Elixir Livebook examples

A collection of Livebook .livemd examples with training datasets.

Salary prediction

Elixir Livebook

Training time: 0.637s

Compiling EXLA

First time you run the Livebook notebook it will probably need to compile exla, which requires bazel to be installed.

EXLA on macOS Big Sur

On macOS Big Sur this worked:

% export BAZEL_VERSION=3.1.0
% curl -fLO "https://github.com/bazelbuild/bazel/releases/download/${BAZEL_VERSION}/$ bazel-${BAZEL_VERSION}-installer-darwin-x86_64.sh"
% chmod +x "bazel-${BAZEL_VERSION}-installer-darwin-x86_64.sh"
% ./bazel-${BAZEL_VERSION}-installer-darwin-x86_64.sh --user

EXLA on RaspberryPi 4

$ wget https://github.com/bazelbuild/bazel/releases/download/3.4.0/bazel-3.4.0-linux-arm64
$ chmod u+x bazel-3.4.0-linux-arm64
$ ./bazel-3.4.0-linux-arm64
$ cd ~/bin
$ ln -s /home/pi/dev/bazel/bazel-3.4.0-linux-arm64 bazel

Erlang prerequisites on Linux

$ apt-get -y install build-essential autoconf libncurses5-dev libwxbase3.0-dev libwxgtk-webview3.0-gtk3-dev libgl1-mesa-dev libglu1-mesa-dev libpng-dev libssh-dev unixodbc-dev xsltproc fop libxml2-utils openjdk-11-jdk

Install Erlang and Elixir:

Next step is to run Livebook, which requires Erlang and Elixir to be installed.

% git clone https://github.com/asdf-vm/asdf.git ~/.asdf
% . $HOME/.asdf/asdf.sh
% asdf plugin add erlang https://github.com/asdf-vm/asdf-erlang.git
% export KERL_CONFIGURE_OPTIONS="--without-javac --enable-lock-counter --with-microstate-accounting=extra"
% asdf install erlang 24.0-rc3
% asdf global erlang 24.0-rc3
% asdf plugin-add elixir https://github.com/asdf-vm/asdf-elixir.git
% asdf install elixir 1.12.0-rc.1-otp-24
% asdf global elixir 1.12.0-rc.1-otp-24

% cat ~/.zshrc
export PATH="$PATH:$HOME/bin"

. $HOME/.asdf/asdf.sh

Compile Livebook and start it:

% git clone https://github.com/elixir-nx/livebook.git
% cd livebook
% mix deps.get --only prod
% MIX_ENV=prod mix phx.server

Python3 version

Training time: 2.860s

% cd salary_prediction
% python3 salary_prediction.py

Jupyter Notebook

Training time: 12.639s

% cd salary_prediction
% jupyter notebook

Google Colab

Training time: 23.478s

% cd salary_prediction
% jupyter notebook  --NotebookApp.allow_origin='https://colab.research.google.com' --port=8888 --NotebookApp.port_retries=0

Upload salaries.csv to your Google Drive and add this at the top of the notebook:

from google.colab import drive
drive.mount('/content/gdrive')
!cat ./gdrive/MyDrive/MachineLearning/salaries.csv

Then update the cell to read_csv from your actual path.