Quick start guide to using Anaconda for Python on University-managed computers including installation without administrator credentials, utilization of unique conda environments for each project, adding additional channels for packages and backing up configurations for portability and reproducibility. Please see linked articles for detailed how-to instructions and the additional assistance links for more documents related to the subject.
Quick start guide to using Miniconda for Python on University-managed computers including installation without administrator credentials, utilization of unique conda environments for each project, adding additional channels for packages and backing up configurations for portability and reproducibility. Please see linked articles for detailed how-to instructions and the additional assistance links for more documents related to the subject.
Install Miniconda as user without administrator permissions in Windows.
Install Miniconda as user without administrator permissions on a Mac.
Activate a Conda Environment in the command line interface in Windows.
Add Channel to Bottom of Channel List in a Conda Environment via Command Line Interface
Add Channel to Top of Channel List in a Conda Environment via Command Line Interface
Backup a Conda Environment Configuration via Command Line Interface into a YAML formatted file.
Create a Conda Environment from a YAML formatted file via Command Line Interface .
Create a Conda Environment in the command line interface in Windows.
Deactivate Conda Environment via Command Line Interface
Install a Package in a Conda Environment via Command Line Interface
Install a PyPI Package with PIP in a Conda Environment via Command Line Interface
Install an R package from CRAN in a virtual conda environment using the command line interface.
Open Windows Anaconda Prompt and MacOS Terminal Command Line Interface in a Virtual Conda Environment
Remove a Channel in a Conda Environment via Command Line Interface
Remove a Virtual Conda Environment via Command Line Interface
Remove a Package in a Conda Environment via Command Line Interface.
Update a Package in a Conda Environment via Command Line Interface
Update a Package in a Conda Environment in Anaconda Navigator
Update all Packages in a Conda Environment via Command Line Interface
Upgrade a PyPI Package with PIP in a Conda Environment via Command Line Interface
Compare three versions of the Conda engine to help you pick the best one for your technical skills and computer type. You can choose Anaconda Navigator for a beginner-friendly, "point-and-click" experience, or go with Miniconda or Miniforge if you prefer a lightweight, professional setup that saves disk space. While they differ in size and interface, all three create isolated "sandboxes" to ensure your software projects don't interfere with each other.
A central guide for researchers to build stable and high-performance coding environments using the Conda ecosystem. It brings together best practices—such as choosing the right distribution and using "blueprints" to share work—to help you avoid technical conflicts and ensure your research can be perfectly recreated by others. By following these strategies, you can protect your productivity and make your software projects both portable and reliable.
To make your work reliable and easy to share, this article recommends using a special blueprint file (called environment.yml) to list all the software tools your project needs. By using this file, anyone can recreate your exact workspace with a single command, ensuring your code runs the same way on every computer. Keeping your projects in their own separate folders and "freezing" your settings when finished prevents future updates from breaking your work.
Conda channels act like specialized "app stores" or online libraries where you can find and download different software packages for your projects. This article explains how to choose between major stores like the curated Anaconda Defaults or the massive, community-run Conda-Forge. It also covers how to set a "priority" for these stores so your computer always knows which one to check first to keep your software stable and up-to-date.
Think of Conda as a way to create separate, private "workspaces" on your computer for different projects so their software settings don't get mixed up or cause errors. While other tools only handle one coding language, Conda is a "heavy hitter" that can manage almost any software component, making it the top choice for complex fields like Data Science and AI. By using these isolated spaces, you keep your computer stable and make it easy to share your exact setup with others.
Index of Articles for Managed Virtual Conda Environments