethod |
| # def from_path(cls, model_dir): |
| # """Creates an instance of Predictor using the given path. |
| # |
| # Loading of the predictor should be done in this method. |
| # |
| # Args: |
| # model_dir: The local directory that contains the exported model |
| # file along with any additional files uploaded when creating the |
| # version resource. |
| # |
| # Returns: |
| # An instance implementing this Predictor class. |
| # """ |
| # raise NotImplementedError() |
| # </pre> |
| # |
| # Learn more about [the Predictor interface and custom prediction |
| # routines](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| "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. |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a model from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform model updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `GetVersion`, and |
| # systems are expected to put that etag in the request to `UpdateVersion` to |
| # ensure that their change will be applied to the model as intended. |
| "serviceAccount": "A String", # Optional. Specifies the service account for resource access control. |
| "errorMessage": "A String", # Output only. The details of a failure or a cancellation. |
| "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to |
| # create the version. See the |
| # [guide to model |
| # deployment](/ml-engine/docs/tensorflow/deploying-models) for more |
| # information. |
| # |
| # When passing Version to |
| # projects.models.versions.create |
| # 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. |
| "runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. |
| # |
| # For more information, see the |
| # [runtime version list](/ml-engine/docs/runtime-version-list) and |
| # [how to manage runtime versions](/ml-engine/docs/versioning). |
| "description": "A String", # Optional. The description specified for the version when it was created. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="list">list(parent, filter=None, pageToken=None, pageSize=None, x__xgafv=None)</code> |
| <pre>Gets basic information about all the versions of a model. |
| |
| If you expect that a model has many versions, or if you need to handle |
| only a limited number of results at a time, you can request that the list |
| be retrieved in batches (called pages). |
| |
| If there are no versions that match the request parameters, the list |
| request returns an empty response body: {}. |
| |
| Args: |
| parent: string, Required. The name of the model for which to list the version. (required) |
| filter: string, Optional. Specifies the subset of versions to retrieve. |
| 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. |
| 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. |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # Response message for the ListVersions method. |
| "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a |
| # subsequent call. |
| "versions": [ # The list of versions. |
| { # Represents a version of the model. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "labels": { # Optional. One or more labels that you can add, to organize your model |
| # versions. Each label is a key-value pair, where both the key and the value |
| # are arbitrary strings that you supply. |
| # For more information, see the documentation on |
| # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>. |
| "a_key": "A String", |
| }, |
| "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only |
| # applies to online prediction service. If this field is not specified, it |
| # defaults to `mls1-c1-m2`. |
| # |
| # Online prediction supports the following machine types: |
| # |
| # * `mls1-c1-m2` |
| # * `mls1-c4-m2` |
| # * `n1-standard-2` |
| # * `n1-standard-4` |
| # * `n1-standard-8` |
| # * `n1-standard-16` |
| # * `n1-standard-32` |
| # * `n1-highmem-2` |
| # * `n1-highmem-4` |
| # * `n1-highmem-8` |
| # * `n1-highmem-16` |
| # * `n1-highmem-32` |
| # * `n1-highcpu-2` |
| # * `n1-highcpu-4` |
| # * `n1-highcpu-8` |
| # * `n1-highcpu-16` |
| # * `n1-highcpu-32` |
| # |
| # `mls1-c1-m2` is generally available. All other machine types are available |
| # in beta. Learn more about the [differences between machine |
| # types](/ml-engine/docs/machine-types-online-prediction). |
| "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom |
| # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) |
| # or [scikit-learn pipelines with custom |
| # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). |
| # |
| # For a custom prediction routine, one of these packages must contain your |
| # Predictor class (see |
| # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, |
| # include any dependencies used by your Predictor or scikit-learn pipeline |
| # uses that are not already included in your selected [runtime |
| # version](/ml-engine/docs/tensorflow/runtime-version-list). |
| # |
| # If you specify this field, you must also set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater. |
| "A String", |
| ], |
| "acceleratorConfig": { # Represents a hardware accelerator request config. # Optional. Accelerator config for using GPUs for online prediction (beta). |
| # Only specify this field if you have specified a Compute Engine (N1) machine |
| # type in the `machineType` field. Learn more about [using GPUs for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| # Note that the AcceleratorConfig can be used in both Jobs and Versions. |
| # Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and |
| # [accelerators for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| "type": "A String", # The type of accelerator to use. |
| "count": "A String", # The number of accelerators to attach to each machine running the job. |
| }, |
| "state": "A String", # Output only. The state of a version. |
| "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. |
| "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in |
| # response to increases and decreases in traffic. Care should be |
| # taken to ramp up traffic according to the model's ability to scale |
| # or you will start seeing increases in latency and 429 response codes. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify |
