Multi-GPU training is a double-edged sword. If you are at the stage where you are running your code in a iPython notebook then you are almost certainly not goi

Multi-GPU training is a double-edged sword. If you are at the stage where you are running your code in a iPython notebook then you are almost certainly not going to benefit from the multiple GPUs, and I strongly suspect you'd be better with fewer and larger GPUs, even if training time is prolonged. The reason I say that is, if we go with PyTorch, you basically have 2 options for multi-GPU training. - DataParallel - where you clone your model over each GPU, but each one functions independently, and after each 'training step' they pool their data. This has downsides, in that you don't get to process intermediate layer outputs and synchronise your batch normalisation layers - so you can't use it to train 'big' models. It just makes your smaller models train more quickly. However, you can at least use these in a 'normal' training script. - DistributedDataParallel - this is 'proper' multi-GPU training - you can now train big models and put a little bit of data on each GPU, and have then synchronise their results after each layer. However, this can be very annoying to use - each GPU runs in its own background process which is either spawned or forked (depending on Windows/Linux) and you therefore cannot run it in an iPython notebook, or an interactive Python console. It also makes tracking metrics etc. MUCH harder - because you need to reduce your metrics over each GPU process (because otherwise you get 4 accuracies, 4 mean squared errors etc. if you have 4 GPUs, and each process only sees one of them). I personally prefer having 1 GPU with 24 Gb RAM over 3 GPU with 12 Gb RAM - because I can have a larger batch size on each GPU, which is VERY VERY advantageous in large models where you can only have small batch sizes, and batch normalisation starts falling down. I'd rather wait 2x as long for a 'better' model to train.