1// Copyright 2021 Google LLC
2//
3// Licensed under the Apache License, Version 2.0 (the "License");
4// you may not use this file except in compliance with the License.
5// You may obtain a copy of the License at
6//
7//     http://www.apache.org/licenses/LICENSE-2.0
8//
9// Unless required by applicable law or agreed to in writing, software
10// distributed under the License is distributed on an "AS IS" BASIS,
11// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12// See the License for the specific language governing permissions and
13// limitations under the License.
14
15syntax = "proto3";
16
17package google.cloud.automl.v1;
18
19import "google/api/field_behavior.proto";
20import "google/cloud/automl/v1/classification.proto";
21
22option csharp_namespace = "Google.Cloud.AutoML.V1";
23option go_package = "cloud.google.com/go/automl/apiv1/automlpb;automlpb";
24option java_multiple_files = true;
25option java_outer_classname = "ImageProto";
26option java_package = "com.google.cloud.automl.v1";
27option php_namespace = "Google\\Cloud\\AutoMl\\V1";
28option ruby_package = "Google::Cloud::AutoML::V1";
29
30// Dataset metadata that is specific to image classification.
31message ImageClassificationDatasetMetadata {
32  // Required. Type of the classification problem.
33  ClassificationType classification_type = 1 [(google.api.field_behavior) = REQUIRED];
34}
35
36// Dataset metadata specific to image object detection.
37message ImageObjectDetectionDatasetMetadata {
38
39}
40
41// Model metadata for image classification.
42message ImageClassificationModelMetadata {
43  // Optional. The ID of the `base` model. If it is specified, the new model
44  // will be created based on the `base` model. Otherwise, the new model will be
45  // created from scratch. The `base` model must be in the same
46  // `project` and `location` as the new model to create, and have the same
47  // `model_type`.
48  string base_model_id = 1 [(google.api.field_behavior) = OPTIONAL];
49
50  // Optional. The train budget of creating this model, expressed in milli node
51  // hours i.e. 1,000 value in this field means 1 node hour. The actual
52  // `train_cost` will be equal or less than this value. If further model
53  // training ceases to provide any improvements, it will stop without using
54  // full budget and the stop_reason will be `MODEL_CONVERGED`.
55  // Note, node_hour  = actual_hour * number_of_nodes_invovled.
56  // For model type `cloud`(default), the train budget must be between 8,000
57  // and 800,000 milli node hours, inclusive. The default value is 192, 000
58  // which represents one day in wall time. For model type
59  // `mobile-low-latency-1`, `mobile-versatile-1`, `mobile-high-accuracy-1`,
60  // `mobile-core-ml-low-latency-1`, `mobile-core-ml-versatile-1`,
61  // `mobile-core-ml-high-accuracy-1`, the train budget must be between 1,000
62  // and 100,000 milli node hours, inclusive. The default value is 24, 000 which
63  // represents one day in wall time.
64  int64 train_budget_milli_node_hours = 16 [(google.api.field_behavior) = OPTIONAL];
65
66  // Output only. The actual train cost of creating this model, expressed in
67  // milli node hours, i.e. 1,000 value in this field means 1 node hour.
68  // Guaranteed to not exceed the train budget.
69  int64 train_cost_milli_node_hours = 17 [(google.api.field_behavior) = OUTPUT_ONLY];
70
71  // Output only. The reason that this create model operation stopped,
72  // e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
73  string stop_reason = 5 [(google.api.field_behavior) = OUTPUT_ONLY];
74
75  // Optional. Type of the model. The available values are:
76  // *   `cloud` - Model to be used via prediction calls to AutoML API.
77  //               This is the default value.
78  // *   `mobile-low-latency-1` - A model that, in addition to providing
79  //               prediction via AutoML API, can also be exported (see
80  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
81  //               with TensorFlow afterwards. Expected to have low latency, but
82  //               may have lower prediction quality than other models.
83  // *   `mobile-versatile-1` - A model that, in addition to providing
84  //               prediction via AutoML API, can also be exported (see
85  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
86  //               with TensorFlow afterwards.
87  // *   `mobile-high-accuracy-1` - A model that, in addition to providing
88  //               prediction via AutoML API, can also be exported (see
89  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
90  //               with TensorFlow afterwards.  Expected to have a higher
91  //               latency, but should also have a higher prediction quality
92  //               than other models.
93  // *   `mobile-core-ml-low-latency-1` - A model that, in addition to providing
94  //               prediction via AutoML API, can also be exported (see
95  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
96  //               ML afterwards. Expected to have low latency, but may have
97  //               lower prediction quality than other models.
98  // *   `mobile-core-ml-versatile-1` - A model that, in addition to providing
99  //               prediction via AutoML API, can also be exported (see
100  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with Core
101  //               ML afterwards.
