Api-roll 44: 2017-01-23

generated/googleapis_beta/lib/ml/v1beta1.dart

 // This is a generated file (see the discoveryapis_generator project).
 
 library googleapis_beta.ml.v1beta1;
 
 import 'dart:core' as core;
 import 'dart:async' as async;
 import 'dart:convert' as convert;
 
 import 'package:_discoveryapis_commons/_discoveryapis_commons.dart' as commons;
 import 'package:http/http.dart' as http;
@@ skipping 1578 matching lines @@
    */
   GoogleCloudMlV1beta1Version defaultVersion;
   /** Optional. The description specified for the model when it was created. */
   core.String description;
   /**
    * Required. The name specified for the model when it was created.
    *
    * The model name must be unique within the project it is created in.
    */
   core.String name;
+  /**
+   * Optional. The list of regions where the model is going to be deployed.
+   * Currently only one region per model is supported.
+   * Defaults to 'us-central1' if nothing is set.
+   */
+  core.List<core.String> regions;
 
   GoogleCloudMlV1beta1Model();
 
   GoogleCloudMlV1beta1Model.fromJson(core.Map _json) {
     if (_json.containsKey("defaultVersion")) {
       defaultVersion = new GoogleCloudMlV1beta1Version.fromJson(_json["defaultVersion"]);
     }
     if (_json.containsKey("description")) {
       description = _json["description"];
     }
     if (_json.containsKey("name")) {
       name = _json["name"];
     }
+    if (_json.containsKey("regions")) {
+      regions = _json["regions"];
+    }
   }
 
   core.Map toJson() {
     var _json = new core.Map();
     if (defaultVersion != null) {
       _json["defaultVersion"] = (defaultVersion).toJson();
     }
     if (description != null) {
       _json["description"] = description;
     }
     if (name != null) {
       _json["name"] = name;
     }
+    if (regions != null) {
+      _json["regions"] = regions;
+    }
     return _json;
   }
 }
 
 /** Represents the metadata of the long-running operation. */
 class GoogleCloudMlV1beta1OperationMetadata {
   /** The time the operation was submitted. */
   core.String createTime;
   /** The time operation processing completed. */
   core.String endTime;
@@ skipping 334 matching lines @@
    * `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
    */
   core.String modelName;
   /** Required. The output Google Cloud Storage location. */
   core.String outputPath;
   /**
    * Required. The Google Compute Engine region to run the prediction job in.
    */
   core.String region;
   /**
+   * Optional. The Google Cloud ML runtime version to use for this batch
+   * prediction. If not set, Google Cloud ML will choose a version.
+   */
+  core.String runtimeVersion;
+  /**
    * Use this field if you want to specify a version of the model to use. The
    * string is formatted the same way as `model_version`, with the addition
    * of the version information:
    *
    * `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
    */
   core.String versionName;
 
   GoogleCloudMlV1beta1PredictionInput();
 
   GoogleCloudMlV1beta1PredictionInput.fromJson(core.Map _json) {
     if (_json.containsKey("dataFormat")) {
       dataFormat = _json["dataFormat"];
     }
     if (_json.containsKey("inputPaths")) {
       inputPaths = _json["inputPaths"];
     }
     if (_json.containsKey("maxWorkerCount")) {
       maxWorkerCount = _json["maxWorkerCount"];
     }
     if (_json.containsKey("modelName")) {
       modelName = _json["modelName"];
     }
     if (_json.containsKey("outputPath")) {
       outputPath = _json["outputPath"];
     }
     if (_json.containsKey("region")) {
       region = _json["region"];
     }
+    if (_json.containsKey("runtimeVersion")) {
+      runtimeVersion = _json["runtimeVersion"];
+    }
     if (_json.containsKey("versionName")) {
       versionName = _json["versionName"];
     }
   }
 
   core.Map toJson() {
     var _json = new core.Map();
     if (dataFormat != null) {
       _json["dataFormat"] = dataFormat;
     }
     if (inputPaths != null) {
       _json["inputPaths"] = inputPaths;
     }
     if (maxWorkerCount != null) {
       _json["maxWorkerCount"] = maxWorkerCount;
     }
     if (modelName != null) {
       _json["modelName"] = modelName;
     }
     if (outputPath != null) {
       _json["outputPath"] = outputPath;
     }
     if (region != null) {
       _json["region"] = region;
     }
+    if (runtimeVersion != null) {
+      _json["runtimeVersion"] = runtimeVersion;
+    }
     if (versionName != null) {
       _json["versionName"] = versionName;
     }
     return _json;
   }
 }
 
