| Index: discovery/googleapis/ml__v1.json
|
| diff --git a/discovery/googleapis/ml__v1.json b/discovery/googleapis/ml__v1.json
|
| index 7e8f8d218220db66e6331a330b4172aa2d4e90b2..cb58ad59b377cad9f283af6c2d6202619782188e 100644
|
| --- a/discovery/googleapis/ml__v1.json
|
| +++ b/discovery/googleapis/ml__v1.json
|
| @@ -25,24 +25,19 @@
|
| "ownerDomain": "google.com",
|
| "ownerName": "Google",
|
| "parameters": {
|
| - "quotaUser": {
|
| - "description": "Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.",
|
| - "location": "query",
|
| - "type": "string"
|
| - },
|
| "pp": {
|
| "default": "true",
|
| "description": "Pretty-print response.",
|
| "location": "query",
|
| "type": "boolean"
|
| },
|
| - "bearer_token": {
|
| - "description": "OAuth bearer token.",
|
| + "oauth_token": {
|
| + "description": "OAuth 2.0 token for the current user.",
|
| "location": "query",
|
| "type": "string"
|
| },
|
| - "oauth_token": {
|
| - "description": "OAuth 2.0 token for the current user.",
|
| + "bearer_token": {
|
| + "description": "OAuth bearer token.",
|
| "location": "query",
|
| "type": "string"
|
| },
|
| @@ -57,18 +52,13 @@
|
| "location": "query",
|
| "type": "boolean"
|
| },
|
| - "uploadType": {
|
| - "description": "Legacy upload protocol for media (e.g. \"media\", \"multipart\").",
|
| - "location": "query",
|
| - "type": "string"
|
| - },
|
| "fields": {
|
| "description": "Selector specifying which fields to include in a partial response.",
|
| "location": "query",
|
| "type": "string"
|
| },
|
| - "callback": {
|
| - "description": "JSONP",
|
| + "uploadType": {
|
| + "description": "Legacy upload protocol for media (e.g. \"media\", \"multipart\").",
|
| "location": "query",
|
| "type": "string"
|
| },
|
| @@ -85,6 +75,11 @@
|
| "location": "query",
|
| "type": "string"
|
| },
|
| + "callback": {
|
| + "description": "JSONP",
|
| + "location": "query",
|
| + "type": "string"
|
| + },
|
| "alt": {
|
| "default": "json",
|
| "description": "Data format for response.",
|
| @@ -101,13 +96,18 @@
|
| "location": "query",
|
| "type": "string"
|
| },
|
| + "access_token": {
|
| + "description": "OAuth access token.",
|
| + "location": "query",
|
| + "type": "string"
|
| + },
|
| "key": {
|
| "description": "API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.",
|
| "location": "query",
|
| "type": "string"
|
| },
|
| - "access_token": {
|
| - "description": "OAuth access token.",
|
| + "quotaUser": {
|
| + "description": "Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.",
|
| "location": "query",
|
| "type": "string"
|
| }
|
| @@ -287,30 +287,6 @@
|
| },
|
| "models": {
|
| "methods": {
|
| - "delete": {
|
| - "description": "Deletes a model.\n\nYou can only delete a model if there are no versions in it. You can delete\nversions by calling\n[projects.models.versions.delete](/ml-engine/reference/rest/v1/projects.models.versions/delete).",
|
| - "httpMethod": "DELETE",
|
| - "id": "ml.projects.models.delete",
|
| - "parameterOrder": [
|
| - "name"
|
| - ],
|
| - "parameters": {
|
| - "name": {
|
| - "description": "Required. The name of the model.\n\nAuthorization: requires `Editor` role on the parent project.",
|
| - "location": "path",
|
| - "pattern": "^projects/[^/]+/models/[^/]+$",
|
| - "required": true,
|
| - "type": "string"
|
| - }
|
| - },
|
| - "path": "v1/{+name}",
|
| - "response": {
|
| - "$ref": "GoogleLongrunning__Operation"
|
| - },
|
| - "scopes": [
|
| - "https://www.googleapis.com/auth/cloud-platform"
|
| - ]
|
| - },
|
| "list": {
|
| "description": "Lists the models in a project.\n\nEach project can contain multiple models, and each model can have multiple\nversions.",
|
| "httpMethod": "GET",
|
| @@ -319,11 +295,6 @@
|
| "parent"
|
| ],
|
| "parameters": {
|
| - "pageToken": {
|
| - "description": "Optional. A page token to request the next page of results.\n\nYou get the token from the `next_page_token` field of the response from\nthe previous call.",
|
| - "location": "query",
|
| - "type": "string"
|
| - },
|
| "pageSize": {
|
| "description": "Optional. The number of models to retrieve per \"page\" of results. If there\nare more remaining results than this number, the response message will\ncontain a valid value in the `next_page_token` field.\n\nThe default value is 20, and the maximum page size is 100.",
|
| "format": "int32",
|
| @@ -336,6 +307,11 @@
|
| "pattern": "^projects/[^/]+$",
|
| "required": true,
|
| "type": "string"
|
| + },
|
| + "pageToken": {
|
| + "description": "Optional. A page token to request the next page of results.\n\nYou get the token from the `next_page_token` field of the response from\nthe previous call.",
|
| + "location": "query",
|
| + "type": "string"
|
| }
|
| },
|
| "path": "v1/{+parent}/models",
|
| @@ -396,6 +372,30 @@
|
| "scopes": [
|
| "https://www.googleapis.com/auth/cloud-platform"
|
| ]
|
| + },
|
| + "delete": {
|
| + "description": "Deletes a model.\n\nYou can only delete a model if there are no versions in it. You can delete\nversions by calling\n[projects.models.versions.delete](/ml-engine/reference/rest/v1/projects.models.versions/delete).",
|
| + "httpMethod": "DELETE",
|
| + "id": "ml.projects.models.delete",
|
| + "parameterOrder": [
|
| + "name"
|
| + ],
|
| + "parameters": {
|
| + "name": {
|
| + "description": "Required. The name of the model.\n\nAuthorization: requires `Editor` role on the parent project.",
|
| + "location": "path",
|
| + "pattern": "^projects/[^/]+/models/[^/]+$",
|
| + "required": true,
|
| + "type": "string"
|
| + }
|
| + },
|
| + "path": "v1/{+name}",
|
| + "response": {
|
| + "$ref": "GoogleLongrunning__Operation"
|
| + },
|
| + "scopes": [
|
| + "https://www.googleapis.com/auth/cloud-platform"
|
| + ]
|
| }
|
| },
|
| "resources": {
|
| @@ -460,6 +460,12 @@
|
| "parent"
|
| ],
|
| "parameters": {
|
| + "pageSize": {
|
| + "description": "Optional. The number of versions to retrieve per \"page\" of results. If\nthere are more remaining results than this number, the response message\nwill contain a valid value in the `next_page_token` field.\n\nThe default value is 20, and the maximum page size is 100.",
|
| + "format": "int32",
|
| + "location": "query",
|
| + "type": "integer"
|
| + },
|
| "parent": {
|
| "description": "Required. The name of the model for which to list the version.\n\nAuthorization: requires `Viewer` role on the parent project.",
|
| "location": "path",
|
| @@ -471,12 +477,6 @@
|
| "description": "Optional. A page token to request the next page of results.\n\nYou get the token from the `next_page_token` field of the response from\nthe previous call.",
|
| "location": "query",
|
| "type": "string"
|
| - },
|
| - "pageSize": {
|
| - "description": "Optional. The number of versions to retrieve per \"page\" of results. If\nthere are more remaining results than this number, the response message\nwill contain a valid value in the `next_page_token` field.\n\nThe default value is 20, and the maximum page size is 100.",
|
| - "format": "int32",
|
| - "location": "query",
|
| - "type": "integer"
|
| }
|
| },
|
| "path": "v1/{+parent}/versions",
|
| @@ -544,6 +544,33 @@
|
| },
|
| "jobs": {
|
| "methods": {
|
| + "cancel": {
|
| + "description": "Cancels a running job.",
|
| + "httpMethod": "POST",
|
| + "id": "ml.projects.jobs.cancel",
|
| + "parameterOrder": [
|
| + "name"
|
| + ],
|
| + "parameters": {
|
| + "name": {
|
| + "description": "Required. The name of the job to cancel.\n\nAuthorization: requires `Editor` role on the parent project.",
|
| + "location": "path",
|
| + "pattern": "^projects/[^/]+/jobs/[^/]+$",
|
| + "required": true,
|
| + "type": "string"
|
| + }
|
| + },
|
| + "path": "v1/{+name}:cancel",
|
| + "request": {
|
| + "$ref": "GoogleCloudMlV1__CancelJobRequest"
|
| + },
|
| + "response": {
|
| + "$ref": "GoogleProtobuf__Empty"
|
| + },
|
| + "scopes": [
|
| + "https://www.googleapis.com/auth/cloud-platform"
|
| + ]
|
| + },
|
| "list": {
|
| "description": "Lists the jobs in the project.",
|
| "httpMethod": "GET",
|
| @@ -552,6 +579,12 @@
|
| "parent"
|
| ],
|
| "parameters": {
|
| + "pageSize": {
|
| + "description": "Optional. The number of jobs to retrieve per \"page\" of results. If there\nare more remaining results than this number, the response message will\ncontain a valid value in the `next_page_token` field.\n\nThe default value is 20, and the maximum page size is 100.",
|
| + "format": "int32",
|
| + "location": "query",
|
| + "type": "integer"
|
| + },
|
| "parent": {
|
| "description": "Required. The name of the project for which to list jobs.\n\nAuthorization: requires `Viewer` role on the specified project.",
|
| "location": "path",
|
| @@ -568,12 +601,6 @@
|
| "description": "Optional. A page token to request the next page of results.\n\nYou get the token from the `next_page_token` field of the response from\nthe previous call.",
|
| "location": "query",
|
| "type": "string"
|
| - },
|
| - "pageSize": {
|
| - "description": "Optional. The number of jobs to retrieve per \"page\" of results. If there\nare more remaining results than this number, the response message will\ncontain a valid value in the `next_page_token` field.\n\nThe default value is 20, and the maximum page size is 100.",
|
| - "format": "int32",
|
| - "location": "query",
|
| - "type": "integer"
|
| }
|
| },
|
| "path": "v1/{+parent}/jobs",
|
| @@ -634,175 +661,179 @@
|
| "scopes": [
|
| "https://www.googleapis.com/auth/cloud-platform"
|
| ]
|
| - },
|
| - "cancel": {
|
| - "description": "Cancels a running job.",
|
| - "httpMethod": "POST",
|
| - "id": "ml.projects.jobs.cancel",
|
| - "parameterOrder": [
|
| - "name"
|
| - ],
|
| - "parameters": {
|
| - "name": {
|
| - "description": "Required. The name of the job to cancel.\n\nAuthorization: requires `Editor` role on the parent project.",
|
| - "location": "path",
|
| - "pattern": "^projects/[^/]+/jobs/[^/]+$",
|
| - "required": true,
|
| - "type": "string"
|
| - }
|
| - },
|
| - "path": "v1/{+name}:cancel",
|
| - "request": {
|
| - "$ref": "GoogleCloudMlV1__CancelJobRequest"
|
| - },
|
| - "response": {
|
| - "$ref": "GoogleProtobuf__Empty"
|
| - },
|
| - "scopes": [
|
| - "https://www.googleapis.com/auth/cloud-platform"
|
| - ]
|
| }
|
| }
|
| }
|
| }
|
| }
|
| },
|
| - "revision": "20170320",
|
| + "revision": "20170407",
|
| "rootUrl": "https://ml.googleapis.com/",
|
| "schemas": {
|
| - "GoogleCloudMlV1beta1__Version": {
|
| - "description": "Represents a version of the model.\n\nEach version is a trained model deployed in the cloud, ready to handle\nprediction requests. A model can have multiple versions. You can get\ninformation about all of the versions of a given model by calling\n[projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.models.versions/list).",
|
| - "id": "GoogleCloudMlV1beta1__Version",
|
| + "GoogleCloudMlV1__HyperparameterSpec": {
|
| + "description": "Represents a set of hyperparameters to optimize.",
|
| + "id": "GoogleCloudMlV1__HyperparameterSpec",
|
| "properties": {
|
| - "runtimeVersion": {
|
| - "description": "Optional. The Google Cloud ML runtime version to use for this deployment.\nIf not set, Google Cloud ML will choose a version.",
|
| - "type": "string"
|
| - },
|
| - "lastUseTime": {
|
| - "description": "Output only. The time the version was last used for prediction.",
|
| - "format": "google-datetime",
|
| - "type": "string"
|
| - },
|
| - "description": {
|
| - "description": "Optional. The description specified for the version when it was created.",
|
| + "goal": {
|
| + "description": "Required. The type of goal to use for tuning. Available types are\n`MAXIMIZE` and `MINIMIZE`.\n\nDefaults to `MAXIMIZE`.",
|
| + "enum": [
|
| + "GOAL_TYPE_UNSPECIFIED",
|
| + "MAXIMIZE",
|
| + "MINIMIZE"
|
| + ],
|
| + "enumDescriptions": [
|
| + "Goal Type will default to maximize.",
|
| + "Maximize the goal metric.",
|
| + "Minimize the goal metric."