102  // *   `mobile-core-ml-high-accuracy-1` - A model that, in addition to
103  //               providing prediction via AutoML API, can also be exported
104  //               (see [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile device with
105  //               Core ML afterwards.  Expected to have a higher latency, but
106  //               should also have a higher prediction quality than other
107  //               models.
108  string model_type = 7 [(google.api.field_behavior) = OPTIONAL];
109
110  // Output only. An approximate number of online prediction QPS that can
111  // be supported by this model per each node on which it is deployed.
112  double node_qps = 13 [(google.api.field_behavior) = OUTPUT_ONLY];
113
114  // Output only. The number of nodes this model is deployed on. A node is an
115  // abstraction of a machine resource, which can handle online prediction QPS
116  // as given in the node_qps field.
117  int64 node_count = 14 [(google.api.field_behavior) = OUTPUT_ONLY];
118}
119
120// Model metadata specific to image object detection.
121message ImageObjectDetectionModelMetadata {
122  // Optional. Type of the model. The available values are:
123  // *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
124  //               calls to AutoML API. Expected to have a higher latency, but
125  //               should also have a higher prediction quality than other
126  //               models.
127  // *   `cloud-low-latency-1` -  A model to be used via prediction
128  //               calls to AutoML API. Expected to have low latency, but may
129  //               have lower prediction quality than other models.
130  // *   `mobile-low-latency-1` - A model that, in addition to providing
131  //               prediction via AutoML API, can also be exported (see
132  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
133  //               with TensorFlow afterwards. Expected to have low latency, but
134  //               may have lower prediction quality than other models.
135  // *   `mobile-versatile-1` - A model that, in addition to providing
136  //               prediction via AutoML API, can also be exported (see
137  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
138  //               with TensorFlow afterwards.
139  // *   `mobile-high-accuracy-1` - A model that, in addition to providing
140  //               prediction via AutoML API, can also be exported (see
141  //               [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
142  //               with TensorFlow afterwards.  Expected to have a higher
143  //               latency, but should also have a higher prediction quality
144  //               than other models.
145  string model_type = 1 [(google.api.field_behavior) = OPTIONAL];
146
147  // Output only. The number of nodes this model is deployed on. A node is an
148  // abstraction of a machine resource, which can handle online prediction QPS
149  // as given in the qps_per_node field.
150  int64 node_count = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
151
152  // Output only. An approximate number of online prediction QPS that can
153  // be supported by this model per each node on which it is deployed.
154  double node_qps = 4 [(google.api.field_behavior) = OUTPUT_ONLY];
155
156  // Output only. The reason that this create model operation stopped,
157  // e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
158  string stop_reason = 5 [(google.api.field_behavior) = OUTPUT_ONLY];
159
160  // Optional. The train budget of creating this model, expressed in milli node
161  // hours i.e. 1,000 value in this field means 1 node hour. The actual
162  // `train_cost` will be equal or less than this value. If further model
163  // training ceases to provide any improvements, it will stop without using
164  // full budget and the stop_reason will be `MODEL_CONVERGED`.
165  // Note, node_hour  = actual_hour * number_of_nodes_invovled.
166  // For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
167  // the train budget must be between 20,000 and 900,000 milli node hours,
168  // inclusive. The default value is 216, 000 which represents one day in
169  // wall time.
170  // For model type `mobile-low-latency-1`, `mobile-versatile-1`,
171  // `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
172  // `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
173  // budget must be between 1,000 and 100,000 milli node hours, inclusive.
174  // The default value is 24, 000 which represents one day in wall time.
175  int64 train_budget_milli_node_hours = 6 [(google.api.field_behavior) = OPTIONAL];
176
177  // Output only. The actual train cost of creating this model, expressed in
178  // milli node hours, i.e. 1,000 value in this field means 1 node hour.
179  // Guaranteed to not exceed the train budget.
180  int64 train_cost_milli_node_hours = 7 [(google.api.field_behavior) = OUTPUT_ONLY];
181}
182
183// Model deployment metadata specific to Image Classification.
184message ImageClassificationModelDeploymentMetadata {
185  // Input only. The number of nodes to deploy the model on. A node is an
186  // abstraction of a machine resource, which can handle online prediction QPS
187  // as given in the model's
188  // [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps].
189  // Must be between 1 and 100, inclusive on both ends.
190  int64 node_count = 1 [(google.api.field_behavior) = INPUT_ONLY];
191}
192
193// Model deployment metadata specific to Image Object Detection.
194message ImageObjectDetectionModelDeploymentMetadata {
195  // Input only. The number of nodes to deploy the model on. A node is an
196  // abstraction of a machine resource, which can handle online prediction QPS
197  // as given in the model's
198  // [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
199  // Must be between 1 and 100, inclusive on both ends.
200  int64 node_count = 1 [(google.api.field_behavior) = INPUT_ONLY];
201}
202