I ran into the same issue while trying to train a model using my own data in lesson1 and managed to get past it using python3’s urllib.request, tarfile and Path modules. Thought I would share this here in case it helps. There may be a fastai library way to do this (?).
I am on Colab and also have a local setup where I use python3.6 and fastai 1.0.5 with an NVIDIA RTX 2080 GPU.
Basically, using lesson1’s code as a reference, I did the following things differently.
First, in the notebook cell on imports:
from pathlib import Path
Next, instead of using untar_data, I used the following code. Note that I am not at liberty to share the exact URL I used and you should replace the url with your url to make this work. Note also that I don’t have checks in place to see if the file downloaded is a tar file or not as well as checks to see if the functions return successfully or not. These can be added easily.
url = "http://myserver/output/proj1_training_data.tar.gz"
local_tgz_path = Path("/root/.fastai/data/proj1_training_data.tar.gz")
print("Downloading from %s..." % (url,))
print("Opening using tarfile from %s..." % (local_tgz_path,))
tarred_file = tarfile.open(local_tgz_path)
path = Path("/root/.fastai/data/proj1_training_data/")
The rest of the lesson1 notebook can remain the same and it all works out. My training data is also organized the same as the Oxford Pet data used in lesson1.