Package com.google.cloud.automl.v1beta1

Interface ModelExportOutputConfigOrBuilder

  • All Superinterfaces:
    com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
    All Known Implementing Classes:
    ModelExportOutputConfig, ModelExportOutputConfig.Builder
    public interface ModelExportOutputConfigOrBuilder
extends com.google.protobuf.MessageOrBuilder

    • Method Detail

      • hasGcsDestination

        boolean hasGcsDestination()
         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", "tf_saved_model", "tf_js", "core_ml".

  Under the directory given as the destination a new one with name
  "model-export-<model-display-name>-<timestamp-of-export-call>",
  where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format,
  will be created. Inside the model and any of its supporting files
  will be written.
        .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
        Returns:
        Whether the gcsDestination field is set.

      • getGcsDestination

        GcsDestination getGcsDestination()
         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", "tf_saved_model", "tf_js", "core_ml".

  Under the directory given as the destination a new one with name
  "model-export-<model-display-name>-<timestamp-of-export-call>",
  where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format,
  will be created. Inside the model and any of its supporting files
  will be written.
        .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;
        Returns:
        The gcsDestination.

      • getGcsDestinationOrBuilder

        GcsDestinationOrBuilder getGcsDestinationOrBuilder()
         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", "tf_saved_model", "tf_js", "core_ml".

  Under the directory given as the destination a new one with name
  "model-export-<model-display-name>-<timestamp-of-export-call>",
  where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format,
  will be created. Inside the model and any of its supporting files
  will be written.
        .google.cloud.automl.v1beta1.GcsDestination gcs_destination = 1;

      • hasGcrDestination

        boolean hasGcrDestination()
         The GCR location where model image is to be pushed to. This location
 may only be set for the following model formats:
   "docker".

 The model image will be created under the given URI.
        .google.cloud.automl.v1beta1.GcrDestination gcr_destination = 3;
        Returns:
        Whether the gcrDestination field is set.

      • getGcrDestination

        GcrDestination getGcrDestination()
         The GCR location where model image is to be pushed to. This location
 may only be set for the following model formats:
   "docker".

 The model image will be created under the given URI.
        .google.cloud.automl.v1beta1.GcrDestination gcr_destination = 3;
        Returns:
        The gcrDestination.

      • getGcrDestinationOrBuilder

        GcrDestinationOrBuilder getGcrDestinationOrBuilder()
         The GCR location where model image is to be pushed to. This location
 may only be set for the following model formats:
   "docker".

 The model image will be created under the given URI.
        .google.cloud.automl.v1beta1.GcrDestination gcr_destination = 3;

      • getModelFormat

        String getModelFormat()
         The format in which the model must be exported. The available, and default,
 formats depend on the problem and model type (if given problem and type
 combination doesn't have a format listed, it means its models are not
 exportable):

 *  For Image Classification mobile-low-latency-1, mobile-versatile-1,
        mobile-high-accuracy-1:
      "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:
      "core_ml" (default).

 *  For Image Object Detection mobile-low-latency-1, mobile-versatile-1,
        mobile-high-accuracy-1:
      "tflite", "tf_saved_model", "tf_js".

 *  For Video Classification cloud,
      "tf_saved_model".

 *  For Video Object Tracking cloud,
      "tf_saved_model".

 *  For Video Object Tracking mobile-versatile-1:
      "tflite", "edgetpu_tflite", "tf_saved_model", "docker".

 *  For Video Object Tracking mobile-coral-versatile-1:
      "tflite", "edgetpu_tflite", "docker".

 *  For Video Object Tracking mobile-coral-low-latency-1:
      "tflite", "edgetpu_tflite", "docker".

 *  For Video Object Tracking mobile-jetson-versatile-1:
      "tf_saved_model", "docker".

 *  For Tables:
      "docker".

 Formats description:

 * tflite - Used for Android mobile devices.
 * 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
            [containers

 quickstart](https:
 //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
 * core_ml - Used for iOS mobile devices.
        string model_format = 4;
        Returns:
        The modelFormat.

      • getModelFormatBytes

        com.google.protobuf.ByteString getModelFormatBytes()
         The format in which the model must be exported. The available, and default,
 formats depend on the problem and model type (if given problem and type
 combination doesn't have a format listed, it means its models are not
 exportable):

 *  For Image Classification mobile-low-latency-1, mobile-versatile-1,
        mobile-high-accuracy-1:
      "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:
      "core_ml" (default).

 *  For Image Object Detection mobile-low-latency-1, mobile-versatile-1,
        mobile-high-accuracy-1:
      "tflite", "tf_saved_model", "tf_js".

 *  For Video Classification cloud,
      "tf_saved_model".

 *  For Video Object Tracking cloud,
      "tf_saved_model".

 *  For Video Object Tracking mobile-versatile-1:
      "tflite", "edgetpu_tflite", "tf_saved_model", "docker".

 *  For Video Object Tracking mobile-coral-versatile-1:
      "tflite", "edgetpu_tflite", "docker".

 *  For Video Object Tracking mobile-coral-low-latency-1:
      "tflite", "edgetpu_tflite", "docker".

 *  For Video Object Tracking mobile-jetson-versatile-1:
      "tf_saved_model", "docker".

 *  For Tables:
      "docker".

 Formats description:

 * tflite - Used for Android mobile devices.
 * 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
            [containers

 quickstart](https:
 //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
 * core_ml - Used for iOS mobile devices.
        string model_format = 4;
        Returns:
        The bytes for modelFormat.

      • getParamsCount

        int getParamsCount()
         Additional model-type and format specific parameters describing the
 requirements for the to be exported model files, any string must be up to
 25000 characters long.

  * For `docker` format:
     `cpu_architecture` - (string) "x86_64" (default).
     `gpu_architecture` - (string) "none" (default), "nvidia".
        map<string, string> params = 2;

      • containsParams

        boolean containsParams​(String key)
         Additional model-type and format specific parameters describing the
 requirements for the to be exported model files, any string must be up to
 25000 characters long.

  * For `docker` format:
     `cpu_architecture` - (string) "x86_64" (default).
     `gpu_architecture` - (string) "none" (default), "nvidia".
        map<string, string> params = 2;

      • getParamsMap

        Map<String,​String> getParamsMap()
         Additional model-type and format specific parameters describing the
 requirements for the to be exported model files, any string must be up to
 25000 characters long.

  * For `docker` format:
     `cpu_architecture` - (string) "x86_64" (default).
     `gpu_architecture` - (string) "none" (default), "nvidia".
        map<string, string> params = 2;

      • getParamsOrDefault

        String getParamsOrDefault​(String key,
                          String defaultValue)
         Additional model-type and format specific parameters describing the
 requirements for the to be exported model files, any string must be up to
 25000 characters long.

  * For `docker` format:
     `cpu_architecture` - (string) "x86_64" (default).
     `gpu_architecture` - (string) "none" (default), "nvidia".
        map<string, string> params = 2;

      • getParamsOrThrow

        String getParamsOrThrow​(String key)
         Additional model-type and format specific parameters describing the
 requirements for the to be exported model files, any string must be up to
 25000 characters long.

  * For `docker` format:
     `cpu_architecture` - (string) "x86_64" (default).
     `gpu_architecture` - (string) "none" (default), "nvidia".
        map<string, string> params = 2;