Reference documentation and code samples for the Cloud AutoML V1beta1 Client class ModelExportOutputConfig.
Output configuration for ModelExport Action.
Generated from protobuf message google.cloud.automl.v1beta1.ModelExportOutputConfig
Methods
__construct
Constructor.
data
array
Optional. Data for populating the Message object.
↳ gcs_destination
Google\Cloud\AutoMl\V1beta1\GcsDestination
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-
↳ gcr_destination
Google\Cloud\AutoMl\V1beta1\GcrDestination
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.
↳ 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". * 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 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](https: //cloud.google.com/vision/automl/docs/containers-gcs-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
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-
hasGcsDestination
setGcsDestination
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-
$this
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.
hasGcrDestination
setGcrDestination
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.
$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".
- 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 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](https: //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
- core_ml - Used for iOS mobile devices.
string
setModelFormat
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 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](https: //cloud.google.com/vision/automl/docs/containers-gcs-quickstart)
- core_ml - Used for iOS mobile devices.
var
string
$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
dockerformat:cpu_architecture- (string) "x86_64" (default).gpu_architecture- (string) "none" (default), "nvidia".
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
dockerformat:cpu_architecture- (string) "x86_64" (default).gpu_architecture- (string) "none" (default), "nvidia".
$this
getDestination
string
