LEADERG AI ZOO - Advanced AI Software (Low Code, Jupyter Lab User Interface)










Provide organized and useful artificial intelligence algorithms, which can be applied to product defect detection, medical image analysis, artificial intelligence teaching, crime detection and prevention, access control and attendance, smart long-term photography, public safety, etc. Help to save development time.



Turn the artificial intelligence algorithms into a web user interface that everyone can use. You can perform deep learning and analyze data without programing, and help to solve the industry's lack of AI talents.



Provide the source code and reference source. You can customize AI products for mass production by yourself. Free of royalties.



Built-in Python, CUDA, cuDNN, TensorFlow, Keras, PyTorch and other software packages and sample codes. Help to save the trouble of installing packages and finding sample codes.




[ Architecture ]



LEADERG-AI-ZOO-AI Software-Architecture-20200505.png






[ Features ]


    • Software with Python, PyTorch, TensorFlow, Keras, OpenVINO, OpenCV, etc.


    • Support deep learning


    • Support AI algorithms


    • Support image processing


    • Support many image analysis methods


    • Support template comparison


    • Support object analysis


    • Support barcode recognition


    • Support QR code


    • Support multi-core and multi-processor computers


    • Support GPU acceleration


    • Support custom libraries


    • Support TensorFlow 2.4


    • Support PyTorch 1.7.0


    • Support CUDA 11.0


    • Support cuDNN 8.0.4


    • No need to upload data to public cloud




[ Supported Operating Systems ]


Please choose one of these two operating systems. If you need to support two operating systems, please buy two licenses.


    •  Windows 10 64 bit


    •  Ubuntu Linux 18.04 64 bit




[ Hardware Requirements ]


    • Intel x64 CPU with AVX2 instruction set


    • 16 GB RAM or higher


    • 100 GB of free space on a disk


    • Screen resolution is 1280 x 720 or higher


    • Mouse or other pointing device


    • NVIDIA GPU with CUDA support. (GPU memory 16 GB or higher is required)


    • Intel Movidius Myriad X. (Option for Acceleration)





[ Software Advantages ]



    • Update for the latest algorithms every month


    • Easy to get started. Development integration is fast


    • Applications: semiconductor industry, panel industry, petrochemical industry, PCB industry, school lessons, etc.


    • Won the champion in the International AI Competition. The prize is 100K USD.


    • Support Python, PyTorch, TensorFlow, Keras. With CPU and GPU support.


    • With verified deep learning solutions: SSD, VGG, ResNet, YOLOv3, MaskRCNN, data analysis, stock forecast, etc.





[ Edition Description ]



LEADERG AI ZOO - One Site License:


For one computer.



LEADERG AI ZOO - Enterprise License for One Department:


For one department of one company.



LEADERG AI ZOO - Education License for One Department:


For one department of one school.






[ Software Comparison ]



Algorithm Jupyter-BERT
Description BERT text training, text generation, sentence judgment


Algorithm Jupyter-Chatterbot
Description Chatbot


Algorithm Jupyter-COM
Description Serial port


Algorithm Jupyter-CURL
Description Using the CURL library to fetching web pages 


Algorithm Jupyter-Data-Conv1D-Keras
Description Data analysis using a 1D convolutional neural network


Algorithm Jupyter-Data-Dense-Sin-PyTorch
Description Using Densenet to learn Sine wave and inference


Algorithm Jupyter-Data-Dense-Stock-PyTorch
Description Using XGBoost to predict stocks


Algorithm Jupyter-Data-Dense-Fraud-Detection
Description Using XGBoost to fraud detection


Algorithm Jupyter-Data-Genetic-Algorithm-Flow-Shop
Description Genetic Algorithm Factory Scheduling


Algorithm Jupyter-Data-Genetic-Algorithm-Job-Shop
Description Genetic Algorithm Factory Scheduling


Algorithm Jupyter-Data-Genetic-Algorithm-Job-Shop-NSGA-II
Description Genetic Algorithm Factory Scheduling


Algorithm Jupyter-Data-Gradient-Boosting-Classification
Description Using Gradient-Boosting to predict Titanic survivors


