5 tips for multi-GPU training with Keras

Deep Learning (the favourite buzzword of late 2010s along with blockchain/bitcoin and Data Science/Machine Learning) has enabled us to do some really cool stuff the last few years. Other than the advances in algorithms (which admittedly are based on ideas already known since 1990s aka "Data Mining era"), the main reasons of its success can be attributed to the availability of large free datasets, the introduction of open-source libraries and the use of GPUs. In this blog post I will focus on the last two and I'll share with you some tips that I learned the hard way. TensorFlow is a very popular Deep Learning library developed by Google which allows you to prototype quickly complex networks. It comes with lots of interesting features such as auto-differentiation (which saves you from estimating/coding the gradients of the cost functions) and GPU support (which allows you to get easily a 200x speed improvement using decent hardware).