| Index: discovery/googleapis/ml__v1.json
|
| diff --git a/discovery/googleapis/ml__v1.json b/discovery/googleapis/ml__v1.json
|
| index 746c306156a706c8720a10876964b70b2d4e0865..a162f6864478046535aa7f242851cb7764c17dfd 100644
|
| --- a/discovery/googleapis/ml__v1.json
|
| +++ b/discovery/googleapis/ml__v1.json
|
| @@ -25,27 +25,6 @@
|
| "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"
|
| - },
|
| - "oauth_token": {
|
| - "description": "OAuth 2.0 token for the current user.",
|
| - "location": "query",
|
| - "type": "string"
|
| - },
|
| - "bearer_token": {
|
| - "description": "OAuth bearer token.",
|
| - "location": "query",
|
| - "type": "string"
|
| - },
|
| "upload_protocol": {
|
| "description": "Upload protocol for media (e.g. \"raw\", \"multipart\").",
|
| "location": "query",
|
| @@ -110,58 +89,79 @@
|
| "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"
|
| + },
|
| + "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"
|
| + },
|
| + "oauth_token": {
|
| + "description": "OAuth 2.0 token for the current user.",
|
| + "location": "query",
|
| + "type": "string"
|
| + },
|
| + "bearer_token": {
|
| + "description": "OAuth bearer token.",
|
| + "location": "query",
|
| + "type": "string"
|
| }
|
| },
|
| "protocol": "rest",
|
| "resources": {
|
| "projects": {
|
| "methods": {
|
| - "getConfig": {
|
| - "description": "Get the service account information associated with your project. You need\nthis information in order to grant the service account persmissions for\nthe Google Cloud Storage location where you put your model training code\nfor training the model with Google Cloud Machine Learning.",
|
| - "httpMethod": "GET",
|
| - "id": "ml.projects.getConfig",
|
| + "predict": {
|
| + "description": "Performs prediction on the data in the request.\n\n**** REMOVE FROM GENERATED DOCUMENTATION",
|
| + "httpMethod": "POST",
|
| + "id": "ml.projects.predict",
|
| "parameterOrder": [
|
| "name"
|
| ],
|
| "parameters": {
|
| "name": {
|
| - "description": "Required. The project name.\n\nAuthorization: requires `Viewer` role on the specified project.",
|
| + "description": "Required. The resource name of a model or a version.\n\nAuthorization: requires `Viewer` role on the parent project.",
|
| "location": "path",
|
| - "pattern": "^projects/[^/]+$",
|
| + "pattern": "^projects/.+$",
|
| "required": true,
|
| "type": "string"
|
| }
|
| },
|
| - "path": "v1/{+name}:getConfig",
|
| + "path": "v1/{+name}:predict",
|
| + "request": {
|
| + "$ref": "GoogleCloudMlV1__PredictRequest"
|
| + },
|
| "response": {
|
| - "$ref": "GoogleCloudMlV1__GetConfigResponse"
|
| + "$ref": "GoogleApi__HttpBody"
|
| },
|
| "scopes": [
|
| "https://www.googleapis.com/auth/cloud-platform"
|
| ]
|
| },
|
| - "predict": {
|
| - "description": "Performs prediction on the data in the request.\n\n**** REMOVE FROM GENERATED DOCUMENTATION",
|
| - "httpMethod": "POST",
|
| - "id": "ml.projects.predict",
|
| + "getConfig": {
|
| + "description": "Get the service account information associated with your project. You need\nthis information in order to grant the service account persmissions for\nthe Google Cloud Storage location where you put your model training code\nfor training the model with Google Cloud Machine Learning.",
|
| + "httpMethod": "GET",
|
| + "id": "ml.projects.getConfig",
|
| "parameterOrder": [
|
| "name"
|
| ],
|
| "parameters": {
|
| "name": {
|
| - "description": "Required. The resource name of a model or a version.\n\nAuthorization: requires `Viewer` role on the parent project.",
|
| + "description": "Required. The project name.\n\nAuthorization: requires `Viewer` role on the specified project.",
|
| "location": "path",
|
| - "pattern": "^projects/.+$",
|
| + "pattern": "^projects/[^/]+$",
|
| "required": true,
|
| "type": "string"
|
| }
|
| },
|
| - "path": "v1/{+name}:predict",
|
| - "request": {
|
| - "$ref": "GoogleCloudMlV1__PredictRequest"
|
| - },
|
| + "path": "v1/{+name}:getConfig",
|
| "response": {
|
| - "$ref": "GoogleApi__HttpBody"
|
| + "$ref": "GoogleCloudMlV1__GetConfigResponse"
|
| },
|
| "scopes": [
|
| "https://www.googleapis.com/auth/cloud-platform"
|
| @@ -196,26 +196,20 @@
|
| ]
|
| },
|
| "list": {
|
| - "description": "Lists operations that match the specified filter in the request. If the\nserver doesn't support this method, it returns `UNIMPLEMENTED`.\n\nNOTE: the `name` binding below allows API services to override the binding\nto use different resource name schemes, such as `users/*/operations`.",
|
| + "description": "Lists operations that match the specified filter in the request. If the\nserver doesn't support this method, it returns `UNIMPLEMENTED`.\n\nNOTE: the `name` binding allows API services to override the binding\nto use different resource name schemes, such as `users/*/operations`. To\noverride the binding, API services can add a binding such as\n`\"/v1/{name=users/*}/operations\"` to their service configuration.\nFor backwards compatibility, the default name includes the operations\ncollection id, however overriding users must ensure the name binding\nis the parent resource, without the operations collection id.",
|
| "httpMethod": "GET",
|
| "id": "ml.projects.operations.list",
|
| "parameterOrder": [
|
| "name"
|
| ],
|
