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@@ -87,8 +87,7 @@ Oh and I swear I'll document the code properly...eventually. Admittedly I'm *on
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### Getting Started Yourself
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This project is built around the wonderful Fast.AI library. Unfortunately, it's the -old- version and I have yet to upgrade it to the new version. (That's definitely on the agenda.) So prereqs, in summary:
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-* ***Old* Fast.AI library** After being buried in this project for two months I'm a bit lost as to what happened to the old Fast.AI library because the one marked "old" doesn't really look like the one I have. This all changed in the past two months or so. So if all else fails you should be able to use the one I forked here: https://github.com/jantic/fastai . Again, getting upgraded to the latest Fast.AI is on the agenda fo sho, and I apologize in advance. **UPDATE** Easiest thing to do in my mind is just to take the fastai/fastai folder and drop it in the root of this project, right next to fasterai's folder. Also added a requirements.txt file to help people along with getting through dependency hell.
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-* **Whatever dependencies Fast.AI has** – there's already convenient requirements.txt and environment.yml there.
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+* ***Old* Fast.AI library** **UDATED 11/7/2018** Easiest thing to do in my mind is just to take the fastai/fastai folder and drop it in the root of this project, right next to fasterai's folder. Just today, I found this thread on installing fast.ai 0.7- This is probably your best resource on this subject! https://forums.fast.ai/t/fastai-v0-7-install-issues-thread/24652 . Do this first- this will take you most of the way, including dependencies.
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* **Pytorch 0.4.1** (needs spectral_norm, so latest stable release is needed).
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* **Jupyter Lab**
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* **Tensorboard** (i.e. install Tensorflow) and **TensorboardX** (https://github.com/lanpa/tensorboardX). I guess you don't *have* to but man, life is so much better with it. And I've conveniently provided hooks/callbacks to automatically write all kinds of stuff to tensorboard for you already! The notebooks have examples of these being instantiated (or commented out since I didn't really need the ones doing histograms of the model weights). Noteably, progress images will be written to Tensorboard every 200 iterations by default, so you get a constant and convenient look at what the model is doing.
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Jason Antic