|
| + ],
|
| "type": "string"
|
| },
|
| - "deploymentUri": {
|
| - "description": "Required. The Google Cloud Storage location of the trained model used to\ncreate the version. See the\n[overview of model deployment](/ml-engine/docs/concepts/deployment-overview) for\nmore informaiton.\n\nWhen passing Version to\n[projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.models.versions/create)\nthe model service uses the specified location as the source of the model.\nOnce deployed, the model version is hosted by the prediction service, so\nthis location is useful only as a historical record.",
|
| + "hyperparameterMetricTag": {
|
| + "description": "Optional. The Tensorflow summary tag name to use for optimizing trials. For\ncurrent versions of Tensorflow, this tag name should exactly match what is\nshown in Tensorboard, including all scopes. For versions of Tensorflow\nprior to 0.12, this should be only the tag passed to tf.Summary.\nBy default, \"training/hptuning/metric\" will be used.",
|
| "type": "string"
|
| },
|
| - "isDefault": {
|
| - "description": "Output only. If true, this version will be used to handle prediction\nrequests that do not specify a version.\n\nYou can change the default version by calling\n[projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/projects.models.versions/setDefault).",
|
| - "type": "boolean"
|
| - },
|
| - "createTime": {
|
| - "description": "Output only. The time the version was created.",
|
| - "format": "google-datetime",
|
| - "type": "string"
|
| + "params": {
|
| + "description": "Required. The set of parameters to tune.",
|
| + "items": {
|
| + "$ref": "GoogleCloudMlV1__ParameterSpec"
|
| + },
|
| + "type": "array"
|
| },
|
| - "manualScaling": {
|
| - "$ref": "GoogleCloudMlV1beta1__ManualScaling",
|
| - "description": "Optional. Manually select the number of nodes to use for serving the\nmodel. If unset (i.e., by default), the number of nodes used to serve\nthe model automatically scales with traffic. However, care should be\ntaken to ramp up traffic according to the model's ability to scale. If\nyour model needs to handle bursts of traffic beyond it's ability to\nscale, it is recommended you set this field appropriately."
|
| + "maxTrials": {
|
| + "description": "Optional. How many training trials should be attempted to optimize\nthe specified hyperparameters.\n\nDefaults to one.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| },
|
| - "name": {
|
| - "description": "Required.The name specified for the version when it was created.\n\nThe version name must be unique within the model it is created in.",
|
| - "type": "string"
|
| + "maxParallelTrials": {
|
| + "description": "Optional. The number of training trials to run concurrently.\nYou can reduce the time it takes to perform hyperparameter tuning by adding\ntrials in parallel. However, each trail only benefits from the information\ngained in completed trials. That means that a trial does not get access to\nthe results of trials running at the same time, which could reduce the\nquality of the overall optimization.\n\nEach trial will use the same scale tier and machine types.\n\nDefaults to one.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__GetConfigResponse": {
|
| - "description": "Returns service account information associated with a project.",
|
| - "id": "GoogleCloudMlV1__GetConfigResponse",
|
| + "GoogleCloudMlV1__ListJobsResponse": {
|
| + "description": "Response message for the ListJobs method.",
|
| + "id": "GoogleCloudMlV1__ListJobsResponse",
|
| "properties": {
|
| - "serviceAccountProject": {
|
| - "description": "The project number for `service_account`.",
|
| - "format": "int64",
|
| - "type": "string"
|
| + "jobs": {
|
| + "description": "The list of jobs.",
|
| + "items": {
|
| + "$ref": "GoogleCloudMlV1__Job"
|
| + },
|
| + "type": "array"
|
| },
|
| - "serviceAccount": {
|
| - "description": "The service account Cloud ML uses to access resources in the project.",
|
| + "nextPageToken": {
|
| + "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__HyperparameterOutput": {
|
| - "description": "Represents the result of a single hyperparameter tuning trial from a\ntraining job. The TrainingOutput object that is returned on successful\ncompletion of a training job with hyperparameter tuning includes a list\nof HyperparameterOutput objects, one for each successful trial.",
|
| - "id": "GoogleCloudMlV1__HyperparameterOutput",
|
| - "properties": {
|
| - "hyperparameters": {
|
| + "GoogleCloudMlV1__SetDefaultVersionRequest": {
|
| + "description": "Request message for the SetDefaultVersion request.",
|
| + "id": "GoogleCloudMlV1__SetDefaultVersionRequest",
|
| + "properties": {},
|
| + "type": "object"
|
| + },
|
| + "GoogleLongrunning__Operation": {
|
| + "description": "This resource represents a long-running operation that is the result of a\nnetwork API call.",
|
| + "id": "GoogleLongrunning__Operation",
|
| + "properties": {
|
| + "error": {
|
| + "$ref": "GoogleRpc__Status",
|
| + "description": "The error result of the operation in case of failure or cancellation."
|
| + },
|
| + "metadata": {
|
| "additionalProperties": {
|
| - "type": "string"
|
| + "description": "Properties of the object. Contains field @type with type URL.",
|
| + "type": "any"
|
| },
|
| - "description": "The hyperparameters given to this trial.",
|
| + "description": "Service-specific metadata associated with the operation. It typically\ncontains progress information and common metadata such as create time.\nSome services might not provide such metadata. Any method that returns a\nlong-running operation should document the metadata type, if any.",
|
| "type": "object"
|
| },
|
| - "trialId": {
|
| - "description": "The trial id for these results.",
|
| - "type": "string"
|
| + "done": {
|
| + "description": "If the value is `false`, it means the operation is still in progress.\nIf true, the operation is completed, and either `error` or `response` is\navailable.",
|
| + "type": "boolean"
|
| },
|
| - "allMetrics": {
|
| - "description": "All recorded object metrics for this trial.",
|
| - "items": {
|
| - "$ref": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric"
|
| + "response": {
|
| + "additionalProperties": {
|
| + "description": "Properties of the object. Contains field @type with type URL.",
|
| + "type": "any"
|
| },
|
| - "type": "array"
|
| + "description": "The normal response of the operation in case of success. If the original\nmethod returns no data on success, such as `Delete`, the response is\n`google.protobuf.Empty`. If the original method is standard\n`Get`/`Create`/`Update`, the response should be the resource. For other\nmethods, the response should have the type `XxxResponse`, where `Xxx`\nis the original method name. For example, if the original method name\nis `TakeSnapshot()`, the inferred response type is\n`TakeSnapshotResponse`.",
|
| + "type": "object"
|
| },
|
| - "finalMetric": {
|
| - "$ref": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric",
|
| - "description": "The final objective metric seen for this trial."
|
| + "name": {
|
| + "description": "The server-assigned name, which is only unique within the same service that\noriginally returns it. If you use the default HTTP mapping, the\n`name` should have the format of `operations/some/unique/name`.",
|
| + "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__PredictionOutput": {
|
| - "description": "Represents results of a prediction job.",
|
| - "id": "GoogleCloudMlV1__PredictionOutput",
|
| + "GoogleCloudMlV1__Model": {
|
| + "description": "Represents a machine learning solution.\n\nA model can have multiple versions, each of which is a deployed, trained\nmodel ready to receive prediction requests. The model itself is just a\ncontainer.",
|
| + "id": "GoogleCloudMlV1__Model",
|
| "properties": {
|
| - "outputPath": {
|
| - "description": "The output Google Cloud Storage location provided at the job creation time.",
|
| + "description": {
|
| + "description": "Optional. The description specified for the model when it was created.",
|
| "type": "string"
|
| },
|
| - "nodeHours": {
|
| - "description": "Node hours used by the batch prediction job.",
|
| - "format": "double",
|
| - "type": "number"
|
| + "onlinePredictionLogging": {
|
| + "description": "Optional. If true, enables StackDriver Logging for online prediction.\nDefault is false.",
|
| + "type": "boolean"
|
| },
|
| - "predictionCount": {
|
| - "description": "The number of generated predictions.",
|
| - "format": "int64",
|
| - "type": "string"
|
| + "defaultVersion": {
|
| + "$ref": "GoogleCloudMlV1__Version",
|
| + "description": "Output only. The default version of the model. This version will be used to\nhandle prediction requests that do not specify a version.\n\nYou can change the default version by calling\n[projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault)."
