--- /dev/null
+// Copyright 2017 Google Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto3";
+
+package google.cloud.ml.v1;
+
+import "google/api/annotations.proto";
+import "google/api/httpbody.proto";
+
+option go_package = "google.golang.org/genproto/googleapis/cloud/ml/v1;ml";
+option java_multiple_files = true;
+option java_outer_classname = "PredictionServiceProto";
+option java_package = "com.google.cloud.ml.api.v1";
+
+// Copyright 2017 Google Inc. All Rights Reserved.
+//
+// Proto file for the Google Cloud Machine Learning Engine.
+// Describes the online prediction service.
+
+// The Prediction API, which serves predictions for models managed by
+// ModelService.
+service OnlinePredictionService {
+ // Performs prediction on the data in the request.
+ //
+ // **** REMOVE FROM GENERATED DOCUMENTATION
+ rpc Predict(PredictRequest) returns (google.api.HttpBody) {
+ option (google.api.http) = {
+ post: "/v1/{name=projects/**}:predict"
+ body: "*"
+ };
+ }
+}
+
+// Request for predictions to be issued against a trained model.
+//
+// The body of the request is a single JSON object with a single top-level
+// field:
+//
+// <dl>
+// <dt>instances</dt>
+// <dd>A JSON array containing values representing the instances to use for
+// prediction.</dd>
+// </dl>
+//
+// The structure of each element of the instances list is determined by your
+// model's input definition. Instances can include named inputs or can contain
+// only unlabeled values.
+//
+// Not all data includes named inputs. Some instances will be simple
+// JSON values (boolean, number, or string). However, instances are often lists
+// of simple values, or complex nested lists. Here are some examples of request
+// bodies:
+//
+// CSV data with each row encoded as a string value:
+// <pre>
+// {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}
+// </pre>
+// Plain text:
+// <pre>
+// {"instances": ["the quick brown fox", "la bruja le dio"]}
+// </pre>
+// Sentences encoded as lists of words (vectors of strings):
+// <pre>
+// {
+// "instances": [
+// ["the","quick","brown"],
+// ["la","bruja","le"],
+// ...
+// ]
+// }
+// </pre>
+// Floating point scalar values:
+// <pre>
+// {"instances": [0.0, 1.1, 2.2]}
+// </pre>
+// Vectors of integers:
+// <pre>
+// {
+// "instances": [
+// [0, 1, 2],
+// [3, 4, 5],
+// ...
+// ]
+// }
+// </pre>
+// Tensors (in this case, two-dimensional tensors):
+// <pre>
+// {
+// "instances": [
+// [
+// [0, 1, 2],
+// [3, 4, 5]
+// ],
+// ...
+// ]
+// }
+// </pre>
+// Images can be represented different ways. In this encoding scheme the first
+// two dimensions represent the rows and columns of the image, and the third
+// contains lists (vectors) of the R, G, and B values for each pixel.
+// <pre>
+// {
+// "instances": [
+// [
+// [
+// [138, 30, 66],
+// [130, 20, 56],
+// ...
+// ],
+// [
+// [126, 38, 61],
+// [122, 24, 57],
+// ...
+// ],
+// ...
+// ],
+// ...
+// ]
+// }
+// </pre>
+// JSON strings must be encoded as UTF-8. To send binary data, you must
+// base64-encode the data and mark it as binary. To mark a JSON string
+// as binary, replace it with a JSON object with a single attribute named `b64`:
+// <pre>{"b64": "..."} </pre>
+// For example:
+//
+// Two Serialized tf.Examples (fake data, for illustrative purposes only):
+// <pre>
+// {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}
+// </pre>
+// Two JPEG image byte strings (fake data, for illustrative purposes only):
+// <pre>
+// {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}
+// </pre>
+// If your data includes named references, format each instance as a JSON object
+// with the named references as the keys:
+//
+// JSON input data to be preprocessed:
+// <pre>
+// {
+// "instances": [
+// {
+// "a": 1.0,
+// "b": true,
+// "c": "x"
+// },
+// {
+// "a": -2.0,
+// "b": false,
+// "c": "y"
+// }
+// ]
+// }
+// </pre>
+// Some models have an underlying TensorFlow graph that accepts multiple input
+// tensors. In this case, you should use the names of JSON name/value pairs to
+// identify the input tensors, as shown in the following exmaples:
+//
+// For a graph with input tensor aliases "tag" (string) and "image"
+// (base64-encoded string):
+// <pre>
+// {
+// "instances": [
+// {
+// "tag": "beach",
+// "image": {"b64": "ASa8asdf"}
+// },
+// {
+// "tag": "car",
+// "image": {"b64": "JLK7ljk3"}
+// }
+// ]
+// }
+// </pre>
+// For a graph with input tensor aliases "tag" (string) and "image"
+// (3-dimensional array of 8-bit ints):
+// <pre>
+// {
+// "instances": [
+// {
+// "tag": "beach",
+// "image": [
+// [
+// [138, 30, 66],
+// [130, 20, 56],
+// ...
+// ],
+// [
+// [126, 38, 61],
+// [122, 24, 57],
+// ...
+// ],
+// ...
+// ]
+// },
+// {
+// "tag": "car",
+// "image": [
+// [
+// [255, 0, 102],
+// [255, 0, 97],
+// ...
+// ],
+// [
+// [254, 1, 101],
+// [254, 2, 93],
+// ...
+// ],
+// ...
+// ]
+// },
+// ...
+// ]
+// }
+// </pre>
+// If the call is successful, the response body will contain one prediction
+// entry per instance in the request body. If prediction fails for any
+// instance, the response body will contain no predictions and will contian
+// a single error entry instead.
+message PredictRequest {
+ // Required. The resource name of a model or a version.
+ //
+ // Authorization: requires `Viewer` role on the parent project.
+ string name = 1;
+
+ //
+ // Required. The prediction request body.
+ google.api.HttpBody http_body = 2;
+}
git://repos.xcallymotion.com / motion2.git / blobdiff