# DL_SegmentInstances_Deploy

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

### Syntax

void avl::DL_SegmentInstances_Deploy
(
const avl::SegmentInstancesModelDirectory& inModelDirectory,
const atl::Optional<avl::DeviceType::Type>& inTargetDevice,
const atl::Optional<int>& inMaxObjectsCountHint,
avl::SegmentInstancesModelId& outModelId
)


### Parameters

Name Type Default Description
inModelDirectory const SegmentInstancesModelDirectory& A Segment Instances model stored in a specific disk directory.
inTargetDevice const Optional<DeviceType::Type>& NIL 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.
inMaxObjectsCountHint const Optional<int>& NIL Prepares the model for an execution with specific inMaxObjectsCount
outModelId SegmentInstancesModelId& 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_SegmentInstances 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.
• If any subsequent DL_SegmentInstances filter using deployed model has inMaxObjectsCount set to not-NIL, it is advisable to set inMaxObjectsCountHint to the maximum from the values set to this parameter. Following this guidelines should ensure an optimal memory usage and no performance hit on first call to DL_SegmentInstances.

### Remarks

• Passing NIL as inTargetDevice (which is default), is identical to passing DeviceType::CUDA on GPU version of Deep Learning Addon and DeviceType::CPU on CPU version on Deep Learning Addon.
• GPU version of Deep Learning Addon supports DeviceType::CUDA and DeviceType::CPU as inTargetDevice value.
• CPU version of Deep Learning Addon supports only DeviceType::CPU as inTargetDevice value.