|
| },
|
| - "errorCount": {
|
| - "description": "The number of data instances which resulted in errors.",
|
| - "format": "int64",
|
| + "regions": {
|
| + "description": "Optional. The list of regions where the model is going to be deployed.\nCurrently only one region per model is supported.\nDefaults to 'us-central1' if nothing is set.\nNote:\n* No matter where a model is deployed, it can always be accessed by\n users from anywhere, both for online and batch prediction.\n* The region for a batch prediction job is set by the region field when\n submitting the batch prediction job and does not take its value from\n this field.",
|
| + "items": {
|
| + "type": "string"
|
| + },
|
| + "type": "array"
|
| + },
|
| + "name": {
|
| + "description": "Required. The name specified for the model when it was created.\n\nThe model name must be unique within the project it is created in.",
|
| "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleLongrunning__ListOperationsResponse": {
|
| - "description": "The response message for Operations.ListOperations.",
|
| - "id": "GoogleLongrunning__ListOperationsResponse",
|
| + "GoogleProtobuf__Empty": {
|
| + "description": "A generic empty message that you can re-use to avoid defining duplicated\nempty messages in your APIs. A typical example is to use it as the request\nor the response type of an API method. For instance:\n\n service Foo {\n rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);\n }\n\nThe JSON representation for `Empty` is empty JSON object `{}`.",
|
| + "id": "GoogleProtobuf__Empty",
|
| + "properties": {},
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__CancelJobRequest": {
|
| + "description": "Request message for the CancelJob method.",
|
| + "id": "GoogleCloudMlV1__CancelJobRequest",
|
| + "properties": {},
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__ListVersionsResponse": {
|
| + "description": "Response message for the ListVersions method.",
|
| + "id": "GoogleCloudMlV1__ListVersionsResponse",
|
| "properties": {
|
| - "nextPageToken": {
|
| - "description": "The standard List next-page token.",
|
| - "type": "string"
|
| - },
|
| - "operations": {
|
| - "description": "A list of operations that matches the specified filter in the request.",
|
| + "versions": {
|
| + "description": "The list of versions.",
|
| "items": {
|
| - "$ref": "GoogleLongrunning__Operation"
|
| + "$ref": "GoogleCloudMlV1__Version"
|
| },
|
| "type": "array"
|
| + },
|
| + "nextPageToken": {
|
| + "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| + "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__ManualScaling": {
|
| + "GoogleCloudMlV1beta1__ManualScaling": {
|
| "description": "Options for manually scaling a model.",
|
| - "id": "GoogleCloudMlV1__ManualScaling",
|
| + "id": "GoogleCloudMlV1beta1__ManualScaling",
|
| "properties": {
|
| "nodes": {
|
| "description": "The number of nodes to allocate for this model. These nodes are always up,\nstarting from the time the model is deployed, so the cost of operating\nthis model will be proportional to nodes * number of hours since\ndeployment.",
|
| @@ -812,712 +843,681 @@
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__TrainingOutput": {
|
| - "description": "Represents results of a training job. Output only.",
|
| - "id": "GoogleCloudMlV1__TrainingOutput",
|
| + "GoogleRpc__Status": {
|
| + "description": "The `Status` type defines a logical error model that is suitable for different\nprogramming environments, including REST APIs and RPC APIs. It is used by\n[gRPC](https://github.com/grpc). The error model is designed to be:\n\n- Simple to use and understand for most users\n- Flexible enough to meet unexpected needs\n\n# Overview\n\nThe `Status` message contains three pieces of data: error code, error message,\nand error details. The error code should be an enum value of\ngoogle.rpc.Code, but it may accept additional error codes if needed. The\nerror message should be a developer-facing English message that helps\ndevelopers *understand* and *resolve* the error. If a localized user-facing\nerror message is needed, put the localized message in the error details or\nlocalize it in the client. The optional error details may contain arbitrary\ninformation about the error. There is a predefined set of error detail types\nin the package `google.rpc` which can be used for common error conditions.\n\n# Language mapping\n\nThe `Status` message is the logical representation of the error model, but it\nis not necessarily the actual wire format. When the `Status` message is\nexposed in different client libraries and different wire protocols, it can be\nmapped differently. For example, it will likely be mapped to some exceptions\nin Java, but more likely mapped to some error codes in C.\n\n# Other uses\n\nThe error model and the `Status` message can be used in a variety of\nenvironments, either with or without APIs, to provide a\nconsistent developer experience across different environments.\n\nExample uses of this error model include:\n\n- Partial errors. If a service needs to return partial errors to the client,\n it may embed the `Status` in the normal response to indicate the partial\n errors.\n\n- Workflow errors. A typical workflow has multiple steps. Each step may\n have a `Status` message for error reporting purpose.\n\n- Batch operations. If a client uses batch request and batch response, the\n `Status` message should be used directly inside batch response, one for\n each error sub-response.\n\n- Asynchronous operations. If an API call embeds asynchronous operation\n results in its response, the status of those operations should be\n represented directly using the `Status` message.\n\n- Logging. If some API errors are stored in logs, the message `Status` could\n be used directly after any stripping needed for security/privacy reasons.",
|
| + "id": "GoogleRpc__Status",
|
| "properties": {
|
| - "completedTrialCount": {
|
| - "description": "The number of hyperparameter tuning trials that completed successfully.\nOnly set for hyperparameter tuning jobs.",
|
| - "format": "int64",
|
| - "type": "string"
|
| - },
|
| - "isHyperparameterTuningJob": {
|
| - "description": "Whether this job is a hyperparameter tuning job.",
|
| - "type": "boolean"
|
| + "code": {
|
| + "description": "The status code, which should be an enum value of google.rpc.Code.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| },
|
| - "consumedMLUnits": {
|
| - "description": "The amount of ML units consumed by the job.",
|
| - "format": "double",
|
| - "type": "number"
|
| + "message": {
|
| + "description": "A developer-facing error message, which should be in English. Any\nuser-facing error message should be localized and sent in the\ngoogle.rpc.Status.details field, or localized by the client.",
|
| + "type": "string"
|
| },
|
| - "trials": {
|
| - "description": "Results for individual Hyperparameter trials.\nOnly set for hyperparameter tuning jobs.",
|
| + "details": {
|
| + "description": "A list of messages that carry the error details. There will be a\ncommon set of message types for APIs to use.",
|
| "items": {
|
| - "$ref": "GoogleCloudMlV1__HyperparameterOutput"
|
| + "additionalProperties": {
|
| + "description": "Properties of the object. Contains field @type with type URL.",
|
| + "type": "any"
|
| + },
|
| + "type": "object"
|
| },
|
| "type": "array"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__PredictRequest": {
|
| - "description": "Request for predictions to be issued against a trained model.\n\nThe body of the request is a single JSON object with a single top-level\nfield:\n\n<dl>\n <dt>instances</dt>\n <dd>A JSON array containing values representing the instances to use for\n prediction.</dd>\n</dl>\n\nThe structure of each element of the instances list is determined by your\nmodel's input definition. Instances can include named inputs or can contain\nonly unlabeled values.\n\nNot all data includes named inputs. Some instances will be simple\nJSON values (boolean, number, or string). However, instances are often lists\nof simple values, or complex nested lists. Here are some examples of request\nbodies:\n\nCSV data with each row encoded as a string value:\n<pre>\n{\"instances\": [\"1.0,true,\\\\\"x\\\\\"\", \"-2.0,false,\\\\\"y\\\\\"\"]}\n</pre>\nPlain text:\n<pre>\n{\"instances\": [\"the quick brown fox\", \"la bruja le dio\"]}\n</pre>\nSentences encoded as lists of words (vectors of strings):\n<pre>\n{\n \"instances\": [\n [\"the\",\"quick\",\"brown\"],\n [\"la\",\"bruja\",\"le\"],\n ...\n ]\n}\n</pre>\nFloating point scalar values:\n<pre>\n{\"instances\": [0.0, 1.1, 2.2]}\n</pre>\nVectors of integers:\n<pre>\n{\n \"instances\": [\n [0, 1, 2],\n [3, 4, 5],\n ...\n ]\n}\n</pre>\nTensors (in this case, two-dimensional tensors):\n<pre>\n{\n \"instances\": [\n [\n [0, 1, 2],\n [3, 4, 5]\n ],\n ...\n ]\n}\n</pre>\nImages can be represented different ways. In this encoding scheme the first\ntwo dimensions represent the rows and columns of the image, and the third\ncontains lists (vectors) of the R, G, and B values for each pixel.\n<pre>\n{\n \"instances\": [\n [\n [\n [138, 30, 66],\n [130, 20, 56],\n ...\n ],\n [\n [126, 38, 61],\n [122, 24, 57],\n ...\n ],\n ...\n ],\n ...\n ]\n}\n</pre>\nJSON strings must be encoded as UTF-8. To send binary data, you must\nbase64-encode the data and mark it as binary. To mark a JSON string\nas binary, replace it with a JSON object with a single attribute named `b64`:\n<pre>{\"b64\": \"...\"} </pre>\nFor example:\n\nTwo Serialized tf.Examples (fake data, for illustrative purposes only):\n<pre>\n{\"instances\": [{\"b64\": \"X5ad6u\"}, {\"b64\": \"IA9j4nx\"}]}\n</pre>\nTwo JPEG image byte strings (fake data, for illustrative purposes only):\n<pre>\n{\"instances\": [{\"b64\": \"ASa8asdf\"}, {\"b64\": \"JLK7ljk3\"}]}\n</pre>\nIf your data includes named references, format each instance as a JSON object\nwith the named references as the keys:\n\nJSON input data to be preprocessed:\n<pre>\n{\n \"instances\": [\n {\n \"a\": 1.0,\n \"b\": true,\n \"c\": \"x\"\n },\n {\n \"a\": -2.0,\n \"b\": false,\n \"c\": \"y\"\n }\n ]\n}\n</pre>\nSome models have an underlying TensorFlow graph that accepts multiple input\ntensors. In this case, you should use the names of JSON name/value pairs to\nidentify the input tensors, as shown in the following exmaples:\n\nFor a graph with input tensor aliases \"tag\" (string) and \"image\"\n(base64-encoded string):\n<pre>\n{\n \"instances\": [\n {\n \"tag\": \"beach\",\n \"image\": {\"b64\": \"ASa8asdf\"}\n },\n {\n \"tag\": \"car\",\n \"image\": {\"b64\": \"JLK7ljk3\"}\n }\n ]\n}\n</pre>\nFor a graph with input tensor aliases \"tag\" (string) and \"image\"\n(3-dimensional array of 8-bit ints):\n<pre>\n{\n \"instances\": [\n {\n \"tag\": \"beach\",\n \"image\": [\n [\n [138, 30, 66],\n [130, 20, 56],\n ...\n ],\n [\n [126, 38, 61],\n [122, 24, 57],\n ...\n ],\n ...\n ]\n },\n {\n \"tag\": \"car\",\n \"image\": [\n [\n [255, 0, 102],\n [255, 0, 97],\n ...\n ],\n [\n [254, 1, 101],\n [254, 2, 93],\n ...\n ],\n ...\n ]\n },\n ...\n ]\n}\n</pre>\nIf the call is successful, the response body will contain one prediction\nentry per instance in the request body. If prediction fails for any\ninstance, the response body will contain no predictions and will contian\na single error entry instead.",
|
| - "id": "GoogleCloudMlV1__PredictRequest",
|
| + "GoogleCloudMlV1__ListModelsResponse": {
|
| + "description": "Response message for the ListModels method.",
|
| + "id": "GoogleCloudMlV1__ListModelsResponse",
|
| "properties": {
|
| - "httpBody": {
|
| - "$ref": "GoogleApi__HttpBody",
|
| - "description": "\nRequired. The prediction request body."
|
| + "models": {
|
| + "description": "The list of models.",
|
| + "items": {
|
| + "$ref": "GoogleCloudMlV1__Model"
|
| + },
|
| + "type": "array"
|
| + },
|
| + "nextPageToken": {
|
| + "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| + "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric": {
|
| - "description": "An observed value of a metric.",
|
| - "id": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric",
|
| + "GoogleCloudMlV1__TrainingInput": {
|
| + "description": "Represents input parameters for a training job.",
|
| + "id": "GoogleCloudMlV1__TrainingInput",
|
| "properties": {
|
| - "trainingStep": {
|
| - "description": "The global training step for this metric.",
|
| + "hyperparameters": {
|
| + "$ref": "GoogleCloudMlV1__HyperparameterSpec",
|
| + "description": "Optional. The set of Hyperparameters to tune."
