Jupyter

Memory Usage#

def memory():
    with open('/proc/meminfo', 'r') as mem:
        ret = {}
        tmp = 0
        for i in mem:
            sline = i.split()
            if str(sline[0]) == 'MemTotal:':
                ret['total'] = int(sline[1])
            elif str(sline[0]) in ('MemFree:', 'Buffers:', 'Cached:'):
                tmp += int(sline[1])
        ret['free'] = tmp
        ret['used'] = int(ret['total']) - int(ret['free'])
    return ret

No Hang Up#

nohup jupyter notebook --no-browser > notebook.log 2>&1 &

Workaround: no cells output#

se = time.time()
print(train.rdd.getNumPartitions())
print(test.rdd.getNumPartitions())
e = time.time()
print("Training time = {}".format(e - se))

your_float_variable = (e - se)
comment = "Training time for getnumpartition:"

# Open the file in append mode and write the comment and variable
with open('output.txt', 'a') as f:
    f.write(f"{comment} {your_float_variable}\n")

VS Code Configuration & Set-up

Configuration#

Remote SSH#

Host machine
    Hostname machine.com
    User user_name
    IdentityFile path/to/ssh/key

Remote SSH - SSH Tunnel#

Host tunnel_machine
    Hostname machine.com
    User user_name
    IdentityFile path/to/ssh/key

Host machine_after_tunnel
    Hostname machine_after_tunnel.com
    User user_name
    IdentityFile path/to/ssh/key
    ForwardAgent yes
    ProxyJump tunnel_machine

PC Configuration#

Authorize your windows local machine to connect to remote machine.

$USER_AT_HOST="your-user-name-on-host@hostname"
$PUBKEYPATH="$HOME\.ssh\id_ed25519.pub"

$pubKey=(Get-Content "$PUBKEYPATH" | Out-String); ssh "$USER_AT_HOST" "mkdir -p ~/.ssh && chmod 700 ~/.ssh && echo '${pubKey}' >> ~/.ssh/authorized_keys && chmod 600 ~/.ssh/authorized_keys"

Verify that the authorized_keys file in the .ssh folder for your remote user on the SSH host is owned by you and no other user has permission to access it.

Building a website using Hugo and Hosting it on GitHub Pages

Installations#

Configuration#

  • To create a new Hugo website, run:
hugo new site mynewsite
  • then cd to the directory
cd mynewsite
  • Initialize the site as a git repository
git init
  • Choose the hugo theme that suits you.
    Hugo offer a selection of themes developed by the community. This site for example was built using Hugo-Book.
  • Add the theme as a submodule
# For example:
git submodule add https://github.com/alex-shpak/hugo-book themes/hugo-book
  • Add the theme to your site configuration file
# Could be config.toml OR config.yaml OR hugo.toml OR hugo.yaml
echo "theme = 'hugo-book'" >> config.toml
  • You will be able to see a first version of your website locally by running:
hugo server --minify 
  • Edit your configuration file
baseURL = 'http://example.org/'
languageCode = 'en-us'
title = 'My New Hugo Site'
Theme ConfigurationGuidelines
Themes’ publishers offer guidelines to configure your webiste in accordance to the theme. Check your theme publisher page on hugo themes or their theme github repo for guidance and help.

Hosting on Github Pages#

  • On your project settings, go to Pages. You’ll be able to see your site’s link.
  • Choose a Build and deployment source (Github actions OR deploy from branch).
  • You can also choose to publish it on a custom domain.
  • Edit your configuration file
baseURL = 'https://username.github.io/repository'
languageCode = 'en-us'
title = 'My New Hugo Site'
theme = 'hugo-book'

Other Great Tools For Building Static Websites#

Run plotly in JupyterLab

    1  pip uninstall plotly
    2  jupyter labextension uninstall @jupyterlab/plotly-extension
    3  jupyter labextension uninstall jupyterlab-plotly 
    4  jupyter labextension uninstall plotlywidget
    5  jupyter labextension update --all
    6  pip install plotly==5.17.0
    7  pip install "jupyterlab>=3" "ipywidgets>=7.6"
    8  pip install jupyter-dash
    9  jupyter labextension list

Install python packages offline

1- Download packages locally using a requirements file or download a single package

pip download -r requirements.txt
## Example - single package
python -m pip download \
--only-binary=:all: \
--platform manylinux1_x86_64 --platform linux_x86_64 --platform any \
--python-version 39 \
--implementation cp \
--abi cp39m --abi cp39 --abi abi3 --abi none \
scipy

2- Copy them to the a temporary folder in your remote machine 3- On your machine, Activate conda and then install them using pip - specify installation options

Running PySpark & Jupyter With Docker

Thanks to the Jupyter community, it’s now much easier to run PySpark on Jupyter using Docker. There are two ways you can do this : 1. the “direct” way and 2. the customized way.

The “direct” way#

  • verify your local settings are aligned with the pre-requisites to run this container, grosso modo make sure docker is installed, of course !

    You have to have about 4 GB of free space