# Work around  with Jupyter-Hub and  conda env

I recently have to work around with python compatible environment between my colleagues. Since we are mix team that switch from Python to R, I'm use to set up the best environment to go through this kind of issues. Here I note some usefull pieace of code to set up.  

## A tasty solution: Juyter-Hub

From this [official site](https://jupyter.org/hub) and [Littelest-hub](https://tljh.jupyter.org/en/latest/install/custom-server.html) I managed to install a Jupyter Hub server from me and collaborators to face different aspect. 


Run the following code on a fresh server. I would use a DigitalOcean $15 2 CPU / 2 GB Memory / 60 GB Disk / Ubuntu 20.04 (LTS) x64


![Screenshot_2020-12-12 Create Droplets - DigitalOcean(1).png](https://cdn.hashnode.com/res/hashnode/image/upload/v1607782050637/2DwFXhj_Y.png)

It's take just a few minute to deploy. Then access using ssh protocol and public IP - IPv4

> ssh root@IPv4

```
sudo apt install python3 python3-dev git curl
``` 

Replace <admin-user with the name of the first admin user for this JupyterHub

```
curl -L https://tljh.jupyter.org/bootstrap.py | sudo -E python3 - --admin <admin-user>
``` 

## Open in http://IPv4

![Screenshot_2020-12-02 JupyterHub.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1606905132321/LMdRSdeFW.png)

## Git Clone 
Clone you Git Project and install your favorite conda packages. I like to use '.yml' file which I easily can use to install all my dependency and activate. 


## Resolve conda environment installation troubleshooting permission

```
NotWritableError: The current user does not have write permissions to a required path.
  path: /opt/tljh/user
  uid: 1009
  gid: 1009
``` 

If you face permission problems as me, use the following code to resolve it:

> sudo chown -R $USER:$USER /opt/tljh/user

```
conda env create -f file.yml
conda activate $env
``` 

### Install ipykernel to manage the diferent python conda enviroment available in conda


```
# Install ipykernel to make available for ipykernel
conda install -c anaconda ipykernel

# this command make available in Jupyter 
python -m ipykernel install --user --name=$env
``` 

![Screenshot_2020-12-02 Home Page - Select or create a notebook.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1606903591321/ncN9LWvFN.png)


### Remove the Kernel

```
rm -rf /home/jupyter-user/local/share/jupyter/kernels/$env
``` 
### Swappiness makes life happier

If you want to deploy heavy consuming processes like me (remote sensing data processing), it's better to increase your swappiness in case you consume all your RAM space. **Swap** is an space design to temporarily store data that it can no longer hold in RAM. This give us the ability to increase the amount of information that your server can keep in its working memory. [More info in DigitalOcean](https://www.digitalocean.com/community/tutorials/how-to-add-swap-space-on-ubuntu-18-04)

```
sudo fallocate -l 2G /swapfile 
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile

## Making the Swap File Permanent
sudo cp /etc/fstab /etc/fstab.bak
echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab
``` 

### Adjusting the Swappiness Property
> sudo nano /etc/sysctl.conf
### ADD the following line at the bottom
> vm.swappiness=10


Excellent 
Enjoy :)

PD: Next steps would be Jupyter-Lab, take a look on this [nice tutorial](https://jupyterhub.readthedocs.io/en/stable/installation-guide-hard.html)