| # `manual_scaling`. |
| "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These |
| # nodes are always up, starting from the time the model is deployed. |
| # Therefore, the cost of operating this model will be at least |
| # `rate` * `min_nodes` * number of hours since last billing cycle, |
| # where `rate` is the cost per node-hour as documented in the |
| # [pricing guide](/ml-engine/docs/pricing), |
| # even if no predictions are performed. There is additional cost for each |
| # prediction performed. |
| # |
| # Unlike manual scaling, if the load gets too heavy for the nodes |
| # that are up, the service will automatically add nodes to handle the |
| # increased load as well as scale back as traffic drops, always maintaining |
| # at least `min_nodes`. You will be charged for the time in which additional |
| # nodes are used. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [legacy |
| # (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `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. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [Compute |
| # Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a |
| # Compute Engine machine type. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use |
| # ManualScaling. |
| # |
| # You can set `min_nodes` when creating the model version, and you can also |
| # update `min_nodes` for an existing version: |
| # <pre> |
| # update_body.json: |
| # { |
| # 'autoScaling': { |
| # 'minNodes': 5 |
| # } |
| # } |
| # </pre> |
| # HTTP request: |
| # <pre style="max-width: 626px;"> |
| # PATCH |
| # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes |
| # -d @./update_body.json |
| # </pre> |
| }, |
| "explanationConfig": { # Message holding configuration options for explaining model predictions. # Optional. Configures explainability features on the model's version. |
| # Some explanation features require additional metadata to be loaded |
| # as part of the model payload. |
| # There are two feature attribution methods supported for TensorFlow models: |
| # integrated gradients and sampled Shapley. |
| # [Learn more about feature |
| # attributions.](/ai-platform/prediction/docs/ai-explanations/overview) |
| "integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage # Attributes credit by computing the Aumann-Shapley value taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: http://proceedings.mlr.press/v70/sundararajan17a.html |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1703.01365 |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "xraiAttribution": { # Attributes credit by computing the XRAI taking advantage # Attributes credit by computing the XRAI taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that # An attribution method that approximates Shapley values for features that |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| "numPaths": 42, # The number of feature permutations to consider when approximating the |
| # Shapley values. |
| }, |
| }, |
| "pythonVersion": "A String", # Required. The version of Python used in prediction. |
| # |
| # The following Python versions are available: |
| # |
| # * Python '3.7' is available when `runtime_version` is set to '1.15' or |
| # later. |
| # * Python '3.5' is available when `runtime_version` is set to a version |
| # from '1.4' to '1.14'. |
| # * Python '2.7' is available when `runtime_version` is set to '1.15' or |
| # earlier. |
| # |
| # Read more about the Python versions available for [each runtime |
| # version](/ml-engine/docs/runtime-version-list). |
| "requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. # Optional. *Only* specify this field in a |
| # projects.models.versions.patch |
| # request. Specifying it in a |
| # projects.models.versions.create |
| # request has no effect. |
| # |
| # Configures the request-response pair logging on predictions from this |
| # Version. |
| # Online prediction requests to a model version and the responses to these |
| # requests are converted to raw strings and saved to the specified BigQuery |
| # table. Logging is constrained by [BigQuery quotas and |
| # limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, |
| # AI Platform Prediction does not log request-response pairs, but it continues |
| # to serve predictions. |
| # |
| # If you are using [continuous |
| # evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to |
| # specify this configuration manually. Setting up continuous evaluation |
| # automatically enables logging of request-response pairs. |
| "samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. |
| # For example, if you want to log 10% of requests, enter `0.1`. The sampling |
| # window is the lifetime of the model version. Defaults to 0. |
| "bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: |
| # "<var>project_id</var>.<var>dataset_name</var>.<var>table_name</var>" |
| # |
| # The specified table must already exist, and the "Cloud ML Service Agent" |
| # for your project must have permission to write to it. The table must have |
| # the following [schema](/bigquery/docs/schemas): |
| # |
| # <table> |
| # <tr><th>Field name</th><th style="display: table-cell">Type</th> |
| # <th style="display: table-cell">Mode</th></tr> |
| # <tr><td>model</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>model_version</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>time</td><td>TIMESTAMP</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_data</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_prediction</td><td>STRING</td><td>NULLABLE</td></tr> |
| # <tr><td>groundtruth</td><td>STRING</td><td>NULLABLE</td></tr> |
| # </table> |
| }, |
| "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the |
| # model. You should generally use `auto_scaling` with an appropriate |
| # `min_nodes` instead, but this option is available if you want more |
| # predictable billing. Beware that latency and error rates will increase |
| # if the traffic exceeds that capability of the system to serve it based |
| # on the selected number of nodes. |
| "nodes": 42, # The number of nodes to allocate for this model. These nodes are always up, |
| # starting from the time the model is deployed, so the cost of operating |
