public sealed class ModelExportOutputConfig : IMessage<ModelExportOutputConfig>, IEquatable<ModelExportOutputConfig>, IDeepCloneable<ModelExportOutputConfig>, IBufferMessage, IMessageReference documentation and code samples for the Google AutoML v1 API class ModelExportOutputConfig.
Output configuration for ModelExport Action.
Implements
IMessageModelExportOutputConfig, IEquatableModelExportOutputConfig, IDeepCloneableModelExportOutputConfig, IBufferMessage, IMessageNamespace
Google.Cloud.AutoML.V1Assembly
Google.Cloud.AutoML.V1.dll
Constructors
ModelExportOutputConfig()
public ModelExportOutputConfig()ModelExportOutputConfig(ModelExportOutputConfig)
public ModelExportOutputConfig(ModelExportOutputConfig other)| Parameter | |
|---|---|
| Name | Description |
other |
ModelExportOutputConfig |
Properties
DestinationCase
public ModelExportOutputConfig.DestinationOneofCase DestinationCase { get; }| Property Value | |
|---|---|
| Type | Description |
ModelExportOutputConfigDestinationOneofCase |
|
GcsDestination
public GcsDestination GcsDestination { get; set; }Required. 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.
| Property Value | |
|---|---|
| Type | Description |
GcsDestination |
|
ModelFormat
public string ModelFormat { get; set; }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". Formats description:
tflite - Used for Android mobile devices.
- edgetpu_tflite - Used for Edge TPU devices.
- tf_saved_model - A tensorflow model in SavedModel format.
- tf_js - A TensorFlow.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
- core_ml - Used for iOS mobile devices.
| Property Value | |
|---|---|
| Type | Description |
string |
|
Params
public MapField<string, string> Params { get; }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
dockerformat:cpu_architecture- (string) "x86_64" (default).gpu_architecture- (string) "none" (default), "nvidia".
| Property Value | |
|---|---|
| Type | Description |
MapFieldstringstring |
|