I’m trying to run notebook tests from the command line and getting an error. What am I doing wrong?
> python run_notebook.py --fn 00_test.ipynb
Error in 00_test.ipynb
Traceback (most recent call last):
File "run_notebook.py", line 18, in <module>
slow:Param("Run slow tests", bool)=False, cpp:Param("Run tests that require c++ extensions", bool)=False):
File "/home/jupyter/fastai_dev/dev/local/script.py", line 37, in call_parse
func(**args.__dict__)
File "run_notebook.py", line 24, in main
for f in sorted(fns): test_nb(f, flags=flags)
File "/home/jupyter/fastai_dev/dev/local/notebook/test.py", line 113, in test_nb
raise e
File "/home/jupyter/fastai_dev/dev/local/notebook/test.py", line 110, in test_nb
ep.preprocess(pnb)
TypeError: preprocess() missing 1 required positional argument: 'resources'
Zoom transform - does it only zoom in and not out? There’s a max_zoom(>=1.0) parameter, but couldn’t find a way to let it zoom say from x0.6 (smaller) to x1.3 (larger)
Where is the “suggested LR” from lr_find? It’s mentioned in v1 docs, but I don’t see it now.
I was looking for a way to quickly pickle some data structure, and stumbled upon this post about not using pickle and instead use MessagePack (which I haven’t tried and no experience with).
What is your opinion on that matter? For example when exporting model/learner.
What’s the equivalent of PyTorch’s Dataloader sampler to balance classes when wanting to rebalance classes to prevent overfitting with an imbalanced dataset?
I am unsure if these questions are for mainly v2 or both v1 and v2. I might be able to answer some of these questions:
I am unsure but I guess you could probably try this yourself and see
Don’t know
For fastai v1, you could do learn.recorder.plot(suggestion=True) but I am not sure if this is the same for v2
Don’t know
For fastai v1 we have the OverSamplingCallback that uses the weighted random sampler. I will probably write a version for fastai v2 in the next couple weeks.
Ah ok I will try to answer again based on what’s in the code now:
Here is the code for the zoom. It seems it uses PyTorch .uniform_ from 1 to max_zoom, but I am not sure if it will work if max_zoom<1 and the upper bound is actually smaller than the lower bound.
Not sure
The code for the LR finder plot is here. There is no suggestion arguments right now.