| # this model will be proportional to `nodes` * number of hours since |
| # last billing cycle plus the cost for each prediction performed. |
| }, |
| "createTime": "A String", # Output only. The time the version was created. |
| "lastUseTime": "A String", # Output only. The time the version was last used for prediction. |
| "framework": "A String", # Optional. The machine learning framework AI Platform uses to train |
| # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, |
| # `XGBOOST`. If you do not specify a framework, AI Platform |
| # will analyze files in the deployment_uri to determine a framework. If you |
| # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version |
| # of the model to 1.4 or greater. |
| # |
| # Do **not** specify a framework if you're deploying a [custom |
| # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # |
| # If you specify a [Compute Engine (N1) machine |
| # type](/ml-engine/docs/machine-types-online-prediction) in the |
| # `machineType` field, you must specify `TENSORFLOW` |
| # for the framework. |
| "predictionClass": "A String", # Optional. The fully qualified name |
| # (<var>module_name</var>.<var>class_name</var>) of a class that implements |
| # the Predictor interface described in this reference field. The module |
| # containing this class should be included in a package provided to the |
| # [`packageUris` field](#Version.FIELDS.package_uris). |
| # |
| # Specify this field if and only if you are deploying a [custom prediction |
| # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # If you specify this field, you must set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and |
| # you must set `machineType` to a [legacy (MLS1) |
| # machine type](/ml-engine/docs/machine-types-online-prediction). |
| # |
| # The following code sample provides the Predictor interface: |
| # |
| # <pre style="max-width: 626px;"> |
| # class Predictor(object): |
| # """Interface for constructing custom predictors.""" |
| # |
| # def predict(self, instances, **kwargs): |
| # """Performs custom prediction. |
| # |
| # Instances are the decoded values from the request. They have already |
| # been deserialized from JSON. |
| # |
| # Args: |
| # instances: A list of prediction input instances. |
| # **kwargs: A dictionary of keyword args provided as additional |
| # fields on the predict request body. |
| # |
| # Returns: |
| # A list of outputs containing the prediction results. This list must |
| # be JSON serializable. |
| # """ |
| # raise NotImplementedError() |
| # |
| # @classmethod |
| # def from_path(cls, model_dir): |
| # """Creates an instance of Predictor using the given path. |
| # |
| # Loading of the predictor should be done in this method. |
| # |
| # Args: |
| # model_dir: The local directory that contains the exported model |
| # file along with any additional files uploaded when creating the |
| # version resource. |
| # |
| # Returns: |
| # An instance implementing this Predictor class. |
| # """ |
| # raise NotImplementedError() |
| # </pre> |
| # |
| # Learn more about [the Predictor interface and custom prediction |
| # routines](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| "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. |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a model from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform model updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `GetVersion`, and |
| # systems are expected to put that etag in the request to `UpdateVersion` to |
| # ensure that their change will be applied to the model as intended. |
| "serviceAccount": "A String", # Optional. Specifies the service account for resource access control. |
| "errorMessage": "A String", # Output only. The details of a failure or a cancellation. |
| "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to |
| # create the version. See the |
| # [guide to model |
| # deployment](/ml-engine/docs/tensorflow/deploying-models) for more |
| # information. |
| # |
| # When passing Version to |
| # projects.models.versions.create |
| # 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. |
| "runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. |
| # |
| # For more information, see the |
| # [runtime version list](/ml-engine/docs/runtime-version-list) and |
| # [how to manage runtime versions](/ml-engine/docs/versioning). |
| "description": "A String", # Optional. The description specified for the version when it was created. |
| }, |
| ], |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="list_next">list_next(previous_request, previous_response)</code> |
| <pre>Retrieves the next page of results. |
| |
| Args: |
| previous_request: The request for the previous page. (required) |
| previous_response: The response from the request for the previous page. (required) |
| |
| Returns: |
| A request object that you can call 'execute()' on to request the next |
| page. Returns None if there are no more items in the collection. |
| </pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="patch">patch(name, body=None, updateMask=None, x__xgafv=None)</code> |
| <pre>Updates the specified Version resource. |
| |
| Currently the only update-able fields are `description`, |
| `requestLoggingConfig`, `autoScaling.minNodes`, and `manualScaling.nodes`. |
| |
| Args: |
| name: string, Required. The name of the model. (required) |
| body: object, The request body. |
| 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. |
| "labels": { # Optional. One or more labels that you can add, to organize your model |
| # versions. Each label is a key-value pair, where both the key and the value |
| # are arbitrary strings that you supply. |
| # For more information, see the documentation on |
| # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>. |
| "a_key": "A String", |
| }, |
| "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only |
| # applies to online prediction service. If this field is not specified, it |
| # defaults to `mls1-c1-m2`. |
| # |
| # Online prediction supports the following machine types: |
| # |
| # * `mls1-c1-m2` |
| # * `mls1-c4-m2` |
| # * `n1-standard-2` |
| # * `n1-standard-4` |
| # * `n1-standard-8` |
| # * `n1-standard-16` |
| # * `n1-standard-32` |
