Nvidia Cuda Container Implementation

This repository provides implementation paths to dockerize some DL Python frameworks and libraries.

Tested on Ubuntu 20.04 LTS with Nvidia GTX 1050.

Requirements:

1. Install Docker as container manager. Found some privilege uncompatibilities with Docker Desktop

2. Install Nvidia Container Toolkit

   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 \
     && \
       sudo apt-get update
   

   sudo apt-get update \
       && sudo apt-get install -y nvidia-container-toolkit
   

3. To confirm succesful install:

   nvidia-ctk --version
   

Check the following implementation docs:

• Tensorflow Cuda Docker
• Pytorch Cuda Docker
• YoloV8 Cuda Docker

To do list:

• Finish current implementations
• Kubernetes Cluster for highly available infering context
• Kubernetes Cluster for GPU management
• Provide safe API to inject custom prediction func
• Code and model optimizations