|
| + },
|
| + "parameterServerCount": {
|
| + "description": "Optional. The number of parameter server replicas to use for the training\njob. Each replica in the cluster will be of the type specified in\n`parameter_server_type`.\n\nThis value can only be used when `scale_tier` is set to `CUSTOM`.If you\nset this value, you must also set `parameter_server_type`.",
|
| "format": "int64",
|
| "type": "string"
|
| },
|
| - "objectiveValue": {
|
| - "description": "The objective value at this training step.",
|
| - "format": "double",
|
| - "type": "number"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__Version": {
|
| - "description": "Represents a version of the model.\n\nEach version is a trained model deployed in the cloud, ready to handle\nprediction requests. A model can have multiple versions. You can get\ninformation about all of the versions of a given model by calling\n[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list).",
|
| - "id": "GoogleCloudMlV1__Version",
|
| - "properties": {
|
| - "runtimeVersion": {
|
| - "description": "Optional. The Google Cloud ML runtime version to use for this deployment.\nIf not set, Google Cloud ML will choose a version.",
|
| + "packageUris": {
|
| + "description": "Required. The Google Cloud Storage location of the packages with\nthe training program and any additional dependencies.\nThe maximum number of package URIs is 100.",
|
| + "items": {
|
| + "type": "string"
|
| + },
|
| + "type": "array"
|
| + },
|
| + "workerCount": {
|
| + "description": "Optional. The number of worker replicas to use for the training job. Each\nreplica in the cluster will be of the type specified in `worker_type`.\n\nThis value can only be used when `scale_tier` is set to `CUSTOM`. If you\nset this value, you must also set `worker_type`.",
|
| + "format": "int64",
|
| "type": "string"
|
| },
|
| - "lastUseTime": {
|
| - "description": "Output only. The time the version was last used for prediction.",
|
| - "format": "google-datetime",
|
| + "masterType": {
|
| + "description": "Optional. Specifies the type of virtual machine to use for your training\njob's master worker.\n\nThe following types are supported:\n\n<dl>\n <dt>standard</dt>\n <dd>\n A basic machine configuration suitable for training simple models with\n small to moderate datasets.\n </dd>\n <dt>large_model</dt>\n <dd>\n A machine with a lot of memory, specially suited for parameter servers\n when your model is large (having many hidden layers or layers with very\n large numbers of nodes).\n </dd>\n <dt>complex_model_s</dt>\n <dd>\n A machine suitable for the master and workers of the cluster when your\n model requires more computation than the standard machine can handle\n satisfactorily.\n </dd>\n <dt>complex_model_m</dt>\n <dd>\n A machine with roughly twice the number of cores and roughly double the\n memory of <code suppresswarning=\"true\">complex_model_s</code>.\n </dd>\n <dt>complex_model_l</dt>\n <dd>\n A machine with roughly twice the number of cores and roughly double the\n memory of <code suppresswarning=\"true\">complex_model_m</code>.\n </dd>\n <dt>standard_gpu</dt>\n <dd>\n A machine equivalent to <code suppresswarning=\"true\">standard</code> that\n also includes a\n <a href=\"/ml-engine/docs/how-tos/using-gpus\">\n GPU that you can use in your trainer</a>.\n </dd>\n <dt>complex_model_m_gpu</dt>\n <dd>\n A machine equivalent to\n <code suppresswarning=\"true\">coplex_model_m</code> that also includes\n four GPUs.\n </dd>\n</dl>\n\nYou must set this value when `scaleTier` is set to `CUSTOM`.",
|
| "type": "string"
|
| },
|
| - "description": {
|
| - "description": "Optional. The description specified for the version when it was created.",
|
| + "runtimeVersion": {
|
| + "description": "Optional. The Google Cloud ML runtime version to use for training. If not\nset, Google Cloud ML will choose the latest stable version.",
|
| "type": "string"
|
| },
|
| - "deploymentUri": {
|
| - "description": "Required. The Google Cloud Storage location of the trained model used to\ncreate the version. See the\n[overview of model deployment](/ml-engine/docs/concepts/deployment-overview) for\nmore informaiton.\n\nWhen passing Version to\n[projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create)\nthe model service uses the specified location as the source of the model.\nOnce deployed, the model version is hosted by the prediction service, so\nthis location is useful only as a historical record.",
|
| + "pythonModule": {
|
| + "description": "Required. The Python module name to run after installing the packages.",
|
| "type": "string"
|
| },
|
| - "isDefault": {
|
| - "description": "Output only. If true, this version will be used to handle prediction\nrequests that do not specify a version.\n\nYou can change the default version by calling\n[projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).",
|
| - "type": "boolean"
|
| + "args": {
|
| + "description": "Optional. Command line arguments to pass to the program.",
|
| + "items": {
|
| + "type": "string"
|
| + },
|
| + "type": "array"
|
| },
|
| - "createTime": {
|
| - "description": "Output only. The time the version was created.",
|
| - "format": "google-datetime",
|
| + "workerType": {
|
| + "description": "Optional. Specifies the type of virtual machine to use for your training\njob's worker nodes.\n\nThe supported values are the same as those described in the entry for\n`masterType`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`workerCount` is greater than zero.",
|
| "type": "string"
|
| },
|
| - "manualScaling": {
|
| - "$ref": "GoogleCloudMlV1__ManualScaling",
|
| - "description": "Optional. Manually select the number of nodes to use for serving the\nmodel. If unset (i.e., by default), the number of nodes used to serve\nthe model automatically scales with traffic. However, care should be\ntaken to ramp up traffic according to the model's ability to scale. If\nyour model needs to handle bursts of traffic beyond it's ability to\nscale, it is recommended you set this field appropriately."
|
| + "region": {
|
| + "description": "Required. The Google Compute Engine region to run the training job in.",
|
| + "type": "string"
|
| },
|
| - "name": {
|
| - "description": "Required.The name specified for the version when it was created.\n\nThe version name must be unique within the model it is created in.",
|
| + "parameterServerType": {
|
| + "description": "Optional. Specifies the type of virtual machine to use for your training\njob's parameter server.\n\nThe supported values are the same as those described in the entry for\n`master_type`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`parameter_server_count` is greater than zero.",
|
| "type": "string"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__ParameterSpec": {
|
| - "description": "Represents a single hyperparameter to optimize.",
|
| - "id": "GoogleCloudMlV1__ParameterSpec",
|
| - "properties": {
|
| - "minValue": {
|
| - "description": "Required if type is `DOUBLE` or `INTEGER`. This field\nshould be unset if type is `CATEGORICAL`. This value should be integers if\ntype is INTEGER.",
|
| - "format": "double",
|
| - "type": "number"
|
| },
|
| - "discreteValues": {
|
| - "description": "Required if type is `DISCRETE`.\nA list of feasible points.\nThe list should be in strictly increasing order. For instance, this\nparameter might have possible settings of 1.5, 2.5, and 4.0. This list\nshould not contain more than 1,000 values.",
|
| - "items": {
|
| - "format": "double",
|
| - "type": "number"
|
| - },
|
| - "type": "array"
|
| - },
|
| - "maxValue": {
|
| - "description": "Required if typeis `DOUBLE` or `INTEGER`. This field\nshould be unset if type is `CATEGORICAL`. This value should be integers if\ntype is `INTEGER`.",
|
| - "format": "double",
|
| - "type": "number"
|
| - },
|
| - "scaleType": {
|
| - "description": "Optional. How the parameter should be scaled to the hypercube.\nLeave unset for categorical parameters.\nSome kind of scaling is strongly recommended for real or integral\nparameters (e.g., `UNIT_LINEAR_SCALE`).",
|
| - "enum": [
|
| - "NONE",
|
| - "UNIT_LINEAR_SCALE",
|
| - "UNIT_LOG_SCALE",
|
| - "UNIT_REVERSE_LOG_SCALE"
|
| - ],
|
| - "enumDescriptions": [
|
| - "By default, no scaling is applied.",
|
| - "Scales the feasible space to (0, 1) linearly.",
|
| - "Scales the feasible space logarithmically to (0, 1). The entire feasible\nspace must be strictly positive.",
|
| - "Scales the feasible space \"reverse\" logarithmically to (0, 1). The result\nis that values close to the top of the feasible space are spread out more\nthan points near the bottom. The entire feasible space must be strictly\npositive."
|
| - ],
|
| - "type": "string"
|
| - },
|
| - "type": {
|
| - "description": "Required. The type of the parameter.",
|
| + "scaleTier": {
|
| + "description": "Required. Specifies the machine types, the number of replicas for workers\nand parameter servers.",
|
| "enum": [
|
| - "PARAMETER_TYPE_UNSPECIFIED",
|
| - "DOUBLE",
|
| - "INTEGER",
|
| - "CATEGORICAL",
|
| - "DISCRETE"
|
| + "BASIC",
|
| + "STANDARD_1",
|
| + "PREMIUM_1",
|
| + "BASIC_GPU",
|
| + "CUSTOM"
|
| ],
|
| "enumDescriptions": [
|
| - "You must specify a valid type. Using this unspecified type will result in\nan error.",
|
| - "Type for real-valued parameters.",
|
| - "Type for integral parameters.",
|
| - "The parameter is categorical, with a value chosen from the categories\nfield.",
|
| - "The parameter is real valued, with a fixed set of feasible points. If\n`type==DISCRETE`, feasible_points must be provided, and\n{`min_value`, `max_value`} will be ignored."
|
| + "A single worker instance. This tier is suitable for learning how to use\nCloud ML, and for experimenting with new models using small datasets.",
|
| + "Many workers and a few parameter servers.",
|
| + "A large number of workers with many parameter servers.",
|
| + "A single worker instance [with a GPU](/ml-engine/docs/how-tos/using-gpus).",
|
| + "The CUSTOM tier is not a set tier, but rather enables you to use your\nown cluster specification. When you use this tier, set values to\nconfigure your processing cluster according to these guidelines:\n\n* You _must_ set `TrainingInput.masterType` to specify the type\n of machine to use for your master node. This is the only required\n setting.\n\n* You _may_ set `TrainingInput.workerCount` to specify the number of\n workers to use. If you specify one or more workers, you _must_ also\n set `TrainingInput.workerType` to specify the type of machine to use\n for your worker nodes.\n\n* You _may_ set `TrainingInput.parameterServerCount` to specify the\n number of parameter servers to use. If you specify one or more\n parameter servers, you _must_ also set\n `TrainingInput.parameterServerType` to specify the type of machine to\n use for your parameter servers.\n\nNote that all of your workers must use the same machine type, which can\nbe different from your parameter server type and master type. Your\nparameter servers must likewise use the same machine type, which can be\ndifferent from your worker type and master type."
|
| ],
|
| "type": "string"
|
| },
|
| - "parameterName": {
|
| - "description": "Required. The parameter name must be unique amongst all ParameterConfigs in\na HyperparameterSpec message. E.g., \"learning_rate\".",
|
| + "jobDir": {
|
| + "description": "Optional. A Google Cloud Storage path in which to store training outputs\nand other data needed for training. This path is passed to your TensorFlow\nprogram as the 'job_dir' command-line argument. The benefit of specifying\nthis field is that Cloud ML validates the path for use in training.",
|
| "type": "string"
|
| - },
|
| - "categoricalValues": {
|
| - "description": "Required if type is `CATEGORICAL`. The list of possible categories.",
|
| - "items": {
|
| - "type": "string"
|
| - },
|
| - "type": "array"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__PredictionInput": {
|
| - "description": "Represents input parameters for a prediction job.",
|
| - "id": "GoogleCloudMlV1__PredictionInput",
|
| + "GoogleCloudMlV1__Job": {
|
| + "description": "Represents a training or prediction job.",
|
| + "id": "GoogleCloudMlV1__Job",
|
| "properties": {
|
| - "modelName": {
|
| - "description": "Use this field if you want to use the default version for the specified\nmodel. The string must use the following format:\n\n`\"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>\"`",
|
| + "endTime": {
|
| + "description": "Output only. When the job processing was completed.",
|
| + "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "outputPath": {
|
| - "description": "Required. The output Google Cloud Storage location.",
|
| + "startTime": {
|
| + "description": "Output only. When the job processing was started.",
|
| + "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "uri": {
|
| - "description": "Use this field if you want to specify a Google Cloud Storage path for\nthe model to use.",
|
| - "type": "string"
|
| + "predictionOutput": {
|
| + "$ref": "GoogleCloudMlV1__PredictionOutput",
|
| + "description": "The current prediction job result."
|
| },
|
| - "maxWorkerCount": {
|
| - "description": "Optional. The maximum number of workers to be used for parallel processing.\nDefaults to 10 if not specified.",
|
| - "format": "int64",
|
| + "trainingOutput": {
|
| + "$ref": "GoogleCloudMlV1__TrainingOutput",
|
| + "description": "The current training job result."
|
| + },
|
| + "trainingInput": {
|
| + "$ref": "GoogleCloudMlV1__TrainingInput",
|
| + "description": "Input parameters to create a training job."
|
| + },
|
| + "createTime": {
|
| + "description": "Output only. When the job was created.",
|
| + "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "dataFormat": {
|
| - "description": "Required. The format of the input data files.",
|
| + "predictionInput": {
|
| + "$ref": "GoogleCloudMlV1__PredictionInput",
|
| + "description": "Input parameters to create a prediction job."
|
| + },
|
| + "state": {
|
| + "description": "Output only. The detailed state of a job.",
|
| "enum": [
|
| - "DATA_FORMAT_UNSPECIFIED",
|
| - "TEXT",
|
| - "TF_RECORD",
|
| - "TF_RECORD_GZIP"
|
| + "STATE_UNSPECIFIED",
|
| + "QUEUED",
|
| + "PREPARING",
|
| + "RUNNING",
|
| + "SUCCEEDED",
|
| + "FAILED",
|
| + "CANCELLING",
|
| + "CANCELLED"
|
| ],
|
| "enumDescriptions": [
|
| - "Unspecified format.",
|
| - "The source file is a text file with instances separated by the\nnew-line character.",
|
| - "The source file is a TFRecord file.",
|
| - "The source file is a GZIP-compressed TFRecord file."
|
| + "The job state is unspecified.",
|
| + "The job has been just created and processing has not yet begun.",
|
| + "The service is preparing to run the job.",
|
| + "The job is in progress.",
|
| + "The job completed successfully.",
|
| + "The job failed.\n`error_message` should contain the details of the failure.",
|
| + "The job is being cancelled.\n`error_message` should describe the reason for the cancellation.",
|
| + "The job has been cancelled.\n`error_message` should describe the reason for the cancellation."