| "parameters": {
|
| - "pageSize": {
|
| - "description": "The standard list page size.",
|
| - "format": "int32",
|
| - "location": "query",
|
| - "type": "integer"
|
| - },
|
| "filter": {
|
| "description": "The standard list filter.",
|
| "location": "query",
|
| "type": "string"
|
| },
|
| "name": {
|
| - "description": "The name of the operation collection.",
|
| + "description": "The name of the operation's parent resource.",
|
| "location": "path",
|
| "pattern": "^projects/[^/]+$",
|
| "required": true,
|
| @@ -225,6 +219,12 @@
|
| "description": "The standard list page token.",
|
| "location": "query",
|
| "type": "string"
|
| + },
|
| + "pageSize": {
|
| + "description": "The standard list page size.",
|
| + "format": "int32",
|
| + "location": "query",
|
| + "type": "integer"
|
| }
|
| },
|
| "path": "v1/{+name}/operations",
|
| @@ -287,6 +287,30 @@
|
| },
|
| "models": {
|
| "methods": {
|
| + "delete": {
|
| + "description": "Deletes a model.\n\nYou can only delete a model if there are no versions in it. You can delete\nversions by calling\n[projects.models.versions.delete](/ml-engine/reference/rest/v1/projects.models.versions/delete).",
|
| + "httpMethod": "DELETE",
|
| + "id": "ml.projects.models.delete",
|
| + "parameterOrder": [
|
| + "name"
|
| + ],
|
| + "parameters": {
|
| + "name": {
|
| + "description": "Required. The name of the model.\n\nAuthorization: requires `Editor` role on the parent project.",
|
| + "location": "path",
|
| + "pattern": "^projects/[^/]+/models/[^/]+$",
|
| + "required": true,
|
| + "type": "string"
|
| + }
|
| + },
|
| + "path": "v1/{+name}",
|
| + "response": {
|
| + "$ref": "GoogleLongrunning__Operation"
|
| + },
|
| + "scopes": [
|
| + "https://www.googleapis.com/auth/cloud-platform"
|
| + ]
|
| + },
|
| "list": {
|
| "description": "Lists the models in a project.\n\nEach project can contain multiple models, and each model can have multiple\nversions.",
|
| "httpMethod": "GET",
|
| @@ -295,6 +319,13 @@
|
| "parent"
|
| ],
|
| "parameters": {
|
| + "parent": {
|
| + "description": "Required. The name of the project whose models are to be listed.\n\nAuthorization: requires `Viewer` role on the specified project.",
|
| + "location": "path",
|
| + "pattern": "^projects/[^/]+$",
|
| + "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",
|
| @@ -305,13 +336,6 @@
|
| "format": "int32",
|
| "location": "query",
|
| "type": "integer"
|
| - },
|
| - "parent": {
|
| - "description": "Required. The name of the project whose models are to be listed.\n\nAuthorization: requires `Viewer` role on the specified project.",
|
| - "location": "path",
|
| - "pattern": "^projects/[^/]+$",
|
| - "required": true,
|
| - "type": "string"
|
| }
|
| },
|
| "path": "v1/{+parent}/models",
|
| @@ -372,59 +396,11 @@
|
| "scopes": [
|
| "https://www.googleapis.com/auth/cloud-platform"
|
| ]
|
| - },
|
| - "delete": {
|
| - "description": "Deletes a model.\n\nYou can only delete a model if there are no versions in it. You can delete\nversions by calling\n[projects.models.versions.delete](/ml-engine/reference/rest/v1/projects.models.versions/delete).",
|
| - "httpMethod": "DELETE",
|
| - "id": "ml.projects.models.delete",
|
| - "parameterOrder": [
|
| - "name"
|
| - ],
|
| - "parameters": {
|
| - "name": {
|
| - "description": "Required. The name of the model.\n\nAuthorization: requires `Editor` role on the parent project.",
|
| - "location": "path",
|
| - "pattern": "^projects/[^/]+/models/[^/]+$",
|
| - "required": true,
|
| - "type": "string"
|
| - }
|
| - },
|
| - "path": "v1/{+name}",
|
| - "response": {
|
| - "$ref": "GoogleLongrunning__Operation"
|
| - },
|
| - "scopes": [
|
| - "https://www.googleapis.com/auth/cloud-platform"
|
| - ]
|
| }
|
| },
|
| "resources": {
|
| "versions": {
|
| "methods": {
|
| - "delete": {
|
| - "description": "Deletes a model version.\n\nEach model can have multiple versions deployed and in use at any given\ntime. Use this method to remove a single version.\n\nNote: You cannot delete the version that is set as the default version\nof the model unless it is the only remaining version.",
|
| - "httpMethod": "DELETE",
|
| - "id": "ml.projects.models.versions.delete",
|
| - "parameterOrder": [
|
| - "name"
|
| - ],
|
| - "parameters": {
|
| - "name": {
|
| - "description": "Required. The name of the version. You can get the names of all the\nversions of a model by calling\n[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list).\n\nAuthorization: requires `Editor` role on the parent project.",
|
| - "location": "path",
|
| - "pattern": "^projects/[^/]+/models/[^/]+/versions/[^/]+$",
|
| - "required": true,
|
| - "type": "string"
|
| - }
|
| - },
|
| - "path": "v1/{+name}",
|
| - "response": {
|
| - "$ref": "GoogleLongrunning__Operation"
|
| - },
|
| - "scopes": [
|
| - "https://www.googleapis.com/auth/cloud-platform"
|
| - ]
|
| - },
|
| "list": {
|
| "description": "Gets basic information about all the versions of a model.\n\nIf you expect that a model has a lot of versions, or if you need to handle\nonly a limited number of results at a time, you can request that the list\nbe retrieved in batches (called pages):",
|
| "httpMethod": "GET",
|
| @@ -433,12 +409,6 @@
|
| "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",