Algorithm Jupyter-Data-Gradient-Boosting-Regression
Description Using Gradient-Boosting to predict Boston house prices


Algorithm Jupyter-Data-JSON
Description Using Python to read in json, print out json, and write to output.json


Algorithm Jupyter-Data-LightGBM-Classification
Description Using LightGBM to predict Titanic survivors


Algorithm Jupyter-Data-LightGBM-Regression
Description Using LightGBM to predict Boston house prices


Algorithm Jupyter-Data-LSTM-PyTorch
Description Using PyTorch LSTM to predict stock prices


Algorithm Jupyter-Data-Matplot-Stock
Description Python, plotting data


Algorithm Jupyter-Data-Read-Sin
Description Python read Sine wave value and draw into table


Algorithm Jupyter-Data-Read-Write-CSV
Description Python, read and store CSV data


Algorithm Jupyter-Data-Read-Write-Excel
Description Python, read and store Excel data


Algorithm Jupyter-Data-Regression-Forest
Description Forecasting Boston house prices with Regression-Forest


Algorithm Jupyter-Data-Regression-Forest-Sin
Description Forecasting Sinusoid with Regression-Forest


Algorithm Jupyter-Data-Regression-Forest-Stock
Description Forecasting Stock with Regression-Forest


Algorithm Jupyter-Data-SVM
Description Use SVM for data clustering


Algorithm Jupyter-Data-Taiwan-Mask
Description Check the information on the stock of masks of the Taiwan pharmacy


Algorithm Jupyter-Data-XGBoost-Classification
Description Using XGBoost to predict Titanic survivors


Algorithm Jupyter-Data-XGBoost-GPU-Test
Description XGBoost GPU Test


Algorithm Jupyter-Data-XGBoost-Regression
Description Using XGBoost to predict Boston house prices


Algorithm Jupyter-Data-XGBoost-Regression-Stock-Taiwan
Description Using XGBoost to predict stock prices


Algorithm Jupyter-Data-XGBoost-Regression-Time-Series
Description Using XGBoost Regression Time Series to predict stock prices


Algorithm Jupyter-File-Batch-Rename
Description Rename files in batch


Algorithm Jupyter-File-Download
Description Download file and display progress bar


Algorithm Jupyter-GPT-2
Description GPT-2 automatically generates text


Algorithm Jupyter-Http-Server-AIOHTTP
Description Python AIOHTTP Web Server


Algorithm Jupyter-Http-Server-Flask
Description Python Flask, a lightweight web application framework


Algorithm Jupyter-Image-Augmentation
Description Image augmentation


Algorithm Jupyter-Image-Barcode
Description Read 1D and 2D barcodes


Algorithm Jupyter-Image-Barcode-Generator
Description Generate 1D barcodes


Algorithm Jupyter-Image-Batch-Resize
Description Image batch resize


Algorithm Jupyter-Image-Classification-AlexNet-PyTorch
Description Using PyTorch AlexNet for defect classification


Algorithm Jupyter-Image-Classification-Attention-PyTorch
Description Using PyTorch Attention for defect classification


Algorithm Jupyter-Image-Classification-CSPResNeXt-50-CPP
Description Using PyTorch CSPResNeXt for defect classification


Algorithm Jupyter-Image-Classification-DenseNet-OCR-Keras
Description Recognize characters with DenseNet


Algorithm Jupyter-Image-Classification-DenseNet121-PyTorch
Description Using PyTorch DenseNet121 for defect classification


Algorithm Jupyter-Image-Classification-EfficientNet-PyTorch
Description Using PyTorch EfficientNet for defect classification


Algorithm Jupyter-Image-Classification-GoogleNet-PyTorch
Description Using PyTorch GoogleNet for defect classification


Algorithm Jupyter-Image-Classification-InceptionV2-PyTorch
Description Using PyTorch InceptionV2 for defect classification


Algorithm Jupyter-Image-Classification-InceptionV3-CAM-PyTorch
Description Use InceptionV3 and Class Activation Mapping visualization to classify images under PyTorch.


