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Deep Learning by Ngene - Toolkit for LabVIEW Download

Deep Learning Toolkit for LabVIEW

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Version8.1.1.260
ReleasedMay 30, 2025
Publisher Ngene
License Ngene Custom
LabVIEW VersionLabVIEW>=20.0
Operating System Windows
Used By ngene_deepltk_patchcore_anomaly_detection_addon  
Project links Homepage   Documentation   Repository   Discussion

Description

Empowering LabVIEW with Deep Learning
DeepLTK is a Deep Learning Toolkit for LabVIEW providing high-level API to build, configure, visualize, train, analyze and deploy Deep Neural Networks within LabVIEW. The toolkit is completely developed in LabVIEW and does not have any outer dependencies, which simplifies the installation, development, deployment and distribution of toolkit based applications and systems (particularly, can be easily deployed on NI's Real Time targets).

Main Features
Create, configure, train, and deploy deep neural networks (DNNs) in LabVIEW
Accelerate training and deployment of DNNs on GPUs
Save trained networks and load for deployment
Visualize network topology and common metrics (memory footprint, computational complexity)
Deploy pre-trained networks on NI's LabVIEW Real-Time target for inference
Speed up pre-trained networks by employing network graph optimization utilities
Analyze and evaluate network's performance
Start with ready-to-run real-world examples
Accelerate inference on FPGAs (with help of DeepLTK FPGA Add-on)

Supported Layers:
Input (1D, 3D)
Augmentations: Noise, Flip(Vertical, Horizontal), Brightness, Contrast, Hue, Saturation, Shear, Scale(Zoom), Blur, Move.
Fully Connected - FC
Convolutional - Conv2D
Convolutional 1D - Conv1D
Convolutional Advanced - Conv2D_Adv
Upsampling
ShortCut (Residual)
Concatenation
Batch Normalization
Activation
Pooling (MaxPool, AvgPool, GlobalMax, GlobalAvg)
DropOut (1D, 3D)
SoftMax (1D, 3D)
YOLO_v2 (object detection)
YOLO_v4 (object detection)

Activation types:
Linear
Sigmoid
Hyperbolic Tangent
ReLU
Leaky ReLU
ReLU6
Mish
Swish

Solver (Optimization Algorithm):
Stochastic Gradient Descend (SGD) based Backpropagation algorithm with Momentum and Weight decay
Adam - Stochastic gradient descent method which is based on adaptive estimation of first-order and second-order moments.

Loss Functions:
MSE - Mean Squared Error
Cross Entropy (LogLoss)
Object Detection (YOLO_v2)
Object Detection (YOLO_v4)

Examples:
Examples are available to demonstrate the applications of the toolkit in:
1. MNIST_Classifier_MLP.vi - training the deep neural network for image classification task in handwritten digit recognition problem (based on MNIST database) on 1 dimensional dataset using MLP (Multilayer Perceptron) architecture
2. MNIST_Classifier_CNN(Train).vi - training the deep neural network for image classification task in handwritten digit recognition problem using CNN (Convolutional Neural Network) architecture
3. MNIST_Classifier(Deploy).vi - deploying pretrained network by automatically loading network configuration and weights files generated from the examples above.
4. MNIST(RT_Deployment) project - deployment of pretrained model on NI's Real Time targets.
5. YOLO_Object_Detection(Cam).vi - automatically building and loading pretrained network for object detection based on YOLO (You Only Look Once) architecture.
6. Object_Detection project - demonstrates training of neural network for object detection on simple dataset.

More Examples: https://github.com/ngenehub/deepltk_examples

Release Notes

8.1.1.260 (May 30, 2025)

v8.1.1
This version brings a couple of new functionalities and bug fixes. It does not break backward compatibility with all v8.x.x toolkit versions.

Features
1. Added the ability to disable CUDA library loading at compile time to prevent issues on machines without GPUs. Implemented wit help of “NO_CUDA” == “TRUE” Conditional Disabling variable.
2. Added support for OpenBLAS acceleration on Real-Time (RT) targets with Intel Atom CPUs.

Bug Fixes
1. Fixed Receptive field calculation bug in MaxPool layer.
2. Fixed automatic padding size calculation in MaxPool, AvgPool layers when stride is equal to 1.
3. Fixed an issue where the full call chain was not provided in error messages.
4. Enhanced error handling and UI in “GPU Info” tool.
5. Enhanced error handling in “NN_Get_T_dT” utility.
6. Enhanced Workspace memory management to allow layer deletion from the network.
7. Fixed a bug preventing BN merge functionality on RT targets (incorrect GPU API calls).
8. Fixed missing context help in several Vis.
9. Resolved memory leakage issue in the following APIs.
a. NN_Create.vi
b. NN_GPU_Get_Info.vi
c. NN_Layer_Create(SoftMax).vi
d. NN_BN_merge.vi


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