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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></h1> |
| <h2>Instance Methods</h2> |
| <p class="toc_element"> |
| <code><a href="ml_v1.projects.models.versions.html">versions()</a></code> |
| </p> |
| <p class="firstline">Returns the versions Resource.</p> |
| |
| <p class="toc_element"> |
| <code><a href="#create">create(parent, body=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Creates a model which will later contain one or more versions.</p> |
| <p class="toc_element"> |
| <code><a href="#delete">delete(name, x__xgafv=None)</a></code></p> |
| <p class="firstline">Deletes a model.</p> |
| <p class="toc_element"> |
| <code><a href="#get">get(name, x__xgafv=None)</a></code></p> |
| <p class="firstline">Gets information about a model, including its name, the description (if</p> |
| <p class="toc_element"> |
| <code><a href="#getIamPolicy">getIamPolicy(resource, options_requestedPolicyVersion=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Gets the access control policy for a resource.</p> |
| <p class="toc_element"> |
| <code><a href="#list">list(parent, pageSize=None, pageToken=None, filter=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Lists the models in a project.</p> |
| <p class="toc_element"> |
| <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p> |
| <p class="firstline">Retrieves the next page of results.</p> |
| <p class="toc_element"> |
| <code><a href="#patch">patch(name, body=None, updateMask=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Updates a specific model resource.</p> |
| <p class="toc_element"> |
| <code><a href="#setIamPolicy">setIamPolicy(resource, body=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Sets the access control policy on the specified resource. Replaces any</p> |
| <p class="toc_element"> |
| <code><a href="#testIamPermissions">testIamPermissions(resource, body=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Returns permissions that a caller has on the specified resource.</p> |
| <h3>Method Details</h3> |
| <div class="method"> |
| <code class="details" id="create">create(parent, body=None, x__xgafv=None)</code> |
| <pre>Creates a model which will later contain one or more versions. |
| |
| You must add at least one version before you can request predictions from |
| the model. Add versions by calling |
| projects.models.versions.create. |
| |
| Args: |
| parent: string, Required. The project name. (required) |
| body: object, The request body. |
| The object takes the form of: |
| |
| { # Represents a machine learning solution. |
| # |
| # A model can have multiple versions, each of which is a deployed, trained |
| # model ready to receive prediction requests. The model itself is just a |
| # container. |
| "description": "A String", # Optional. The description specified for the model when it was created. |
| "regions": [ # Optional. The list of regions where the model is going to be deployed. |
| # Only one region per model is supported. |
| # Defaults to 'us-central1' if nothing is set. |
| # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a> |
| # for AI Platform services. |
| # Note: |
| # * No matter where a model is deployed, it can always be accessed by |
| # users from anywhere, both for online and batch prediction. |
| # * The region for a batch prediction job is set by the region field when |
| # submitting the batch prediction job and does not take its value from |
| # this field. |
| "A String", |
| ], |
| "name": "A String", # Required. The name specified for the model when it was created. |
| # |
| # The model name must be unique within the project it is created in. |
| "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout` |
| # streams to Stackdriver Logging. These can be more verbose than the standard |
| # access logs (see `onlinePredictionLogging`) and can incur higher cost. |
| # However, they are helpful for debugging. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high QPS. Estimate your |
| # costs before enabling this option. |
| # |
| # Default is false. |
| "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 `GetModel`, and |
| # systems are expected to put that etag in the request to `UpdateModel` to |
| # ensure that their change will be applied to the model as intended. |
| "labels": { # Optional. One or more labels that you can add, to organize your models. |
| # 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", |
| }, |
| "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to |
| # handle prediction requests that do not specify a version. |
| # |
| # You can change the default version by calling |
| # projects.models.versions.setDefault. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "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. |
| }, |
| "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver |
| # Logging. These logs are like standard server access logs, containing |
| # information like timestamp and latency for each request. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high queries per second rate |
| # (QPS). Estimate your costs before enabling this option. |
| # |
| # Default is false. |
| } |
| |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # Represents a machine learning solution. |
| # |
| # A model can have multiple versions, each of which is a deployed, trained |
| # model ready to receive prediction requests. The model itself is just a |
| # container. |
| "description": "A String", # Optional. The description specified for the model when it was created. |
| "regions": [ # Optional. The list of regions where the model is going to be deployed. |
| # Only one region per model is supported. |
| # Defaults to 'us-central1' if nothing is set. |
| # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a> |
| # for AI Platform services. |
| # Note: |
| # * No matter where a model is deployed, it can always be accessed by |
| # users from anywhere, both for online and batch prediction. |
| # * The region for a batch prediction job is set by the region field when |
| # submitting the batch prediction job and does not take its value from |
| # this field. |
| "A String", |
| ], |
| "name": "A String", # Required. The name specified for the model when it was created. |
| # |
| # The model name must be unique within the project it is created in. |
| "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout` |
| # streams to Stackdriver Logging. These can be more verbose than the standard |
| # access logs (see `onlinePredictionLogging`) and can incur higher cost. |
| # However, they are helpful for debugging. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high QPS. Estimate your |
| # costs before enabling this option. |
| # |
| # Default is false. |
| "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 `GetModel`, and |
| # systems are expected to put that etag in the request to `UpdateModel` to |
| # ensure that their change will be applied to the model as intended. |
| "labels": { # Optional. One or more labels that you can add, to organize your models. |
| # 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", |
| }, |
| "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to |
| # handle prediction requests that do not specify a version. |