Algorithm Jupyter-Image-Classification-InceptionV3-PyTorch
Description Using PyTorch InceptionV3 for defect classification


Algorithm Jupyter-Image-Classification-InceptionV4-PyTorch
Description Using PyTorch InceptionV4 for defect classification


Algorithm Jupyter-Image-Classification-Inception_ResNet_v1-PyTorch
Description Using PyTorch Inception_ResNet_v1 for defect classification


Algorithm Jupyter-Image-Classification-Inception_ResNet_v2-PyTorch
Description Using PyTorch Inception_ResNet_v2 for defect classification


Algorithm Jupyter-Image-Classification-MNASNet-PyTorch
Description Using PyTorch MNASNet for defect classification


Algorithm Jupyter-Image-Classification-MobileNetV1-PyTorch
Description Using PyTorch MobileNetV1 for defect classification


Algorithm Jupyter-Image-Classification-MobileNetV2-PyTorch
Description Using PyTorch MobileNetV2 for defect classification


Algorithm Jupyter-Image-Classification-MobileNetV3-PyTorch
Description Using PyTorch MobileNetV3 for defect classification


Algorithm Jupyter-Image-Classification-PreactresNet18-PyTorch
Description Using PyTorch PreactresNet18 for defect classification


Algorithm Jupyter-Image-Classification-RegNet-PyTorch
Description Using PyTorch RegNet for defect classification


Algorithm Jupyter-Image-Classification-Resnet_in_Resnet-PyTorch
Description Using PyTorch Resnet_in_Resnet for defect classification


Algorithm Jupyter-Image-Classification-ResNet50-PyTorch
Description Using PyTorch ResNet50 for defect classification


Algorithm Jupyter-Image-Classification-ResNeXt101-PyTorch
Description Using PyTorch ResNeXt101 for defect classification


Algorithm Jupyter-Image-Classification-SENet-PyTorch
Description Using PyTorch SENet for defect classification


Algorithm Jupyter-Image-Classification-ShuffleNetV1-PyTorch
Description Using PyTorch ShuffleNetV1 for defect classification


Algorithm Jupyter-Image-Classification-ShuffleNetV2-PyTorch
Description Using PyTorch ShuffleNetV2 for defect classification


Algorithm Jupyter-Image-Classification-SqueezeNet-PyTorch
Description Using PyTorch SqueezeNet for defect classification


Algorithm Jupyter-Image-Classification-VGG16-PyTorch
Description Using PyTorch VGG16 for defect classification


Algorithm Jupyter-Image-Classification-Xception-PyTorch
Description Using PyTorch Xception for defect classification


Algorithm Jupyter-Image-CycleGAN-PyTorch
Description Use CycleGAN to convert horses to zebras


Algorithm Jupyter-Image-DICOM
Description DICOM


Algorithm Jupyter-Image-FaceNet
Description FaceNet face recognition


Algorithm Jupyter-Image-Fingerprint-Recognition
Description Image fingerprint recognition


Algorithm Jupyter-Image-GAN-Compression-PyTorch
Description GAN compression model use


Algorithm Jupyter-Image-Ganomaly
Description Use GANomaly for defect detection


Algorithm Jupyter-Image-Gauge-Reader
Description Gauge scale detection


Algorithm Jupyter-Image-Human-Pose-PyTorch
Description Detect human posture


Algorithm Jupyter-Image-LPRNet-PyTorch
Description Use LPRNet to identify license plates


Algorithm Jupyter-Image-Object-Detection-CSPResNeXt50-PANet-SPP-CPP
Description PyTorch CSPResNeXt50 PANet for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-DETR-PyTorch
Description PyTorch DETR for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-EfficientDet-Keras
Description Keras EfficientDet for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-FasterRCNN-Keras
Description Keras FasterRCNN for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-MobileNetV1-SSD300-PyTorch
Description PyTorch MobileNetV1-SSD300 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-MobileNetV1-SSD512-PyTorch
Description PyTorch MobileNetV1-SSD512 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-MobileNetV2-SSD300-PyTorch
Description PyTorch MobileNetV2-SSD300 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-MobileNetV2-SSD512-PyTorch
Description PyTorch MobileNetV2-SSD512 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-MobileNetV3-SSD300-PyTorch
Description PyTorch MobileNetV3-SSD300 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-MobileNetV3-SSD512-PyTorch
Description PyTorch MobileNetV3-SSD512 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-ResNet152-SSD512-PyTorch
Description PyTorch ResNet152-SSD512 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-ResNet50-SSD300-PyTorch
Description PyTorch ResNet50-SSD300 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-ResNet50-SSD512-PyTorch
Description PyTorch ResNet50-SSD512 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-VGG16-SSD512-PyTorch
Description PyTorch VGG16-SSD512 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-VGG19-SSD512-PyTorch
Description PyTorch VGG19-SSD512 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-YOLOv4-CPP
Description YOLOv4 for detecting surface defects


