IdDecoder

Basic Requirements

• Ubuntu 20.04
• Pytorch 1.7.1
• Cuda Toolkit 11.2.2

Hardware recommendations

This framework has been successfully tested on:

• CPU: Intel core i7 10th generation
• GPU: Nvidia RTX 3090 (this is not a requirement, any GPU with at least 12GB of VRAM should be enough)
• RAM: 32 GB DDR4

Software Requirements

• Anaconda/Mini conda
• Latest Nvidia driver

1. We recommend to set up the virtual environment by Mini Conda:

git clonehttps://github.com/minha12/IdDecoder.git
cd IdDecoder
conda env create -n iddecoder --file ./requirements.yaml
wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip
sudo unzip ninja-linux.zip -d /usr/local/bin/
sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force

Special requirements for Nvidia RTX 30 series

• Install Cuda Toolkit 11.2.2
• Pytorch 1.7.1:

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html

Using Docker

Prerequisites:

• Docker installed on your machine.
• NVIDIA GPU with the NVIDIA Container Toolkit (for GPU support).

Step 1: Clone the Repository

• Clone the repository containing the Gradio app and Dockerfile to your local machine.

Step 2: Build the Docker Image

• Open a terminal or command prompt.
• Navigate to the directory containing the Dockerfile.
• Run the following command to build the Docker image:

docker build -t iddecoder_docker .

This command builds a Docker image named iddecoder_docker from the Dockerfile in the current directory.

Step 3: Run the Docker Container

• Once the image is built, you can run the container with the following command:

docker run -d --gpus all -p 7860:7860 iddecoder_docker tail -f /dev/null

This command runs the container in detached mode (-d), enables GPU access (--gpus all), and maps port 7860 of the container to port 7860 on your host machine.

Step 4: Stop the Docker Container

• If you need to stop the running Docker container, first find the container ID using:

  docker ps
  

• Then stop the container using the following command:

  docker stop [container_id]
  

  Replace [container_id] with the actual ID of your container.