| # |
| # You can change the default version by calling |
| # projects.models.versions.setDefault. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "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. |
| }, |
| "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver |
| # Logging. These logs are like standard server access logs, containing |
| # information like timestamp and latency for each request. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high queries per second rate |
| # (QPS). Estimate your costs before enabling this option. |
| # |
| # Default is false. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="delete">delete(name, x__xgafv=None)</code> |
| <pre>Deletes a model. |
| |
| You can only delete a model if there are no versions in it. You can delete |
| versions by calling |
| projects.models.versions.delete. |
| |
| Args: |
| name: string, Required. The name of the model. (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, including its name, the description (if |
| set), and the default version (if at least one version of the model has |
| been deployed). |
| |
| Args: |
| name: string, Required. The name of the model. (required) |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # Represents a machine learning solution. |
| # |
| # A model can have multiple versions, each of which is a deployed, trained |
| # model ready to receive prediction requests. The model itself is just a |
| # container. |
| "description": "A String", # Optional. The description specified for the model when it was created. |
| "regions": [ # Optional. The list of regions where the model is going to be deployed. |
| # Only one region per model is supported. |
| # Defaults to 'us-central1' if nothing is set. |
| # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a> |
| # for AI Platform services. |
| # Note: |
| # * No matter where a model is deployed, it can always be accessed by |
| # users from anywhere, both for online and batch prediction. |
| # * The region for a batch prediction job is set by the region field when |
| # submitting the batch prediction job and does not take its value from |
| # this field. |
| "A String", |
| ], |
| "name": "A String", # Required. The name specified for the model when it was created. |
| # |
| # The model name must be unique within the project it is created in. |
| "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout` |
| # streams to Stackdriver Logging. These can be more verbose than the standard |
| # access logs (see `onlinePredictionLogging`) and can incur higher cost. |
| # However, they are helpful for debugging. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high QPS. Estimate your |
| # costs before enabling this option. |
| # |
| # Default is false. |
| "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 `GetModel`, and |
| # systems are expected to put that etag in the request to `UpdateModel` to |
| # ensure that their change will be applied to the model as intended. |
| "labels": { # Optional. One or more labels that you can add, to organize your models. |
| # 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", |
| }, |
| "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to |
| # handle prediction requests that do not specify a version. |
| # |
| # You can change the default version by calling |
| # projects.models.versions.setDefault. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "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. |
| }, |
| "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver |
| # Logging. These logs are like standard server access logs, containing |
| # information like timestamp and latency for each request. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high queries per second rate |
| # (QPS). Estimate your costs before enabling this option. |
| # |
| # Default is false. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="getIamPolicy">getIamPolicy(resource, options_requestedPolicyVersion=None, x__xgafv=None)</code> |
| <pre>Gets the access control policy for a resource. |
| Returns an empty policy if the resource exists and does not have a policy |
| set. |
| |
| Args: |
| resource: string, REQUIRED: The resource for which the policy is being requested. |
| See the operation documentation for the appropriate value for this field. (required) |
| options_requestedPolicyVersion: integer, Optional. The policy format version to be returned. |
| |
| Valid values are 0, 1, and 3. Requests specifying an invalid value will be |
| rejected. |
| |
| Requests for policies with any conditional bindings must specify version 3. |
| Policies without any conditional bindings may specify any valid value or |
| leave the field unset. |
| |
| To learn which resources support conditions in their IAM policies, see the |
| [IAM |
| documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # An Identity and Access Management (IAM) policy, which specifies access |
| # controls for Google Cloud resources. |
| # |
| # |
| # A `Policy` is a collection of `bindings`. A `binding` binds one or more |
| # `members` to a single `role`. Members can be user accounts, service accounts, |
| # Google groups, and domains (such as G Suite). A `role` is a named list of |
| # permissions; each `role` can be an IAM predefined role or a user-created |
| # custom role. |
| # |
| # For some types of Google Cloud resources, a `binding` can also specify a |
| # `condition`, which is a logical expression that allows access to a resource |
| # only if the expression evaluates to `true`. A condition can add constraints |
| # based on attributes of the request, the resource, or both. To learn which |
| # resources support conditions in their IAM policies, see the |
| # [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| # |
| # **JSON example:** |
| # |
| # { |
| # "bindings": [ |
| # { |
| # "role": "roles/resourcemanager.organizationAdmin", |
| # "members": [ |
| # "user:mike@example.com", |
| # "group:admins@example.com", |
| # "domain:google.com", |
| # "serviceAccount:my-project-id@appspot.gserviceaccount.com" |
| # ] |
| # }, |
| # { |
| # "role": "roles/resourcemanager.organizationViewer", |
| # "members": [ |
| # "user:eve@example.com" |
| # ], |
| # "condition": { |
| # "title": "expirable access", |
| # "description": "Does not grant access after Sep 2020", |
| # "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", |
| # } |
| # } |
| # ], |
| # "etag": "BwWWja0YfJA=", |
| # "version": 3 |
| # } |
| # |
| # **YAML example:** |
| # |
| # bindings: |
| # - members: |
| # - user:mike@example.com |
| # - group:admins@example.com |
| # - domain:google.com |
| # - serviceAccount:my-project-id@appspot.gserviceaccount.com |
| # role: roles/resourcemanager.organizationAdmin |
| # - members: |
| # - user:eve@example.com |
| # role: roles/resourcemanager.organizationViewer |
| # condition: |
| # title: expirable access |
| # description: Does not grant access after Sep 2020 |
| # expression: request.time < timestamp('2020-10-01T00:00:00.000Z') |
| # - etag: BwWWja0YfJA= |
| # - version: 3 |
| # |
| # For a description of IAM and its features, see the |
