# DL_DetectAnomalies2_Deploy

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

### Syntax

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
inModelDirectory const DetectAnomalies2ModelDirectory& A Detect Anomalies 2 model stored in a specific disk directory.
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.
inDeviceIndex const int 0 - 0 An index of a device selected for deploying and executing the model.
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

• 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.