If we’ve got Rust installed, we can open up a shell, and type this: $ rustc -version Uninstalling Rust is as easy as installing it: $ rustup self uninstall Troubleshooting It will start the installation in a console and present the above message on success.įor other installation options and information, visit the install page of the Rust website. Installing on Windows is nearly as easy: download and run rustup-init.exe. If everything goes well, you’ll see this appear: Rust is installed now. It will download a script, and start the installation. Installing RustĪll you need to do on Unix systems like Linux and macOS is open a terminal and type this: $ curl -sSf | sh For full details on Rust platform support see the website. There are official builds of the Rust compiler and standard library for these platforms and more. The Rust compiler runs on, and compiles to, a great number of platforms, but is best supported on Linux, Mac, and Windows, on the x86 and x86–64 CPU architecture. Generally speaking, you’ll need an Internet connection to run the commands in this section, as we’ll be downloading Rust from the Internet. You can find installation instructions for Miniconda on their website, but if you're using Linux (e.g.The first step to using Rust is to install it. You can skip Miniconda entirely if you prefer and install Jupyter Lab directly, however, I prefer using it to manage other environments too. It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others. Miniconda is a free minimal installer for conda. One approach I can recommend is to install and use Miniconda. There are many different ways to get up and running with an environment that will facilitate our work. We will write all of our code within a Jupyter Notebook, but you are free to use other IDEs.įigure 1 - A Jupyter Notebook being edited within Jupyter Lab. We will be using Rust along with packages that will form our scientific stack, such as ndarray (for multi-dimensional containers) and plotly (for interactive graphing), etc. As such, we need the right tools and environments available in order to keep up with the examples and exercises. We are taking a practical approach in the following sections.

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