| # * `n1-highmem-2` |
| # * `n1-highmem-4` |
| # * `n1-highmem-8` |
| # * `n1-highmem-16` |
| # * `n1-highmem-32` |
| # * `n1-highcpu-2` |
| # * `n1-highcpu-4` |
| # * `n1-highcpu-8` |
| # * `n1-highcpu-16` |
| # * `n1-highcpu-32` |
| # |
| # `mls1-c1-m2` is generally available. All other machine types are available |
| # in beta. Learn more about the [differences between machine |
| # types](/ml-engine/docs/machine-types-online-prediction). |
| "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom |
| # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) |
| # or [scikit-learn pipelines with custom |
| # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). |
| # |
| # For a custom prediction routine, one of these packages must contain your |
| # Predictor class (see |
| # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, |
| # include any dependencies used by your Predictor or scikit-learn pipeline |
| # uses that are not already included in your selected [runtime |
| # version](/ml-engine/docs/tensorflow/runtime-version-list). |
| # |
| # If you specify this field, you must also set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater. |
| "A String", |
| ], |
| "acceleratorConfig": { # Represents a hardware accelerator request config. # Optional. Accelerator config for using GPUs for online prediction (beta). |
| # Only specify this field if you have specified a Compute Engine (N1) machine |
| # type in the `machineType` field. Learn more about [using GPUs for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| # Note that the AcceleratorConfig can be used in both Jobs and Versions. |
| # Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and |
| # [accelerators for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| "type": "A String", # The type of accelerator to use. |
| "count": "A String", # The number of accelerators to attach to each machine running the job. |
| }, |
| "state": "A String", # Output only. The state of a version. |
| "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. |
| "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in |
| # response to increases and decreases in traffic. Care should be |
| # taken to ramp up traffic according to the model's ability to scale |
| # or you will start seeing increases in latency and 429 response codes. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify |
| # `manual_scaling`. |
| "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These |
| # nodes are always up, starting from the time the model is deployed. |
| # Therefore, the cost of operating this model will be at least |
| # `rate` * `min_nodes` * number of hours since last billing cycle, |
| # where `rate` is the cost per node-hour as documented in the |
| # [pricing guide](/ml-engine/docs/pricing), |
| # even if no predictions are performed. There is additional cost for each |
| # prediction performed. |
| # |
| # Unlike manual scaling, if the load gets too heavy for the nodes |
| # that are up, the service will automatically add nodes to handle the |
| # increased load as well as scale back as traffic drops, always maintaining |
| # at least `min_nodes`. You will be charged for the time in which additional |
| # nodes are used. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [legacy |
| # (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `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. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [Compute |
| # Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a |
| # Compute Engine machine type. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use |
| # ManualScaling. |
| # |
| # You can set `min_nodes` when creating the model version, and you can also |
| # update `min_nodes` for an existing version: |
| # <pre> |
| # update_body.json: |
| # { |
| # 'autoScaling': { |
| # 'minNodes': 5 |
| # } |
| # } |
| # </pre> |
| # HTTP request: |
| # <pre style="max-width: 626px;"> |
| # PATCH |
| # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes |
| # -d @./update_body.json |
| # </pre> |
| }, |
| "explanationConfig": { # Message holding configuration options for explaining model predictions. # Optional. Configures explainability features on the model's version. |
| # Some explanation features require additional metadata to be loaded |
| # as part of the model payload. |
| # There are two feature attribution methods supported for TensorFlow models: |
| # integrated gradients and sampled Shapley. |
| # [Learn more about feature |
| # attributions.](/ai-platform/prediction/docs/ai-explanations/overview) |
| "integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage # Attributes credit by computing the Aumann-Shapley value taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: http://proceedings.mlr.press/v70/sundararajan17a.html |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1703.01365 |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "xraiAttribution": { # Attributes credit by computing the XRAI taking advantage # Attributes credit by computing the XRAI taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that # An attribution method that approximates Shapley values for features that |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| "numPaths": 42, # The number of feature permutations to consider when approximating the |
| # Shapley values. |
| }, |
| }, |
| "pythonVersion": "A String", # Required. The version of Python used in prediction. |
| # |
| # The following Python versions are available: |
| # |
| # * Python '3.7' is available when `runtime_version` is set to '1.15' or |
| # later. |
| # * Python '3.5' is available when `runtime_version` is set to a version |
| # from '1.4' to '1.14'. |
| # * Python '2.7' is available when `runtime_version` is set to '1.15' or |
| # earlier. |
| # |
| # Read more about the Python versions available for [each runtime |
| # version](/ml-engine/docs/runtime-version-list). |
| "requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. # Optional. *Only* specify this field in a |
| # projects.models.versions.patch |
| # request. Specifying it in a |
| # projects.models.versions.create |
| # request has no effect. |
| # |
| # Configures the request-response pair logging on predictions from this |
