I recently discovered Conda after I was having trouble installing SciPy, specifically on a Heroku app that I am developing. The Microsoft Python Extension for Visual Studio Code is actively developed in our GitHub repository, and is the most downloaded extension in the VS Code marketplace with over 6 million downloads to-date, and a 4.7/5.0 rating. Download Anaconda 5.1 now and check out the Visual Studio Code Python. With Conda you create environments, very similar to what virtualenv does. My questions are:
JohanJohan
6 Answers
For example: lists all installed packages in your current environment.Conda-installed packages show up like this: and the ones installed via
Mike MüllerMike Müller
Short answer is, you only need conda.
Here is a link to the conda page comparing conda, pip and virtualenv: https://docs.conda.io/projects/conda/en/latest/commands.html#conda-vs-pip-vs-virtualenv-commands.
Mad PhysicistMad Physicist
Virtual Environments and I will add that creating and removing conda environments is simple with Anaconda. In an activated environment, install packages via These environments are strongly tied to conda's pip-like package management, so it is simple to create environments and install both Python and non-Python packages. Jupyter In addition, installing Reliability In my experience, conda is faster and more reliable at installing large libraries such as
pylangpylang
![]() Installing Conda will enable you to create and remove python environments as you wish, therefore providing you with same functionality as virtialenv would. In case of both distributions you would be able to create an isolated filesystem tree, where you can install and remove python packages (probably, with pip) as you wish. Which might come in handy if you want to have different versions of same library for different use cases or you just want to try some distribution and remove it afterwards conserving your disk space. Differences:Licence agreement. While virtualenv comes under most liberal MIT license, Conda uses 3 clause BSD license. Conda provides you with their own package control system. This package control system often provides precompiled versions (for most popular systems) of popular non-python software, which can easy ones way getting some machine learning packages working. Namely you don't have to compile optimized C/C++ code for you system. While it is a great relief for most of us, it might affect performance of such libraries. Unlike virtualenv, Conda duplicating some system libraries at least on Linux system. This libraries can get out of sync leading to inconsistent behaviour of your programs. Verdict:Conda is great and should be your default choice while starting your way with machine learning. It will save you some time messing with gcc and numerous packages. Yet, Conda does not replace virtualenv. It introduces some additional complexity which might not always be desired. It comes under different license. You might want to avoid using conda on a distributed environments or on HPC hardware.
y.selivonchyky.selivonchyk
Another new option and my current preferred method of getting an environment up and running is Pipenv It is currently the officially recommended Python packaging tool from Python.org
JohanJohan
Yes,
Liang HuangLiang Huang
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