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It is designed to be installed inside a virtual environment (created by venv) and wraps pip to install defined packages. Pip-tools is a composable tool that solves one specific problem - environment reproducibility. How does pip-tools interact with pip and other tools? It solves the second problem I’ve described.
![conda python version mess conda python version mess](https://i.stack.imgur.com/soEAL.png)
Pip-sync takes a compiled requirements.txt and ensures that your current environment has exact versions and only packages defined in the requirements.txt. It solves the first problem I’ve described. Some tools (npm, yarn, bundle) call it a lockfile. Pip-compile takes setup.py/ requirements.in file and compiles a requirements.txt with all dependencies locked to specific versions. uninstalling a package will leave its dependencies in the existing environment, but they won’t exist if I create a new environment and install everything from requirements.txt). Both eventually lead to the app breaking inconsistently (e.g. The second most common is running different environments, depending on the time you’ve created them (2). The most common one is running different package versions in different environments (1).
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Unlisted dependencies cause several problems. These dependencies often have other dependencies not listed in requirements.txt.
![conda python version mess conda python version mess](https://miro.medium.com/max/1200/1*wxe5vtzS7xZeW0B9pvKBdQ.png)
The most common way to define dependencies is to create a requirements.txt file and list them with specific versions. People who care about it the most are production engineers. They’re relatively simple, but tackle a very important problem - keeping your environment reproducible and consistent. Pip-tools is packaging two different tools: pip-compile and pip-sync. You’re encouraged to use pip for installing packages within virtual environments. They’re both the part of standard python tooling, tackle very different problems, and play together really well. Each environment can use any version of the same package and they won’t collide. It makes sure apps don’t share packages between themselves and they don’t share packages with the system’s python installation. The most common use case is creating an environment per app. Venv is a tool for creating lightweight virtual environments. It knows nothing about different python versions or Jupyter kernels. That command talks to PyPI (The Python Package Index), downloads the package, and makes it available to the current python installation. Installing a package is as simple as running pip install torch. It comes preinstalled with most python installations so it’s likely you never had to install it yourself. Pip ( package installer for python) is the most basic package installer in the python land. Jump to: pip | venv | pip-tools | pyenv | conda | pipenv | poetry | Docker.Īll solutions compared | Usual setups pip At the end of the post, you can find a table summarizing all the information and the usual setups people use. I’ll briefly describe each tool, why it’s created and the problems it’s tackling. The world of Python dependency management is a mess, but once you understand the tools and why they exist, it’s going to be easier to choose the one you want and deal with the others in environments where you can’t choose your favorite ones. Totally confused by all the tools for managing dependencies? Pip, venv, Docker, conda, virtualenvwrapper, pipenv, … Which one should you use? Why do we even have all these different tools? Can they work together?
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