| # Version. |
| # Online prediction requests to a model version and the responses to these |
| # requests are converted to raw strings and saved to the specified BigQuery |
| # table. Logging is constrained by [BigQuery quotas and |
| # limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, |
| # AI Platform Prediction does not log request-response pairs, but it continues |
| # to serve predictions. |
| # |
| # If you are using [continuous |
| # evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to |
| # specify this configuration manually. Setting up continuous evaluation |
| # automatically enables logging of request-response pairs. |
| "samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. |
| # For example, if you want to log 10% of requests, enter `0.1`. The sampling |
| # window is the lifetime of the model version. Defaults to 0. |
| "bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: |
| # "<var>project_id</var>.<var>dataset_name</var>.<var>table_name</var>" |
| # |
| # The specified table must already exist, and the "Cloud ML Service Agent" |
| # for your project must have permission to write to it. The table must have |
| # the following [schema](/bigquery/docs/schemas): |
| # |
| # <table> |
| # <tr><th>Field name</th><th style="display: table-cell">Type</th> |
| # <th style="display: table-cell">Mode</th></tr> |
| # <tr><td>model</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>model_version</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>time</td><td>TIMESTAMP</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_data</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_prediction</td><td>STRING</td><td>NULLABLE</td></tr> |
| # <tr><td>groundtruth</td><td>STRING</td><td>NULLABLE</td></tr> |
| # </table> |
| }, |
| "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the |
| # model. You should generally use `auto_scaling` with an appropriate |
| # `min_nodes` instead, but this option is available if you want more |
| # predictable billing. Beware that latency and error rates will increase |
| # if the traffic exceeds that capability of the system to serve it based |
| # on the selected number of nodes. |
| "nodes": 42, # The number of nodes to allocate for this model. These nodes are always up, |
| # starting from the time the model is deployed, so the cost of operating |
| # this model will be proportional to `nodes` * number of hours since |
| # last billing cycle plus the cost for each prediction performed. |
| }, |
| "createTime": "A String", # Output only. The time the version was created. |
| "lastUseTime": "A String", # Output only. The time the version was last used for prediction. |
| "framework": "A String", # Optional. The machine learning framework AI Platform uses to train |
| # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, |
| # `XGBOOST`. If you do not specify a framework, AI Platform |
| # will analyze files in the deployment_uri to determine a framework. If you |
| # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version |
| # of the model to 1.4 or greater. |
| # |
| # Do **not** specify a framework if you're deploying a [custom |
| # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # |
| # If you specify a [Compute Engine (N1) machine |
| # type](/ml-engine/docs/machine-types-online-prediction) in the |
| # `machineType` field, you must specify `TENSORFLOW` |
| # for the framework. |
| "predictionClass": "A String", # Optional. The fully qualified name |
| # (<var>module_name</var>.<var>class_name</var>) of a class that implements |
| # the Predictor interface described in this reference field. The module |
| # containing this class should be included in a package provided to the |
| # [`packageUris` field](#Version.FIELDS.package_uris). |
| # |
| # Specify this field if and only if you are deploying a [custom prediction |
| # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # If you specify this field, you must set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and |
| # you must set `machineType` to a [legacy (MLS1) |
| # machine type](/ml-engine/docs/machine-types-online-prediction). |
| # |
| # The following code sample provides the Predictor interface: |
| # |
| # <pre style="max-width: 626px;"> |
| # class Predictor(object): |
| # """Interface for constructing custom predictors.""" |
| # |
| # def predict(self, instances, **kwargs): |
| # """Performs custom prediction. |
| # |
| # Instances are the decoded values from the request. They have already |
| # been deserialized from JSON. |
| # |
| # Args: |
| # instances: A list of prediction input instances. |
| # **kwargs: A dictionary of keyword args provided as additional |
| # fields on the predict request body. |
| # |
| # Returns: |
| # A list of outputs containing the prediction results. This list must |
| # be JSON serializable. |
| # """ |
| # raise NotImplementedError() |
| # |
| # @classmethod |
| # def from_path(cls, model_dir): |
| # """Creates an instance of Predictor using the given path. |
| # |
| # Loading of the predictor should be done in this method. |
| # |
| # Args: |
| # model_dir: The local directory that contains the exported model |
| # file along with any additional files uploaded when creating the |
| # version resource. |
| # |
| # Returns: |
| # An instance implementing this Predictor class. |
| # """ |
| # raise NotImplementedError() |
| # </pre> |
| # |
| # Learn more about [the Predictor interface and custom prediction |
| # routines](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| "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. |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a model from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform model updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `GetVersion`, and |
| # systems are expected to put that etag in the request to `UpdateVersion` to |
| # ensure that their change will be applied to the model as intended. |
| "serviceAccount": "A String", # Optional. Specifies the service account for resource access control. |
| "errorMessage": "A String", # Output only. The details of a failure or a cancellation. |
| "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to |