|
| @@ -450,6 +420,12 @@
|
| "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",
|
| @@ -537,6 +513,30 @@
|
| "scopes": [
|
| "https://www.googleapis.com/auth/cloud-platform"
|
| ]
|
| + },
|
| + "delete": {
|
| + "description": "Deletes a model version.\n\nEach model can have multiple versions deployed and in use at any given\ntime. Use this method to remove a single version.\n\nNote: You cannot delete the version that is set as the default version\nof the model unless it is the only remaining version.",
|
| + "httpMethod": "DELETE",
|
| + "id": "ml.projects.models.versions.delete",
|
| + "parameterOrder": [
|
| + "name"
|
| + ],
|
| + "parameters": {
|
| + "name": {
|
| + "description": "Required. The name of the version. You can get the names of all the\nversions of a model by calling\n[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list).\n\nAuthorization: requires `Editor` role on the parent project.",
|
| + "location": "path",
|
| + "pattern": "^projects/[^/]+/models/[^/]+/versions/[^/]+$",
|
| + "required": true,
|
| + "type": "string"
|
| + }
|
| + },
|
| + "path": "v1/{+name}",
|
| + "response": {
|
| + "$ref": "GoogleLongrunning__Operation"
|
| + },
|
| + "scopes": [
|
| + "https://www.googleapis.com/auth/cloud-platform"
|
| + ]
|
| }
|
| }
|
| }
|
| @@ -552,17 +552,6 @@
|
| "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 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",
|
| @@ -574,6 +563,17 @@
|
| "description": "Optional. Specifies the subset of jobs to retrieve.",
|
| "location": "query",
|
| "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"
|
| + },
|
| + "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",
|
| @@ -667,125 +667,254 @@
|
| }
|
| }
|
| },
|
| - "revision": "20170515",
|
| + "revision": "20170604",
|
| "rootUrl": "https://ml.googleapis.com/",
|
| "schemas": {
|
| - "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` that 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.\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__OperationMetadata": {
|
| + "description": "Represents the metadata of the long-running operation.",
|
| + "id": "GoogleCloudMlV1__OperationMetadata",
|
| "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.",
|
| - "items": {
|
| - "additionalProperties": {
|
| - "description": "Properties of the object. Contains field @type with type URL.",
|
| - "type": "any"
|
| - },
|
| - "type": "object"
|
| - },
|
| - "type": "array"
|
| + "startTime": {
|
| + "description": "The time operation processing started.",
|
| + "format": "google-datetime",
|
| + "type": "string"
|
| },
|
| - "code": {
|
| - "description": "The status code, which should be an enum value of google.rpc.Code.",
|
| - "format": "int32",
|
| - "type": "integer"
|
| + "isCancellationRequested": {
|
| + "description": "Indicates whether a request to cancel this operation has been made.",
|
| + "type": "boolean"
|
| },
|
| - "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.",
|
| + "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.",
|
| + "type": "string"
|
| + },
|
| + "version": {
|
| + "$ref": "GoogleCloudMlV1__Version",
|
| + "description": "Contains the version associated with the operation."
|
| + },
|
| + "endTime": {
|
| + "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"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__TrainingInput": {
|
| - "description": "Represents input parameters for a training job.",
|
| - "id": "GoogleCloudMlV1__TrainingInput",
|
| + "GoogleCloudMlV1beta1__OperationMetadata": {
|
| + "description": "Represents the metadata of the long-running operation.",
|
| + "id": "GoogleCloudMlV1beta1__OperationMetadata",
|
| "properties": {
|
| - "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\">complex_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`.",
|
| + "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"
|
| },
|
| - "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.",
|
| + "modelName": {
|
| + "description": "Contains the name of the model associated with the operation.",
|
| "type": "string"
|
| },
|
| - "pythonModule": {
|
| - "description": "Required. The Python module name to run after installing the packages.",
|
| + "version": {
|
| + "$ref": "GoogleCloudMlV1beta1__Version",
|
| + "description": "Contains the version associated with the operation."
|
| + },
|
| + "endTime": {
|
| + "description": "The time operation processing completed.",
|
| + "format": "google-datetime",
|
| "type": "string"
|
| },
|
| - "workerType": {
|
| - "description": "Optional. Specifies the type of virtual machine to use for your training\njob's worker nodes.\n\nThe supported values are the same as those described in the entry for\n`masterType`.\n\nThis value must be present when `scaleTier` is set to `CUSTOM` and\n`workerCount` is greater than zero.",
|
| + "operationType": {
|
| + "description": "The operation type.",
|
| + "enum": [
|
| + "OPERATION_TYPE_UNSPECIFIED",
|
| + "CREATE_VERSION",
|
| + "DELETE_VERSION",
|
| + "DELETE_MODEL"
|
| + ],
|
| + "enumDescriptions": [
|
| + "Unspecified operation type.",
|
| + "An operation to create a new version.",
|
| + "An operation to delete an existing version.",
|
| + "An operation to delete an existing model."
