Recently, I’ve been working on some projects for fun, trying out some things I hadn’t worked with before, such as profiling.
But after profiling my code, I found out that my average GPU activity is around 50%. Apparently, the code frequently hangs for a few hundred milliseconds on the dataloader process. I’ve tried a few things in the dataloader: increasing/decreasing the number of workers, setting pin-memory to true or false, but neither seems to really matter. I have an NVME drive, so the disk is not the problem either. I’ve concluded that the bottleneck must be the CPU.
Now, I’ve read that pre-processing the data might help, so that the dataloader doesn’t have to decode the images, for example, but I don’t really know how to go about this. I have around 2TB of NVME storage, and I’ve got a couple datasets on the disk (ImageNet and INaturalist are the two biggest ones), so I don’t suppose I’ll be able to store them on the disk uncompressed.
Is there anything I can do to lighten the load on the CPU during training so that I can take advantage of the 50% of the GPU that I’m not using at the moment?
CPU bottlenecks can be easily found by monitoring the CPU usage during training. If all of your cores are constantly at 100% your cpu might be too slow. If both the cpu and gpu are idle from time to time your storage could be the bottleneck.
To increase data loading performance, you could try out Nvidias Dali or FFCV which are both libraries optimized for that purpose. They replace some of the inefficient python code with highly optimised code. FFCV is quite nice but it requires you to convert your dataset into a specific format.