| # create the version. See the |
| # [guide to model |
| # deployment](/ml-engine/docs/tensorflow/deploying-models) for more |
| # information. |
| # |
| # When passing Version to |
| # projects.models.versions.create |
| # 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. |
| "runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. |
| # |
| # For more information, see the |
| # [runtime version list](/ml-engine/docs/runtime-version-list) and |
| # [how to manage runtime versions](/ml-engine/docs/versioning). |
| "description": "A String", # Optional. The description specified for the version when it was created. |
| } |
| |
| updateMask: string, Required. Specifies the path, relative to `Version`, of the field to |
| update. Must be present and non-empty. |
| |
| For example, to change the description of a version to "foo", the |
| `update_mask` parameter would be specified as `description`, and the |
| `PATCH` request body would specify the new value, as follows: |
| |
| ``` |
| { |
| "description": "foo" |
| } |
| ``` |
| |
| Currently the only supported update mask fields are `description`, |
| `requestLoggingConfig`, `autoScaling.minNodes`, and `manualScaling.nodes`. |
| However, you can only update `manualScaling.nodes` if the version uses a |
| [Compute Engine (N1) |
| machine type](/ml-engine/docs/machine-types-online-prediction). |
| 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. |
| "error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation. |
| # different programming environments, including REST APIs and RPC APIs. It is |
| # used by [gRPC](https://github.com/grpc). Each `Status` message contains |
| # three pieces of data: error code, error message, and error details. |
| # |
| # You can find out more about this error model and how to work with it in the |
| # [API Design Guide](https://cloud.google.com/apis/design/errors). |
| "details": [ # A list of messages that carry the error details. There is a common set of |
| # message types for APIs to use. |
| { |
| "a_key": "", # Properties of the object. Contains field @type with type URL. |
| }, |
| ], |
| "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. |
| }, |
| "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. |
| }, |
| "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. |
| }, |
| "name": "A String", # The server-assigned name, which is only unique within the same service that |
| # originally returns it. If you use the default HTTP mapping, the |
| # `name` should be a resource name ending with `operations/{unique_id}`. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="setDefault">setDefault(name, body=None, x__xgafv=None)</code> |
| <pre>Designates a version to be the default for the model. |
| |
| The default version is used for prediction requests made against the model |
| that don't specify a version. |
| |
| The first version to be created for a model is automatically set as the |
| default. You must make any subsequent changes to the default version |
| setting manually using this method. |
| |
| Args: |
| name: string, Required. The name of the version to make the default for the model. You |
| can get the names of all the versions of a model by calling |
| projects.models.versions.list. (required) |
| body: object, The request body. |
| The object takes the form of: |
| |
| { # Request message for the SetDefaultVersion request. |
| } |
| |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # Represents a version of the model. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "labels": { # Optional. One or more labels that you can add, to organize your model |
| # versions. Each label is a key-value pair, where both the key and the value |
| # are arbitrary strings that you supply. |
| # For more information, see the documentation on |
| # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>. |
| "a_key": "A String", |
| }, |
| "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only |
| # applies to online prediction service. If this field is not specified, it |
| # defaults to `mls1-c1-m2`. |
| # |
| # Online prediction supports the following machine types: |
| # |
| # * `mls1-c1-m2` |
| # * `mls1-c4-m2` |
| # * `n1-standard-2` |
| # * `n1-standard-4` |
| # * `n1-standard-8` |
| # * `n1-standard-16` |
| # * `n1-standard-32` |
| # * `n1-highmem-2` |
| # * `n1-highmem-4` |
| # * `n1-highmem-8` |
| # * `n1-highmem-16` |
| # * `n1-highmem-32` |
| # * `n1-highcpu-2` |
| # * `n1-highcpu-4` |
| # * `n1-highcpu-8` |
| # * `n1-highcpu-16` |
| # * `n1-highcpu-32` |
| # |
| # `mls1-c1-m2` is generally available. All other machine types are available |
| # in beta. Learn more about the [differences between machine |
| # types](/ml-engine/docs/machine-types-online-prediction). |
| "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom |
| # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines) |
| # or [scikit-learn pipelines with custom |
| # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code). |
| # |
| # For a custom prediction routine, one of these packages must contain your |
| # Predictor class (see |
| # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally, |
| # include any dependencies used by your Predictor or scikit-learn pipeline |
| # uses that are not already included in your selected [runtime |
| # version](/ml-engine/docs/tensorflow/runtime-version-list). |
| # |
| # If you specify this field, you must also set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater. |
| "A String", |
| ], |
| "acceleratorConfig": { # Represents a hardware accelerator request config. # Optional. Accelerator config for using GPUs for online prediction (beta). |
| # Only specify this field if you have specified a Compute Engine (N1) machine |
| # type in the `machineType` field. Learn more about [using GPUs for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| # Note that the AcceleratorConfig can be used in both Jobs and Versions. |
| # Learn more about [accelerators for training](/ml-engine/docs/using-gpus) and |
| # [accelerators for online |
| # prediction](/ml-engine/docs/machine-types-online-prediction#gpus). |
| "type": "A String", # The type of accelerator to use. |