|
| + ],
|
| "type": "string"
|
| },
|
| - "args": {
|
| - "description": "Optional. Command line arguments to pass to the program.",
|
| + "startTime": {
|
| + "description": "The time operation processing started.",
|
| + "format": "google-datetime",
|
| + "type": "string"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__HyperparameterSpec": {
|
| + "description": "Represents a set of hyperparameters to optimize.",
|
| + "id": "GoogleCloudMlV1__HyperparameterSpec",
|
| + "properties": {
|
| + "params": {
|
| + "description": "Required. The set of parameters to tune.",
|
| "items": {
|
| - "type": "string"
|
| + "$ref": "GoogleCloudMlV1__ParameterSpec"
|
| },
|
| "type": "array"
|
| },
|
| - "region": {
|
| - "description": "Required. The Google Compute Engine region to run the training job in.",
|
| - "type": "string"
|
| + "maxTrials": {
|
| + "description": "Optional. How many training trials should be attempted to optimize\nthe specified hyperparameters.\n\nDefaults to one.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| },
|
| - "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"
|
| + "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"
|
| },
|
| - "scaleTier": {
|
| - "description": "Required. Specifies the machine types, the number of replicas for workers\nand parameter servers.",
|
| + "goal": {
|
| + "description": "Required. The type of goal to use for tuning. Available types are\n`MAXIMIZE` and `MINIMIZE`.\n\nDefaults to `MAXIMIZE`.",
|
| "enum": [
|
| - "BASIC",
|
| - "STANDARD_1",
|
| - "PREMIUM_1",
|
| - "BASIC_GPU",
|
| - "CUSTOM"
|
| + "GOAL_TYPE_UNSPECIFIED",
|
| + "MAXIMIZE",
|
| + "MINIMIZE"
|
| ],
|
| "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."
|
| + "Goal Type will default to maximize.",
|
| + "Maximize the goal metric.",
|
| + "Minimize the goal metric."
|
| ],
|
| "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.",
|
| + "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"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__ListJobsResponse": {
|
| + "description": "Response message for the ListJobs method.",
|
| + "id": "GoogleCloudMlV1__ListJobsResponse",
|
| + "properties": {
|
| + "nextPageToken": {
|
| + "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| "type": "string"
|
| },
|
| - "hyperparameters": {
|
| - "$ref": "GoogleCloudMlV1__HyperparameterSpec",
|
| - "description": "Optional. The set of Hyperparameters to tune."
|
| + "jobs": {
|
| + "description": "The list of jobs.",
|
| + "items": {
|
| + "$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": {
|
| + "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"
|
| },
|
| - "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",
|
| + "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"
|
| + },
|
| + "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"
|
| },
|
| - "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.",
|
| + "error": {
|
| + "$ref": "GoogleRpc__Status",
|
| + "description": "The error result of the operation in case of failure or cancellation."
|
| + },
|
| + "metadata": {
|
| + "additionalProperties": {
|
| + "description": "Properties of the object. Contains field @type with type URL.",
|
| + "type": "any"
|
| + },
|
| + "description": "Service-specific metadata associated with the operation. It typically\ncontains progress information and common metadata such as create time.\nSome services might not provide such metadata. Any method that returns a\nlong-running operation should document the metadata type, if any.",
|
| + "type": "object"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__Model": {
|
| + "description": "Represents a machine learning solution.\n\nA model can have multiple versions, each of which is a deployed, trained\nmodel ready to receive prediction requests. The model itself is just a\ncontainer.",
|
| + "id": "GoogleCloudMlV1__Model",
|
| + "properties": {
|
| + "defaultVersion": {
|
| + "$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)."
