Using environment.yml to lock Anaconda environments for reproducible builds
Learn how to write an environment.yml file that pins exact package versions and channels, then create, export, and reproduce Anaconda environments across machines.
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Learn how to write an environment.yml file that pins exact package versions and channels, then create, export, and reproduce Anaconda environments across machines.
An environment.yml records what you asked for, not what got installed. Keep a hand-edited request file plus a platform-specific explicit lock, and verify both in a clean prefix.
Learn how to lock an Anaconda environment using environment.yml see a worked example and understand the limits of this reproducibility method.
Learn how to use Anaconda's environment.yml to declare exact package sets, create reproducible environments, and avoid version mismatches across machines and CI pipelines.
Goal To determine whether explicitly pinning build strings in an environment.yml file guarantees that the same binary artifacts are installed on Windows, macOS, and Linux, thereby yielding identical runtime behavior. Constraints and Uncertainty While the file can list exact package names, versions, and optional build strings, conda’s solver may still choose
Conda's channel_priority: strict setting in .condarc is documented to require all packages to originate from the highest-priority channel that provides them. When an environment.yml lists both defaults and conda-forge , and a package version exists in both channels with different build strings, the documentation does not specify whether strict priority manda
Goal Restore a broken conda environment after disk loss while ensuring reproducibility. Constraints Need bit‑for‑bit exactness on the same platform, but also consider portability to a different OS/arch. Uncertainty The conda documentation lists two restore paths: conda list --explicit followed by conda create --file , and conda env export / conda env create
Goal: Recreate an environment defined in an environment.yml file on a macOS developer workstation inside a Linux-based production container with identical package versions and builds. Uncertainty arises because conda resolves platform-specific package builds at install time, channel priority settings can shift package sources, transitive dependencies are re‑