How to use GPU-accelerated Docker (NVIDIA Container Toolkit) for machine learning and artificial intelligence tasks?

Running heavy computations related to neural networks and machine learning requires the use of graphics processors. Historically, configuring drivers and CUDA libraries inside containers was a difficult task due to strict version coupling with the host system. With the advent of NVIDIA Container Toolkit, this process has become standardized and transparent.

To work with a hardware accelerator inside an isolated environment, the container needs direct access to the physical hardware and special host libraries. At the same time, the base image itself must contain the correct version of the CUDA runtime matching the drivers installed on the server.

Using containerization for artificial intelligence tasks makes it easy to move complex models between different training and inference servers. This ensures complete reproducibility of experimental results and eliminates library version conflicts that often occur when developing multiple projects simultaneously on a single machine.