|
| ],
|
| "type": "string"
|
| },
|
| - "runtimeVersion": {
|
| - "description": "Optional. The Google Cloud ML runtime version to use for this batch\nprediction. If not set, Google Cloud ML will pick the runtime version used\nduring the CreateVersion request for this model version, or choose the\nlatest stable version when model version information is not available\nsuch as when the model is specified by uri.",
|
| - "type": "string"
|
| - },
|
| - "inputPaths": {
|
| - "description": "Required. The Google Cloud Storage location of the input data files.\nMay contain wildcards.",
|
| - "items": {
|
| - "type": "string"
|
| - },
|
| - "type": "array"
|
| - },
|
| - "region": {
|
| - "description": "Required. The Google Compute Engine region to run the prediction job in.",
|
| + "jobId": {
|
| + "description": "Required. The user-specified id of the job.",
|
| "type": "string"
|
| },
|
| - "versionName": {
|
| - "description": "Use this field if you want to specify a version of the model to use. The\nstring is formatted the same way as `model_version`, with the addition\nof the version information:\n\n`\"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>\"`",
|
| + "errorMessage": {
|
| + "description": "Output only. The details of a failure or a cancellation.",
|
| "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__OperationMetadata": {
|
| - "description": "Represents the metadata of the long-running operation.",
|
| - "id": "GoogleCloudMlV1__OperationMetadata",
|
| + "GoogleApi__HttpBody": {
|
| + "description": "Message that represents an arbitrary HTTP body. It should only be used for\npayload formats that can't be represented as JSON, such as raw binary or\nan HTML page.\n\n\nThis message can be used both in streaming and non-streaming API methods in\nthe request as well as the response.\n\nIt can be used as a top-level request field, which is convenient if one\nwants to extract parameters from either the URL or HTTP template into the\nrequest fields and also want access to the raw HTTP body.\n\nExample:\n\n message GetResourceRequest {\n // A unique request id.\n string request_id = 1;\n\n // The raw HTTP body is bound to this field.\n google.api.HttpBody http_body = 2;\n }\n\n service ResourceService {\n rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);\n rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);\n }\n\nExample with streaming methods:\n\n service CaldavService {\n rpc GetCalendar(stream google.api.HttpBody)\n returns (stream google.api.HttpBody);\n rpc UpdateCalendar(stream google.api.HttpBody)\n returns (stream google.api.HttpBody);\n }\n\nUse of this type only changes how the request and response bodies are\nhandled, all other features will continue to work unchanged.",
|
| + "id": "GoogleApi__HttpBody",
|
| "properties": {
|
| - "isCancellationRequested": {
|
| - "description": "Indicates whether a request to cancel this operation has been made.",
|
| - "type": "boolean"
|
| - },
|
| - "createTime": {
|
| - "description": "The time the operation was submitted.",
|
| - "format": "google-datetime",
|
| - "type": "string"
|
| - },
|
| - "modelName": {
|
| - "description": "Contains the name of the model associated with the operation.",
|
| + "data": {
|
| + "description": "HTTP body binary data.",
|
| + "format": "byte",
|
| "type": "string"
|
| },
|
| - "version": {
|
| - "$ref": "GoogleCloudMlV1__Version",
|
| - "description": "Contains the version associated with the operation."
|
| - },
|
| - "endTime": {
|
| - "description": "The time operation processing completed.",
|
| - "format": "google-datetime",
|
| + "contentType": {
|
| + "description": "The HTTP Content-Type string representing the content type of the body.",
|
| "type": "string"
|
| - },
|
| - "operationType": {
|
| - "description": "The operation type.",
|
| - "enum": [
|
| - "OPERATION_TYPE_UNSPECIFIED",
|
| - "CREATE_VERSION",
|
| - "DELETE_VERSION",
|
| - "DELETE_MODEL"
|
| - ],
|
| - "enumDescriptions": [
|
| - "Unspecified operation type.",
|
| - "An operation to create a new version.",
|
| - "An operation to delete an existing version.",
|
| - "An operation to delete an existing model."
|
| - ],
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__GetConfigResponse": {
|
| + "description": "Returns service account information associated with a project.",
|
| + "id": "GoogleCloudMlV1__GetConfigResponse",
|
| + "properties": {
|
| + "serviceAccountProject": {
|
| + "description": "The project number for `service_account`.",
|
| + "format": "int64",
|
| "type": "string"
|
| },
|
| - "startTime": {
|
| - "description": "The time operation processing started.",
|
| - "format": "google-datetime",
|
| + "serviceAccount": {
|
| + "description": "The service account Cloud ML uses to access resources in the project.",
|
| "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1beta1__OperationMetadata": {
|
| - "description": "Represents the metadata of the long-running operation.",
|
| - "id": "GoogleCloudMlV1beta1__OperationMetadata",
|
| + "GoogleCloudMlV1beta1__Version": {
|
| + "description": "Represents a version of the model.\n\nEach version is a trained model deployed in the cloud, ready to handle\nprediction requests. A model can have multiple versions. You can get\ninformation about all of the versions of a given model by calling\n[projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.models.versions/list).",
|
| + "id": "GoogleCloudMlV1beta1__Version",
|
| "properties": {
|
| - "createTime": {
|
| - "description": "The time the operation was submitted.",
|
| + "lastUseTime": {
|
| + "description": "Output only. The time the version was last used for prediction.",
|
| "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "modelName": {
|
| - "description": "Contains the name of the model associated with the operation.",
|
| + "runtimeVersion": {
|
| + "description": "Optional. The Google Cloud ML runtime version to use for this deployment.\nIf not set, Google Cloud ML will choose a version.",
|
| "type": "string"
|
| },
|
| - "version": {
|
| - "$ref": "GoogleCloudMlV1beta1__Version",
|
| - "description": "Contains the version associated with the operation."
|
| - },
|
| - "endTime": {
|
| - "description": "The time operation processing completed.",
|
| - "format": "google-datetime",
|
| + "description": {
|
| + "description": "Optional. The description specified for the version when it was created.",
|
| "type": "string"
|
| },
|
| - "operationType": {
|
| - "description": "The operation type.",
|
| - "enum": [
|
| - "OPERATION_TYPE_UNSPECIFIED",
|
| - "CREATE_VERSION",
|
| - "DELETE_VERSION",
|
| - "DELETE_MODEL"
|
| - ],
|
| - "enumDescriptions": [
|
| - "Unspecified operation type.",
|
| - "An operation to create a new version.",
|
| - "An operation to delete an existing version.",
|
| - "An operation to delete an existing model."
|
| - ],
|
| + "deploymentUri": {
|
| + "description": "Required. The Google Cloud Storage location of the trained model used to\ncreate the version. See the\n[overview of model\ndeployment](/ml-engine/docs/concepts/deployment-overview) for more\ninformaiton.\n\nWhen passing Version to\n[projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.models.versions/create)\nthe model service uses the specified location as the source of the model.\nOnce deployed, the model version is hosted by the prediction service, so\nthis location is useful only as a historical record.\nThe total number of model files can't exceed 1000.",
|
| "type": "string"
|
| },
|
| - "startTime": {
|
| - "description": "The time operation processing started.",
|
| + "isDefault": {
|
| + "description": "Output only. If true, this version will be used to handle prediction\nrequests that do not specify a version.\n\nYou can change the default version by calling\n[projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/projects.models.versions/setDefault).",
|
| + "type": "boolean"
|
| + },
|
| + "createTime": {
|
| + "description": "Output only. The time the version was created.",
|
| "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "isCancellationRequested": {
|
| - "description": "Indicates whether a request to cancel this operation has been made.",
|
| - "type": "boolean"
|
| + "manualScaling": {
|
| + "$ref": "GoogleCloudMlV1beta1__ManualScaling",
|
| + "description": "Optional. Manually select the number of nodes to use for serving the\nmodel. If unset (i.e., by default), the number of nodes used to serve\nthe model automatically scales with traffic. However, care should be\ntaken to ramp up traffic according to the model's ability to scale. If\nyour model needs to handle bursts of traffic beyond it's ability to\nscale, it is recommended you set this field appropriately."
|
| + },
|
| + "name": {
|
| + "description": "Required.The name specified for the version when it was created.\n\nThe version name must be unique within the model it is created in.",
|
| + "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__HyperparameterSpec": {
|
| - "description": "Represents a set of hyperparameters to optimize.",
|
| - "id": "GoogleCloudMlV1__HyperparameterSpec",
|
| + "GoogleCloudMlV1__HyperparameterOutput": {
|
| + "description": "Represents the result of a single hyperparameter tuning trial from a\ntraining job. The TrainingOutput object that is returned on successful\ncompletion of a training job with hyperparameter tuning includes a list\nof HyperparameterOutput objects, one for each successful trial.",
|
| + "id": "GoogleCloudMlV1__HyperparameterOutput",
|
| "properties": {
|
| - "params": {
|
| - "description": "Required. The set of parameters to tune.",
|
| + "allMetrics": {
|
| + "description": "All recorded object metrics for this trial.",
|
| "items": {
|
| - "$ref": "GoogleCloudMlV1__ParameterSpec"
|
| + "$ref": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric"
|
| },
|
| "type": "array"
|
| },
|
| - "maxTrials": {
|
| - "description": "Optional. How many training trials should be attempted to optimize\nthe specified hyperparameters.\n\nDefaults to one.",
|
| - "format": "int32",
|
| - "type": "integer"
|
| - },
|
| - "maxParallelTrials": {
|
| - "description": "Optional. The number of training trials to run concurrently.\nYou can reduce the time it takes to perform hyperparameter tuning by adding\ntrials in parallel. However, each trail only benefits from the information\ngained in completed trials. That means that a trial does not get access to\nthe results of trials running at the same time, which could reduce the\nquality of the overall optimization.\n\nEach trial will use the same scale tier and machine types.\n\nDefaults to one.",
|
| - "format": "int32",
|
| - "type": "integer"
|
| + "finalMetric": {
|
| + "$ref": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric",
|
| + "description": "The final objective metric seen for this trial."
|
| },
|
| - "goal": {
|
| - "description": "Required. The type of goal to use for tuning. Available types are\n`MAXIMIZE` and `MINIMIZE`.\n\nDefaults to `MAXIMIZE`.",
|
| - "enum": [
|
| - "GOAL_TYPE_UNSPECIFIED",
|
| - "MAXIMIZE",
|
| - "MINIMIZE"
|
| - ],
|
| - "enumDescriptions": [
|
| - "Goal Type will default to maximize.",
|
| - "Maximize the goal metric.",
|
| - "Minimize the goal metric."
|
| - ],
|
| - "type": "string"
|
| + "hyperparameters": {
|
| + "additionalProperties": {
|
| + "type": "string"
|
| + },
|
| + "description": "The hyperparameters given to this trial.",
|
| + "type": "object"
|
| },
|
| - "hyperparameterMetricTag": {
|
| - "description": "Optional. The Tensorflow summary tag name to use for optimizing trials. For\ncurrent versions of Tensorflow, this tag name should exactly match what is\nshown in Tensorboard, including all scopes. For versions of Tensorflow\nprior to 0.12, this should be only the tag passed to tf.Summary.\nBy default, \"training/hptuning/metric\" will be used.",
|
| + "trialId": {
|
| + "description": "The trial id for these results.",
|
| "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__ListJobsResponse": {
|
| - "description": "Response message for the ListJobs method.",
|
| - "id": "GoogleCloudMlV1__ListJobsResponse",
|
| + "GoogleCloudMlV1__PredictionOutput": {
|
| + "description": "Represents results of a prediction job.",
|
| + "id": "GoogleCloudMlV1__PredictionOutput",
|
| "properties": {
|
| - "nextPageToken": {
|
| - "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| + "outputPath": {
|
| + "description": "The output Google Cloud Storage location provided at the job creation time.",
|
| "type": "string"
|
| },
|
| - "jobs": {
|
| - "description": "The list of jobs.",
|
| - "items": {
|
| - "$ref": "GoogleCloudMlV1__Job"
|
| - },
|
| - "type": "array"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__SetDefaultVersionRequest": {
|
| - "description": "Request message for the SetDefaultVersion request.",
|
| - "id": "GoogleCloudMlV1__SetDefaultVersionRequest",
|
| - "properties": {},
|
| - "type": "object"
|
| - },
|
| - "GoogleLongrunning__Operation": {
|
| - "description": "This resource represents a long-running operation that is the result of a\nnetwork API call.",
|
| - "id": "GoogleLongrunning__Operation",
|
| - "properties": {
|
| - "response": {
|
| - "additionalProperties": {
|
| - "description": "Properties of the object. Contains field @type with type URL.",
|
| - "type": "any"
|
| - },
|
| - "description": "The normal response of the operation in case of success. If the original\nmethod returns no data on success, such as `Delete`, the response is\n`google.protobuf.Empty`. If the original method is standard\n`Get`/`Create`/`Update`, the response should be the resource. For other\nmethods, the response should have the type `XxxResponse`, where `Xxx`\nis the original method name. For example, if the original method name\nis `TakeSnapshot()`, the inferred response type is\n`TakeSnapshotResponse`.",
|
| - "type": "object"
|
| + "nodeHours": {
|
| + "description": "Node hours used by the batch prediction job.",
|
| + "format": "double",
|
| + "type": "number"
|
| },
|
| - "name": {
|
| - "description": "The server-assigned name, which is only unique within the same service that\noriginally returns it. If you use the default HTTP mapping, the\n`name` should have the format of `operations/some/unique/name`.",
|
| + "predictionCount": {
|
| + "description": "The number of generated predictions.",
|
| + "format": "int64",
|
| "type": "string"
|
| },
|
| - "error": {
|
| - "$ref": "GoogleRpc__Status",
|
| - "description": "The error result of the operation in case of failure or cancellation."
