public static final class ModelExportOutputConfig.Builder extends GeneratedMessageV3.Builder<ModelExportOutputConfig.Builder> implements ModelExportOutputConfigOrBuilder
Output configuration for ModelExport Action.
Protobuf type google.cloud.automl.v1beta1.ModelExportOutputConfig
Static Methods
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()
Returns
Methods
addRepeatedField(Descriptors.FieldDescriptor field, Object value)
public ModelExportOutputConfig.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
Returns
Overrides
build()
public ModelExportOutputConfig build()
Returns
buildPartial()
public ModelExportOutputConfig buildPartial()
Returns
clear()
public ModelExportOutputConfig.Builder clear()
Returns
Overrides
clearDestination()
public ModelExportOutputConfig.Builder clearDestination()
Returns
clearField(Descriptors.FieldDescriptor field)
public ModelExportOutputConfig.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
Returns
Overrides
clearGcrDestination()
public ModelExportOutputConfig.Builder clearGcrDestination()
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;
clearGcsDestination()
public ModelExportOutputConfig.Builder clearGcsDestination()
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;
public ModelExportOutputConfig.Builder clearModelFormat()
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
- core_ml - Used for iOS mobile devices.
string model_format = 4;
clearOneof(Descriptors.OneofDescriptor oneof)
public ModelExportOutputConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
Returns
Overrides
clearParams()
public ModelExportOutputConfig.Builder clearParams()
Returns
clone()
public ModelExportOutputConfig.Builder clone()
Returns
Overrides
containsParams(String key)
public 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;
getDefaultInstanceForType()
public ModelExportOutputConfig getDefaultInstanceForType()
Returns
getDescriptorForType()
public Descriptors.Descriptor getDescriptorForType()
Returns
Overrides
getDestinationCase()
public ModelExportOutputConfig.DestinationCase getDestinationCase()
Returns
getGcrDestination()
public 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;
getGcrDestinationBuilder()
public GcrDestination.Builder getGcrDestinationBuilder()
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;
getGcrDestinationOrBuilder()
public 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;
getGcsDestination()
public 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;
getGcsDestinationBuilder()
public GcsDestination.Builder getGcsDestinationBuilder()
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;
getGcsDestinationOrBuilder()
public 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;
public 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
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.
string model_format = 4;
| Type | Description |
| String | The modelFormat.
|
public 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
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.
string model_format = 4;
| Type | Description |
| ByteString | The bytes for modelFormat.
|
getMutableParams()
public Map<String,String> getMutableParams()
Use alternate mutation accessors instead.
getParams()
public Map<String,String> getParams()
Returns
getParamsCount()
public 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;
getParamsMap()
public 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 key, String defaultValue)
public 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 key)
public 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;
hasGcrDestination()
public 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;
| Type | Description |
| boolean | Whether the gcrDestination field is set.
|
hasGcsDestination()
public 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;
| Type | Description |
| boolean | Whether the gcsDestination field is set.
|
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
Overrides
internalGetMapField(int number)
protected MapField internalGetMapField(int number)
Parameter
Returns
Overrides
internalGetMutableMapField(int number)
protected MapField internalGetMutableMapField(int number)
Parameter
Returns
Overrides
isInitialized()
public final boolean isInitialized()
Returns
Overrides
mergeFrom(ModelExportOutputConfig other)
public ModelExportOutputConfig.Builder mergeFrom(ModelExportOutputConfig other)
Parameter
Returns
public ModelExportOutputConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
Returns
Overrides
Exceptions
mergeFrom(Message other)
public ModelExportOutputConfig.Builder mergeFrom(Message other)
Parameter
Returns
Overrides
mergeGcrDestination(GcrDestination value)
public ModelExportOutputConfig.Builder mergeGcrDestination(GcrDestination value)
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;
mergeGcsDestination(GcsDestination value)
public ModelExportOutputConfig.Builder mergeGcsDestination(GcsDestination value)
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;
mergeUnknownFields(UnknownFieldSet unknownFields)
public final ModelExportOutputConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
Returns
Overrides
putAllParams(Map<String,String> values)
public ModelExportOutputConfig.Builder putAllParams(Map<String,String> values)
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;
putParams(String key, String value)
public ModelExportOutputConfig.Builder putParams(String key, String value)
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;
removeParams(String key)
public ModelExportOutputConfig.Builder removeParams(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;
setField(Descriptors.FieldDescriptor field, Object value)
public ModelExportOutputConfig.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
Returns
Overrides
setGcrDestination(GcrDestination value)
public ModelExportOutputConfig.Builder setGcrDestination(GcrDestination value)
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;
setGcrDestination(GcrDestination.Builder builderForValue)
public ModelExportOutputConfig.Builder setGcrDestination(GcrDestination.Builder builderForValue)
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;
setGcsDestination(GcsDestination value)
public ModelExportOutputConfig.Builder setGcsDestination(GcsDestination value)
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;
setGcsDestination(GcsDestination.Builder builderForValue)
public ModelExportOutputConfig.Builder setGcsDestination(GcsDestination.Builder builderForValue)
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;
public ModelExportOutputConfig.Builder setModelFormat(String value)
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
- core_ml - Used for iOS mobile devices.
string model_format = 4;
| Name | Description |
| value | String
The modelFormat to set.
|
Returns
public ModelExportOutputConfig.Builder setModelFormatBytes(ByteString value)
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
- core_ml - Used for iOS mobile devices.
string model_format = 4;
| Name | Description |
| value | ByteString
The bytes for modelFormat to set.
|
Returns
setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
public ModelExportOutputConfig.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
Parameters
Returns
Overrides
setUnknownFields(UnknownFieldSet unknownFields)
public final ModelExportOutputConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
Returns
Overrides
