diff --git a/google/cloud/automl/v1beta1/classification.proto b/google/cloud/automl/v1beta1/classification.proto index 7e54238e..c8475542 100644 --- a/google/cloud/automl/v1beta1/classification.proto +++ b/google/cloud/automl/v1beta1/classification.proto @@ -17,8 +17,8 @@ syntax = "proto3"; package google.cloud.automl.v1beta1; -import "google/cloud/automl/v1beta1/temporal.proto"; import "google/api/annotations.proto"; +import "google/cloud/automl/v1beta1/temporal.proto"; option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl"; option java_outer_classname = "ClassificationProto"; @@ -126,7 +126,10 @@ message ClassificationEvaluationMetrics { // for each example. float false_positive_rate_at1 = 9; - // Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1]. + // Output only. The harmonic mean of + // [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] + // and + // [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1]. float f1_score_at1 = 7; // Output only. The number of model created labels that match a ground truth @@ -153,7 +156,9 @@ message ClassificationEvaluationMetrics { // Output only. Value of the specific cell in the confusion matrix. // The number of values each row has (i.e. the length of the row) is equal // to the length of the `annotation_spec_id` field or, if that one is not - // populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field. + // populated, length of the + // [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] + // field. repeated int32 example_count = 1; } @@ -175,9 +180,9 @@ message ClassificationEvaluationMetrics { // Output only. Rows in the confusion matrix. The number of rows is equal to // the size of `annotation_spec_id`. - // `row[i].value[j]` is the number of examples that have ground truth of the - // `annotation_spec_id[i]` and are predicted as `annotation_spec_id[j]` by - // the model being evaluated. + // `row[i].example_count[j]` is the number of examples that have ground + // truth of the `annotation_spec_id[i]` and are predicted as + // `annotation_spec_id[j]` by the model being evaluated. repeated Row row = 2; } diff --git a/google/cloud/automl/v1beta1/data_items.proto b/google/cloud/automl/v1beta1/data_items.proto index fdbea704..424a0c64 100644 --- a/google/cloud/automl/v1beta1/data_items.proto +++ b/google/cloud/automl/v1beta1/data_items.proto @@ -35,9 +35,11 @@ option ruby_package = "Google::Cloud::AutoML::V1beta1"; // Only images up to 30MB in size are supported. message Image { // Input only. The data representing the image. - // For Predict calls [image_bytes][] must be set, as other options are not - // currently supported by prediction API. You can read the contents of an - // uploaded image by using the [content_uri][] field. + // For Predict calls + // [image_bytes][google.cloud.automl.v1beta1.Image.image_bytes] must be set, + // as other options are not currently supported by prediction API. You can + // read the contents of an uploaded image by using the + // [content_uri][google.cloud.automl.v1beta1.Image.content_uri] field. oneof data { // Image content represented as a stream of bytes. // Note: As with all `bytes` fields, protobuffers use a pure binary diff --git a/google/cloud/automl/v1beta1/image.proto b/google/cloud/automl/v1beta1/image.proto index 61e48d12..5995efc6 100644 --- a/google/cloud/automl/v1beta1/image.proto +++ b/google/cloud/automl/v1beta1/image.proto @@ -98,6 +98,15 @@ message ImageClassificationModelMetadata { // to have a higher latency, but should also have a higher // prediction quality than other models. string model_type = 7; + + // Output only. An approximate number of online prediction QPS that can + // be supported by this model per each node on which it is deployed. + double node_qps = 13; + + // Output only. The number of nodes this model is deployed on. A node is an + // abstraction of a machine resource, which can handle online prediction QPS + // as given in the node_qps field. + int64 node_count = 14; } // Model metadata specific to image object detection. @@ -132,7 +141,7 @@ message ImageObjectDetectionModelMetadata { // full budget and the stop_reason