![]() How I combine virtual environments and conda environments Even if we don't have too much control on the underlying libraries, it may be just what we need. It prevents problems with incompatible cross-dependencies, allows careful updates and testing. ![]() If our projects are exclusively Python-dependent, virtual environments are so lightweight that there's only upside in using them.Įven if we have control on the computer where the code is going to be deployed, adding the layer of a virtual environment gives plenty of peace of mind. We could rely on the operating system's package manager. If we have control on the packages installed on a server, for example, then I could argue that conda environments are not necessarily adding value. If we are delivering software, either by deploying it on a server accessible by others, or as part of an embedded system, or as a program that others will install in their computers, we probably want to minimize the maintenance burden. However, thinking about ourselves as the developers and consumers of the same code hides the real problem. In these update cycles it may happen that there's a new release of Python, which in turn makes our program misbehave. For example, Windows updates itself giving very little control to the user. One of the largest risks when deploying application is that an un-intended software update generates a cascading effect. The choice between one and the other comes down to understanding how much control we have on the underlying operating system, and what would the risks be. How to choose between conda environments and virtual environments Virtual environments are a very good tool to isolate dependencies across projects that rely mostly on Python packages. For example, Python itself is grabbed from the operating system and not installed purposefully on each environment. However, many of these packages still rely on libraries available at an operating-system level. Today, no one has issues installing numpy, pyqt, or scikit with a simple pip command. The Python package manager improved a lot in the past decade. In these cases, one must rely on the operating system's ability to install programs. Libraries and programs that do not belong to the Python ecosystem can't be installed with these tools. ![]() The main difference is that they rely on the Python package manager. Virtual environments are a native tool to Python developers, and they provide a functionality similar to that of the conda environments. Python virtual environments are the light version of the conda environments. How do virtual environments compare to conda environments You know you can bring the same environment to the next computer you work on, and the results will be the same. Conda environments are ideal for ensuring reproducibility. ![]() We can track the version of the underlying libraries, for example those on which numpy relies to perform calculations. An update of one of the libraries will have an impact only on that specific project structure, and not on the others.Ĭonda environments are so specific that they allow to select the version of Python we want to run, for example. That's the root idea of a conda environment: isolating the dependencies for each project in separated spaces. When developing several projects on the same computer, managing dependencies can become crucial to ensure quality and compatibility. Installing packages is only half the problem, being able to isolate project dependencies is the other half. Not so many years ago getting a decent development setup on Windows was a total nightmare, and Anaconda solved that problem. ![]() Many libraries such as numpy have dependencies that go much beyond what the default Python installer can take care of. This removes a lot of duplication (programs shipping dependencies) and makes the system easier to maintain (a single source of truth for versions and requirements).Īnaconda brought the idea of a package manager to windows, focusing on scientific/engineering Python developers. A package manager is a tool that allows to install programs AND their dependencies. What is Anaconda and the conda package managerĪnaconda is a full-fledged package manager that works on most operating systems.įor people coming from a Linux background, this is not novelty, but for Mac and Windows users, package managers are unknown. ![]()
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