| # [IAM documentation](https://cloud.google.com/iam/docs/). |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a policy from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform policy updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `getIamPolicy`, and |
| # systems are expected to put that etag in the request to `setIamPolicy` to |
| # ensure that their change will be applied to the same version of the policy. |
| # |
| # **Important:** If you use IAM Conditions, you must include the `etag` field |
| # whenever you call `setIamPolicy`. If you omit this field, then IAM allows |
| # you to overwrite a version `3` policy with a version `1` policy, and all of |
| # the conditions in the version `3` policy are lost. |
| "auditConfigs": [ # Specifies cloud audit logging configuration for this policy. |
| { # Specifies the audit configuration for a service. |
| # The configuration determines which permission types are logged, and what |
| # identities, if any, are exempted from logging. |
| # An AuditConfig must have one or more AuditLogConfigs. |
| # |
| # If there are AuditConfigs for both `allServices` and a specific service, |
| # the union of the two AuditConfigs is used for that service: the log_types |
| # specified in each AuditConfig are enabled, and the exempted_members in each |
| # AuditLogConfig are exempted. |
| # |
| # Example Policy with multiple AuditConfigs: |
| # |
| # { |
| # "audit_configs": [ |
| # { |
| # "service": "allServices", |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ", |
| # "exempted_members": [ |
| # "user:jose@example.com" |
| # ] |
| # }, |
| # { |
| # "log_type": "DATA_WRITE" |
| # }, |
| # { |
| # "log_type": "ADMIN_READ" |
| # } |
| # ] |
| # }, |
| # { |
| # "service": "sampleservice.googleapis.com", |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ" |
| # }, |
| # { |
| # "log_type": "DATA_WRITE", |
| # "exempted_members": [ |
| # "user:aliya@example.com" |
| # ] |
| # } |
| # ] |
| # } |
| # ] |
| # } |
| # |
| # For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ |
| # logging. It also exempts jose@example.com from DATA_READ logging, and |
| # aliya@example.com from DATA_WRITE logging. |
| "service": "A String", # Specifies a service that will be enabled for audit logging. |
| # For example, `storage.googleapis.com`, `cloudsql.googleapis.com`. |
| # `allServices` is a special value that covers all services. |
| "auditLogConfigs": [ # The configuration for logging of each type of permission. |
| { # Provides the configuration for logging a type of permissions. |
| # Example: |
| # |
| # { |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ", |
| # "exempted_members": [ |
| # "user:jose@example.com" |
| # ] |
| # }, |
| # { |
| # "log_type": "DATA_WRITE" |
| # } |
| # ] |
| # } |
| # |
| # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting |
| # jose@example.com from DATA_READ logging. |
| "logType": "A String", # The log type that this config enables. |
| "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of |
| # permission. |
| # Follows the same format of Binding.members. |
| "A String", |
| ], |
| }, |
| ], |
| }, |
| ], |
| "version": 42, # Specifies the format of the policy. |
| # |
| # Valid values are `0`, `1`, and `3`. Requests that specify an invalid value |
| # are rejected. |
| # |
| # Any operation that affects conditional role bindings must specify version |
| # `3`. This requirement applies to the following operations: |
| # |
| # * Getting a policy that includes a conditional role binding |
| # * Adding a conditional role binding to a policy |
| # * Changing a conditional role binding in a policy |
| # * Removing any role binding, with or without a condition, from a policy |
| # that includes conditions |
| # |
| # **Important:** If you use IAM Conditions, you must include the `etag` field |
| # whenever you call `setIamPolicy`. If you omit this field, then IAM allows |
| # you to overwrite a version `3` policy with a version `1` policy, and all of |
| # the conditions in the version `3` policy are lost. |
| # |
| # If a policy does not include any conditions, operations on that policy may |
| # specify any valid version or leave the field unset. |
| # |
| # To learn which resources support conditions in their IAM policies, see the |
| # [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| "bindings": [ # Associates a list of `members` to a `role`. Optionally, may specify a |
| # `condition` that determines how and when the `bindings` are applied. Each |
| # of the `bindings` must contain at least one member. |
| { # Associates `members` with a `role`. |
| "role": "A String", # Role that is assigned to `members`. |
| # For example, `roles/viewer`, `roles/editor`, or `roles/owner`. |
| "condition": { # Represents a textual expression in the Common Expression Language (CEL) # The condition that is associated with this binding. |
| # |
| # If the condition evaluates to `true`, then this binding applies to the |
| # current request. |
| # |
| # If the condition evaluates to `false`, then this binding does not apply to |
| # the current request. However, a different role binding might grant the same |
| # role to one or more of the members in this binding. |
| # |
| # To learn which resources support conditions in their IAM policies, see the |
| # [IAM |
| # documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| # syntax. CEL is a C-like expression language. The syntax and semantics of CEL |
| # are documented at https://github.com/google/cel-spec. |
| # |
| # Example (Comparison): |
| # |
| # title: "Summary size limit" |
| # description: "Determines if a summary is less than 100 chars" |
| # expression: "document.summary.size() < 100" |
| # |
| # Example (Equality): |
| # |
| # title: "Requestor is owner" |
| # description: "Determines if requestor is the document owner" |
| # expression: "document.owner == request.auth.claims.email" |
| # |
| # Example (Logic): |
| # |
| # title: "Public documents" |
| # description: "Determine whether the document should be publicly visible" |
| # expression: "document.type != 'private' && document.type != 'internal'" |
| # |
| # Example (Data Manipulation): |
| # |
| # title: "Notification string" |
| # description: "Create a notification string with a timestamp." |
| # expression: "'New message received at ' + string(document.create_time)" |
| # |
| # The exact variables and functions that may be referenced within an expression |
| # are determined by the service that evaluates it. See the service |
| # documentation for additional information. |
| "expression": "A String", # Textual representation of an expression in Common Expression Language |
| # syntax. |
| "title": "A String", # Optional. Title for the expression, i.e. a short string describing |
| # its purpose. This can be used e.g. in UIs which allow to enter the |
| # expression. |
| "location": "A String", # Optional. String indicating the location of the expression for error |
| # reporting, e.g. a file name and a position in the file. |
| "description": "A String", # Optional. Description of the expression. This is a longer text which |
| # describes the expression, e.g. when hovered over it in a UI. |
| }, |
| "members": [ # Specifies the identities requesting access for a Cloud Platform resource. |
| # `members` can have the following values: |
| # |
