“Docker is the easiest way to run TensorFlow on a GPU…”
One of the fastest ways to get started with TensorFlow and Keras on your local developer machine is using Docker. This is especially useful if you are working on a Windows machine with an NVIDIA GPU to speed up model training and inference. I have put together the following detailed setup guide to help you get up and running more quickly.
Continue reading “Setup TensorFlow with GPU acceleration using Windows, Docker and WSL2”
Save Machine Learning models, time, and energy (and maybe even the planet)
I’ve noticed when going through lengthy ML tutorials for Keras and TensorFlow that I often run the same models repeatedly in Jupyter Notebook, regardless of the fact they have no changes. This is natural for me with a notebook with multiple models. I also find that typing the code into a new notebook, rather than just clicking
Continue reading “Caching Deep Learning Models with TensorFlow, Keras and PTMLib”
Shift + Enter, aids my understanding and retention of what I have learned.