|
| + },
|
| + "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"
|
| },
|
| - "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",
|
| + "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.",
|
| "type": "string"
|
| + },
|
| + "onlinePredictionLogging": {
|
| + "description": "Optional. If true, enables StackDriver Logging for online prediction.\nDefault is false.",
|
| + "type": "boolean"
|
| }
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__ListModelsResponse": {
|
| - "description": "Response message for the ListModels method.",
|
| - "id": "GoogleCloudMlV1__ListModelsResponse",
|
| + "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__ListVersionsResponse": {
|
| + "description": "Response message for the ListVersions method.",
|
| + "id": "GoogleCloudMlV1__ListVersionsResponse",
|
| "properties": {
|
| - "models": {
|
| - "description": "The list of models.",
|
| + "versions": {
|
| + "description": "The list of versions.",
|
| "items": {
|
| - "$ref": "GoogleCloudMlV1__Model"
|
| + "$ref": "GoogleCloudMlV1__Version"
|
| },
|
| "type": "array"
|
| },
|
| @@ -796,13 +925,166 @@
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__Job": {
|
| - "description": "Represents a training or prediction job.",
|
| - "id": "GoogleCloudMlV1__Job",
|
| - "properties": {
|
| - "state": {
|
| - "description": "Output only. The detailed state of a job.",
|
| - "enum": [
|
| + "GoogleCloudMlV1__CancelJobRequest": {
|
| + "description": "Request message for the CancelJob method.",
|
| + "id": "GoogleCloudMlV1__CancelJobRequest",
|
| + "properties": {},
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1beta1__ManualScaling": {
|
| + "description": "Options for manually scaling a model.",
|
| + "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\nlast billing cycle.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| + }
|
| + },
|
| + "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` that 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.\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": {
|
| + "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"
|
| + },
|
| + "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": {
|
| + "additionalProperties": {
|
| + "description": "Properties of the object. Contains field @type with type URL.",
|
| + "type": "any"
|
| + },
|
| + "type": "object"
|
| + },
|
| + "type": "array"
|
| + },
|
| + "code": {
|
| + "description": "The status code, which should be an enum value of google.rpc.Code.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| + }
|
| + },
|
| + "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.",
|
| + "type": "string"
|
| + },
|
| + "models": {
|
| + "description": "The list of models.",
|
| + "items": {
|
| + "$ref": "GoogleCloudMlV1__Model"
|
| + },
|
| + "type": "array"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__TrainingInput": {
|
| + "description": "Represents input parameters for a training job.",
|
| + "id": "GoogleCloudMlV1__TrainingInput",
|
| + "properties": {
|
| + "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"
|
| + },
|
| + "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\">complex_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"
|
| + },
|
| + "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"
|
| + },
|
| + "pythonModule": {
|
| + "description": "Required. The Python module name to run after installing the packages.",
|
| + "type": "string"
|
| + },
|
| + "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"
|
| + },
|
| + "region": {
|
| + "description": "Required. The Google Compute Engine region to run the training job in.",
|
| + "type": "string"
|
| + },
|
| + "args": {
|
| + "description": "Optional. Command line arguments to pass to the program.",
|
| + "items": {
|
| + "type": "string"
|
| + },
|
| + "type": "array"
|
| + },
|
| + "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"
|
| + },
|
| + "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."
|
| + ],
|
| + "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.",
|
| + "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",
|
| + "type": "string"
|
| + },
|
| + "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"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__Job": {
|
| + "description": "Represents a training or prediction job.",
|
| + "id": "GoogleCloudMlV1__Job",
|
| + "properties": {
|
| + "createTime": {
|
| + "description": "Output only. When the job was created.",
|
| + "format": "google-datetime",
|
| + "type": "string"
|
| + },
|
| + "trainingInput": {
|
| + "$ref": "GoogleCloudMlV1__TrainingInput",
|
| + "description": "Input parameters to create a training job."
|
| + },
|
| + "state": {
|
| + "description": "Output only. The detailed state of a job.",
|
| + "enum": [
|
| "STATE_UNSPECIFIED",
|
| "QUEUED",
|
| "PREPARING",
|
| @@ -853,15 +1135,6 @@
|
| "trainingOutput": {
|
| "$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"
|
| }
|
| },
|
| "type": "object"
|
| @@ -870,6 +1143,17 @@
|
| "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": {
|
| + "extensions": {
|
| + "description": "Application specific response metadata. Must be set in the first response\nfor streaming APIs.",
|
| + "items": {
|
| + "additionalProperties": {
|
| + "description": "Properties of the object. Contains field @type with type URL.",
|
| + "type": "any"
|
| + },
|
| + "type": "object"
|
| + },
|
| + "type": "array"
|
| + },
|
| "data": {
|
| "description": "HTTP body binary data.",
|
| "format": "byte",
|
| @@ -882,6 +1166,22 @@
|
| },
|
| "type": "object"
|
| },
|
| + "GoogleCloudMlV1__GetConfigResponse": {
|
| + "description": "Returns service account information associated with a project.",
|
| + "id": "GoogleCloudMlV1__GetConfigResponse",
|
| + "properties": {
|
| + "serviceAccount": {
|
| + "description": "The service account Cloud ML uses to access resources in the project.",
|
| + "type": "string"
|
| + },
|
| + "serviceAccountProject": {
|
| + "description": "The project number for `service_account`.",
|
| + "format": "int64",
|
| + "type": "string"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| "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",
|
| @@ -905,12 +1205,16 @@
|
| },
|
| "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."
|
| + "description": "Manually select the number of nodes to use for serving the\nmodel. You should generally use `automatic_scaling` with an appropriate\n`min_nodes` instead, but this option is available if you want predictable\nbilling. Beware that latency and error rates will increase if the\ntraffic exceeds that capability of the system to serve it based on\nthe selected number of nodes."
|
| },
|
| "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"
|
| },
|
| + "automaticScaling": {
|
| + "$ref": "GoogleCloudMlV1beta1__AutomaticScaling",
|
| + "description": "Automatically scale the number of nodes used to serve the model in\nresponse to increases and decreases in traffic. Care should be\ntaken to ramp up traffic according to the model's ability to scale\nor you will start seeing increases in latency and 429 response codes."
|
| + },
|
| "runtimeVersion": {
|
| "description": "Optional. The Google Cloud ML runtime version to use for this deployment.\nIf not set, Google Cloud ML will choose a version.",
|
| "type": "string"
|
| @@ -923,30 +1227,10 @@
|
| },
|
| "type": "object"
|
| },
|
| - "GoogleCloudMlV1__GetConfigResponse": {
|
| - "description": "Returns service account information associated with a project.",
|
| - "id": "GoogleCloudMlV1__GetConfigResponse",
|
| - "properties": {
|
| - "serviceAccountProject": {
|
| - "description": "The project number for `service_account`.",
|
| - "format": "int64",
|
| - "type": "string"
|
| - },
|
| - "serviceAccount": {
|
| - "description": "The service account Cloud ML uses to access resources in the project.",
|
| - "type": "string"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| "GoogleCloudMlV1__HyperparameterOutput": {
|
| "description": "Represents the result of a single hyperparameter tuning trial from a\ntraining job. The TrainingOutput object that is returned on successful\ncompletion of a training job with hyperparameter tuning includes a list\nof HyperparameterOutput objects, one for each successful trial.",
|
| "id": "GoogleCloudMlV1__HyperparameterOutput",
|
| "properties": {
|
| - "finalMetric": {
|
| - "$ref": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric",
|
| - "description": "The final objective metric seen for this trial."
