A Notebook on Quickly getting a deep learning base AMI running

First of all, getting miniconda installed

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

chmod u+x Miniconda3-latest-Linux-x86_64.sh && ./Miniconda3-latest-Linux-x86_64.sh

source .bashrc

conda install -y pytorch torchvision torchaudio cudatoolkit -c pytorch

conda install -y pandas scikit-learn matplotlib tqdm seaborn tensorboard

pip install wandb

conda clean -a

We then get going with setting up jupyterlab

installing jupyter lab

conda install -c conda-forge jupyterlab

server-configurations:

touch .jupyter/jupyter_server_config.py

vi .jupyter/jupyter_server_config.py

then inside you can add this

c.ServerApp.ip = '*' # bind to any network interface
c.ServerApp.password = u'sha256:bcd259ccf...<your hashed password here>'
c.ServerApp.open_browser = False
c.ServerApp.port = 8888 # or any other ports you'd like

then to get the password we

jupyter server password

then

cat .jupyter/jupyter_server_config.json

copy the hashed password into the .py file you created above

Getting the lab launched

screen -S Jupyter

then

jupyter lab

ctrl+A

ctrl+D

go here

:8888/lab

Mounting EBS

First let us see the name of the storage:

lsblk

We then make our file system

sudo mkfs -t xfs /dev/[drive name]

We create a dir to mount the drive in

mkdir ~/data

We mount the drive

sudo mount /dev/[drive name] ~/data

We change the permission to be able to access the drive

cd ~/data

sudo chmod go+rw .

cd ..