Algorithm Jupyter-Image-Object-Detection-YOLOv4-Multiple-Object-Tracking-CPP
Description Use YOLOv4 for multi-object tracking


Algorithm Jupyter-Image-Object-Detection-YOLOv4-Tiny-CPP
Description YOLOv4 Tiny for detecting surface defects


Algorithm Jupyter-Image-OCR
Description Python. Character recognition with Tesseract-OCR


Algorithm Jupyter-Image-ONNX
Description Use ONNX model for image classification, image detection, image segmentation


Algorithm Jupyter-Image-OpenCV-Adaptive-Threshold
Description Python. OpenCV adaptive threshold


Algorithm Jupyter-Image-OpenCV-Add
Description Python. OpenCV image add


Algorithm Jupyter-Image-OpenCV-Bilateral-Filter
Description Python. OpenCV bilateral filter


Algorithm Jupyter-Image-OpenCV-Binarize
Description Python. OpenCV image binarization


Algorithm Jupyter-Image-OpenCV-Black-Hat
Description Python OpenCV for morphology Black Hat


Algorithm Jupyter-Image-OpenCV-Blob
Description Python OpenCV Blob. binary image geometry extraction and separation


Algorithm Jupyter-Image-OpenCV-Blur
Description Python. OpenCV image blur


Algorithm Jupyter-Image-OpenCV-Brightness
Description Python. OpenCV image brightness


Algorithm Jupyter-Image-OpenCV-Canny
Description Python. OpenCV Canny edge detection


Algorithm Jupyter-Image-OpenCV-Capture-Image
Description Python OpenCV. Continuously capture images from webcam and display on screen


Algorithm Jupyter-Image-OpenCV-Connected-Components
Description Python OpenCV image connected component labeling method


Algorithm Jupyter-Image-OpenCV-Copy
Description Python. OpenCV image copy


Algorithm Jupyter-Image-OpenCV-Create-And-Fill
Description Python. OpenCV create and fill


Algorithm Jupyter-Image-OpenCV-Crop
Description Python. OpenCV image crop


Algorithm Jupyter-Image-OpenCV-DCT
Description Python. OpenCV image DCT frequency domain


Algorithm Jupyter-Image-OpenCV-DeBlur
Description Python. OpenCV image deblurring


Algorithm Jupyter-Image-OpenCV-DFT
Description Python. OpenCV image DFT frequency domain


Algorithm Jupyter-Image-OpenCV-Dilation
Description OpenCV image dilation example


Algorithm Jupyter-Image-OpenCV-Erosion
Description OpenCV image erosion example


Algorithm Jupyter-Image-OpenCV-Filter2D
Description OpenCV filter2D example


Algorithm Jupyter-Image-OpenCV-Find-Contours
Description OpenCV image find contours


Algorithm Jupyter-Image-OpenCV-Gaussian-Blur
Description OpenCV image Gaussian blur example


Algorithm Jupyter-Image-OpenCV-GetWH
Description OpenCV example of getting image width and height


Algorithm Jupyter-Image-OpenCV-Gray
Description Python OpenCV. Read the color image file of input.png and convert it to grayscale and display it on the screen


Algorithm Jupyter-Image-OpenCV-Histogram-Calculation
Description OpenCV histogram calculation example


Algorithm Jupyter-Image-OpenCV-Histogram-Comparison
Description OpenCV histogram comparison example


Algorithm Jupyter-Image-OpenCV-Histogram-Equalization
Description OpenCV histogram equalization example


Algorithm Jupyter-Image-OpenCV-Hough-Circle-Transform
Description OpenCV image Hough circle transform