|
| - },
|
| - "metadata": {
|
| - "additionalProperties": {
|
| - "description": "Properties of the object. Contains field @type with type URL.",
|
| - "type": "any"
|
| - },
|
| - "description": "Service-specific metadata associated with the operation. It typically\ncontains progress information and common metadata such as create time.\nSome services might not provide such metadata. Any method that returns a\nlong-running operation should document the metadata type, if any.",
|
| - "type": "object"
|
| - },
|
| - "done": {
|
| - "description": "If the value is `false`, it means the operation is still in progress.\nIf true, the operation is completed, and either `error` or `response` is\navailable.",
|
| - "type": "boolean"
|
| + "errorCount": {
|
| + "description": "The number of data instances which resulted in errors.",
|
| + "format": "int64",
|
| + "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__Model": {
|
| - "description": "Represents a machine learning solution.\n\nA model can have multiple versions, each of which is a deployed, trained\nmodel ready to receive prediction requests. The model itself is just a\ncontainer.",
|
| - "id": "GoogleCloudMlV1__Model",
|
| + "GoogleLongrunning__ListOperationsResponse": {
|
| + "description": "The response message for Operations.ListOperations.",
|
| + "id": "GoogleLongrunning__ListOperationsResponse",
|
| "properties": {
|
| - "regions": {
|
| - "description": "Optional. The list of regions where the model is going to be deployed.\nCurrently only one region per model is supported.\nDefaults to 'us-central1' if nothing is set.\nNote:\n* No matter where a model is deployed, it can always be accessed by\n users from anywhere, both for online and batch prediction.\n* The region for a batch prediction job is set by the region field when\n submitting the batch prediction job and does not take its value from\n this field.",
|
| + "operations": {
|
| + "description": "A list of operations that matches the specified filter in the request.",
|
| "items": {
|
| - "type": "string"
|
| + "$ref": "GoogleLongrunning__Operation"
|
| },
|
| "type": "array"
|
| },
|
| - "name": {
|
| - "description": "Required. The name specified for the model when it was created.\n\nThe model name must be unique within the project it is created in.",
|
| - "type": "string"
|
| - },
|
| - "description": {
|
| - "description": "Optional. The description specified for the model when it was created.",
|
| + "nextPageToken": {
|
| + "description": "The standard List next-page token.",
|
| "type": "string"
|
| - },
|
| - "onlinePredictionLogging": {
|
| - "description": "Optional. If true, enables StackDriver Logging for online prediction.\nDefault is false.",
|
| - "type": "boolean"
|
| - },
|
| - "defaultVersion": {
|
| - "$ref": "GoogleCloudMlV1__Version",
|
| - "description": "Output only. The default version of the model. This version will be used to\nhandle prediction requests that do not specify a version.\n\nYou can change the default version by calling\n[projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault)."
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleProtobuf__Empty": {
|
| - "description": "A generic empty message that you can re-use to avoid defining duplicated\nempty messages in your APIs. A typical example is to use it as the request\nor the response type of an API method. For instance:\n\n service Foo {\n rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);\n }\n\nThe JSON representation for `Empty` is empty JSON object `{}`.",
|
| - "id": "GoogleProtobuf__Empty",
|
| - "properties": {},
|
| + "GoogleCloudMlV1__ManualScaling": {
|
| + "description": "Options for manually scaling a model.",
|
| + "id": "GoogleCloudMlV1__ManualScaling",
|
| + "properties": {
|
| + "nodes": {
|
| + "description": "The number of nodes to allocate for this model. These nodes are always up,\nstarting from the time the model is deployed, so the cost of operating\nthis model will be proportional to nodes * number of hours since\ndeployment.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| + }
|
| + },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__ListVersionsResponse": {
|
| - "description": "Response message for the ListVersions method.",
|
| - "id": "GoogleCloudMlV1__ListVersionsResponse",
|
| + "GoogleCloudMlV1__TrainingOutput": {
|
| + "description": "Represents results of a training job. Output only.",
|
| + "id": "GoogleCloudMlV1__TrainingOutput",
|
| "properties": {
|
| - "versions": {
|
| - "description": "The list of versions.",
|
| + "completedTrialCount": {
|
| + "description": "The number of hyperparameter tuning trials that completed successfully.\nOnly set for hyperparameter tuning jobs.",
|
| + "format": "int64",
|
| + "type": "string"
|
| + },
|
| + "isHyperparameterTuningJob": {
|
| + "description": "Whether this job is a hyperparameter tuning job.",
|
| + "type": "boolean"
|
| + },
|
| + "consumedMLUnits": {
|
| + "description": "The amount of ML units consumed by the job.",
|
| + "format": "double",
|
| + "type": "number"
|
| + },
|
| + "trials": {
|
| + "description": "Results for individual Hyperparameter trials.\nOnly set for hyperparameter tuning jobs.",
|
| "items": {
|
| - "$ref": "GoogleCloudMlV1__Version"
|
| + "$ref": "GoogleCloudMlV1__HyperparameterOutput"
|
| },
|
| "type": "array"
|
| - },
|
| - "nextPageToken": {
|
| - "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| - "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__CancelJobRequest": {
|
| - "description": "Request message for the CancelJob method.",
|
| - "id": "GoogleCloudMlV1__CancelJobRequest",
|
| - "properties": {},
|
| + "GoogleCloudMlV1__PredictRequest": {
|
| + "description": "Request for predictions to be issued against a trained model.\n\nThe body of the request is a single JSON object with a single top-level\nfield:\n\n<dl>\n <dt>instances</dt>\n <dd>A JSON array containing values representing the instances to use for\n prediction.</dd>\n</dl>\n\nThe structure of each element of the instances list is determined by your\nmodel's input definition. Instances can include named inputs or can contain\nonly unlabeled values.\n\nNot all data includes named inputs. Some instances will be simple\nJSON values (boolean, number, or string). However, instances are often lists\nof simple values, or complex nested lists. Here are some examples of request\nbodies:\n\nCSV data with each row encoded as a string value:\n<pre>\n{\"instances\": [\"1.0,true,\\\\\"x\\\\\"\", \"-2.0,false,\\\\\"y\\\\\"\"]}\n</pre>\nPlain text:\n<pre>\n{\"instances\": [\"the quick brown fox\", \"la bruja le dio\"]}\n</pre>\nSentences encoded as lists of words (vectors of strings):\n<pre>\n{\n \"instances\": [\n [\"the\",\"quick\",\"brown\"],\n [\"la\",\"bruja\",\"le\"],\n ...\n ]\n}\n</pre>\nFloating point scalar values:\n<pre>\n{\"instances\": [0.0, 1.1, 2.2]}\n</pre>\nVectors of integers:\n<pre>\n{\n \"instances\": [\n [0, 1, 2],\n [3, 4, 5],\n ...\n ]\n}\n</pre>\nTensors (in this case, two-dimensional tensors):\n<pre>\n{\n \"instances\": [\n [\n [0, 1, 2],\n [3, 4, 5]\n ],\n ...\n ]\n}\n</pre>\nImages can be represented different ways. In this encoding scheme the first\ntwo dimensions represent the rows and columns of the image, and the third\ncontains lists (vectors) of the R, G, and B values for each pixel.\n<pre>\n{\n \"instances\": [\n [\n [\n [138, 30, 66],\n [130, 20, 56],\n ...\n ],\n [\n [126, 38, 61],\n [122, 24, 57],\n ...\n ],\n ...\n ],\n ...\n ]\n}\n</pre>\nJSON strings must be encoded as UTF-8. To send binary data, you must\nbase64-encode the data and mark it as binary. To mark a JSON string\nas binary, replace it with a JSON object with a single attribute named `b64`:\n<pre>{\"b64\": \"...\"} </pre>\nFor example:\n\nTwo Serialized tf.Examples (fake data, for illustrative purposes only):\n<pre>\n{\"instances\": [{\"b64\": \"X5ad6u\"}, {\"b64\": \"IA9j4nx\"}]}\n</pre>\nTwo JPEG image byte strings (fake data, for illustrative purposes only):\n<pre>\n{\"instances\": [{\"b64\": \"ASa8asdf\"}, {\"b64\": \"JLK7ljk3\"}]}\n</pre>\nIf your data includes named references, format each instance as a JSON object\nwith the named references as the keys:\n\nJSON input data to be preprocessed:\n<pre>\n{\n \"instances\": [\n {\n \"a\": 1.0,\n \"b\": true,\n \"c\": \"x\"\n },\n {\n \"a\": -2.0,\n \"b\": false,\n \"c\": \"y\"\n }\n ]\n}\n</pre>\nSome models have an underlying TensorFlow graph that accepts multiple input\ntensors. In this case, you should use the names of JSON name/value pairs to\nidentify the input tensors, as shown in the following exmaples:\n\nFor a graph with input tensor aliases \"tag\" (string) and \"image\"\n(base64-encoded string):\n<pre>\n{\n \"instances\": [\n {\n \"tag\": \"beach\",\n \"image\": {\"b64\": \"ASa8asdf\"}\n },\n {\n \"tag\": \"car\",\n \"image\": {\"b64\": \"JLK7ljk3\"}\n }\n ]\n}\n</pre>\nFor a graph with input tensor aliases \"tag\" (string) and \"image\"\n(3-dimensional array of 8-bit ints):\n<pre>\n{\n \"instances\": [\n {\n \"tag\": \"beach\",\n \"image\": [\n [\n [138, 30, 66],\n [130, 20, 56],\n ...\n ],\n [\n [126, 38, 61],\n [122, 24, 57],\n ...\n ],\n ...\n ]\n },\n {\n \"tag\": \"car\",\n \"image\": [\n [\n [255, 0, 102],\n [255, 0, 97],\n ...\n ],\n [\n [254, 1, 101],\n [254, 2, 93],\n ...\n ],\n ...\n ]\n },\n ...\n ]\n}\n</pre>\nIf the call is successful, the response body will contain one prediction\nentry per instance in the request body. If prediction fails for any\ninstance, the response body will contain no predictions and will contian\na single error entry instead.",
|
| + "id": "GoogleCloudMlV1__PredictRequest",
|
| + "properties": {
|
| + "httpBody": {
|
| + "$ref": "GoogleApi__HttpBody",
|
| + "description": "\nRequired. The prediction request body."
