Environments & Packages

ARCH provides flexible tools to create isolated environments for Python, R, and multi-language workflows. By working within a virtual environment, users can install libraries, manage packages, and build reproducible projects without interfering with system-wide software.

This section covers multiple environment types and how to connect them to common platforms:

• Virtual Environments — Python venvs, Conda environments, and Spack packages

• Conda R — Creating and managing R environments with Conda

• Link Conda to Jupyter — Making your Conda environments available as Jupyter kernels

• Link Conda to RStudio — Running RStudio Server sessions using a Conda-managed R

Whether you’re developing pure Python code, working with machine learning frameworks, managing R packages, or preparing complex HPC workflows, these guides will help you get set up correctly.

Questions? Contact help@arch.jhu.edu for assistance.

• Virtual Environments
• Conda R
• Link Conda to Jupyter
• Link Conda to RStudio