Algorithm Jupyter-Image-OpenCV-Hough-Transform
Description OpenCV image Hough line transform


Algorithm Jupyter-Image-OpenCV-InRange
Description OpenCV inRange example


Algorithm Jupyter-Image-OpenCV-Laplace
Description OpenCV Laplace example


Algorithm Jupyter-Image-OpenCV-Median-Blur
Description OpenCV image blur example


Algorithm Jupyter-Image-OpenCV-Merge
Description OpenCV image merge example


Algorithm Jupyter-Image-OpenCV-Morphological-Gradient
Description OpenCV image gradient example


Algorithm Jupyter-Image-OpenCV-Opening-And-Closing
Description OpenCV morphology examples of open and close


Algorithm Jupyter-Image-OpenCV-Read-Write-Image-File
Description Python OpenCV. Reads input.png and saves it as output.jpg


Algorithm Jupyter-Image-OpenCV-ReMap
Description OpenCV image remap example


Algorithm Jupyter-Image-OpenCV-Resize
Description OpenCV image resize example


Algorithm Jupyter-Image-OpenCV-Rotate
Description OpenCV image rotation example


Algorithm Jupyter-Image-OpenCV-Sharpness
Description OpenCV image sharpness example


Algorithm Jupyter-Image-OpenCV-Shift
Description OpenCV image shift example


Algorithm Jupyter-Image-OpenCV-Sobel
Description OpenCV Sobel algorithm example


Algorithm Jupyter-Image-OpenCV-Split
Description OpenCV split example


Algorithm Jupyter-Image-OpenCV-Top-Hat
Description OpenCV top hat calculation example


Algorithm Jupyter-Image-Pix2Pix-PyTorch
Description Use pix2pix GAN for map conversion


Algorithm Jupyter-Image-PSGAN-PyTorch
Description Use PSGAN for face makeup


Algorithm Jupyter-Image-QRcode-Generator
Description Generate QR code barcode


Algorithm Jupyter-Image-Screen-Capture
Description Python. After capturing the desktop screen, save it to output.png


Algorithm Jupyter-Image-Segmentation-3D-UNet-PyTorch
Description Use U-Net 3D for image segmentation


Algorithm Jupyter-Image-Segmentation-MaskRCNN-Keras
Description Image instance segmentation with  MaskRCNN


Algorithm Jupyter-Image-Segmentation-UNet-Keras
Description Image segmentation drugs using UNet


Algorithm Jupyter-Image-Segmentation-YOLACT-PyTorch
Description Image object segmentation using YOLACT


Algorithm Jupyter-Image-Stitching
Description Image stitching using brisk feature extraction algorithm


Algorithm Jupyter-Image-Super-Resolution-PyTorch
Description Use SRGAN for super resolution


Algorithm Jupyter-Keyboard
Description Python. Send keyboard signal


Algorithm Jupyter-Model-Keras-to-ONNX
Description Convert Keras model to ONNX model


Algorithm Jupyter-Model-ONNX-To-OpenVINO
Description Convert ONNX model to OpenVINO model


Algorithm Jupyter-Model-ONNX-To-TensorRT
Description Convert ONNX model to TensorRT model


Algorithm Jupyter-Model-PyTorch-To-ONNX
Description Convert PyTorch model to ONNX model


Algorithm Jupyter-Model-TensorFlow-To-ONNX
Description Convert TensorFlow model to ONNX model


Algorithm Jupyter-Model-View-Netron
Description Show model architecture


Algorithm Jupyter-Model-YOLOv3-CPP-to-OpenVINO
Description Convert YOLOv3 CPP model to OpenVINO model


Algorithm Jupyter-Model-YOLOv4-CPP-to-PyTorch
Description Convert YOLOv4 CPP model to PyTorch model


Algorithm Jupyter-Mouse
Description Python. Send a command to move the mouse


Algorithm Jupyter-MySQL
Description Python. MySQL connection, delete, modify, query data


