utils.ipython

IPython Utilities

Utilities to help work with ipython/jupyter environment.

To import from fastai.utils.ipython do:

  1. from fastai.utils.ipython import *

Workarounds to the leaky ipython traceback on exception

ipython has a feature where it stores tb with all the locals() tied in, which prevents gc.collect() from freeing those variables and leading to a leakage.

Therefore we cleanse the tb before handing it over to ipython. The 2 ways of doing it are by either using the gpu_mem_restore decorator or the gpu_mem_restore_ctx context manager which are described next:

gpu_mem_restore[source][test]

gpu_mem_restore(func) No tests found for gpu_mem_restore. To contribute a test please refer to this guide and this discussion.

Reclaim GPU RAM if CUDA out of memory happened, or execution was interrupted

gpu_mem_restore is a decorator to be used with any functions that interact with CUDA (top-level is fine)

  • under non-ipython environment it doesn’t do anything.
  • under ipython currently it strips tb by default only for the “CUDA out of memory” exception.

The env var FASTAI_TB_CLEAR_FRAMES changes this behavior when run under ipython, depending on its value:

  • “0”: never strip tb (makes it possible to always use %debug magic, but with leaks)
  • “1”: always strip tb (never need to worry about leaks, but %debug won’t work)

e.g. os.environ['FASTAI_TB_CLEAR_FRAMES']="0" will set it to 0.

class gpu_mem_restore_ctx[source][test]

gpu_mem_restore_ctx() No tests found for gpu_mem_restore_ctx. To contribute a test please refer to this guide and this discussion.

context manager to reclaim RAM if an exception happened under ipython

if function decorator is not a good option, you can use a context manager instead. For example:

  1. with gpu_mem_restore_ctx():
  2.    learn.fit_one_cycle(1,1e-2)

This particular one will clear tb on any exception.

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