Daily Blog Post: June 9th, 2023

June 9th, 2023

Getting Started with Julia: Setting up a Coding Environment on Linux

Welcome to the first post in our series on Julia, a high-level, high-performance, dynamic programming language for technical computing. Julia has a syntax that is familiar to users of other technical computing environments and is easy to learn, making it an ideal choice for a wide range of applications, including data science and machine learning. In this post, we will focus on setting up a coding environment for Julia on Linux.

Step 1: Installing Julia

First, let's install Julia on your Linux system. You can download the latest version of Julia from the official website: HERE

Alternatively, you can use the following commands in your terminal to install Julia:

wget https://julialang-s3.julialang.org/bin/linux/x64/1.7/julia-1.7.0-linux-x86_64.tar.gz
tar -xvzf julia-1.7.0-linux-x86_64.tar.gz
sudo mv julia-1.7.0 /opt/
sudo ln -s /opt/julia-1.7.0/bin/julia /usr/local/bin/julia

Now you can run Julia by simply typing julia in your terminal.

Step 2: Setting up a Text Editor or IDE

Next, you'll want to set up a text editor or an integrated development environment (IDE) to write and edit your Julia code. There are several options available, including:

Visual Studio Code (VSCode) - A popular open-source code editor with built-in support for Julia, including syntax highlighting, code completion, and debugging. To get started with VSCode, follow the installation instructions for your Linux distribution here. After installing VSCode, install the Julia extension by searching for "Julia" in the Extensions marketplace.
Atom - Another open-source text editor with support for Julia through the julia-client and ink packages. Install Atom from the official website: here. After installing Atom, install the uber-juno package using the package manager, which will automatically install the necessary packages for Julia support.
Jupyter Notebooks - A web-based interactive computing environment that allows you to create documents containing live code, equations, visualizations, and more. To use Jupyter Notebooks with Julia, first install Jupyter by following the instructions here. Then, install the IJulia package by running the following command in your Julia terminal: using Pkg && Pkg.add("IJulia") Once installed, you can start a new Jupyter Notebook with Julia by running jupyter notebook in your terminal.

Choose the option that best suits your preferences and workflow.

Step 3: Exploring the Julia Ecosystem

With your coding environment set up, you can now start exploring the rich ecosystem of packages available for Julia. The package manager, Pkg, makes it easy to install and manage these packages.

To install a package, simply run the following command in your Julia terminal:

using Pkg
Pkg.add("PackageName")

Replace "PackageName" with the name of the package you want to install.

Some popular Julia packages for data science and machine learning include:

DataFrames.jl for working with tabular data
Plots.jl for creating visualizations
Flux.jl for deep learning
MLJ.jl for a wide range of machine learning algorithms

In the next post, we will dive deeper into Julia programming, covering its syntax, unique features, and how to write your first Julia program. We will also explore some best practices for Julia development to help you get started on the right foot.

Stay tuned for the upcoming posts in this series, where we will delve into advanced topics like data science, machine learning, and more. By the end of this series, you will have a solid understanding of the Julia language and its capabilities, enabling you to tackle a wide variety of projects with ease.

Happy coding!