This article describes how to setup your DataCrunch.io server to use it with fast.ai and jupyter notebooks.
The extended steps to install can be found here. Below you will find a simplified list of steps that are sufficient when using Ubuntu 18.04. We start by downloading the installer script and running it:
chmod +x Anaconda3-2020.02-Linux-x86_64.sh
Choose yes for default installation path and yes for conda to initialize.
When installation is done, close your SSH session and open it up again. Let’s confirm our installation is up to date by running:
conda install conda
We will get started by creating an Anaconda environment for fastai and activating it in tmux;
conda create --name fastai
conda activate fastai
We run our commands in tmux. That way, if we disconnect our SSH session, we can still pick up our session later, which is useful when running notebooks. You can find more info about tmux below.
Next we go ahead and install the fastai environment;
conda install -c fastai -c pytorch -c anaconda fastai gh anaconda
To get started with the course materials, we will need to clone the git repository.
git clone https://github.com/fastai/fastbook.git
Jupyter notebooks are great for exploring data and trying out things before taking it into production. Lets add it to our fastai conda environment by executing following commands (make sure you run ‘conda activate fastai’ first if you haven’t activated it yet).
conda install jupyter notebook
conda install -c conda-forge jupyter_contrib_nbextensions
Almost there! We can start jupyter notebook with this command: jupyter notebook --ip *your-IP-here* --port 8888
(This requires port 8888 to be open, in case you are not sure check 'sudo ufw status', if status is active and 8888 is not listed, run 'sudo ufw allow 8888')
So in this case we run:
jupyter notebook --ip 188.8.131.52 --port 8888
Copy the the output string (in Putty: selecting the text with your cursor will copy it into your clipboard) into a browser and you should be greeted with your home screen! It should look like this;
Now in jupyter notebooks you can browse the files in your copy of the repository and start following the course.
Pro tip: before starting jupyter notebooks, I recommend using a screen manager such as Tmux on your server (you might need to run ‘sudo apt install tmux’ first to install it):
sudo apt install tmux
Then press ctrl+b followed by ", you can switch between top & bottom via ctrl+b followed by ctrl + up or down arrow. You can run a command like ‘htop’ or ‘watch nvidia-smi’ on the other panel after starting jupyter notebook, or open a new window.
When we disconnect SSH or lose our SSH connection, we can simply pick up where we left by running 'tmux a' to grab our active session.