Cloud AutoML V1 Client - Class ModelExportOutputConfig (1.6.3)

Reference documentation and code samples for the Cloud AutoML V1 Client class ModelExportOutputConfig.

Output configuration for ModelExport Action.

Generated from protobuf message google.cloud.automl.v1.ModelExportOutputConfig

Namespace

Google \ Cloud \ AutoMl \ V1

Methods

__construct

Constructor.

Parameters
Name	Description
`data`	`array` Optional. Data for populating the Message object.
`↳ gcs_destination`	`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-
`↳ model_format`	`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.
`↳ params`	`array\|Google\Protobuf\Internal\MapField` 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".

getGcsDestination

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
Type	Description
`Google\Cloud\AutoMl\V1\GcsDestination\|null`

hasGcsDestination

setGcsDestination

Required. The Google Cloud Storage location where the model is to be written to.

Parameter
Name	Description
`var`	`Google\Cloud\AutoMl\V1\GcsDestination`

Returns
Type	Description
`$this`

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". 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
Type	Description
`string`

setModelFormat

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.

Parameter
Name	Description
`var`	`string`

Returns
Type	Description
`$this`

getParams

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
Type	Description
`Google\Protobuf\Internal\MapField`

setParams

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".

Parameter
Name	Description
`var`	`array\|Google\Protobuf\Internal\MapField`

Returns
Type	Description
`$this`

getDestination

Returns
Type	Description
`string`