|
| - },
|
| "hyperparameters": {
|
| "additionalProperties": {
|
| "type": "string"
|
| @@ -964,6 +1248,22 @@
|
| "$ref": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric"
|
| },
|
| "type": "array"
|
| + },
|
| + "finalMetric": {
|
| + "$ref": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric",
|
| + "description": "The final objective metric seen for this trial."
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| + "GoogleCloudMlV1__AutomaticScaling": {
|
| + "description": "Options for automatically scaling a model.",
|
| + "id": "GoogleCloudMlV1__AutomaticScaling",
|
| + "properties": {
|
| + "minNodes": {
|
| + "description": "Optional. The minimum number of nodes to allocate for this model. These\nnodes are always up, starting from the time the model is deployed, so the\ncost of operating this model will be at least\n`rate` * `min_nodes` * number of hours since last billing cycle,\nwhere `rate` is the cost per node-hour as documented in\n[pricing](https://cloud.google.com/ml-engine/pricing#prediction_pricing),\neven if no predictions are performed. There is additional cost for each\nprediction performed.\n\nUnlike manual scaling, if the load gets too heavy for the nodes\nthat are up, the service will automatically add nodes to handle the\nincreased load as well as scale back as traffic drops, always maintaining\nat least `min_nodes`. You will be charged for the time in which additional\nnodes are used.\n\nIf not specified, `min_nodes` defaults to 0, in which case, when traffic\nto a model stops (and after a cool-down period), nodes will be shut down\nand no charges will be incurred until traffic to the model resumes.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| }
|
| },
|
| "type": "object"
|
| @@ -994,20 +1294,32 @@
|
| },
|
| "type": "object"
|
| },
|
| + "GoogleCloudMlV1beta1__AutomaticScaling": {
|
| + "description": "Options for automatically scaling a model.",
|
| + "id": "GoogleCloudMlV1beta1__AutomaticScaling",
|
| + "properties": {
|
| + "minNodes": {
|
| + "description": "Optional. The minimum number of nodes to allocate for this model. These\nnodes are always up, starting from the time the model is deployed, so the\ncost of operating this model will be at least\n`rate` * `min_nodes` * number of hours since last billing cycle,\nwhere `rate` is the cost per node-hour as documented in\n[pricing](https://cloud.google.com/ml-engine/pricing#prediction_pricing),\neven if no predictions are performed. There is additional cost for each\nprediction performed.\n\nUnlike manual scaling, if the load gets too heavy for the nodes\nthat are up, the service will automatically add nodes to handle the\nincreased load as well as scale back as traffic drops, always maintaining\nat least `min_nodes`. You will be charged for the time in which additional\nnodes are used.\n\nIf not specified, `min_nodes` defaults to 0, in which case, when traffic\nto a model stops (and after a cool-down period), nodes will be shut down\nand no charges will be incurred until traffic to the model resumes.",
|
| + "format": "int32",
|
| + "type": "integer"
|
| + }
|
| + },
|
| + "type": "object"
|
| + },
|
| "GoogleLongrunning__ListOperationsResponse": {
|
| "description": "The response message for Operations.ListOperations.",
|
| "id": "GoogleLongrunning__ListOperationsResponse",
|
| "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.",
|
| "items": {
|
| "$ref": "GoogleLongrunning__Operation"
|
| },
|
| "type": "array"
|
| + },
|
| + "nextPageToken": {
|
| + "description": "The standard List next-page token.",
|
| + "type": "string"
|
| }
|
| },
|
| "type": "object"
|
| @@ -1017,7 +1329,7 @@
|
| "id": "GoogleCloudMlV1__ManualScaling",
|
| "properties": {
|
| "nodes": {
|
| - "description": "The number of nodes to allocate for this model. These nodes are always up,\nstarting from the time the model is deployed, so the cost of operating\nthis model will be proportional to nodes * number of hours since\ndeployment.",
|
| + "description": "The number of nodes to allocate for this model. These nodes are always up,\nstarting from the time the model is deployed, so the cost of operating\nthis model will be proportional to `nodes` * number of hours since\nlast billing cycle plus the cost for each prediction performed.",
|
| "format": "int32",
|
| "type": "integer"
|
| }
|
| @@ -1028,15 +1340,6 @@
|
| "description": "Represents results of a training job. Output only.",
|
| "id": "GoogleCloudMlV1__TrainingOutput",
|
| "properties": {
|
| - "completedTrialCount": {
|
| - "description": "The number of hyperparameter tuning trials that completed successfully.\nOnly set for hyperparameter tuning jobs.",
|
| - "format": "int64",
|
| - "type": "string"
|
| - },
|
| - "isHyperparameterTuningJob": {
|
| - "description": "Whether this job is a hyperparameter tuning job.",
|
| - "type": "boolean"
|
| - },
|
| "consumedMLUnits": {
|
| "description": "The amount of ML units consumed by the job.",
|
| "format": "double",
|
| @@ -1048,13 +1351,22 @@
|
| "$ref": "GoogleCloudMlV1__HyperparameterOutput"
|
| },
|
| "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",
|
| + },
|
| + "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"
|
| + }
|
| + },
|
| + "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",
|
| "properties": {
|