| # * `allUsers`: A special identifier that represents anyone who is |
| # on the internet; with or without a Google account. |
| # |
| # * `allAuthenticatedUsers`: A special identifier that represents anyone |
| # who is authenticated with a Google account or a service account. |
| # |
| # * `user:{emailid}`: An email address that represents a specific Google |
| # account. For example, `alice@example.com` . |
| # |
| # |
| # * `serviceAccount:{emailid}`: An email address that represents a service |
| # account. For example, `my-other-app@appspot.gserviceaccount.com`. |
| # |
| # * `group:{emailid}`: An email address that represents a Google group. |
| # For example, `admins@example.com`. |
| # |
| # * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique |
| # identifier) representing a user that has been recently deleted. For |
| # example, `alice@example.com?uid=123456789012345678901`. If the user is |
| # recovered, this value reverts to `user:{emailid}` and the recovered user |
| # retains the role in the binding. |
| # |
| # * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus |
| # unique identifier) representing a service account that has been recently |
| # deleted. For example, |
| # `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. |
| # If the service account is undeleted, this value reverts to |
| # `serviceAccount:{emailid}` and the undeleted service account retains the |
| # role in the binding. |
| # |
| # * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique |
| # identifier) representing a Google group that has been recently |
| # deleted. For example, `admins@example.com?uid=123456789012345678901`. If |
| # the group is recovered, this value reverts to `group:{emailid}` and the |
| # recovered group retains the role in the binding. |
| # |
| # |
| # * `domain:{domain}`: The G Suite domain (primary) that represents all the |
| # users of that domain. For example, `google.com` or `example.com`. |
| # |
| "A String", |
| ], |
| }, |
| ], |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="list">list(parent, pageSize=None, pageToken=None, filter=None, x__xgafv=None)</code> |
| <pre>Lists the models in a project. |
| |
| Each project can contain multiple models, and each model can have multiple |
| versions. |
| |
| If there are no models that match the request parameters, the list request |
| returns an empty response body: {}. |
| |
| Args: |
| parent: string, Required. The name of the project whose models are to be listed. (required) |
| pageSize: integer, Optional. The number of models to retrieve per "page" of results. If there |
| are more remaining results than this number, the response message will |
| contain a valid value in the `next_page_token` field. |
| |
| The default value is 20, and the maximum page size is 100. |
| 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. |
| filter: string, Optional. Specifies the subset of models to retrieve. |
| 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 ListModels method. |
| "models": [ # The list of models. |
| { # Represents a machine learning solution. |
| # |
| # A model can have multiple versions, each of which is a deployed, trained |
| # model ready to receive prediction requests. The model itself is just a |
| # container. |
| "description": "A String", # Optional. The description specified for the model when it was created. |
| "regions": [ # Optional. The list of regions where the model is going to be deployed. |
| # Only one region per model is supported. |
| # Defaults to 'us-central1' if nothing is set. |
| # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a> |
| # for AI Platform services. |
| # Note: |
| # * No matter where a model is deployed, it can always be accessed by |
| # users from anywhere, both for online and batch prediction. |
| # * The region for a batch prediction job is set by the region field when |
| # submitting the batch prediction job and does not take its value from |
| # this field. |
| "A String", |
| ], |
| "name": "A String", # Required. The name specified for the model when it was created. |
| # |
| # The model name must be unique within the project it is created in. |
| "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout` |
| # streams to Stackdriver Logging. These can be more verbose than the standard |
| # access logs (see `onlinePredictionLogging`) and can incur higher cost. |
| # However, they are helpful for debugging. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high QPS. Estimate your |
| # costs before enabling this option. |
| # |
| # Default is false. |
| "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 `GetModel`, and |
| # systems are expected to put that etag in the request to `UpdateModel` to |
| # ensure that their change will be applied to the model as intended. |
| "labels": { # Optional. One or more labels that you can add, to organize your models. |
| # 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", |
| }, |
| "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to |
| # handle prediction requests that do not specify a version. |
| # |
| # You can change the default version by calling |
| # projects.models.versions.setDefault. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "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. |
| }, |
| "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver |
| # Logging. These logs are like standard server access logs, containing |
| # information like timestamp and latency for each request. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high queries per second rate |
| # (QPS). Estimate your costs before enabling this option. |
| # |
| # Default is false. |
| }, |
| ], |
| "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a |
| # subsequent call. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="list_next">list_next(previous_request, previous_response)</code> |
| <pre>Retrieves the next page of results. |
| |
| Args: |
| previous_request: The request for the previous page. (required) |
| previous_response: The response from the request for the previous page. (required) |
| |
| Returns: |
| A request object that you can call 'execute()' on to request the next |
| page. Returns None if there are no more items in the collection. |
| </pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="patch">patch(name, body=None, updateMask=None, x__xgafv=None)</code> |
| <pre>Updates a specific model resource. |
| |
| Currently the only supported fields to update are `description` and |
| `default_version.name`. |
| |
| Args: |
| name: string, Required. The project name. (required) |
| body: object, The request body. |
| The object takes the form of: |
| |
| { # Represents a machine learning solution. |
| # |
| # A model can have multiple versions, each of which is a deployed, trained |
| # model ready to receive prediction requests. The model itself is just a |
| # container. |
| "description": "A String", # Optional. The description specified for the model when it was created. |
| "regions": [ # Optional. The list of regions where the model is going to be deployed. |
| # Only one region per model is supported. |
| # Defaults to 'us-central1' if nothing is set. |
| # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a> |
