👋 Welcome to ISOAI! Our mission is to develop cutting-edge technology solutions, focusing on face recognition, speech recognition, speaker diarization, and facial emotion detection using Python. We proudly serve the Istanbul Chamber of Industry by innovating and optimizing their technological needs.

Questions? Here’s how to reach us:

• Email: [email protected]
• Team: @ISOAI

About the Team

@Fatih Yavuz

@Gorkem Karamolla

Install CUDA 11.8

NVIDIA - CUDA | onnxruntime

Requirements

Please reference the table below for official GPU packages dependencies for the ONNX Runtime inferencing package. Note that ONNX Runtime Training is aligned with PyTorch CUDA versions; refer to the Training tab on onnxruntime.ai for supported versions.

Install CUDA Toolkit 11.8

For Windows

1. Download the installer from the CUDA Toolkit 11.8 Downloads | NVIDIA Developer.
2. Double click cuda_11.8.0_522.06_windows.exe.
3. Follow the on-screen prompts to complete the installation.

To verify the installation:

1. Open the command prompt.

2. Type the following command and press Enter:

   nvcc --version

This command should output information about the NVIDIA CUDA Compiler (nvcc), including the version of the CUDA toolkit that is installed.

If you get an error saying that 'nvcc' is not recognized as an internal or external command, it means either CUDA is not installed or it's not added to the system PATH. If CUDA is installed but not added to the system PATH, you need to add it. The CUDA bin directory (which contains nvcc) is typically located at C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin.

For Linux

1. Download the installer from the CUDA Toolkit 11.8 Downloads | NVIDIA Developer.

2. Open a terminal and navigate to the directory where the installer is downloaded.

3. Install the toolkit by running the following commands:

   sudo dpkg -i cuda-repo-<distro>_<version>_amd64.deb
   sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/<distro>/x86_64/7fa2af80.pub
   sudo apt-get update
   sudo apt-get install cuda

To verify the installation:

1. Open a terminal.

2. Type the following command and press Enter:

   nvcc --version

This command should output information about the NVIDIA CUDA Compiler (nvcc), including the version of the CUDA toolkit that is installed.

If you get an error saying that 'nvcc' is not recognized as an internal or external command, it means either CUDA is not installed or it's not added to the system PATH. If CUDA is installed but not added to the system PATH, you need to add it by appending the following lines to your .bashrc file:

export PATH=/usr/local/cuda-11.8/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Then, reload your .bashrc file:

source ~/.bashrc

Install cuDNN 8.5.0.96

For Windows

1. Download the cuDNN installer from the Index of /compute/redist/cudnn/v8.5.0/local_installers/11.7 (nvidia.com).

2. Specifically, download the archive: cudnn-windows-x86_64-8.5.0.96_cuda11-archive.zip.

3. Extract the contents to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8.

Install zlib123dllx64

1. Download zlib from the provided link: zlib123dllx64.zip.

2. Extract the contents to C:\Program Files\zlib123dllx64.

3. Add the following path to your system environment variables:

   C:\Program Files\zlib123dllx64\dll_x64

For Linux

1. Download the cuDNN installer from the Index of /compute/redist/cudnn/v8.5.0/local_installers/11.7 (nvidia.com).

2. Extract the downloaded archive:

   tar -xzvf cudnn-linux-x86_64-8.5.0.96_cuda11-archive.tar.xz

3. Copy the extracted files to your CUDA directory:

   sudo cp cuda/include/cudnn*.h /usr/local/cuda/include
   sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
   sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*

4. Verify the installation:

   cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2

Install zlib

1. Install zlib using your package manager:

   sudo apt-get update
   sudo apt-get install zlib1g zlib1g-dev

Ensure zlib is correctly installed and available in your system paths.

Manual Installation for Insightface Buffalo_L Model

Download the model

Installing CUDA Container Toolkit with Apt

1. Configure the production repository:

   curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
     && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
       sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
       sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

2. Optionally, configure the repository to use experimental packages:

   sed -i -e '/experimental/ s/^#//g' /etc/apt/sources.list.d/nvidia-container-toolkit.list

3. Update the packages list from the repository:

   sudo apt-get update

4. Install the NVIDIA Container Toolkit packages:

   sudo apt-get install -y nvidia-container-toolkit

For further information, check the installation guide from the NVIDIA website: NVIDIA Container Toolkit Installation Guide.

Configuring Docker

1. Configure the container runtime by using the nvidia-ctk command:

   sudo nvidia-ctk runtime configure --runtime=docker

   The nvidia-ctk command modifies the /etc/docker/daemon.json file on the host. The file is updated so that Docker can use the NVIDIA Container Runtime.

2. Restart the Docker daemon:

   sudo systemctl restart docker

Quick Start

git clone https://github.com/gorkemkaramolla/iso-fr-ai.git
cd iso-fr-ai
docker compose up --build 

Run Frontend Code

Open another terminal

cd iso-fr-ai
cd iso-electron && npm run isoai