Cloud AutoML V1 API - Class Google::Cloud::AutoML::V1::ModelExportOutputConfig (v1.4.0)

Reference documentation and code samples for the Cloud AutoML V1 API class Google::Cloud::AutoML::V1::ModelExportOutputConfig.

Output configuration for ModelExport Action.

Inherits

def gcs_destination() -> ::Google::Cloud::AutoML::V1::GcsDestination

Returns

(::Google::Cloud::AutoML::V1::GcsDestination) — 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-

def gcs_destination=(value) -> ::Google::Cloud::AutoML::V1::GcsDestination

Parameter

value (::Google::Cloud::AutoML::V1::GcsDestination) — 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-

Returns

(::Google::Cloud::AutoML::V1::GcsDestination) — 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-

def model_format() -> ::String

Returns

def model_format=(value) -> ::String

Parameter

value (::String) —
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.

Returns

def params() -> ::Google::Protobuf::Map{::String => ::String}

Returns

(::Google::Protobuf::Map{::String => ::String}) —
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".

def params=(value) -> ::Google::Protobuf::Map{::String => ::String}

Parameter

value (::Google::Protobuf::Map{::String => ::String}) —
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".

Returns

(::Google::Protobuf::Map{::String => ::String}) —
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".