The later versions of conda will create the destination folders for you if it does not exists already. To do that, instead of the name of the environment, you supply the destination location as an argument to the create command.

Create Environment at a given locationĬonda gives you an ability to create your virtual environment at a location of your choosing. In case you run into an error like ‘conda is not recognized internal or external command‘, verify that you have Anaconda’s bin folder added to PATH. Upon successful execution of the command, you should see similar output as below. Open a terminal and run the following command. Let’s cut right to the chase, and create a virtual environment named test-env. In case you don’t have Anaconda installed, follow the steps given in Getting started with Python post to get Anaconda up and running on your system. Install AnacondaĪs a pre-requisite, you’ll need Anaconda installed on your machine. It comes packaged with the Anaconda distribution itself. Create Conda EnvironmentĬonda virtual environments are created using conda command line utility. In this post, we are going to create virtual environments, learn how to use them, install packages within them and how to manage them. However, you will find that Conda is a much better package manager compared to PIP, especially in its ability to resolve non-python dependencies. Conda environments work in a similar way as PIP environments. It gives you an ability to install your preferred versions of packages without conflicting with other projects and users. My Ubuntu version as of time of this writing is 16.04.3 (Xenial Xerus).Conda virtual environments provide users an isolated workspace. My aim, however, is to build OpenCV so that it could be used in both C++ and Python.įurther in this blog post I am going to summarize my notes on building OpenCV 3 on Ubuntu using conda’s Python 3.6, with further symlinking of the Python extension to other conda environments. Their availability vary by operating systems and Python versions, but they are in general a pretty good reproducible solution when OpenCV is only used in Python. It is worth to mention that there exist pre-built OpenCV 3 binaries in the menpo channel on Anaconda Cloud. It especially shines on Raspberry Pies, where conda’s pre-built binaries get installed way faster than the pip-installed ones. However, in my work I mostly use conda for managing my Python environments. Such a solution worked pretty well for me, both on Linux and macOS. In particular, Adrian creates a dedicated virtualenv environment, installs NumPy in it, and builds the whole thing having this environment activated. He provides detailed description of the required steps, as well as motivation for better development practices. When it comes to building and installing OpenCV with Python support on *nix platforms, the collection of tutorials by Adrian Rosebrock is the best.

Install gfortran in conda environment