|
| + }
|
| + },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1beta1__ManualScaling": {
|
| - "description": "Options for manually scaling a model.",
|
| - "id": "GoogleCloudMlV1beta1__ManualScaling",
|
| + "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric": {
|
| + "description": "An observed value of a metric.",
|
| + "id": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric",
|
| "properties": {
|
| - "nodes": {
|
| - "description": "The number of nodes to allocate for this model. These nodes are always up,\nstarting from the time the model is deployed, so the cost of operating\nthis model will be proportional to nodes * number of hours since\ndeployment.",
|
| - "format": "int32",
|
| - "type": "integer"
|
| + "trainingStep": {
|
| + "description": "The global training step for this metric.",
|
| + "format": "int64",
|
| + "type": "string"
|
| + },
|
| + "objectiveValue": {
|
| + "description": "The objective value at this training step.",
|
| + "format": "double",
|
| + "type": "number"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleRpc__Status": {
|
| - "description": "The `Status` type defines a logical error model that is suitable for different\nprogramming environments, including REST APIs and RPC APIs. It is used by\n[gRPC](https://github.com/grpc). The error model is designed to be:\n\n- Simple to use and understand for most users\n- Flexible enough to meet unexpected needs\n\n# Overview\n\nThe `Status` message contains three pieces of data: error code, error message,\nand error details. The error code should be an enum value of\ngoogle.rpc.Code, but it may accept additional error codes if needed. The\nerror message should be a developer-facing English message that helps\ndevelopers *understand* and *resolve* the error. If a localized user-facing\nerror message is needed, put the localized message in the error details or\nlocalize it in the client. The optional error details may contain arbitrary\ninformation about the error. There is a predefined set of error detail types\nin the package `google.rpc` which can be used for common error conditions.\n\n# Language mapping\n\nThe `Status` message is the logical representation of the error model, but it\nis not necessarily the actual wire format. When the `Status` message is\nexposed in different client libraries and different wire protocols, it can be\nmapped differently. For example, it will likely be mapped to some exceptions\nin Java, but more likely mapped to some error codes in C.\n\n# Other uses\n\nThe error model and the `Status` message can be used in a variety of\nenvironments, either with or without APIs, to provide a\nconsistent developer experience across different environments.\n\nExample uses of this error model include:\n\n- Partial errors. If a service needs to return partial errors to the client,\n it may embed the `Status` in the normal response to indicate the partial\n errors.\n\n- Workflow errors. A typical workflow has multiple steps. Each step may\n have a `Status` message for error reporting purpose.\n\n- Batch operations. If a client uses batch request and batch response, the\n `Status` message should be used directly inside batch response, one for\n each error sub-response.\n\n- Asynchronous operations. If an API call embeds asynchronous operation\n results in its response, the status of those operations should be\n represented directly using the `Status` message.\n\n- Logging. If some API errors are stored in logs, the message `Status` could\n be used directly after any stripping needed for security/privacy reasons.",
|
| - "id": "GoogleRpc__Status",
|
| + "GoogleCloudMlV1__Version": {
|
| + "description": "Represents a version of the model.\n\nEach version is a trained model deployed in the cloud, ready to handle\nprediction requests. A model can have multiple versions. You can get\ninformation about all of the versions of a given model by calling\n[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list).",
|
| + "id": "GoogleCloudMlV1__Version",
|
| "properties": {
|
| - "details": {
|
| - "description": "A list of messages that carry the error details. There will be a\ncommon set of message types for APIs to use.",
|
| + "runtimeVersion": {
|
| + "description": "Optional. The Google Cloud ML runtime version to use for this deployment.\nIf not set, Google Cloud ML will choose a version.",
|
| + "type": "string"
|
| + },
|
| + "lastUseTime": {
|
| + "description": "Output only. The time the version was last used for prediction.",
|
| + "format": "google-datetime",
|
| + "type": "string"
|
| + },
|
| + "description": {
|
| + "description": "Optional. The description specified for the version when it was created.",
|
| + "type": "string"
|
| + },
|
| + "deploymentUri": {
|
| + "description": "Required. The Google Cloud Storage location of the trained model used to\ncreate the version. See the\n[overview of model\ndeployment](/ml-engine/docs/concepts/deployment-overview) for more\ninformaiton.\n\nWhen passing Version to\n[projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create)\nthe model service uses the specified location as the source of the model.\nOnce deployed, the model version is hosted by the prediction service, so\nthis location is useful only as a historical record.\nThe total number of model files can't exceed 1000.",
|
| + "type": "string"
|
| + },
|
| + "isDefault": {
|
| + "description": "Output only. If true, this version will be used to handle prediction\nrequests that do not specify a version.\n\nYou can change the default version by calling\n[projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.models.versions/setDefault).",
|
| + "type": "boolean"
|
| + },
|
| + "createTime": {
|
| + "description": "Output only. The time the version was created.",
|
| + "format": "google-datetime",
|
| + "type": "string"
|
| + },
|
| + "manualScaling": {
|
| + "$ref": "GoogleCloudMlV1__ManualScaling",
|
| + "description": "Optional. Manually select the number of nodes to use for serving the\nmodel. If unset (i.e., by default), the number of nodes used to serve\nthe model automatically scales with traffic. However, care should be\ntaken to ramp up traffic according to the model's ability to scale. If\nyour model needs to handle bursts of traffic beyond it's ability to\nscale, it is recommended you set this field appropriately."
|
| + },
|
| + "name": {
|
| + "description": "Required.The name specified for the version when it was created.\n\nThe version name must be unique within the model it is created in.",
|
| + "type": "string"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__ParameterSpec": {
|
| + "description": "Represents a single hyperparameter to optimize.",
|
| + "id": "GoogleCloudMlV1__ParameterSpec",
|
| + "properties": {
|
| + "minValue": {
|
| + "description": "Required if type is `DOUBLE` or `INTEGER`. This field\nshould be unset if type is `CATEGORICAL`. This value should be integers if\ntype is INTEGER.",
|
| + "format": "double",
|
| + "type": "number"
|
| + },
|
| + "discreteValues": {
|
| + "description": "Required if type is `DISCRETE`.\nA list of feasible points.\nThe list should be in strictly increasing order. For instance, this\nparameter might have possible settings of 1.5, 2.5, and 4.0. This list\nshould not contain more than 1,000 values.",
|
| "items": {
|
| - "additionalProperties": {
|
| - "description": "Properties of the object. Contains field @type with type URL.",
|
| - "type": "any"
|
| - },
|
| - "type": "object"
|
| + "format": "double",
|
| + "type": "number"
|
| },
|
| "type": "array"
|
| },
|
| - "code": {
|
| - "description": "The status code, which should be an enum value of google.rpc.Code.",
|
| - "format": "int32",
|
| - "type": "integer"
|
| + "scaleType": {
|
| + "description": "Optional. How the parameter should be scaled to the hypercube.\nLeave unset for categorical parameters.\nSome kind of scaling is strongly recommended for real or integral\nparameters (e.g., `UNIT_LINEAR_SCALE`).",
|
| + "enum": [
|
| + "NONE",
|
| + "UNIT_LINEAR_SCALE",
|
| + "UNIT_LOG_SCALE",
|
| + "UNIT_REVERSE_LOG_SCALE"
|
| + ],
|
| + "enumDescriptions": [
|
| + "By default, no scaling is applied.",
|
| + "Scales the feasible space to (0, 1) linearly.",
|
| + "Scales the feasible space logarithmically to (0, 1). The entire feasible\nspace must be strictly positive.",
|
| + "Scales the feasible space \"reverse\" logarithmically to (0, 1). The result\nis that values close to the top of the feasible space are spread out more\nthan points near the bottom. The entire feasible space must be strictly\npositive."
|
| + ],
|
| + "type": "string"
|
| + },
|
| + "maxValue": {
|
| + "description": "Required if typeis `DOUBLE` or `INTEGER`. This field\nshould be unset if type is `CATEGORICAL`. This value should be integers if\ntype is `INTEGER`.",
|
| + "format": "double",
|
| + "type": "number"
|
| },
|
| - "message": {
|
| - "description": "A developer-facing error message, which should be in English. Any\nuser-facing error message should be localized and sent in the\ngoogle.rpc.Status.details field, or localized by the client.",
|
| + "type": {
|
| + "description": "Required. The type of the parameter.",
|
| + "enum": [
|
| + "PARAMETER_TYPE_UNSPECIFIED",
|
| + "DOUBLE",
|
| + "INTEGER",
|
| + "CATEGORICAL",
|
| + "DISCRETE"
|
| + ],
|
| + "enumDescriptions": [
|
| + "You must specify a valid type. Using this unspecified type will result in\nan error.",
|
| + "Type for real-valued parameters.",
|
| + "Type for integral parameters.",
|
| + "The parameter is categorical, with a value chosen from the categories\nfield.",
|
| + "The parameter is real valued, with a fixed set of feasible points. If\n`type==DISCRETE`, feasible_points must be provided, and\n{`min_value`, `max_value`} will be ignored."
|
| + ],
|
| "type": "string"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__ListModelsResponse": {
|
| - "description": "Response message for the ListModels method.",
|
| - "id": "GoogleCloudMlV1__ListModelsResponse",
|
| - "properties": {
|
| - "nextPageToken": {
|
| - "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| + },
|
| + "parameterName": {
|
| + "description": "Required. The parameter name must be unique amongst all ParameterConfigs in\na HyperparameterSpec message. E.g., \"learning_rate\".",
|
| "type": "string"
|
| },
|
| - "models": {
|
| - "description": "The list of models.",
|
| + "categoricalValues": {
|
| + "description": "Required if type is `CATEGORICAL`. The list of possible categories.",
|
| "items": {
|
| - "$ref": "GoogleCloudMlV1__Model"
|
| + "type": "string"
|
| },
|
| "type": "array"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__TrainingInput": {
|
| - "description": "Represents input parameters for a training job.",
|
| - "id": "GoogleCloudMlV1__TrainingInput",
|
| + "GoogleCloudMlV1__PredictionInput": {
|
| + "description": "Represents input parameters for a prediction job.",
|
| + "id": "GoogleCloudMlV1__PredictionInput",
|
| "properties": {
|
| - "workerType": {
|
| - "description": "Optional. Specifies the type of virtual machine to use for your training\njob's worker nodes.\n\nThe supported values are the same as those described in the entry for\n`masterType`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`workerCount` is greater than zero.",
|
| - "type": "string"
|
| - },
|
| - "args": {
|
| - "description": "Optional. Command line arguments to pass to the program.",
|
| - "items": {
|
| - "type": "string"
|
| - },
|
| - "type": "array"
|
| - },
|
| "region": {
|
| - "description": "Required. The Google Compute Engine region to run the training job in.",
|
| + "description": "Required. The Google Compute Engine region to run the prediction job in.",
|
| "type": "string"
|
| },
|
| - "parameterServerType": {
|
| - "description": "Optional. Specifies the type of virtual machine to use for your training\njob's parameter server.\n\nThe supported values are the same as those described in the entry for\n`master_type`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`parameter_server_count` is greater than zero.",
|
| + "versionName": {
|
| + "description": "Use this field if you want to specify a version of the model to use. The\nstring is formatted the same way as `model_version`, with the addition\nof the version information:\n\n`\"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>\"`",
|
| "type": "string"
|
| },
|
| - "scaleTier": {
|
| - "description": "Required. Specifies the machine types, the number of replicas for workers\nand parameter servers.",
|
| - "enum": [
|
| - "BASIC",
|
| - "STANDARD_1",
|
| - "PREMIUM_1",
|
| - "BASIC_GPU",
|
| - "CUSTOM"
|
| - ],
|
| - "enumDescriptions": [
|
| - "A single worker instance. This tier is suitable for learning how to use\nCloud ML, and for experimenting with new models using small datasets.",
|
| - "Many workers and a few parameter servers.",
|
| - "A large number of workers with many parameter servers.",
|
| - "A single worker instance [with a GPU](/ml-engine/docs/how-tos/using-gpus).",
|
| - "The CUSTOM tier is not a set tier, but rather enables you to use your\nown cluster specification. When you use this tier, set values to\nconfigure your processing cluster according to these guidelines:\n\n* You _must_ set `TrainingInput.masterType` to specify the type\n of machine to use for your master node. This is the only required\n setting.\n\n* You _may_ set `TrainingInput.workerCount` to specify the number of\n workers to use. If you specify one or more workers, you _must_ also\n set `TrainingInput.workerType` to specify the type of machine to use\n for your worker nodes.\n\n* You _may_ set `TrainingInput.parameterServerCount` to specify the\n number of parameter servers to use. If you specify one or more\n parameter servers, you _must_ also set\n `TrainingInput.parameterServerType` to specify the type of machine to\n use for your parameter servers.\n\nNote that all of your workers must use the same machine type, which can\nbe different from your parameter server type and master type. Your\nparameter servers must likewise use the same machine type, which can be\ndifferent from your worker type and master type."
