- CONDA INSTALL PACKAGE DEPENDENCIES HOW TO
- CONDA INSTALL PACKAGE DEPENDENCIES WINDOWS
Beyond pure Python: Packaging compiled extensions That’s a big enough topic that it gets a whole new section, next.
CONDA INSTALL PACKAGE DEPENDENCIES HOW TO
Consistent configuration: You don’t need to install system packages and Python packages in two different ways (almost) everything can go in one file, the environment.yml.īut it also addresses another problem: how to deal with Python libraries that require compiled code. Reproducibility: It’s possible to pin almost the whole stack, from the Python interpreter upwards. Portability across operating systems: Instead of installing Python in three different ways on Linux, macOS, and Windows, you can use the same environment.yml on all three. In part it’s about portability and reproducibility. Why did Conda make the decision to package everything, Python interpreter included? Need to ship quickly, and don’t have time to figure out every detail on your own? Read the concise, action-oriented Python on Docker Production Handbook. Note: Outside any specific best practice being demonstrated, the Dockerfiles in this article are not examples of best practices, since the added complexity would obscure the main point of the article. 
This base image ships with Conda pre-installed, but we’re not relying on any existing Python install, we’re installing a new one in the new environment. Here’s what the pip requirements.txt would look like:įROM continuumio/miniconda3 COPY environment.yml.
Conda packages include Python libraries (NumPy or matplotlib), C libraries ( libjpeg), and executables (like C compilers, and even the Python interpreter itself).įor example, let’s say you want to install Python 3.9 with NumPy, Pandas, and the gnuplot rendering tool, a tool that is unrelated to Python. Pip packages are Python libraries like NumPy or matplotlib. The fundamental difference between pip and Conda packaging is what they put in packages. The starting point: which kind of dependencies? Focusing on the Conda-Forge package repository Conda has multiple package repositories, or “channels”.īy the end you should understand why Conda exists, when you might want to use it, and the tradeoffs between choosing each one. Linux, including running on Docker, though with some mention of macOS and Windows. Python only Conda has support for other languages but I won’t go into that. While it’s not possible to answer this question for every situation, in this article you will learn the basic differences, constrained to: What are the tradeoffs between the two?. Pugh: Changed to use python -m pip to ensure the dependency is installed on the right place.If you’re using Python in the world of data science or scientific computing, you will soon discover that Python has two different packaging systems: pip and Conda. Taught people to use pyenv and pyenv-win. What does Audrey use?Īudrey uses pyenv and loves it. My classes focused more on teaching Python and Django rather than Docker. Once people got it installed we got to teaching instead of dealing with setup issues. Conda is a universal installation system that works for everyone regardless of operating system. 
Didn't handle non-python binaries well, requiring use of Chocalateyģ. CONDA INSTALL PACKAGE DEPENDENCIES WINDOWS
I always struggled to get pyenv-win to work on Windows.
On Windows, conda install is sometimes useful because it handles non-python binaries that otherwise require Docker, Chocolately, or other tools. I rarely use conda install on MacOS or Linux. What about conda install for dependencies?
Conda has worked great with pip for years.īecause I know you have them. I'm using pip to manage packages in a conda env. Just like many any other virtual environments for Python, use pip to install/uninstall dependencies (credible) python -m pip install typer (credible) Step 3: Install Dependencies Using Pip Into a Conda Env This prefixes the CLI shell with (credible), exactly like venv or virtualenv. Works /Users/drg/opt/miniconda3/envs/worksĮverywhere /Users/drg/opt/miniconda3/envs/everywhere Step 2: Activate a Virtual Environment conda activate credible System /Users/drg/opt/miniconda3/envs/system Get the list of current envs: $ conda env listĬredible /Users/drg/opt/miniconda3/envs/credible Here's where they are on my work, personal laptops, and a 2018-era Windows laptop I sometimes test on: Step 1: Create a Virtual Environment conda create -n credible python=3.10Īfter agreeing to install some dependencies, it places them in an out-of-the way location. I prefer to use Conda because once installed it works essentially the same everywhere.
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