Algorithm Jupyter-NVR
Description Network Video Recorder recording


Algorithm Jupyter-OpenVINO

OpenVINO example










Image_Object_Detection_YOLOv3 Image_Pedestrian_Tracker









Algorithm Jupyter-PySide2
Description Python PySide2


Algorithm Jupyter-PySide2-OpenCV-Webcam
Description PySide2 interface displays webcam live images


Algorithm Jupyter-Python-DLL
Description Python. DLL example


Algorithm Jupyter-Python-For
Description Python. For example


Algorithm Jupyter-Python-Function
Description Python. Function example


Algorithm Jupyter-Python-Function-Call
Description Python call function example


Algorithm Jupyter-Python-Hello-World
Description Python print example


Algorithm Jupyter-Python-If
Description Python If example


Algorithm Jupyter-Python-Import
Description Python. Import example


Algorithm Jupyter-Python-Print
Description Python. Print example


Algorithm Jupyter-Python-Thread
Description Python. Thread example


Algorithm Jupyter-Python-Variable
Description Python. Variable example


Algorithm Jupyter-Python-While
Description Python. While example


Algorithm Jupyter-PyTorch-CUDA-Test
Description PyTorch CUDA Test


Algorithm Jupyter-Quote
Description Python Quote


Algorithm Jupyter-SMTP
Description Python. Simple Mail Transfer


Algorithm Jupyter-Sound-Play-Music
Description Python. Play mp3


Algorithm Jupyter-Sound-Play-Sound
Description Python. Play wav


Algorithm Jupyter-Sound-Spectrogram
Description Python. Sound spectrum


Algorithm Jupyter-Speech-Simple-Recognizer
Description Speech analysis


Algorithm Jupyter-Speech-To-Text
Description Python. Speech to text


Algorithm Jupyter-TensorFlow-CUDA-Test
Description Python TensorFlow CUDA Test


Algorithm Jupyter-Text-To-Speech
Description Python. Text-to-speech


Algorithm VS-OpenCV-Webcam
Description C# OpenCV Webcam example


Algorithm Web-OpenCV-CPP-Webcam
Description OpenCV reads the Webcam image and displays it on the web page


Algorithm Web-OpenCV-CPP-Python-Webcam
Description Use Python OpenCV to read Webcam images and display them on the web


Algorithm Web-OpenCV-CPP-Python-Webcam
Description Use Python OpenCV to read Webcam images and display them on the web




[ Education Videos ]













[Operation steps and instructions]


[Python Tools]


1. Unzip the file. After the download is complete, please use 7zip to unzip the file.




2. Open the app. Open the folder, find LEADERG-APP.exe, double click to open the application.




3. Activate the application. If you have already activated the application, you can skip this step.

First, please click the [Activate] button, then enter your activation code and press enter key in the command window. When OK appears on the screen, the application is successfully activated. Finally, just restart the application and you can use AI-ZOO.





4. Download Python. If your device supports NVIDIA GPU accelerated computing, please click [Download Python (GPU)]; if it does not support NVIDIA GPU accelerated computing, please click [Download Python (CPU)].

After clicking download, please wait patiently. As shown in the figure below, the screen will display the download progress bar, decompression progress bar, and update path one by one. Finally restart, you can start using LEADERG-AI-ZOO.


Download Python-Button.png

Download Python-Downloading.png

Download Python-Unzipping.png

Download Python-Complete.png


5. Execute command in Terminal. If your device supports NVIDIA GPU accelerated computing, please click [Terminal (GPU)]; if it does not support NVIDIA GPU accelerated computing, please click [Terminal (CPU)].

After opening the command prompt window, you can start typing commands.




6. Jupyter Lab

If your device supports NVIDIA GPU accelerated computing, please click [Jupyter Lab (GPU)]; if it does not support NVIDIA GPU accelerated computing, please click [Jupyter Lab (CPU)].


Jupyter Lab.png



7. Spyder. Python language development environment. If your device supports NVIDIA GPU accelerated computing, please click [Spyder (GPU)]; if it does not support NVIDIA GPU accelerated computing, please click [Spyder (CPU)].

After opening Spyder, you can start writing your own code, or load the .py source code.


Spyder-Python IDE.png




1. How to download solutions : Please click [Download Solutions]. Then select the solution you are interested in and then click [Download].