| "count": "A String", # The number of accelerators to attach to each machine running the job. |
| }, |
| "state": "A String", # Output only. The state of a version. |
| "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. |
| "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in |
| # response to increases and decreases in traffic. Care should be |
| # taken to ramp up traffic according to the model's ability to scale |
| # or you will start seeing increases in latency and 429 response codes. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use specify |
| # `manual_scaling`. |
| "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These |
| # nodes are always up, starting from the time the model is deployed. |
| # Therefore, the cost of operating this model will be at least |
| # `rate` * `min_nodes` * number of hours since last billing cycle, |
| # where `rate` is the cost per node-hour as documented in the |
| # [pricing guide](/ml-engine/docs/pricing), |
| # even if no predictions are performed. There is additional cost for each |
| # prediction performed. |
| # |
| # Unlike manual scaling, if the load gets too heavy for the nodes |
| # that are up, the service will automatically add nodes to handle the |
| # increased load as well as scale back as traffic drops, always maintaining |
| # at least `min_nodes`. You will be charged for the time in which additional |
| # nodes are used. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [legacy |
| # (MLS1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `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. |
| # |
| # If `min_nodes` is not specified and AutoScaling is used with a [Compute |
| # Engine (N1) machine type](/ml-engine/docs/machine-types-online-prediction), |
| # `min_nodes` defaults to 1. `min_nodes` must be at least 1 for use with a |
| # Compute Engine machine type. |
| # |
| # Note that you cannot use AutoScaling if your version uses |
| # [GPUs](#Version.FIELDS.accelerator_config). Instead, you must use |
| # ManualScaling. |
| # |
| # You can set `min_nodes` when creating the model version, and you can also |
| # update `min_nodes` for an existing version: |
| # <pre> |
| # update_body.json: |
| # { |
| # 'autoScaling': { |
| # 'minNodes': 5 |
| # } |
| # } |
| # </pre> |
| # HTTP request: |
| # <pre style="max-width: 626px;"> |
| # PATCH |
| # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes |
| # -d @./update_body.json |
| # </pre> |
| }, |
| "explanationConfig": { # Message holding configuration options for explaining model predictions. # Optional. Configures explainability features on the model's version. |
| # Some explanation features require additional metadata to be loaded |
| # as part of the model payload. |
| # There are two feature attribution methods supported for TensorFlow models: |
| # integrated gradients and sampled Shapley. |
| # [Learn more about feature |
| # attributions.](/ai-platform/prediction/docs/ai-explanations/overview) |
| "integratedGradientsAttribution": { # Attributes credit by computing the Aumann-Shapley value taking advantage # Attributes credit by computing the Aumann-Shapley value taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: http://proceedings.mlr.press/v70/sundararajan17a.html |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1703.01365 |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "xraiAttribution": { # Attributes credit by computing the XRAI taking advantage # Attributes credit by computing the XRAI taking advantage |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| # of the model's fully differentiable structure. Refer to this paper for |
| # more details: https://arxiv.org/abs/1906.02825 |
| # Currently only implemented for models with natural image inputs. |
| "numIntegralSteps": 42, # Number of steps for approximating the path integral. |
| # A good value to start is 50 and gradually increase until the |
| # sum to diff property is met within the desired error range. |
| }, |
| "sampledShapleyAttribution": { # An attribution method that approximates Shapley values for features that # An attribution method that approximates Shapley values for features that |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| # contribute to the label being predicted. A sampling strategy is used to |
| # approximate the value rather than considering all subsets of features. |
| "numPaths": 42, # The number of feature permutations to consider when approximating the |
| # Shapley values. |
| }, |
| }, |
| "pythonVersion": "A String", # Required. The version of Python used in prediction. |
| # |
| # The following Python versions are available: |
| # |
| # * Python '3.7' is available when `runtime_version` is set to '1.15' or |
| # later. |
| # * Python '3.5' is available when `runtime_version` is set to a version |
| # from '1.4' to '1.14'. |
| # * Python '2.7' is available when `runtime_version` is set to '1.15' or |
| # earlier. |
| # |
| # Read more about the Python versions available for [each runtime |
| # version](/ml-engine/docs/runtime-version-list). |
| "requestLoggingConfig": { # Configuration for logging request-response pairs to a BigQuery table. # Optional. *Only* specify this field in a |
| # projects.models.versions.patch |
| # request. Specifying it in a |
| # projects.models.versions.create |
| # request has no effect. |
| # |
| # Configures the request-response pair logging on predictions from this |
| # Version. |
| # Online prediction requests to a model version and the responses to these |
| # requests are converted to raw strings and saved to the specified BigQuery |
| # table. Logging is constrained by [BigQuery quotas and |
| # limits](/bigquery/quotas). If your project exceeds BigQuery quotas or limits, |
| # AI Platform Prediction does not log request-response pairs, but it continues |
| # to serve predictions. |
| # |
| # If you are using [continuous |
| # evaluation](/ml-engine/docs/continuous-evaluation/), you do not need to |
| # specify this configuration manually. Setting up continuous evaluation |
| # automatically enables logging of request-response pairs. |
| "samplingPercentage": 3.14, # Percentage of requests to be logged, expressed as a fraction from 0 to 1. |
| # For example, if you want to log 10% of requests, enter `0.1`. The sampling |
