Rock Containerized GPU Machine Learning Development With VS Code - AI Summary

Running machine learning algorithms on GPUs is a common practice. Although there are cloud ML services like Paperspace and Colab, the most convenient/flexible way to prototype is still a local machine. Since the beginning of machine learning libraries (e.g., TensorFlow, Torch and Caffe), dealing with Nvidia libraries has been a headache for many data scientists: To summarize, setting up a GPU ML environment will constantly mess up the existing infrastructure and often an OS reinstallation is needed to recover. The better approach is to develop inside a CUDA-enabled container where the development environment is isolated from the host and other projects. Running machine learning algorithms on GPUs is a common practice.