Download Solutions.png


2. How to browse solutions : Click [Browse Solutions] to open the file location of the solutions.


Browse Solutions.png


3. How to run the solution: Please refer to [Python Tools] point six, use Jupyter Lab to run the solution.


Run Solution.png


[Annotation Tools]


1. Annotation is labeling software. Click to use.




3. LabelImg is labeling software. Click to use.




3. Labelme is labeling software. Click to use.




[Advanced Tools]

1. Activate, activate the application. Please refer to the third point of [Python Tools] for detailed usage.

2. Update, software update. Click this button to check whether the current ai-zoo version is the latest. If it is not the latest version, it will be automatically upgraded to the latest released AI-ZOO.


Upgrade Software.png


3. Doc and Video. If you want to get more technical articles and instructions about AI-ZOO, please click the [Doc and Video] button to get more information.


Doc and Video.png


4. How To Buy. If you think AI-ZOO meets your needs and want to use it further, please click this button.


How To Buy.png



[ubuntu 18.04 Operation steps and instructions]


1. Install and build system environment :


1). Use the 7z command to decompress LEADERG-AI-ZOO.7z.

Enter 7z x LEADERG-AI-ZOO.7z on the Terminal to unzip it.


2). Please open the LEADERG-AI-ZOO folder and find Install_Ubuntu.txt.  




3). Install and update the graphics card driver. Please find # Install GPU driver ++ and # Install GPU driver -- in the Install_Ubuntu.txt, and execute this command on the terminal in order.

As shown in the figure below, in the red box area, copy and paste all the instructions one by one from top to bottom to complete the graphics card driver installation.


Install graphics driver.png


Input command.png


4). Install docker. Please find # Install Docker ++ and # Install Docker -- in the Install_Ubuntu.txt, and execute this section of operating instructions on the terminal in order.

In the red box in the figure below, copy and paste the instructions one by one from top to bottom to complete the docker installation.


Install docker.png



2. Install AI-ZOO :


1). Activate AI-ZOO. Find activate.sh under the AI-ZOO folder, open and enter the authorization code, and press enter to complete the activation.


2). Use docker to build AI-ZOO environment. Click to run docker_image_list.sh to confirm whether leadergaizoo exists. If it exists, click to run docker_image_remove.sh, and then run docker_image_load.sh to load the AI-ZOO image.


•    docker_image_list.sh : List all images on the device.


•    docker_image_load.sh : Load the leadergaizoo image placed in the docker_image folder.


•    docker_image_remove.sh : Remove the leadergaizoo image from the device.

(If there is an error that the image has been used, please run stop and remove the container docker_container_all_stop.sh, docker_container_all_remove.sh. After loading the new image, run docker_container_xxxx_run.sh)  


docker image.png



3). Start using AI-ZOO. According to the distinction of users, there are two main types, which can be used at the same time or alternatively according to user needs. Each user has his own independent folder, which does not affect each other:


a). Create a single user container to use more device resources.


•    Click to run docker_container_8001_run.sh, enter the sudo password, after the successful opening, enter on the browser to start using.


•    The default password of Jupyterlab is leadergaizoo. For security reasons, it is recommended to set your own password. For the setting steps, please refer to the next step 4) Set Jupyterlab password.


•    The current initial resource usage is 10 GB memory, 4 CPUs, all GPUs.


•    The user's folder path is : LEADERG-AI-ZOO-Linux/docker_users/user8001


•    Additional port that can be used : 8801 (can be used for tensorboard, visdom, …)



b). Create multiple user containers (default is 3, you can modify the content of .sh to adjust the number of users), and allocate some device resources.


•    Click to run docker_container_9001_run.sh, enter the sudo password, after the successful opening, the three users can individually enter,, http: // to use.


•    The default password of Jupyterlab is leadergaizoo. For security reasons, it is recommended to set your own password. For the setting steps, please refer to the next step 4) Set Jupyterlab password.


•    The current initial settings for each user's resources are 5 GB memory, 1 CPU, 0 GPU, and the solution is read only.


•    The folder path of user 9001 is: LEADERG-AI-ZOO-Linux/docker_users/user9001


•    User 9001 can use additional port: 9901 (can be used for tensorboard, visdom, ...)