| # for AI Platform services. |
| # Note: |
| # * No matter where a model is deployed, it can always be accessed by |
| # users from anywhere, both for online and batch prediction. |
| # * The region for a batch prediction job is set by the region field when |
| # submitting the batch prediction job and does not take its value from |
| # this field. |
| "A String", |
| ], |
| "name": "A String", # Required. The name specified for the model when it was created. |
| # |
| # The model name must be unique within the project it is created in. |
| "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout` |
| # streams to Stackdriver Logging. These can be more verbose than the standard |
| # access logs (see `onlinePredictionLogging`) and can incur higher cost. |
| # However, they are helpful for debugging. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high QPS. Estimate your |
| # costs before enabling this option. |
| # |
| # Default is false. |
| "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 `GetModel`, and |
| # systems are expected to put that etag in the request to `UpdateModel` to |
| # ensure that their change will be applied to the model as intended. |
| "labels": { # Optional. One or more labels that you can add, to organize your models. |
| # 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", |
| }, |
| "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. This version will be used to |
| # handle prediction requests that do not specify a version. |
| # |
| # You can change the default version by calling |
| # projects.models.versions.setDefault. |
| # |
| # Each version is a trained model deployed in the cloud, ready to handle |
| # prediction requests. A model can have multiple versions. You can get |
| # information about all of the versions of a given model by calling |
| # projects.models.versions.list. |
| "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. |
| }, |
| "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver |
| # Logging. These logs are like standard server access logs, containing |
| # information like timestamp and latency for each request. Note that |
| # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if |
| # your project receives prediction requests at a high queries per second rate |
| # (QPS). Estimate your costs before enabling this option. |
| # |
| # Default is false. |
| } |
| |
| updateMask: string, Required. Specifies the path, relative to `Model`, of the field to update. |
| |
| For example, to change the description of a model to "foo" and set its |
| default version to "version_1", the `update_mask` parameter would be |
| specified as `description`, `default_version.name`, and the `PATCH` |
| request body would specify the new value, as follows: |
| { |
| "description": "foo", |
| "defaultVersion": { |
| "name":"version_1" |
| } |
| } |
| |
| Currently the supported update masks are `description` and |
| `default_version.name`. |
| 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="setIamPolicy">setIamPolicy(resource, body=None, x__xgafv=None)</code> |
| <pre>Sets the access control policy on the specified resource. Replaces any |
| existing policy. |
| |
| Can return `NOT_FOUND`, `INVALID_ARGUMENT`, and `PERMISSION_DENIED` errors. |
| |
| Args: |
| resource: string, REQUIRED: The resource for which the policy is being specified. |
| See the operation documentation for the appropriate value for this field. (required) |
| body: object, The request body. |
| The object takes the form of: |
| |
| { # Request message for `SetIamPolicy` method. |
| "policy": { # An Identity and Access Management (IAM) policy, which specifies access # REQUIRED: The complete policy to be applied to the `resource`. The size of |
| # the policy is limited to a few 10s of KB. An empty policy is a |
| # valid policy but certain Cloud Platform services (such as Projects) |
| # might reject them. |
| # controls for Google Cloud resources. |
| # |
| # |
| # A `Policy` is a collection of `bindings`. A `binding` binds one or more |
| # `members` to a single `role`. Members can be user accounts, service accounts, |
| # Google groups, and domains (such as G Suite). A `role` is a named list of |
| # permissions; each `role` can be an IAM predefined role or a user-created |
| # custom role. |
| # |
| # For some types of Google Cloud resources, a `binding` can also specify a |
| # `condition`, which is a logical expression that allows access to a resource |
| # only if the expression evaluates to `true`. A condition can add constraints |
| # based on attributes of the request, the resource, or both. To learn which |
| # resources support conditions in their IAM policies, see the |
| # [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| # |
| # **JSON example:** |
| # |
| # { |
| # "bindings": [ |
| # { |
| # "role": "roles/resourcemanager.organizationAdmin", |
| # "members": [ |
| # "user:mike@example.com", |
| # "group:admins@example.com", |
| # "domain:google.com", |
| # "serviceAccount:my-project-id@appspot.gserviceaccount.com" |
| # ] |
| # }, |
| # { |
| # "role": "roles/resourcemanager.organizationViewer", |
| # "members": [ |
| # "user:eve@example.com" |
| # ], |
| # "condition": { |
| # "title": "expirable access", |
| # "description": "Does not grant access after Sep 2020", |
| # "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", |
| # } |
| # } |
| # ], |
| # "etag": "BwWWja0YfJA=", |
| # "version": 3 |
| # } |
| # |
| # **YAML example:** |
| # |
| # bindings: |
| # - members: |
| # - user:mike@example.com |
| # - group:admins@example.com |
| # - domain:google.com |
| # - serviceAccount:my-project-id@appspot.gserviceaccount.com |
| # role: roles/resourcemanager.organizationAdmin |
| # - members: |
| # - user:eve@example.com |
| # role: roles/resourcemanager.organizationViewer |
| # condition: |
| # title: expirable access |
| # description: Does not grant access after Sep 2020 |
| # expression: request.time < timestamp('2020-10-01T00:00:00.000Z') |
| # - etag: BwWWja0YfJA= |
| # - version: 3 |
| # |
| # For a description of IAM and its features, see the |
| # [IAM documentation](https://cloud.google.com/iam/docs/). |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a policy from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform policy updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `getIamPolicy`, and |
| # systems are expected to put that etag in the request to `setIamPolicy` to |
| # ensure that their change will be applied to the same version of the policy. |
| # |
| # **Important:** If you use IAM Conditions, you must include the `etag` field |
| # whenever you call `setIamPolicy`. If you omit this field, then IAM allows |
| # you to overwrite a version `3` policy with a version `1` policy, and all of |
| # the conditions in the version `3` policy are lost. |
| "auditConfigs": [ # Specifies cloud audit logging configuration for this policy. |
| { # Specifies the audit configuration for a service. |
| # The configuration determines which permission types are logged, and what |
| # identities, if any, are exempted from logging. |
| # An AuditConfig must have one or more AuditLogConfigs. |
| # |
| # If there are AuditConfigs for both `allServices` and a specific service, |