 /** Represents results of a prediction job. */
 class GoogleCloudMlV1beta1PredictionOutput {
   /** The number of data instances which resulted in errors. */
   core.String errorCount;
+  /** Node hours used by the batch prediction job. */
+  core.double nodeHours;
   /**
    * The output Google Cloud Storage location provided at the job creation time.
    */
   core.String outputPath;
   /** The number of generated predictions. */
   core.String predictionCount;
 
   GoogleCloudMlV1beta1PredictionOutput();
 
   GoogleCloudMlV1beta1PredictionOutput.fromJson(core.Map _json) {
     if (_json.containsKey("errorCount")) {
       errorCount = _json["errorCount"];
     }
+    if (_json.containsKey("nodeHours")) {
+      nodeHours = _json["nodeHours"];
+    }
     if (_json.containsKey("outputPath")) {
       outputPath = _json["outputPath"];
     }
     if (_json.containsKey("predictionCount")) {
       predictionCount = _json["predictionCount"];
     }
   }
 
   core.Map toJson() {
     var _json = new core.Map();
     if (errorCount != null) {
       _json["errorCount"] = errorCount;
     }
+    if (nodeHours != null) {
+      _json["nodeHours"] = nodeHours;
+    }
     if (outputPath != null) {
       _json["outputPath"] = outputPath;
     }
     if (predictionCount != null) {
       _json["predictionCount"] = predictionCount;
     }
     return _json;
   }
 }
 
@@ skipping 79 matching lines @@
    *
    * This value must be present when `scaleTier` is set to `CUSTOM` and
    * `parameter_server_count` is greater than zero.
    */
   core.String parameterServerType;
   /** Required. The Python module name to run after installing the packages. */
   core.String pythonModule;
   /** Required. The Google Compute Engine region to run the training job in. */
   core.String region;
   /**
+   * Optional. The Google Cloud ML runtime version to use for training.  If not
+   * set, Google Cloud ML will choose the latest stable version.
+   */
+  core.String runtimeVersion;
+  /**
    * Required. Specifies the machine types, the number of replicas for workers
    * and parameter servers.
    * Possible string values are:
    * - "BASIC" : A single worker instance. This tier is suitable for learning
    * how to use
    * Cloud ML, and for experimenting with new models using small datasets.
    * - "STANDARD_1" : Many workers and a few parameter servers.
    * - "PREMIUM_1" : A large number of workers with many parameter servers.
    * - "CUSTOM" : The CUSTOM tier is not a set tier, but rather enables you to
    * use your
@@ skipping 61 matching lines @@
     }
     if (_json.containsKey("parameterServerType")) {
       parameterServerType = _json["parameterServerType"];
     }
     if (_json.containsKey("pythonModule")) {
       pythonModule = _json["pythonModule"];
     }
     if (_json.containsKey("region")) {
       region = _json["region"];
     }
+    if (_json.containsKey("runtimeVersion")) {
+      runtimeVersion = _json["runtimeVersion"];
+    }
     if (_json.containsKey("scaleTier")) {
       scaleTier = _json["scaleTier"];
     }
     if (_json.containsKey("workerCount")) {
       workerCount = _json["workerCount"];
     }
     if (_json.containsKey("workerType")) {
       workerType = _json["workerType"];
     }
   }
@@ skipping 17 matching lines @@
     }
     if (parameterServerType != null) {
       _json["parameterServerType"] = parameterServerType;
     }
     if (pythonModule != null) {
       _json["pythonModule"] = pythonModule;
     }
     if (region != null) {
       _json["region"] = region;
     }
+    if (runtimeVersion != null) {
+      _json["runtimeVersion"] = runtimeVersion;
+    }
     if (scaleTier != null) {
       _json["scaleTier"] = scaleTier;
     }
     if (workerCount != null) {
       _json["workerCount"] = workerCount;
     }
     if (workerType != null) {
       _json["workerType"] = workerType;
     }
     return _json;
   }
 }
 
-/** Represents results of a training job. */
+/** Represents results of a training job. Output only. */
 class GoogleCloudMlV1beta1TrainingOutput {
   /**
    * The number of hyperparameter tuning trials that completed successfully.
+   * Only set for hyperparameter tuning jobs.
    */
   core.String completedTrialCount;
   /** The amount of ML units consumed by the job. */
-  core.double consumedMlUnits;
-  /** Results for individual Hyperparameter trials. */
+  core.double consumedMLUnits;
+  /** Whether this job is a hyperparameter tuning job. */
+  core.bool isHyperparameterTuningJob;
+  /**
+   * Results for individual Hyperparameter trials.
+   * Only set for hyperparameter tuning jobs.
+   */
   core.List<GoogleCloudMlV1beta1HyperparameterOutput> trials;
 
