Machine Learning on GPU

GPUs are great for highly-parallel processing.

CPUs are more flexible than GPUs.

GPUs are most useful for neural network applications in machine learning.

A GPU needs to be available in order for you to use it.

Not all GPUs are the same.

Both the model and the data must be moved onto the GPU for training.

Data should be moved onto the GPU in batches.

You’ll be back.

Squirrels may mock you.

Using a GPU will not improve your ML performance.

Using a GPU will improve your run time only under certain circumstances.

GPU processes are asynchronous.

GPU memory is not the only consideration when setting the batch size.

Memory limits will depend on both allocated and reserved memory.

You’ll be back.

They’re the jedi of the sea.

Using Multiple-GPUs with PyTorch is trivial

Multi-GPU training is subject to processing bottlenecks.