You’ll use prefix-notation to specify the package version(s) to install. Well with: $ conda install foo-lib=14.3.2 Narrow the installation down to an exact PATCH level, you can specify that as You could spell that as: $ conda install foo-lib=13 May want a particular major version, and prefer conda to select the latestĬompatible MINOR version as well as PATCH level. You could spell that as: $ conda install foo-lib=12.3 ![]() For example, you might want a MAJOR and MINOR version, but want conda to select the most up-to-date PATCH version within that series. Your most common pattern will probably be prefix notation, using semantic versioning. How to install a specific version of a package?Ĭonda allows you to install software versions in several flexible ways. The following command lists all the installed packages. That is, packages you didn’t install explicitly get installed for you to resolve another package’s dependencies. $ conda -Vīecause conda installs packages automatically, it’s hard to know which package versions are actually on your system. Run a command to determine what version of conda you have installed. ![]() You can download the Anaconda Distribution from the official site. With over 15 million users worldwide, it is the industry standard for developing, testing, and training on a single machine. The open-source Anaconda Distribution is the easiest way to perform Python/R data science and machine learning on Linux, Windows, and Mac OS X. Notice that conda supports Python, R, Scala and Julia but we will focus on Python in this post. It is very important when we are working on a project to be reproducible and for that reason, we want to be able to share our working environment with our colleagues, or each project to be in a different environment. This post is a gentle introduction about Anaconda Environments which is like the “ Docker” of the Machine Learning projects.
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