A small Rust CLI example you can use to build on. With an emphasis on Linux and creating automation tools that solve a problem for you. This is the basis for DevOps principles that you can apply in day-to-day work.
💡 Are you just looking for a 👉 Rust template to get started easily with a project? The template has everything you need!
This repository is part of the Python and Rust CLI tools course:
- 1: Resources
- 2: Python CLI
- 3: Rust CLI 👈 You are here!
- 4: Python Advanced CLI
- 5: Rust Advanced CLI
Use the included practice lab to apply the content you've learned in this week. Follow the steps to create your own repository and apply the requirements to complete the lab.
Rust development requires certain tools to be installed on your system. The easiest way to do this is to use the rustup tool. This will install the Rust compiler and Cargo, the Rust package manager. Although you can install it in Linux using the package manager, I recommend using rustup
. Use the following command or go through the rustup.rs website to install it.
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
This repository and video course focuses on the development side of command-line tools in Rust. It uses Visual Studio Code as the editor of choice. You can use any editor you like, but the instructions in this repository will be for VS Code.
These are all the tools and editor extensions I recommend you install to get started:
As part of your development workflow, I highly suggest you use the following programs in the terminal regularly:
cargo fmt
- Formats your code to the Rust standardcargo clippy
- Lints your code and helps you find errors and potential issuescargo check
- Checks your code for errors and allows you to fix them before compiling (which means its faster!)
Explore additional content that you can use to learn more about the topics covered in this course.
Coursera Courses
- Linux and Bash for Data Engineering
- Open Source Platforms for MLOps
- Python Essentials for MLOps
- Web Applications and Command-Line tools for Data Engineering
- Python and Pandas for Data Engineering
- Scripting with Python and SQL for Data Engineering
O'Reilly Courses and Books
- Python for DevOps (Book)
- Practical MLOps (Book)
- Linux For Beginners (Video)
- GitHub Codespaces Course (Video)
- Python Command-line Tools course (Video)