The multitude of Python environments management choices can leave even senior Python developers unsure of the best tool usage. This article describes how Palaimon reduced costs while increase productivity by choosing to use mamba/micromamba for environment management.
Tools like pip, conda, venv, poetry, and others, have served us well over time. But like most things in the dynamic world of programming, we continuously evolve to meet new challenges and improve existing systems.
Palaimon develops complex data projects which requires the usage of multiple dependencies. pip and virtualenv are the “go to” way to manage environment and handle dependencies in Python but Conda packages are a better solution for Machine Learning projects. Conda includes cross-language dependencies and isolated environment setup with more stable packages and GPU support (which is key for us).
Conda’s impressive capability comes with performance issues which leads to workarounds like combining conda with other tools to optimize the setup. In the past we tried to combine Conda and poetry but these kind of workarounds can lead to its own complexities and are not ideal for all scenarios.
That’s where mamba comes into play. If you’re not familiar, mamba is a reimplementation of the conda package manager, written in C++. This transition to a compiled language means that mamba provides a significant performance boost, particularly when resolving dependencies, which can take a lot of time in larger projects.
Mamba offers all the advantages of conda with significantly speed improvement. Faster environment solving means less waiting and more time for coding
Mamba allowed our team to config a better, faster and cheaper CI (Continuos Integration), improving the team productivity. Its compatibility with conda environment and packages made the transition from conda to mamba very easy since all commands the team used are kept the same.
At Palaimon GmbH, we understand the importance of effective Python environment management, and we’ve adopted micromamba, a tiny version of mamba. This tooling addresses many of the challenges we’re faced with when managing complex Python environments while reducing our docker images costs, making the CI maintenance cheaper.
Managing Python environments is a vital aspect of Python development, and choosing the right tool can significantly impact your team productivity, the quality of your work and CI costs. At Palaimon GmbH we are excited about the possibilities mamba/micromamba offers and we highly recommend giving it a try.
Text created with the help of AI