•    The 9002~90xx users can be deduced by analogy. The 9002 folder is user9002 and the additional available port is 9902.  



    Common error handling, if the following error occurs when running docker_container_xxxx_run.sh, xxxx can be replaced with 8001 or 9001 according to your needs :


docker: Error response from daemon: Conflict. The container name "/leadergaizooxxx" is already in use by container …。


=> indicates that the container has been created before, please click docker_container_list.sh to view the list and status of all containers.  


docker container list.png


Check out Leadergaizooxxx’s STATUS:

If it is Up, it means that the container has started, and you can use it directly by entering the URL in the browser.

If it is Exited, it means the container has stopped. Please click docker_container_xxxx_start.sh and enter the URL in the browser to use it.  



    docker_container_8001_run.sh Single container resource allocation setting :


• user_memory=10240MB //The available memory size


• user_cpus=4 //How many CPUs can be used


• gpu_device=all //Available GPU. For example: gpu_device=0 means using the 0th GPU; gpu_device=0,1 means using the 0th and 1st GPU; gpu_device=all means using all GPUs.


Jupyterlab password settings 2.png


    docker_container_9001_run.sh multiple container resource allocation settings :


•    user_memory=5120MB // The amount of memory available to each user


•    user_cpus=1  // How many CPUs can each user use


•    user_start=1 


•    user_end=3


// Create 3 containers from user9001~ user 9003 to 3 users. If user_end=5, from user 9001 to user9005, a total of 5 containers will be created for 5 users.

// If user_start and user_end are modified, other related files need to be modified accordingly: docker_container_9001_restart.sh, docker_container_9001_start.sh, docker_container_9001_stop.sh, docker_container_9001_remove.sh, docker_container_9001_remove_folder.sh  

docker container setting.png


    The user's solution authority is read-only, but the solution needs to be edited.

Enter "cp -rf solutions_read_only/Jupyter-xxx ./" in the terminal of Jupyterlab. Copy the solution to your own folder to edit the file. This action will not change the original solution content.



    docker_container :


•    docker_container_xxxx_run.sh : Create a new container for users to use.


•    docker_container_xxxx_restart.sh : Restart the container.


•    docker_container_xxxx_start.sh : Start the container.


•    docker_container_xxxx_stop.sh : Stop container.


•    docker_container_all_stop.sh : Stop all containers, including user8001, user9001~ user9003, ...


•    docker_container_xxxx_remove.sh : Delete the container. One thing to note is that after removal, the user's environment will be removed, but the user folder will not be deleted.

(If this error occurs "Error response from daemon: You cannot remove a running container. Stop the container before attempting removal or force remove", please stop the container docker_container_xxxx_stop.sh before deleting it.)


•    docker_container_all_remove.sh : Delete all containers, including user8001, user9001~ user9003, … 

(If this error occurs "Error response from daemon: You cannot remove a running container. Stop the container before attempting removal or force remove", please stop the container docker_container_all_stop.sh before deleting it.)


•    docker_container_xxxx_remove_folder.sh : Delete the user's folder. 8001 is to remove the folder of user8001, and 9001 is to remove the folder of user9001~ user9003.


•    docker_container_list.sh : List the containers currently created by the device.  



4). Set Jupyterlab password.

After opening JupyterLab, please open LEADERG-AI-ZOO-Linux /solutions/readme.txt, follow the steps to complete the password setting or resetting.


# Change container password by using terminal

jupyter notebook password

# Please use JupyterLab -> File -> Shut Down -> Shut Down to enable new password

# Remove docker_users/userxxx/.jupyter folder to reset password


The schematic diagram of setting password is as follows:


Jupyterlab password settings 1.png  

Jupyterlab password settings 2.png


Jupyterlab password settings 3.png


Jupyterlab password settings 4.png


Jupyterlab password settings 5.png




[ Software Download Link ]




LEADERG AI ZOO - Windows 64 bit Download:






LEADERG AI ZOO - Ubuntu Linux 18.04 64 bit Download:


Please provide Activation Code, and send email to leaderg@leaderg.com for download link.





[ Software Trial ]


Sorry. Because this is source code product, we don't provide trial.




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