![]() ![]() When appropriate conda and pip requirements should be stored in text files. If modifications are needed to the environment, it is best to create a new environment rather than running conda after pip. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software. In summary, when combining conda and pip, it is best to use an isolated conda environment. Both of these methods have the benefit that the files describing the environment can be checked into a version control system and shared with others. A single file containing both conda and pip requirements can be exported or provided to the conda env command to control an environment. Package requirements can be provided to conda via the -file argument and pip via the -r or -requirement. Again, it is primarily the “statefulness” of pip that causes problems - the more state that exists because of the order of installation of packages, the harder it will be to keep things working.įor environments that will be recreated often, it is a good practice to store the conda and pip package requirements in text files. This new environment can be tested before removing the old one. Rather than running conda, pip and then conda again, a more reliable method is to create a new environment with the combined conda requirements and then run pip. Once pip is used to install software into a conda environment, conda will be unaware of these changes and may make modifications that would break the environment. On the other hand, creating separate conda environments allows you to delete and recreate environments readily, without risking your core conda functionality. If this environment becomes cluttered with a mix of pip and conda installs, it is much harder to recover. Many users rely on simply the “root” conda environment that is created by installing either Anaconda or Miniconda. If a similar set of packages are installed, each new conda environment will require only a small amount of additional disk space. In conda environments, hard links are used when possible rather than copying files to save space. Conda environments are isolated from each other and allow different versions of packages to be installed. If there is an expectation to install software using pip along-side conda packages it is a good practice to do this installation into a purpose-built conda environment to protect other environments from any modifications that pip might make. This is the default when running pip but it should not be changed. Additionally, pip should be run with the “-upgrade-strategy only-if-needed” argument to prevent packages installed via conda from being upgraded unnecessarily. In these cases, using pip only after all other requirements have been installed via conda is the safest practice. For projects available on PyPI, the conda skeleton command (which is part of conda-build) frequently produces a recipe which can be used create a conda package with little or no modifications.Ĭreating conda packages for all additional software needed is a reliably safe method for putting together a data science environment but can be a burden if the environment involves a large number of packages which are only available on PyPI. If software is needed which is not available as a conda package, conda build can be used to create packages for said software. One surefire method is to only use conda packages. There are a few steps which can be used to avoid broken environments when using conda and pip together. In some cases these breakages are cosmetic, where a few files are present that should have been removed, but in other cases the environment may evolve into an unusable state. Similarly, pip may upgrade or remove a package which a conda-installed package requires. Running conda after pip has the potential to overwrite and potentially break packages installed via pip. Most of these issues stem from that fact that conda, like other package managers, has limited abilities to control packages it did not install. ![]() Unfortunately, issues can arise when conda and pip are used together to create an environment, especially when the tools are used back-to-back multiple times, establishing a state that can be hard to reproduce. ![]()
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