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DL_DetectAnomalies2_Deploy


Header: AVLDL.h
Namespace: avl
Module: DL_DA

Loads a deep learning model and prepares its execution on a specific target device.

Syntax

C++
C#
 
void avl::DL_DetectAnomalies2_Deploy
(
	const avl::DetectAnomalies2ModelDirectory& inModelDirectory,
	const atl::Optional<avl::DeviceKind::Type>& inDeviceType,
	const int inDeviceIndex,
	avl::DetectAnomalies2ModelId& outModelId
)

Parameters

Name Type Range Default Description
Input value inModelDirectory const DetectAnomalies2ModelDirectory& A Detect Anomalies 2 model stored in a specific disk directory.
Input value inDeviceType const Optional<DeviceKind::Type>& NIL A type of a device selected for deploying and executing the model. If not set, device depending on version (CPU/GPU) of installed Deep Learning add-on is selected. If not set, device depending on version (CPU/GPU) of installed Deep Learning add-on is selected.
Input value inDeviceIndex const int 0 - 0 An index of a device selected for deploying and executing the model.
Output value outModelId DetectAnomalies2ModelId& Identifier of the deployed model

Hints

  • In most cases, this filter should be placed in the INITIALIZE section.
  • Executing this filter may take several seconds.
  • This filter should be connected to DL_DetectAnomalies2 through the ModelId ports.
  • You can edit the model directly through the inModelDirectory. Another option is to use the Deep Learning Editor application and just copy the path to the created model.

Remarks

This product employs WEAVER
  • Passing NIL as inTargetDevice (which is default), is identical to passing DeviceKind::CUDA on GPU version of Deep Learning add-on and DeviceKind::CPU on CPU version on Deep Learning add-on.
  • GPU version of Deep Learning add-on supports DeviceKind::CUDA and DeviceKind::CPU as inTargetDevice value.
  • CPU version of Deep Learning add-on supports only DeviceKind::CPU as inTargetDevice value.

See Also