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How to configure local computer for FastAI course

📅 2018-Apr-01 ⬩ ✍️ Ashwin Nanjappa ⬩ 📚 Archive

I wanted to check out the Practical Deep Learning for Coders course by FastAI. However, I noticed that the course provided configuration instructions mainly for cloud GPU instance providers like Paperspace. I have a notebook and a desktop computer with powerful NVIDIA GPUs and wanted to try the course on my local machines. The course material is also provided in the form of Jupyter notebooks, while I intended to turn those into Python programs to run locally.

Here are the steps I followed to get my local computer setup for the FastAI course:

$ git clone git@github.com:fastai/fastai.git
$ conda env update
$ conda activate fastai
$ python
>>> import torch
>>> torch.cuda.is_available()
True
>>> torch.backends.cudnn.enabled
True
$ from fastai.imports import *

You are now ready to execute any of the code shown in the course at a Python interpreter or inside Python scripts. Note that you will still need to download any additional datasets needed by the course. You will find these instructions in the Jupyter notebooks or course material.