| "httpBody": {
|
| "$ref": "GoogleApi__HttpBody",
|
| @@ -1067,15 +1379,15 @@
|
| "description": "An observed value of a metric.",
|
| "id": "GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetric",
|
| "properties": {
|
| - "objectiveValue": {
|
| - "description": "The objective value at this training step.",
|
| - "format": "double",
|
| - "type": "number"
|
| - },
|
| "trainingStep": {
|
| "description": "The global training step for this metric.",
|
| "format": "int64",
|
| "type": "string"
|
| + },
|
| + "objectiveValue": {
|
| + "description": "The objective value at this training step.",
|
| + "format": "double",
|
| + "type": "number"
|
| }
|
| },
|
| "type": "object"
|
| @@ -1084,15 +1396,23 @@
|
| "description": "Represents a version of the model.\n\nEach version is a trained model deployed in the cloud, ready to handle\nprediction requests. A model can have multiple versions. You can get\ninformation about all of the versions of a given model by calling\n[projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.versions/list).",
|
| "id": "GoogleCloudMlV1__Version",
|
| "properties": {
|
| - "runtimeVersion": {
|
| - "description": "Optional. The Google Cloud ML runtime version to use for this deployment.\nIf not set, Google Cloud ML will choose a version.",
|
| + "name": {
|
| + "description": "Required.The name specified for the version when it was created.\n\nThe version name must be unique within the model it is created in.",
|
| "type": "string"
|
| },
|
| + "automaticScaling": {
|
| + "$ref": "GoogleCloudMlV1__AutomaticScaling",
|
| + "description": "Automatically scale the number of nodes used to serve the model in\nresponse to increases and decreases in traffic. Care should be\ntaken to ramp up traffic according to the model's ability to scale\nor you will start seeing increases in latency and 429 response codes."
|
| + },
|
| "lastUseTime": {
|
| "description": "Output only. The time the version was last used for prediction.",
|
| "format": "google-datetime",
|
| "type": "string"
|
| },
|
| + "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"
|
| + },
|
| "description": {
|
| "description": "Optional. The description specified for the version when it was created.",
|
| "type": "string"
|
| @@ -1112,11 +1432,7 @@
|
| },
|
| "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"
|
| + "description": "Manually select the number of nodes to use for serving the\nmodel. You should generally use `automatic_scaling` with an appropriate\n`min_nodes` instead, but this option is available if you want more\npredictable billing. Beware that latency and error rates will increase\nif the traffic exceeds that capability of the system to serve it based\non the selected number of nodes."
|
| }
|
| },
|
| "type": "object"
|
| @@ -1125,6 +1441,11 @@
|
| "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": {
|
| @@ -1182,11 +1503,6 @@
|
| "parameterName": {
|
| "description": "Required. The parameter name must be unique amongst all ParameterConfigs in\na HyperparameterSpec message. E.g., \"learning_rate\".",
|
| "type": "string"
|
| - },
|
| - "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"
|
| }
|
| },
|
| "type": "object"
|
| @@ -1211,15 +1527,15 @@
|
| "description": "Required. The output Google Cloud Storage location.",
|
| "type": "string"
|
| },
|
| + "uri": {
|
| + "description": "Use this field if you want to specify a Google Cloud Storage path for\nthe model to use.",
|
| + "type": "string"
|
| + },
|
| "maxWorkerCount": {
|
| "description": "Optional. The maximum number of workers to be used for parallel processing.\nDefaults to 10 if not specified.",
|
| "format": "int64",
|
| "type": "string"
|
| },
|
| - "uri": {
|
| - "description": "Use this field if you want to specify a Google Cloud Storage path for\nthe model to use.",
|
| - "type": "string"
|
| - },
|
| "dataFormat": {
|
| "description": "Required. The format of the input data files.",
|
| "enum": [
|
| @@ -1249,279 +1565,6 @@
|
| }
|
| },
|
| "type": "object"
|
| - },
|
| - "GoogleCloudMlV1beta1__OperationMetadata": {
|
| - "description": "Represents the metadata of the long-running operation.",
|
| - "id": "GoogleCloudMlV1beta1__OperationMetadata",
|
| - "properties": {
|
| - "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.",
|
| - "type": "string"
|
| - },
|
| - "version": {
|
| - "$ref": "GoogleCloudMlV1beta1__Version",
|
| - "description": "Contains the version associated with the operation."
|
| - },
|
| - "endTime": {
|
| - "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": "The time operation processing started.",
|
| - "format": "google-datetime",
|
| - "type": "string"
|
| - },
|
| - "isCancellationRequested": {
|
| - "description": "Indicates whether a request to cancel this operation has been made.",
|
| - "type": "boolean"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__OperationMetadata": {
|
| - "description": "Represents the metadata of the long-running operation.",
|
| - "id": "GoogleCloudMlV1__OperationMetadata",
|
| - "properties": {
|
| - "modelName": {
|
| - "description": "Contains the name of the model associated with the operation.",
|
| - "type": "string"
|
| - },
|
| - "version": {
|
| - "$ref": "GoogleCloudMlV1__Version",
|
| - "description": "Contains the version associated with the operation."