| # window is the lifetime of the model version. Defaults to 0. |
| "bigqueryTableName": "A String", # Required. Fully qualified BigQuery table name in the following format: |
| # "<var>project_id</var>.<var>dataset_name</var>.<var>table_name</var>" |
| # |
| # The specified table must already exist, and the "Cloud ML Service Agent" |
| # for your project must have permission to write to it. The table must have |
| # the following [schema](/bigquery/docs/schemas): |
| # |
| # <table> |
| # <tr><th>Field name</th><th style="display: table-cell">Type</th> |
| # <th style="display: table-cell">Mode</th></tr> |
| # <tr><td>model</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>model_version</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>time</td><td>TIMESTAMP</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_data</td><td>STRING</td><td>REQUIRED</td></tr> |
| # <tr><td>raw_prediction</td><td>STRING</td><td>NULLABLE</td></tr> |
| # <tr><td>groundtruth</td><td>STRING</td><td>NULLABLE</td></tr> |
| # </table> |
| }, |
| "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the |
| # model. You should generally use `auto_scaling` with an appropriate |
| # `min_nodes` instead, but this option is available if you want more |
| # predictable billing. Beware that latency and error rates will increase |
| # if the traffic exceeds that capability of the system to serve it based |
| # on the selected number of nodes. |
| "nodes": 42, # The number of nodes to allocate for this model. These nodes are always up, |
| # starting from the time the model is deployed, so the cost of operating |
| # this model will be proportional to `nodes` * number of hours since |
| # last billing cycle plus the cost for each prediction performed. |
| }, |
| "createTime": "A String", # Output only. The time the version was created. |
| "lastUseTime": "A String", # Output only. The time the version was last used for prediction. |
| "framework": "A String", # Optional. The machine learning framework AI Platform uses to train |
| # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`, |
| # `XGBOOST`. If you do not specify a framework, AI Platform |
| # will analyze files in the deployment_uri to determine a framework. If you |
| # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version |
| # of the model to 1.4 or greater. |
| # |
| # Do **not** specify a framework if you're deploying a [custom |
| # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # |
| # If you specify a [Compute Engine (N1) machine |
| # type](/ml-engine/docs/machine-types-online-prediction) in the |
| # `machineType` field, you must specify `TENSORFLOW` |
| # for the framework. |
| "predictionClass": "A String", # Optional. The fully qualified name |
| # (<var>module_name</var>.<var>class_name</var>) of a class that implements |
| # the Predictor interface described in this reference field. The module |
| # containing this class should be included in a package provided to the |
| # [`packageUris` field](#Version.FIELDS.package_uris). |
| # |
| # Specify this field if and only if you are deploying a [custom prediction |
| # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| # If you specify this field, you must set |
| # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater and |
| # you must set `machineType` to a [legacy (MLS1) |
| # machine type](/ml-engine/docs/machine-types-online-prediction). |
| # |
| # The following code sample provides the Predictor interface: |
| # |
| # <pre style="max-width: 626px;"> |
| # class Predictor(object): |
| # """Interface for constructing custom predictors.""" |
| # |
| # def predict(self, instances, **kwargs): |
| # """Performs custom prediction. |
| # |
| # Instances are the decoded values from the request. They have already |
| # been deserialized from JSON. |
| # |
| # Args: |
| # instances: A list of prediction input instances. |
| # **kwargs: A dictionary of keyword args provided as additional |
| # fields on the predict request body. |
| # |
| # Returns: |
| # A list of outputs containing the prediction results. This list must |
| # be JSON serializable. |
| # """ |
| # raise NotImplementedError() |
| # |
| # @classmethod |
| # def from_path(cls, model_dir): |
| # """Creates an instance of Predictor using the given path. |
| # |
| # Loading of the predictor should be done in this method. |
| # |
| # Args: |
| # model_dir: The local directory that contains the exported model |
| # file along with any additional files uploaded when creating the |
| # version resource. |
| # |
| # Returns: |
| # An instance implementing this Predictor class. |
| # """ |
| # raise NotImplementedError() |
| # </pre> |
| # |
| # Learn more about [the Predictor interface and custom prediction |
| # routines](/ml-engine/docs/tensorflow/custom-prediction-routines). |
| "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. |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a model from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform model updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `GetVersion`, and |
| # systems are expected to put that etag in the request to `UpdateVersion` to |
| # ensure that their change will be applied to the model as intended. |
| "serviceAccount": "A String", # Optional. Specifies the service account for resource access control. |
| "errorMessage": "A String", # Output only. The details of a failure or a cancellation. |
| "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to |
| # create the version. See the |
| # [guide to model |
| # deployment](/ml-engine/docs/tensorflow/deploying-models) for more |
| # information. |
| # |
| # When passing Version to |
| # projects.models.versions.create |
| # 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. |
| "runtimeVersion": "A String", # Required. The AI Platform runtime version to use for this deployment. |
| # |
| # For more information, see the |
| # [runtime version list](/ml-engine/docs/runtime-version-list) and |
| # [how to manage runtime versions](/ml-engine/docs/versioning). |
| "description": "A String", # Optional. The description specified for the version when it was created. |
| }</pre> |
| </div> |
| |
| </body></html> |