| # the union of the two AuditConfigs is used for that service: the log_types |
| # specified in each AuditConfig are enabled, and the exempted_members in each |
| # AuditLogConfig are exempted. |
| # |
| # Example Policy with multiple AuditConfigs: |
| # |
| # { |
| # "audit_configs": [ |
| # { |
| # "service": "allServices", |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ", |
| # "exempted_members": [ |
| # "user:jose@example.com" |
| # ] |
| # }, |
| # { |
| # "log_type": "DATA_WRITE" |
| # }, |
| # { |
| # "log_type": "ADMIN_READ" |
| # } |
| # ] |
| # }, |
| # { |
| # "service": "sampleservice.googleapis.com", |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ" |
| # }, |
| # { |
| # "log_type": "DATA_WRITE", |
| # "exempted_members": [ |
| # "user:aliya@example.com" |
| # ] |
| # } |
| # ] |
| # } |
| # ] |
| # } |
| # |
| # For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ |
| # logging. It also exempts jose@example.com from DATA_READ logging, and |
| # aliya@example.com from DATA_WRITE logging. |
| "service": "A String", # Specifies a service that will be enabled for audit logging. |
| # For example, `storage.googleapis.com`, `cloudsql.googleapis.com`. |
| # `allServices` is a special value that covers all services. |
| "auditLogConfigs": [ # The configuration for logging of each type of permission. |
| { # Provides the configuration for logging a type of permissions. |
| # Example: |
| # |
| # { |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ", |
| # "exempted_members": [ |
| # "user:jose@example.com" |
| # ] |
| # }, |
| # { |
| # "log_type": "DATA_WRITE" |
| # } |
| # ] |
| # } |
| # |
| # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting |
| # jose@example.com from DATA_READ logging. |
| "logType": "A String", # The log type that this config enables. |
| "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of |
| # permission. |
| # Follows the same format of Binding.members. |
| "A String", |
| ], |
| }, |
| ], |
| }, |
| ], |
| "version": 42, # Specifies the format of the policy. |
| # |
| # Valid values are `0`, `1`, and `3`. Requests that specify an invalid value |
| # are rejected. |
| # |
| # Any operation that affects conditional role bindings must specify version |
| # `3`. This requirement applies to the following operations: |
| # |
| # * Getting a policy that includes a conditional role binding |
| # * Adding a conditional role binding to a policy |
| # * Changing a conditional role binding in a policy |
| # * Removing any role binding, with or without a condition, from a policy |
| # that includes conditions |
| # |
| # **Important:** If you use IAM Conditions, you must include the `etag` field |
| # whenever you call `setIamPolicy`. If you omit this field, then IAM allows |
| # you to overwrite a version `3` policy with a version `1` policy, and all of |
| # the conditions in the version `3` policy are lost. |
| # |
| # If a policy does not include any conditions, operations on that policy may |
| # specify any valid version or leave the field unset. |
| # |
| # To learn which resources support conditions in their IAM policies, see the |
| # [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| "bindings": [ # Associates a list of `members` to a `role`. Optionally, may specify a |
| # `condition` that determines how and when the `bindings` are applied. Each |
| # of the `bindings` must contain at least one member. |
| { # Associates `members` with a `role`. |
| "role": "A String", # Role that is assigned to `members`. |
| # For example, `roles/viewer`, `roles/editor`, or `roles/owner`. |
| "condition": { # Represents a textual expression in the Common Expression Language (CEL) # The condition that is associated with this binding. |
| # |
| # If the condition evaluates to `true`, then this binding applies to the |
| # current request. |
| # |
| # If the condition evaluates to `false`, then this binding does not apply to |
| # the current request. However, a different role binding might grant the same |
| # role to one or more of the members in this binding. |
| # |
| # To learn which resources support conditions in their IAM policies, see the |
| # [IAM |
| # documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| # syntax. CEL is a C-like expression language. The syntax and semantics of CEL |
| # are documented at https://github.com/google/cel-spec. |
| # |
| # Example (Comparison): |
| # |
| # title: "Summary size limit" |
| # description: "Determines if a summary is less than 100 chars" |
| # expression: "document.summary.size() < 100" |
| # |
| # Example (Equality): |
| # |
| # title: "Requestor is owner" |
| # description: "Determines if requestor is the document owner" |
| # expression: "document.owner == request.auth.claims.email" |
| # |
| # Example (Logic): |
| # |
| # title: "Public documents" |
| # description: "Determine whether the document should be publicly visible" |
| # expression: "document.type != 'private' && document.type != 'internal'" |
| # |
| # Example (Data Manipulation): |
| # |
| # title: "Notification string" |
| # description: "Create a notification string with a timestamp." |
| # expression: "'New message received at ' + string(document.create_time)" |
| # |
| # The exact variables and functions that may be referenced within an expression |
| # are determined by the service that evaluates it. See the service |
| # documentation for additional information. |
| "expression": "A String", # Textual representation of an expression in Common Expression Language |
| # syntax. |
| "title": "A String", # Optional. Title for the expression, i.e. a short string describing |
| # its purpose. This can be used e.g. in UIs which allow to enter the |
| # expression. |
| "location": "A String", # Optional. String indicating the location of the expression for error |
| # reporting, e.g. a file name and a position in the file. |
| "description": "A String", # Optional. Description of the expression. This is a longer text which |
| # describes the expression, e.g. when hovered over it in a UI. |
| }, |
| "members": [ # Specifies the identities requesting access for a Cloud Platform resource. |
| # `members` can have the following values: |
| # |
| # * `allUsers`: A special identifier that represents anyone who is |
| # on the internet; with or without a Google account. |
| # |
| # * `allAuthenticatedUsers`: A special identifier that represents anyone |
| # who is authenticated with a Google account or a service account. |
| # |
| # * `user:{emailid}`: An email address that represents a specific Google |
| # account. For example, `alice@example.com` . |
| # |
| # |
| # * `serviceAccount:{emailid}`: An email address that represents a service |
| # account. For example, `my-other-app@appspot.gserviceaccount.com`. |
| # |
| # * `group:{emailid}`: An email address that represents a Google group. |
| # For example, `admins@example.com`. |
| # |
| # * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique |
| # identifier) representing a user that has been recently deleted. For |
| # example, `alice@example.com?uid=123456789012345678901`. If the user is |
| # recovered, this value reverts to `user:{emailid}` and the recovered user |
| # retains the role in the binding. |
| # |
| # * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus |
| # unique identifier) representing a service account that has been recently |
| # deleted. For example, |
| # `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. |
| # If the service account is undeleted, this value reverts to |
| # `serviceAccount:{emailid}` and the undeleted service account retains the |
| # role in the binding. |
| # |
| # * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique |
| # identifier) representing a Google group that has been recently |
| # deleted. For example, `admins@example.com?uid=123456789012345678901`. If |
| # the group is recovered, this value reverts to `group:{emailid}` and the |
| # recovered group retains the role in the binding. |
| # |
| # |
| # * `domain:{domain}`: The G Suite domain (primary) that represents all the |
| # users of that domain. For example, `google.com` or `example.com`. |
| # |
| "A String", |
| ], |
| }, |
| ], |
| }, |
| "updateMask": "A String", # OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only |
| # the fields in the mask will be modified. If no mask is provided, the |
| # following default mask is used: |
| # |
| # `paths: "bindings, etag"` |
| } |
| |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # An Identity and Access Management (IAM) policy, which specifies access |
| # controls for Google Cloud resources. |
| # |
| # |
| # A `Policy` is a collection of `bindings`. A `binding` binds one or more |
| # `members` to a single `role`. Members can be user accounts, service accounts, |
| # Google groups, and domains (such as G Suite). A `role` is a named list of |
| # permissions; each `role` can be an IAM predefined role or a user-created |
| # custom role. |
| # |
| # For some types of Google Cloud resources, a `binding` can also specify a |
| # `condition`, which is a logical expression that allows access to a resource |
| # only if the expression evaluates to `true`. A condition can add constraints |
| # based on attributes of the request, the resource, or both. To learn which |
| # resources support conditions in their IAM policies, see the |
| # [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| # |
| # **JSON example:** |
| # |
| # { |
| # "bindings": [ |
| # { |
| # "role": "roles/resourcemanager.organizationAdmin", |
| # "members": [ |
| # "user:mike@example.com", |
| # "group:admins@example.com", |
| # "domain:google.com", |
| # "serviceAccount:my-project-id@appspot.gserviceaccount.com" |
| # ] |
| # }, |
| # { |
| # "role": "roles/resourcemanager.organizationViewer", |
| # "members": [ |
| # "user:eve@example.com" |
| # ], |
| # "condition": { |
| # "title": "expirable access", |
| # "description": "Does not grant access after Sep 2020", |
| # "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", |
| # } |
| # } |
| # ], |
| # "etag": "BwWWja0YfJA=", |
| # "version": 3 |
| # } |
| # |
| # **YAML example:** |
| # |
| # bindings: |
| # - members: |
| # - user:mike@example.com |
| # - group:admins@example.com |
| # - domain:google.com |
| # - serviceAccount:my-project-id@appspot.gserviceaccount.com |
| # role: roles/resourcemanager.organizationAdmin |
| # - members: |
| # - user:eve@example.com |
| # role: roles/resourcemanager.organizationViewer |
| # condition: |
| # title: expirable access |
| # description: Does not grant access after Sep 2020 |
| # expression: request.time < timestamp('2020-10-01T00:00:00.000Z') |
| # - etag: BwWWja0YfJA= |
| # - version: 3 |
| # |
| # For a description of IAM and its features, see the |
| # [IAM documentation](https://cloud.google.com/iam/docs/). |
| "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help |
| # prevent simultaneous updates of a policy from overwriting each other. |
| # It is strongly suggested that systems make use of the `etag` in the |
| # read-modify-write cycle to perform policy updates in order to avoid race |
| # conditions: An `etag` is returned in the response to `getIamPolicy`, and |
| # systems are expected to put that etag in the request to `setIamPolicy` to |
| # ensure that their change will be applied to the same version of the policy. |
| # |
| # **Important:** If you use IAM Conditions, you must include the `etag` field |
| # whenever you call `setIamPolicy`. If you omit this field, then IAM allows |
| # you to overwrite a version `3` policy with a version `1` policy, and all of |
| # the conditions in the version `3` policy are lost. |
| "auditConfigs": [ # Specifies cloud audit logging configuration for this policy. |
| { # Specifies the audit configuration for a service. |
| # The configuration determines which permission types are logged, and what |
| # identities, if any, are exempted from logging. |
| # An AuditConfig must have one or more AuditLogConfigs. |
| # |
| # If there are AuditConfigs for both `allServices` and a specific service, |
| # the union of the two AuditConfigs is used for that service: the log_types |
| # specified in each AuditConfig are enabled, and the exempted_members in each |
| # AuditLogConfig are exempted. |
| # |
| # Example Policy with multiple AuditConfigs: |
| # |
| # { |
| # "audit_configs": [ |
| # { |
| # "service": "allServices", |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ", |
| # "exempted_members": [ |
| # "user:jose@example.com" |
| # ] |
| # }, |
| # { |
| # "log_type": "DATA_WRITE" |
| # }, |
| # { |
| # "log_type": "ADMIN_READ" |
| # } |
| # ] |
| # }, |
| # { |
| # "service": "sampleservice.googleapis.com", |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ" |
| # }, |
| # { |
| # "log_type": "DATA_WRITE", |
| # "exempted_members": [ |
| # "user:aliya@example.com" |
| # ] |
| # } |
| # ] |
| # } |
| # ] |
| # } |
| # |
| # For sampleservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ |
| # logging. It also exempts jose@example.com from DATA_READ logging, and |
| # aliya@example.com from DATA_WRITE logging. |
| "service": "A String", # Specifies a service that will be enabled for audit logging. |
| # For example, `storage.googleapis.com`, `cloudsql.googleapis.com`. |
| # `allServices` is a special value that covers all services. |
| "auditLogConfigs": [ # The configuration for logging of each type of permission. |
| { # Provides the configuration for logging a type of permissions. |
| # Example: |
| # |
| # { |
| # "audit_log_configs": [ |
| # { |
| # "log_type": "DATA_READ", |
| # "exempted_members": [ |
| # "user:jose@example.com" |
| # ] |
| # }, |
| # { |
| # "log_type": "DATA_WRITE" |
| # } |
| # ] |
| # } |
| # |
| # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting |
| # jose@example.com from DATA_READ logging. |
| "logType": "A String", # The log type that this config enables. |
| "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of |
| # permission. |
| # Follows the same format of Binding.members. |
| "A String", |
| ], |
| }, |
| ], |
| }, |
| ], |
| "version": 42, # Specifies the format of the policy. |
| # |
| # Valid values are `0`, `1`, and `3`. Requests that specify an invalid value |
| # are rejected. |
| # |
| # Any operation that affects conditional role bindings must specify version |
| # `3`. This requirement applies to the following operations: |
| # |
| # * Getting a policy that includes a conditional role binding |
| # * Adding a conditional role binding to a policy |
