I have a Pc with Ubuntu 16.04 with fastai 0.7 and lessons 2018. Can I install course-3.fast.ai incl lessons 2019 and still get access to lessons 2018? If yes how do I install and configure 2019?
In the menu at “https://course.fast.ai/” there are under “Server setup” many different plattforms but no “Your own server”. Could you add a link to a page with how to install your own server 2019?
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I’ve extracted some parts from SageMaker setup. You own server should be no different than “my pc”. Keep in mind that you will need a proper GPU (I don’t have one on my MBP, for example) to run these things fast enough (I believe you get 3 to 4 orders of magnitude faster training times on GPUs, please somebody correct me if I am wrong).
The following setup will create a separate conda environment for fastai. It is possible it’s unnecessary, but will be safer so it won’t interact with packages from the rest of the system.
Installation
# install the fastai library and dependencies
echo “Installing the fastai library and dependencies in new conda enviornment”
mkdir -p ~/fastai-course
cd ~/fastai-course
conda create -mqyp envs/fastai -c pytorch -c fastai fastai ipykernel
echo “Finished installing environment”
# clone the course notebooks
echo “Clone the course repo”
git clone https://github.com/fastai/course-v3.git ~/fastai-course/v3
# install jupyter extensions
source activate envs/fastai
echo “Install jupyter nbextension”
pip install jupyter_contrib_nbextensions
jupyter contrib nbextensions install --user
Running jupyter
cd ~/fastai-course
source activate envs/fastai # to enter created environment
jupyter notebook v3
Edit: Edited the setup to have less commands.
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THANK YOU!!! (I have 2 GTX 980)
I believe installing fastai kernel will fail because it’s a little bit SageMaker specific. I will post complete instructions in few hours.
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search the forums for “Local Linux setup” …
There are a number of already excellent posts describing everything you need to do to build your on machine for DL and set it up with all the pytorch/fastai bits required.
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The 2018 repository had an environment.yml file that set up the conda environment perfectly. Why no such file in the current repo. I really don’t want days of configuration hell, or to pay for a commercial service when I have everything at home 
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