conda env will export or create environments based on a file with conda and pip requirementsĬoncur with eatmeimadanish.pip accepts a list of Python packages with -r or -requirements.package requirements can be passed to conda via the -file argument.Store conda and pip requirements in text files to install additional Conda packages it is best to recreate the environment.once pip has been used conda will be unaware of the changes.Recreate the environment if changes are needed care should be taken to avoid running pip in the root environment.environments take up little space thanks to hard links.create a Conda environment to isolate any changes pip makes.Do not use pip with the -user argument, avoid all “users” installs.pip should be run with -upgrade-strategy "only-if-needed" (the default). install as many requirements as possible with conda, then use pip.pip?"Īnaconda Inc's Jonathan Helmus sums this up quite nicely in the post " Using Pip in a Conda Environment." Here's an excerpt from the final best practices recommendation: Best Practices Checklist Use pip only after conda " What is the current (2019) wisdom regarding when to install something with conda vs. Note: The following recommendations are now part of the official documentation. If you want to, say, work with the many Python packages which rely on external dependencies (NumPy, SciPy, and Matplotlib are common examples), while tracking those dependencies in a meaningful way, pip can't help you: by design, it manages Python packages and only Python packages.Ĭonda and pip are not competitors, but rather tools focused on different groups of users and patterns of use. If you want to, say, manage Python packages within an existing system Python installation, conda can't help you: by design, it can only install packages within conda environments. If we focus on just installation of Python packages, conda and pip serve different audiences and different purposes. By isolated environment I mean a conda-env or virtualenv, in which you can install packages without modifying your system Python installation. If all you are doing is installing Python packages within an isolated environment, conda and pip+virtualenv are mostly interchangeable, modulo some difference in dependency handling and package availability. For the user, the most salient distinction is probably this: pip installs python packages within any environment conda installs any package within conda environments. In short, pip is a general-purpose manager for Python packages conda is a language-agnostic cross-platform environment manager. Both pip and PyPI are governed and supported by the Python Packaging Authority (PyPA). Pip, which stands for Pip Installs Packages, is Python's officially-sanctioned package manager, and is most commonly used to install packages published on the Python Package Index (PyPI). For instance psycopg2 is far easier to install in conda than pip. I created a simple Sklearn routine, which utilises a single thread for its calculations.I find I use conda first simply because it installs the binary, than try pip if the package isn't there. One of the advantages to use a Hackintosh is that you can try the same software on the same hardware across three operating systems: how does NumPy - and therefore Sklearn - perform across different operating systems and mathematics libraries?
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