We’re thrilled to announce the release of Anaconda Distribution 5.0! With over 4.5 million active users, Anaconda Distribution is the world’s most popular and trusted distribution for data science. It allows you to easily install 1,000+ Python and R data science packages and manage your packages, dependencies, and environments—all with the single click of a button.



You can now download and install Anaconda Distribution 5.0, or update your current Anaconda Distribution installation to version 5.0 by using conda update conda followed by conda install anaconda=5.0 .



In other news, you may have noticed that Continuum Analytics has been renamed to Anaconda! The name change has been a long time coming, and reinforces our commitment to the growing Anaconda open source community. Check out this blog post from our CEO to learn more.



Highlights of Anaconda Distribution 5.0:

Over 100 packages have been updated or added to the distribution. JupyterLab alpha preview 0.27.0 is now included, and MKL has been updated to 2018.0.0.

The new version features all new compilers on macOS and Linux, providing substantial security and performance improvements.

Where possible, all build recipes are now using conda-forge as a base, via https://github.com/AnacondaRecipes.

A new channel, pkgs/main , has been added to defaults . The new channel is given top priority within defaults and holds packages built with the new compiler stack.

, has been added to . The new channel is given top priority within and holds packages built with the new compiler stack. The new version of Anaconda Distribution now features more flexible dependency pinning of NumPy packages, providing wider ranges of compatibility.

Full release notes: https://docs.anaconda.com/anaconda/release-notes

Additional Details

New Compilers

The Anaconda 5.0 release used very modern compilers to rebuild almost everything (~99.5%) provided in the installers for x86 Linux and MacOS. This enables Anaconda users to get the benefits of the latest compilers— still allowing support for older operating systems—back to MacOS 10.9 and CentOS 6. Our own builds of GCC 7.2 (Linux) and Clang 4.0.1 (MacOS) are used, and every reasonable security flag has been enabled. CFLAGS and CXXFLAGS are no longer managed by each package; instead compiler activation sets them globally.

The packages built with the new compilers are in a different channel from packages built the old way, and as we build out this new channel, we will eventually be able to change the default experience to only using these packages. Interested in using this approach to build your own conda packages? Stay tuned for a more developer-focused blog post!

conda-forge

With the rising popularity of Anaconda and conda, the conda packaging community conda-forge has emerged and grown tremendously in the last couple of years. This flourishing community has already built over 3,200 different packages and contributes new recipes and recipe updates daily.