will be `MODEL_CONVERGED`. // Note, node_hour = actual_hour * number_of_nodes_invovled. // For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`, - // the train budget must be between 20,000 and 2,000,000 milli node hours, + // the train budget must be between 20,000 and 900,000 milli node hours, // inclusive. The default value is 216, 000 which represents one day in // wall time. // For model type `mobile-low-latency-1`, `mobile-versatile-1`, @@ -153,7 +162,8 @@ message ImageClassificationModelDeploymentMetadata { // Input only. The number of nodes to deploy the model on. A node is an // abstraction of a machine resource, which can handle online prediction QPS // as given in the model's - // [node_qps][google.cloud.automl.v1p1beta.ImageClassificationModelMetadata.node_qps]. + // + // [node_qps][google.cloud.automl.v1beta1.ImageClassificationModelMetadata.node_qps]. // Must be between 1 and 100, inclusive on both ends. int64 node_count = 1; } diff --git a/google/cloud/automl/v1beta1/io.proto b/google/cloud/automl/v1beta1/io.proto index 6f007f02..5cc61c5e 100644 --- a/google/cloud/automl/v1beta1/io.proto +++ b/google/cloud/automl/v1beta1/io.proto @@ -986,7 +986,7 @@ message ModelExportOutputConfig { oneof destination { // The Google Cloud Storage location where the model is to be written to. // This location may only be set for the following model formats: - // "tflite", "edgetpu_tflite", "core_ml", "docker". + // "tflite", "edgetpu_tflite", "tf_saved_model", "tf_js", "core_ml". // // Under the directory given as the destination a new one with name // "model-export--", @@ -1010,7 +1010,8 @@ message ModelExportOutputConfig { // // * For Image Classification mobile-low-latency-1, mobile-versatile-1, // mobile-high-accuracy-1: - // "tflite" (default), "edgetpu_tflite", "tf_saved_model", "docker". + // "tflite" (default), "edgetpu_tflite", "tf_saved_model", "tf_js", + // "docker". // // * For Image Classification mobile-core-ml-low-latency-1, // mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1: @@ -1021,6 +1022,8 @@ message ModelExportOutputConfig { // * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/) // devices. // * tf_saved_model - A tensorflow model in SavedModel format. + // * tf_js - A [TensorFlow.js](https://www.tensorflow.org/js) model that can + // be used in the browser and in Node.js using JavaScript. // * docker - Used for Docker containers. Use the params field to customize // the container. The container is verified to work correctly on // ubuntu 16.04 operating system. See more at diff --git a/google/cloud/automl/v1beta1/operations.proto b/google/cloud/automl/v1beta1/operations.proto index aba18cd6..c835aba3 100644 --- a/google/cloud/automl/v1beta1/operations.proto +++ b/google/cloud/automl/v1beta1/operations.proto @@ -17,13 +17,13 @@ syntax = "proto3"; package google.cloud.automl.v1beta1; +import "google/api/annotations.proto"; import "google/cloud/automl/v1beta1/io.proto"; import "google/cloud/automl/v1beta1/model.proto"; import "google/cloud/automl/v1beta1/model_evaluation.proto"; import "google/protobuf/empty.proto"; import "google/protobuf/timestamp.proto"; import "google/rpc/status.proto"; -import "google/api/annotations.proto"; option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl"; option java_multiple_files = true; @@ -61,7 +61,8 @@ message OperationMetadata { ExportModelOperationMetadata export_model_details = 22; // Details of ExportEvaluatedExamples operation. - ExportEvaluatedExamplesOperationMetadata export_evaluated_examples_details = 26; + ExportEvaluatedExamplesOperationMetadata export_evaluated_examples_details = + 26; } // Output only. Progress of operation. Range: [0, 100]. @@ -82,29 +83,19 @@ message OperationMetadata { } // Details of operations that perform deletes of any entities. -message DeleteOperationMetadata { - -} +message DeleteOperationMetadata {} // Details of DeployModel operation. -message DeployModelOperationMetadata { - -} +message DeployModelOperationMetadata {} // Details of UndeployModel operation. -message UndeployModelOperationMetadata { - -} +message UndeployModelOperationMetadata {} // Details of CreateModel operation. -message CreateModelOperationMetadata { - -} +message CreateModelOperationMetadata {} // Details of ImportData operation. -message ImportDataOperationMetadata { - -} +message ImportDataOperationMetadata {} // Details of ExportData operation. message ExportDataOperationMetadata { diff --git a/google/cloud/automl/v1beta1/tables.proto b/google/cloud/automl/v1beta1/tables.proto index c8b7809b..467a330c 100644 --- a/google/cloud/automl/v1beta1/tables.proto +++ b/google/cloud/automl/v1beta1/tables.proto @@ -17,6 +17,7 @@ syntax = "proto3"; package google.cloud.automl.v1beta1; +import "google/api/annotations.proto"; import "google/cloud/automl/v1beta1/classification.proto"; import "google/cloud/automl/v1beta1/column_spec.proto"; import "google/cloud/automl/v1beta1/data_items.proto"; @@ -25,7 +26,6 @@ import "google/cloud/automl/v1beta1/ranges.proto"; import "google/cloud/automl/v1beta1/temporal.proto"; import "google/protobuf/struct.proto"; import "google/protobuf/timestamp.proto"; -import "google/api/annotations.proto"; option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl"; option java_multiple_files = true; @@ -103,6 +103,19 @@ message TablesDatasetMetadata { // Model metadata specific to AutoML Tables. message TablesModelMetadata { + // Additional optimization objective configuration. Required for + // `MAXIMIZE_PRECISION_AT_RECALL` and `MAXIMIZE_RECALL_AT_PRECISION`, + // otherwise unused. + oneof additional_optimization_objective_config { + // Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". + // Must be between 0 and 1, inclusive. + float optimization_objective_recall_value = 17; + + // Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". + // Must be between 0 and 1, inclusive. + float optimization_objective_precision_value = 18; + } + // Column spec of the dataset's primary table's column the model is // predicting. Snapshotted when model creation started. // Only 3 fields are used: diff --git a/google/cloud/automl/v1beta1/text.proto b/google/cloud/automl/v1beta1/text.proto index 6a01b7cf..ca722e07 100644 --- a/google/cloud/automl/v1beta1/text.proto +++ b/google/cloud/automl/v1beta1/text.proto @@ -17,8 +17,8 @@ syntax = "proto3"; package google.cloud.automl.v1beta1; -import "google/cloud/automl/v1beta1/classification.proto"; import "google/api/annotations.proto"; +import "google/cloud/automl/v1beta1/classification.proto"; option go_package = "google.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl"; option java_multiple_files = true; @@ -35,31 +35,25 @@ message TextClassificationDatasetMetadata { // Model metadata that is specific to text classification. message TextClassificationModelMetadata { - + // Output only. Classification type of the dataset used to train this model. + ClassificationType classification_type = 3; } // Dataset metadata that is specific to text extraction -message TextExtractionDatasetMetadata { - -} +message TextExtractionDatasetMetadata {} // Model metadata that is specific to text extraction. -message TextExtractionModelMetadata { - -} +message TextExtractionModelMetadata {} // Dataset metadata for text sentiment. message TextSentimentDatasetMetadata { - // Required. A sentiment is expressed as an integer ordinal, where higher value - // means a more positive sentiment. The range of sentiments that will be used - // is between 0 and sentiment_max (inclusive on both ends), and all the values - // in the range must be represented in the dataset before a model can be - // created. - // sentiment_max value must be between 1 and 10 (inclusive). + // Required. A sentiment is expressed as an integer ordinal, where higher + // value means a more positive sentiment. The range of sentiments that will be + // used is between 0 and sentiment_max (inclusive on both ends), and all the + // values in the range must be represented in the dataset before a model can + // be created. sentiment_max value must be between 1 and 10 (inclusive). int32 sentiment_max = 1; } // Model metadata that is specific to text sentiment. -message TextSentimentModelMetadata { - -} +message TextSentimentModelMetadata {}