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ML dev speed hack #1 - PyTorch over TF - Time to first step is faster b/c no static graph compilation - Easier to get loud errors via assertions within the code - Easier to drop into debugger and inspect tensors (TF2.0 may solve some of these problems but is still raw)
ML dev speed hack #2 - Assert tensor shapes - Wrong shapes due to silent broadcasting or reduction is an extreme hot spot for silent errors, asserting on shapes (in torch or TF) makes them loud - If you're ever tempted to write shapes in a comment, make an assert instead
ML dev speed hack #3 - Add ML test to CI - If more than one entrypoint or more than one person working on the codebase, then add a test that runs for N steps and then checks loss - If you only have one person and entrypoint then an ML test in CI is probably overkill
ML dev speed hack #4 - Use ipdb.set_trace() - It's hard to make an ML job take less than 10 seconds to start, which is too slow to maintain flow - Using the ipdb workflow lets you zero in on a bug and play with tensors with a fast feedback loop
ML dev speed hack #5 - Use nvvp to debug throughput - ML throughput (step time) is one place where we have the tools to make errors loud and feedback fast - You can use torch.cuda.nvtx.range_push to annotate the nvvp timeline to be more readable
Curious what other folks recommend for speeding up ML development feedback loops and for making errors louder.
cc tweeps who build stuff quickly: @karpathy, @catherineols, @jeremyphoward, @Thom_Wolf, @hardmaru, @goodfellow_ian, @soumithchintala, @D_Berthelot_ML, @josh_tobin_ , @mcleavey and @AlecRad.
@nottombrown my understanding is that #pytorch usage will be limited until there as much support for optimization and scaling that we see with #TensorFlow. Is this still accurate?
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