depending on how smart the framework is, you might find yourself installing Conda packages over and over again on every run. This is inefficient, even when using a faster installer like Mamba.

there’s the bootstrapping problem: depending on the framework you’re using, you might need to install Conda and the framework driver before you can get anything going. A Docker image would come prepackaged with both, in addition to your code and its dependencies. So even if your framework supports Conda directly, you might want to use Docker anyway.

Mlflow supports both Conda and Docker-based projects.

The only thing that will depend on the host operating system is glibc, pretty much everything else will be packaged by Conda. So a pinned environment.yml or conda-lock.yml file is a reasonable alternative to a Docker image as far as having consistent dependencies.

To summarize, for the use case of Python development environments, here’s how you might choose alternatives to Docker:

Conda packages everything but the standard C library, from C libraries to the Python interpreter to command-line tools to compilers.

At the time of writing, there is an open bug in Snakemake (version 5.8.2) on Windows systems that prevents requesting specific files from the command line when those files are in a subdirectory.

When running on Windows using Git Bash and Anaconda, the previous code will not work. Multiline strings containing multiple shell commands are not executed correctly. The simplest workaround is to add &&\ to the end of all lines except the last inside the multiline shell command:

Speaking from my (R-biased) viewpoint, conda has posed some problems as well:

Mamba installs these packages in only a third of the time that Conda does. Much of that is due to less CPU usage, but even network downloads seem to be little faster; Mamba uses parallel downloads to speed them up.

So which should you use, pip or Conda? For general Python computing, pip and PyPI are usually fine, and the surrounding tooling tends to be better. For data science or scientific computing, however, Conda’s ability to package third-party libraries, and the centralized infrastructure provided by Conda-Forge, means setup of complex packages will often be easier.

conda env update --prune --quiet --name $envname --file "$envfile"

when using miniconda, you can’t move the Amber install folder fromits original location

pip is a package manager. conda is both a package manager and an environment maanger.

conda and bioconda channel