|
| - },
|
| - "endTime": {
|
| - "description": "The time operation processing completed.",
|
| - "format": "google-datetime",
|
| - "type": "string"
|
| - },
|
| - "operationType": {
|
| - "description": "The operation type.",
|
| - "enum": [
|
| - "OPERATION_TYPE_UNSPECIFIED",
|
| - "CREATE_VERSION",
|
| - "DELETE_VERSION",
|
| - "DELETE_MODEL"
|
| - ],
|
| - "enumDescriptions": [
|
| - "Unspecified operation type.",
|
| - "An operation to create a new version.",
|
| - "An operation to delete an existing version.",
|
| - "An operation to delete an existing model."
|
| - ],
|
| - "type": "string"
|
| - },
|
| - "startTime": {
|
| - "description": "The time operation processing started.",
|
| - "format": "google-datetime",
|
| - "type": "string"
|
| - },
|
| - "isCancellationRequested": {
|
| - "description": "Indicates whether a request to cancel this operation has been made.",
|
| - "type": "boolean"
|
| - },
|
| - "createTime": {
|
| - "description": "The time the operation was submitted.",
|
| - "format": "google-datetime",
|
| - "type": "string"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__HyperparameterSpec": {
|
| - "description": "Represents a set of hyperparameters to optimize.",
|
| - "id": "GoogleCloudMlV1__HyperparameterSpec",
|
| - "properties": {
|
| - "params": {
|
| - "description": "Required. The set of parameters to tune.",
|
| - "items": {
|
| - "$ref": "GoogleCloudMlV1__ParameterSpec"
|
| - },
|
| - "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"
|
| - },
|
| - "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"
|
| - },
|
| - "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"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__ListJobsResponse": {
|
| - "description": "Response message for the ListJobs method.",
|
| - "id": "GoogleCloudMlV1__ListJobsResponse",
|
| - "properties": {
|
| - "jobs": {
|
| - "description": "The list of jobs.",
|
| - "items": {
|
| - "$ref": "GoogleCloudMlV1__Job"
|
| - },
|
| - "type": "array"
|
| - },
|
| - "nextPageToken": {
|
| - "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| - "type": "string"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__SetDefaultVersionRequest": {
|
| - "description": "Request message for the SetDefaultVersion request.",
|
| - "id": "GoogleCloudMlV1__SetDefaultVersionRequest",
|
| - "properties": {},
|
| - "type": "object"
|
| - },
|
| - "GoogleLongrunning__Operation": {
|
| - "description": "This resource represents a long-running operation that is the result of a\nnetwork API call.",
|
| - "id": "GoogleLongrunning__Operation",
|
| - "properties": {
|
| - "done": {
|
| - "description": "If the value is `false`, it means the operation is still in progress.\nIf true, the operation is completed, and either `error` or `response` is\navailable.",
|
| - "type": "boolean"
|
| - },
|
| - "response": {
|
| - "additionalProperties": {
|
| - "description": "Properties of the object. Contains field @type with type URL.",
|
| - "type": "any"
|
| - },
|
| - "description": "The normal response of the operation in case of success. If the original\nmethod returns no data on success, such as `Delete`, the response is\n`google.protobuf.Empty`. If the original method is standard\n`Get`/`Create`/`Update`, the response should be the resource. For other\nmethods, the response should have the type `XxxResponse`, where `Xxx`\nis the original method name. For example, if the original method name\nis `TakeSnapshot()`, the inferred response type is\n`TakeSnapshotResponse`.",
|
| - "type": "object"
|
| - },
|
| - "name": {
|
| - "description": "The server-assigned name, which is only unique within the same service that\noriginally returns it. If you use the default HTTP mapping, the\n`name` should have the format of `operations/some/unique/name`.",
|
| - "type": "string"
|
| - },
|
| - "error": {
|
| - "$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"
|
| - }
|
| - },
|
| - "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",
|
| - "properties": {
|
| - "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)."
|
| - },
|
| - "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"
|
| - },
|
| - "description": {
|
| - "description": "Optional. The description specified for the model when it was created.",
|
| - "type": "string"
|
| - }
|
| - },
|
| - "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": {},
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__ListVersionsResponse": {
|
| - "description": "Response message for the ListVersions method.",
|
| - "id": "GoogleCloudMlV1__ListVersionsResponse",
|
| - "properties": {
|
| - "nextPageToken": {
|
| - "description": "Optional. Pass this token as the `page_token` field of the request for a\nsubsequent call.",
|
| - "type": "string"
|
| - },
|
| - "versions": {
|
| - "description": "The list of versions.",
|
| - "items": {
|
| - "$ref": "GoogleCloudMlV1__Version"
|
| - },
|
| - "type": "array"
|
| - }
|
| - },
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1__CancelJobRequest": {
|
| - "description": "Request message for the CancelJob method.",
|
| - "id": "GoogleCloudMlV1__CancelJobRequest",
|
| - "properties": {},
|
| - "type": "object"
|
| - },
|
| - "GoogleCloudMlV1beta1__ManualScaling": {
|
| - "description": "Options for manually scaling a model.",
|
| - "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.",
|
| - "format": "int32",
|
| - "type": "integer"
|
| - }
|
| - },
|
| - "type": "object"
|
| }
|
| },
|
| "servicePath": "",
|
|
|
Chromium Code Reviews
Issue 2936613002:
Api-Roll 50: 2017-06-12 (Closed)