| # * Changing a conditional role binding in a policy |
| # * Removing any role binding, with or without a condition, from a policy |
| # that includes conditions |
| # |
| # **Important:** If you use IAM Conditions, you must include the `etag` field |
| # whenever you call `setIamPolicy`. If you omit this field, then IAM allows |
| # you to overwrite a version `3` policy with a version `1` policy, and all of |
| # the conditions in the version `3` policy are lost. |
| # |
| # If a policy does not include any conditions, operations on that policy may |
| # specify any valid version or leave the field unset. |
| # |
| # To learn which resources support conditions in their IAM policies, see the |
| # [IAM documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| "bindings": [ # Associates a list of `members` to a `role`. Optionally, may specify a |
| # `condition` that determines how and when the `bindings` are applied. Each |
| # of the `bindings` must contain at least one member. |
| { # Associates `members` with a `role`. |
| "role": "A String", # Role that is assigned to `members`. |
| # For example, `roles/viewer`, `roles/editor`, or `roles/owner`. |
| "condition": { # Represents a textual expression in the Common Expression Language (CEL) # The condition that is associated with this binding. |
| # |
| # If the condition evaluates to `true`, then this binding applies to the |
| # current request. |
| # |
| # If the condition evaluates to `false`, then this binding does not apply to |
| # the current request. However, a different role binding might grant the same |
| # role to one or more of the members in this binding. |
| # |
| # To learn which resources support conditions in their IAM policies, see the |
| # [IAM |
| # documentation](https://cloud.google.com/iam/help/conditions/resource-policies). |
| # syntax. CEL is a C-like expression language. The syntax and semantics of CEL |
| # are documented at https://github.com/google/cel-spec. |
| # |
| # Example (Comparison): |
| # |
| # title: "Summary size limit" |
| # description: "Determines if a summary is less than 100 chars" |
| # expression: "document.summary.size() < 100" |
| # |
| # Example (Equality): |
| # |
| # title: "Requestor is owner" |
| # description: "Determines if requestor is the document owner" |
| # expression: "document.owner == request.auth.claims.email" |
| # |
| # Example (Logic): |
| # |
| # title: "Public documents" |
| # description: "Determine whether the document should be publicly visible" |
| # expression: "document.type != 'private' && document.type != 'internal'" |
| # |
| # Example (Data Manipulation): |
| # |
| # title: "Notification string" |
| # description: "Create a notification string with a timestamp." |
| # expression: "'New message received at ' + string(document.create_time)" |
| # |
| # The exact variables and functions that may be referenced within an expression |
| # are determined by the service that evaluates it. See the service |
| # documentation for additional information. |
| "expression": "A String", # Textual representation of an expression in Common Expression Language |
| # syntax. |
| "title": "A String", # Optional. Title for the expression, i.e. a short string describing |
| # its purpose. This can be used e.g. in UIs which allow to enter the |
| # expression. |
| "location": "A String", # Optional. String indicating the location of the expression for error |
| # reporting, e.g. a file name and a position in the file. |
| "description": "A String", # Optional. Description of the expression. This is a longer text which |
| # describes the expression, e.g. when hovered over it in a UI. |
| }, |
| "members": [ # Specifies the identities requesting access for a Cloud Platform resource. |
| # `members` can have the following values: |
| # |
| # * `allUsers`: A special identifier that represents anyone who is |
| # on the internet; with or without a Google account. |
| # |
| # * `allAuthenticatedUsers`: A special identifier that represents anyone |
| # who is authenticated with a Google account or a service account. |
| # |
| # * `user:{emailid}`: An email address that represents a specific Google |
| # account. For example, `alice@example.com` . |
| # |
| # |
| # * `serviceAccount:{emailid}`: An email address that represents a service |
| # account. For example, `my-other-app@appspot.gserviceaccount.com`. |
| # |
| # * `group:{emailid}`: An email address that represents a Google group. |
| # For example, `admins@example.com`. |
| # |
| # * `deleted:user:{emailid}?uid={uniqueid}`: An email address (plus unique |
| # identifier) representing a user that has been recently deleted. For |
| # example, `alice@example.com?uid=123456789012345678901`. If the user is |
| # recovered, this value reverts to `user:{emailid}` and the recovered user |
| # retains the role in the binding. |
| # |
| # * `deleted:serviceAccount:{emailid}?uid={uniqueid}`: An email address (plus |
| # unique identifier) representing a service account that has been recently |
| # deleted. For example, |
| # `my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901`. |
| # If the service account is undeleted, this value reverts to |
| # `serviceAccount:{emailid}` and the undeleted service account retains the |
| # role in the binding. |
| # |
| # * `deleted:group:{emailid}?uid={uniqueid}`: An email address (plus unique |
| # identifier) representing a Google group that has been recently |
| # deleted. For example, `admins@example.com?uid=123456789012345678901`. If |
| # the group is recovered, this value reverts to `group:{emailid}` and the |
| # recovered group retains the role in the binding. |
| # |
| # |
| # * `domain:{domain}`: The G Suite domain (primary) that represents all the |
| # users of that domain. For example, `google.com` or `example.com`. |
| # |
| "A String", |
| ], |
| }, |
| ], |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="testIamPermissions">testIamPermissions(resource, body=None, x__xgafv=None)</code> |
| <pre>Returns permissions that a caller has on the specified resource. |
| If the resource does not exist, this will return an empty set of |
| permissions, not a `NOT_FOUND` error. |
| |
| Note: This operation is designed to be used for building permission-aware |
| UIs and command-line tools, not for authorization checking. This operation |
| may "fail open" without warning. |
| |
| Args: |
| resource: string, REQUIRED: The resource for which the policy detail is being requested. |
| See the operation documentation for the appropriate value for this field. (required) |
| body: object, The request body. |
| The object takes the form of: |
| |
| { # Request message for `TestIamPermissions` method. |
| "permissions": [ # The set of permissions to check for the `resource`. Permissions with |
| # wildcards (such as '*' or 'storage.*') are not allowed. For more |
| # information see |
| # [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions). |
| "A String", |
| ], |
| } |
| |
| 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 `TestIamPermissions` method. |
| "permissions": [ # A subset of `TestPermissionsRequest.permissions` that the caller is |
| # allowed. |
| "A String", |
| ], |
| }</pre> |
| </div> |
| |
| </body></html> |