   GoogleCloudMlV1beta1TrainingOutput();
 
   GoogleCloudMlV1beta1TrainingOutput.fromJson(core.Map _json) {
     if (_json.containsKey("completedTrialCount")) {
       completedTrialCount = _json["completedTrialCount"];
     }
-    if (_json.containsKey("consumedMlUnits")) {
-      consumedMlUnits = _json["consumedMlUnits"];
+    if (_json.containsKey("consumedMLUnits")) {
+      consumedMLUnits = _json["consumedMLUnits"];
+    }
+    if (_json.containsKey("isHyperparameterTuningJob")) {
+      isHyperparameterTuningJob = _json["isHyperparameterTuningJob"];
     }
     if (_json.containsKey("trials")) {
       trials = _json["trials"].map((value) => new GoogleCloudMlV1beta1HyperparameterOutput.fromJson(value)).toList();
     }
   }
 
   core.Map toJson() {
     var _json = new core.Map();
     if (completedTrialCount != null) {
       _json["completedTrialCount"] = completedTrialCount;
     }
-    if (consumedMlUnits != null) {
-      _json["consumedMlUnits"] = consumedMlUnits;
+    if (consumedMLUnits != null) {
+      _json["consumedMLUnits"] = consumedMLUnits;
+    }
+    if (isHyperparameterTuningJob != null) {
+      _json["isHyperparameterTuningJob"] = isHyperparameterTuningJob;
     }
     if (trials != null) {
       _json["trials"] = trials.map((value) => (value).toJson()).toList();
     }
     return _json;
   }
 }
 
 /**
  * Represents a version of the model.
@@ skipping 32 matching lines @@
    */
   core.bool isDefault;
   /** Output only. The time the version was last used for prediction. */
   core.String lastUseTime;
   /**
    * Required.The name specified for the version when it was created.
    *
    * The version name must be unique within the model it is created in.
    */
   core.String name;
+  /**
+   * Optional. If true, enables StackDriver Logging for online prediction.
+   * Default is false.
+   */
+  core.bool onlinePredictionLogging;
+  /**
+   * Optional. The Google Cloud ML runtime version to use for this deployment.
+   * If not set, Google Cloud ML will choose a version.
+   */
+  core.String runtimeVersion;
 
   GoogleCloudMlV1beta1Version();
 
   GoogleCloudMlV1beta1Version.fromJson(core.Map _json) {
     if (_json.containsKey("createTime")) {
       createTime = _json["createTime"];
     }
     if (_json.containsKey("deploymentUri")) {
       deploymentUri = _json["deploymentUri"];
     }
     if (_json.containsKey("description")) {
       description = _json["description"];
     }
     if (_json.containsKey("isDefault")) {
       isDefault = _json["isDefault"];
     }
     if (_json.containsKey("lastUseTime")) {
       lastUseTime = _json["lastUseTime"];
     }
     if (_json.containsKey("name")) {
       name = _json["name"];
     }
+    if (_json.containsKey("onlinePredictionLogging")) {
+      onlinePredictionLogging = _json["onlinePredictionLogging"];
+    }
+    if (_json.containsKey("runtimeVersion")) {
+      runtimeVersion = _json["runtimeVersion"];
+    }
   }
 
   core.Map toJson() {
     var _json = new core.Map();
     if (createTime != null) {
       _json["createTime"] = createTime;
     }
     if (deploymentUri != null) {
       _json["deploymentUri"] = deploymentUri;
     }
     if (description != null) {
       _json["description"] = description;
     }
     if (isDefault != null) {
       _json["isDefault"] = isDefault;
     }
     if (lastUseTime != null) {
       _json["lastUseTime"] = lastUseTime;
     }
     if (name != null) {
       _json["name"] = name;
     }
+    if (onlinePredictionLogging != null) {
+      _json["onlinePredictionLogging"] = onlinePredictionLogging;
+    }
+    if (runtimeVersion != null) {
+      _json["runtimeVersion"] = runtimeVersion;
+    }
     return _json;
   }
 }
 
 /** The response message for Operations.ListOperations. */
 class GoogleLongrunningListOperationsResponse {
   /** The standard List next-page token. */
   core.String nextPageToken;
   /** A list of operations that matches the specified filter in the request. */
   core.List<GoogleLongrunningOperation> operations;
@@ skipping 225 matching lines @@
     }
     if (details != null) {
       _json["details"] = details;
     }
     if (message != null) {
       _json["message"] = message;
     }
     return _json;
   }
 }