|
| - ],
|
| + "modelName": {
|
| + "description": "Use this field if you want to use the default version for the specified\nmodel. The string must use the following format:\n\n`\"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>\"`",
|
| "type": "string"
|
| },
|
| - "jobDir": {
|
| - "description": "Optional. A Google Cloud Storage path in which to store training outputs\nand other data needed for training. This path is passed to your TensorFlow\nprogram as the 'job_dir' command-line argument. The benefit of specifying\nthis field is that Cloud ML validates the path for use in training.",
|
| + "outputPath": {
|
| + "description": "Required. The output Google Cloud Storage location.",
|
| "type": "string"
|
| },
|
| - "hyperparameters": {
|
| - "$ref": "GoogleCloudMlV1__HyperparameterSpec",
|
| - "description": "Optional. The set of Hyperparameters to tune."
|
| - },
|
| - "parameterServerCount": {
|
| - "description": "Optional. The number of parameter server replicas to use for the training\njob. Each replica in the cluster will be of the type specified in\n`parameter_server_type`.\n\nThis value can only be used when `scale_tier` is set to `CUSTOM`.If you\nset this value, you must also set `parameter_server_type`.",
|
| - "format": "int64",
|
| + "uri": {
|
| + "description": "Use this field if you want to specify a Google Cloud Storage path for\nthe model to use.",
|
| "type": "string"
|
| },
|
| - "packageUris": {
|
| - "description": "Required. The Google Cloud Storage location of the packages with\nthe training program and any additional dependencies.",
|
| - "items": {
|
| - "type": "string"
|
| - },
|
| - "type": "array"
|
| - },
|
| - "workerCount": {
|
| - "description": "Optional. The number of worker replicas to use for the training job. Each\nreplica in the cluster will be of the type specified in `worker_type`.\n\nThis value can only be used when `scale_tier` is set to `CUSTOM`. If you\nset this value, you must also set `worker_type`.",
|
| + "maxWorkerCount": {
|
| + "description": "Optional. The maximum number of workers to be used for parallel processing.\nDefaults to 10 if not specified.",
|
| "format": "int64",
|
| "type": "string"
|
| },
|
| - "masterType": {
|
| - "description": "Optional. Specifies the type of virtual machine to use for your training\njob's master worker.\n\nThe following types are supported:\n\n<dl>\n <dt>standard</dt>\n <dd>\n A basic machine configuration suitable for training simple models with\n small to moderate datasets.\n </dd>\n <dt>large_model</dt>\n <dd>\n A machine with a lot of memory, specially suited for parameter servers\n when your model is large (having many hidden layers or layers with very\n large numbers of nodes).\n </dd>\n <dt>complex_model_s</dt>\n <dd>\n A machine suitable for the master and workers of the cluster when your\n model requires more computation than the standard machine can handle\n satisfactorily.\n </dd>\n <dt>complex_model_m</dt>\n <dd>\n A machine with roughly twice the number of cores and roughly double the\n memory of <code suppresswarning=\"true\">complex_model_s</code>.\n </dd>\n <dt>complex_model_l</dt>\n <dd>\n A machine with roughly twice the number of cores and roughly double the\n memory of <code suppresswarning=\"true\">complex_model_m</code>.\n </dd>\n <dt>standard_gpu</dt>\n <dd>\n A machine equivalent to <code suppresswarning=\"true\">standard</code> that\n also includes a\n <a href=\"/ml-engine/docs/how-tos/using-gpus\">\n GPU that you can use in your trainer</a>.\n </dd>\n <dt>complex_model_m_gpu</dt>\n <dd>\n A machine equivalent to\n <code suppresswarning=\"true\">coplex_model_m</code> that also includes\n four GPUs.\n </dd>\n</dl>\n\nYou must set this value when `scaleTier` is set to `CUSTOM`.",
|
| + "dataFormat": {
|
| + "description": "Required. The format of the input data files.",
|
| + "enum": [
|
| + "DATA_FORMAT_UNSPECIFIED",
|
| + "TEXT",
|
| + "TF_RECORD",
|
| + "TF_RECORD_GZIP"
|
| + ],
|
| + "enumDescriptions": [
|
| + "Unspecified format.",
|
| + "The source file is a text file with instances separated by the\nnew-line character.",
|
| + "The source file is a TFRecord file.",
|
| + "The source file is a GZIP-compressed TFRecord file."
|
| + ],
|
| "type": "string"
|
| },
|
| "runtimeVersion": {
|
| - "description": "Optional. The Google Cloud ML runtime version to use for training. If not\nset, Google Cloud ML will choose the latest stable version.",
|
| + "description": "Optional. The Google Cloud ML runtime version to use for this batch\nprediction. If not set, Google Cloud ML will pick the runtime version used\nduring the CreateVersion request for this model version, or choose the\nlatest stable version when model version information is not available\nsuch as when the model is specified by uri.",
|
| "type": "string"
|
| },
|
| - "pythonModule": {
|
| - "description": "Required. The Python module name to run after installing the packages.",
|
| - "type": "string"
|
| + "inputPaths": {
|
| + "description": "Required. The Google Cloud Storage location of the input data files.\nMay contain wildcards.",
|
| + "items": {
|
| + "type": "string"
|
| + },
|
| + "type": "array"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__Job": {
|
| - "description": "Represents a training or prediction job.",
|
| - "id": "GoogleCloudMlV1__Job",
|
| + "GoogleCloudMlV1__OperationMetadata": {
|
| + "description": "Represents the metadata of the long-running operation.",
|
| + "id": "GoogleCloudMlV1__OperationMetadata",
|
| "properties": {
|
| - "jobId": {
|
| - "description": "Required. The user-specified id of the job.",
|
| + "modelName": {
|
| + "description": "Contains the name of the model associated with the operation.",
|
| "type": "string"
|
| },
|
| - "errorMessage": {
|
| - "description": "Output only. The details of a failure or a cancellation.",
|
| - "type": "string"
|
| + "version": {
|
| + "$ref": "GoogleCloudMlV1__Version",
|
| + "description": "Contains the version associated with the operation."
|
| },
|
| "endTime": {
|
| - "description": "Output only. When the job processing was completed.",
|
| + "description": "The time operation processing completed.",
|
| "format": "google-datetime",
|
| "type": "string"
|
| },
|
| + "operationType": {
|
| + "description": "The operation type.",
|
| + "enum": [
|
| + "OPERATION_TYPE_UNSPECIFIED",
|
| + "CREATE_VERSION",
|
| + "DELETE_VERSION",
|
| + "DELETE_MODEL"
|
| + ],
|
| + "enumDescriptions": [
|
| + "Unspecified operation type.",
|
| + "An operation to create a new version.",
|
| + "An operation to delete an existing version.",
|
| + "An operation to delete an existing model."
|
| + ],
|
| + "type": "string"
|
| + },
|
| "startTime": {
|
| - "description": "Output only. When the job processing was started.",
|
| + "description": "The time operation processing started.",
|
| "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "predictionOutput": {
|
| - "$ref": "GoogleCloudMlV1__PredictionOutput",
|
| - "description": "The current prediction job result."
|
| - },
|
| - "trainingOutput": {
|
| - "$ref": "GoogleCloudMlV1__TrainingOutput",
|
| - "description": "The current training job result."
|
| - },
|
| - "trainingInput": {
|
| - "$ref": "GoogleCloudMlV1__TrainingInput",
|
| - "description": "Input parameters to create a training job."
|
| + "isCancellationRequested": {
|
| + "description": "Indicates whether a request to cancel this operation has been made.",
|
| + "type": "boolean"
|
| },
|
| "createTime": {
|
| - "description": "Output only. When the job was created.",
|
| + "description": "The time the operation was submitted.",
|
| "format": "google-datetime",
|
| "type": "string"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1beta1__OperationMetadata": {
|
| + "description": "Represents the metadata of the long-running operation.",
|
| + "id": "GoogleCloudMlV1beta1__OperationMetadata",
|
| + "properties": {
|
| + "modelName": {
|
| + "description": "Contains the name of the model associated with the operation.",
|
| + "type": "string"
|
| },
|
| - "predictionInput": {
|
| - "$ref": "GoogleCloudMlV1__PredictionInput",
|
| - "description": "Input parameters to create a prediction job."
|
| + "version": {
|
| + "$ref": "GoogleCloudMlV1beta1__Version",
|
| + "description": "Contains the version associated with the operation."
|
| },
|
| - "state": {
|
| - "description": "Output only. The detailed state of a job.",
|
| + "endTime": {
|
| + "description": "The time operation processing completed.",
|
| + "format": "google-datetime",
|
| + "type": "string"
|
| + },
|
| + "operationType": {
|
| + "description": "The operation type.",
|
| "enum": [
|
| - "STATE_UNSPECIFIED",
|
| - "QUEUED",
|
| - "PREPARING",
|
| - "RUNNING",
|
| - "SUCCEEDED",
|
| - "FAILED",
|
| - "CANCELLING",
|
| - "CANCELLED"
|
| + "OPERATION_TYPE_UNSPECIFIED",
|
| + "CREATE_VERSION",
|
| + "DELETE_VERSION",
|
| + "DELETE_MODEL"
|
| ],
|
| "enumDescriptions": [
|
| - "The job state is unspecified.",
|
| - "The job has been just created and processing has not yet begun.",
|
| - "The service is preparing to run the job.",
|
| - "The job is in progress.",
|
| - "The job completed successfully.",
|
| - "The job failed.\n`error_message` should contain the details of the failure.",
|
| - "The job is being cancelled.\n`error_message` should describe the reason for the cancellation.",
|
| - "The job has been cancelled.\n`error_message` should describe the reason for the cancellation."
|
| + "Unspecified operation type.",
|
| + "An operation to create a new version.",
|
| + "An operation to delete an existing version.",
|
| + "An operation to delete an existing model."
|
| ],
|
| "type": "string"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleApi__HttpBody": {
|
| - "description": "Message that represents an arbitrary HTTP body. It should only be used for\npayload formats that can't be represented as JSON, such as raw binary or\nan HTML page.\n\n\nThis message can be used both in streaming and non-streaming API methods in\nthe request as well as the response.\n\nIt can be used as a top-level request field, which is convenient if one\nwants to extract parameters from either the URL or HTTP template into the\nrequest fields and also want access to the raw HTTP body.\n\nExample:\n\n message GetResourceRequest {\n // A unique request id.\n string request_id = 1;\n\n // The raw HTTP body is bound to this field.\n google.api.HttpBody http_body = 2;\n }\n\n service ResourceService {\n rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);\n rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);\n }\n\nExample with streaming methods:\n\n service CaldavService {\n rpc GetCalendar(stream google.api.HttpBody)\n returns (stream google.api.HttpBody);\n rpc UpdateCalendar(stream google.api.HttpBody)\n returns (stream google.api.HttpBody);\n }\n\nUse of this type only changes how the request and response bodies are\nhandled, all other features will continue to work unchanged.",
|
| - "id": "GoogleApi__HttpBody",
|
| - "properties": {
|
| - "data": {
|
| - "description": "HTTP body binary data.",
|
| - "format": "byte",
|
| + },
|
| + "startTime": {
|
| + "description": "The time operation processing started.",
|
| + "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "contentType": {
|
| - "description": "The HTTP Content-Type string representing the content type of the body.",
|
| + "isCancellationRequested": {
|
| + "description": "Indicates whether a request to cancel this operation has been made.",
|
| + "type": "boolean"
|
| + },
|
| + "createTime": {
|
| + "description": "The time the operation was submitted.",
|
| + "format": "google-datetime",
|
| "type": "string"
|
| }
|
| },
|
|
|
Chromium Code Reviews
Issue 2824163002:
Api-roll 48: 2017-04-18 (Closed)
