How to Run Jupyter Notebook on AWS EC2 Instance

How to run jupyter notebook on AWS EC2 instance

After creating EC2 instance follow the following steps to run a jupyter notebook. Jupyter notebook uses port 8888 to access the web interface. When you create the instance make sure to add port 8888 under security policy configurations before launching so that you will be able access your jupyter notebook later.

1. Install Conda (Miniconda)

From the official website https://docs.conda.io/en/latest/miniconda.html find the right link based on your python version.

python-versions

Copy the address link and download the setup file using

    wget https://repo.anaconda.com/miniconda/Miniconda3-py38_4.11.0-Linux-x86_64.sh

Run the setup

    chmod +x ./Miniconda3-py38_4.11.0-Linux-x86_64.sh && ./Miniconda3-py38_4.11.0-Linux-x86_64.sh

Install Jupyter Lab

Using conda install Jupyter lab using

    conda install -c conda-forge jupyterlab

Setup password

Inside ~/.jupyter directory create a new file ` jupyter_server_config.py`

    touch .jupyter/jupyter_server_config.py

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

To setup a jupyter password

    jupyter server password

The above command will ask you to enter a password. Once you’re done, you will get a json file with jupyter_server_config name. Copy the hashed password from this file and past it to ` jupyter_server_config.py`.

You’re done! run jupyter lab and use your public ip to access your notebook.

Attach EFS

— attach EFS to the instance

Create efs You can go to AWS’s EFS and create an efs. This will allow you to store your important data even after you closed your EC2 instance
Mount the efs After creating efs, you will find a button that says “Attach”. Click on it and you’ll find a command to execture on your terminal to attach the efs. But before that make sure to create efs folder in your instance.
```
     sudo mount ******.amazonaws.com:/ efs
```
This is an optional command. It allows you to use the temporary storage instance available along with your EC2.
```
     sudo mkfs -t xfs /dev/nvme1n1
```

Mount the storage

     sudo mkdir ~/data && sudo mount /dev/nvme1n1 ~/data

Allow read/write persmission
```
     sudo chmod go+rw data
```

Extract your data

     tar -xf efs/*.tar && source .bashrc

All Combined

    mkdir efs
    sudo mount *** .amazonaws.com:/ efs
    sudo mkfs -t xfs /dev/nvme1n1
    sudo mkdir ~/data && sudo mount /dev/nvme1n1 ~/data
    sudo chmod go+rw ~/data
    tar -xf efs/*.tar && source .bashrc

Persist config data to EFS

If you want to store your data fter you’re done with your work;

    sudo tar -cf ~/efs/*.tar ./miniconda3 .kaggle .ipython .jupyter .conda .bashrc

Python 3.8

    sudo apt install python3.8
    sudo update-alternatives --install /usr/bin/python python /usr/bin/python3.8.1
    sudo update-alternatives --config python

Note: this note is adapted from https://chen-zhe.github.io/blog/p/aws-user-notes-for-deep-learning/