cosmetic adjustments

Author: jaimergp

community/history.md

@@ -2,7 +2,11 @@
 title: History
 ---
 
-# Context of binary packaging for Python
+# History of conda-forge
+
+<!-- TODO: Add a little introduction -->
+
+## Context of binary packaging for Python
 
 conda-forge's origins are best understood in the context of Python packaging back in the early 2010s. Back then, the installation of Python packages across operating systems was very challenging, especially on Windows, as it often meant compiling dependencies from source.
 
@@ -14,7 +18,7 @@ However, for Linux, you would have to wait until 2016, when [`manylinux` wheels
 
 As an example, take a look at the [PyPI download page for `numpy` 1.7.0](https://pypi.org/project/numpy/1.7.0/#files), released in Feb 2013. The "Built Distributions" section only shows a few `.exe` files for Windows (!), and some `manylinux1` wheels. However, the `manylinux1` wheels were not uploaded until April 2016. There was no mention whatsoever of macOS. Now compare it to [`numpy` 1.11.0](https://pypi.org/project/numpy/1.11.0/#files), released in March 2016: wheels for all platforms!
 
-The reason why it is hard to find packages for a specific system, and why compilation was the preferred option for many, is binary compatibility. Binary compatibility is a window of compatibility where each combination of compiler version, core libraries such as glibc, and dependency libraries present on the build machine are compatible on destination systems. Linux distributions achieve this by freezing compiler versions and library versions for a particular release cycle. Windows achieves this relatively easily because Python standardized on particular Visual Studio compiler versions for each Python release. Where a Windows package executable was reliably redistributable across versions of Windows, so long as Python version was the same, Linux presented a more difficult target because it was (and is) so much harder to account for all of the little details that must line up.
+The reason why it is hard to find packages for a specific system, and why compilation was the preferred option for many, is binary compatibility. Binary compatibility is a window of compatibility where each combination of compiler version, core libraries such as `glibc`, and dependency libraries present on the build machine are compatible on destination systems. Linux distributions achieve this by freezing compiler versions and library versions for a particular release cycle. Windows achieves this relatively easily because Python standardized on particular Visual Studio compiler versions for each Python release. Where a Windows package executable was reliably redistributable across versions of Windows, so long as Python version was the same, Linux presented a more difficult target because it was (and is) so much harder to account for all of the little details that must line up.
 
 ## The origins of `conda`
 
@@ -43,13 +47,15 @@ There was a lot of cross-pollination between projects/channels, but working in s
 
 ## Meanwhile at Continuum
 
-It's a little strange to describe Continuum/Anaconda's history here, but the company history is so deeply intertwined with conda-forge that it is essential for a complete story. During this time, Continuum (especially Ilan Schnell) was developing its own internal recipes for packages. Continuum's Linux toolchain at the time was based on CentOS 5 and GCC 4.8. These details matter, because they effectively set the compatibility bounds of the entire conda package ecosystem. The packages made from these internal recipes were available on the "free" channel, which in turn was part of a metachannel named `defaults`. The `defaults` channel made up the initial channel configuration for the Miniconda and Anaconda installers. Concurrently, Aaron Meurer led the conda and conda-build projects, contributed many recipes to the conda-recipes repository and built many packages on his "asmeurer" binstar.org channel. Aaron left Continuum in late 2015, leaving the community side of the projects in need of new leadership. Continuum hired Kale Franz to fill this role. Kale had huge ambitions for conda, but conda-build was not as much of a priority for him. Michael Sarahan stepped in to maintain Conda-build.
+It's a little strange to describe Continuum Analytics/Anaconda's history here, but the company history is so deeply intertwined with conda-forge that it is essential for a complete story. During this time, Continuum (especially Ilan Schnell) was developing its own internal recipes for packages. Continuum's Linux toolchain at the time was based on CentOS 5 and GCC 4.8. These details matter, because they effectively set the compatibility bounds of the entire conda package ecosystem. The packages made from these internal recipes were available on the [`free` channel][free-channel], which in turn was part of a metachannel named `defaults`. The `defaults` channel made up the initial channel configuration for the Miniconda and Anaconda installers.
 
-In 2016, Rich Signell at USGS connected Filipe and Phil with Travis Oliphant at Continuum, who assigned Michael Sarahan to be Continuum's representative in Conda-Forge. Ray Donnelly joined the team at Continuum soon afterwards, bringing extensive experience in package managers and toolchains from his involvement in the MSYS2 project. There was a period of time where conda-forge and Continuum worked together closely, with conda-forge relying on Continuum to supply several core libraries. This reliance was partly to lower conda-forge's maintenance burden and reduce duplicate work, but it also helped keep mixtures of conda-forge and `defaults` channel packages working by reducing possibility of divergence. Just as there were binary compatibility issues with mixing packages from among the many Binstar channels, mixing packages from `defaults` with `conda-forge` could be fragile and frustrating.
+Concurrently, Aaron Meurer led the `conda` and `conda-build` projects, contributed many recipes to the `conda-recipes` repository and built many packages on his `asmeurer` binstar.org channel. Aaron left Continuum in late 2015, leaving the community side of the projects in need of new leadership. Continuum hired Kale Franz to fill this role. Kale had huge ambitions for `conda`, but `conda-build` was not as much of a priority for him. Michael Sarahan stepped in to maintain `conda-build`.
 
-Around this point in time, GCC 5 arrived with a breaking change in libstdc++. These changes, among other compiler updates, began to make the CentOS 5 toolchain troublesome. Cutting edge packages, such as the nascent TensorFlow project, required cumbersome patching to work with the older toolchain, if they worked at all. There was strong pressure from the community to update the ecosystem (i.e. the toolchain, and implicitly everything built with it). There were two prevailing options. One was Red Hat's devtoolset. This used an older GCC version which statically linked the newer libstdc++ parts into binaries, so that libstdc++ updates were not necessary on end user systems. The other was to build GCC ourselves, and to ship the newer libstdc++ library as a conda package. This was a community decision, and it was split roughly down the middle. In the end, the community decided to take the latter route, for the sake of greater control over updating to the latest toolchains, instead of having to rely on Red Hat. One major advantage of providing our own toolchain was that we could provide the toolchain as a conda package instead of a system dependency, so we could now express toolchain requirements in our recipes and have better control over compiler flags and behavior.
+In 2016, Rich Signell at USGS connected Filipe and Phil with Travis Oliphant at Continuum, who assigned Michael Sarahan to be Continuum's representative in conda-forge. Ray Donnelly joined the team at Continuum soon afterwards, bringing extensive experience in package managers and toolchains from his involvement in the MSYS2 project. There was a period of time where conda-forge and Continuum worked together closely, with conda-forge relying on Continuum to supply several core libraries. This reliance was partly to lower conda-forge's maintenance burden and reduce duplicate work, but it also helped keep mixtures of `conda-forge` and `defaults` channel packages working by reducing possibility of divergence. Just as there were binary compatibility issues with mixing packages from among the many Binstar channels, mixing packages from `defaults` with `conda-forge` could be fragile and frustrating.
 
-As more and more conflicts with `free` channel packages occurred, conda-forge gradually added more and more of their own core dependency packages to avoid those breakages. At the same time, Continuum was working on two contracts that would prove revolutionary. Samsung wanted to use Conda packages to manage their internal toolchains, and Ray suggested that this was complementary to our own internal needs to update our toolchain. Samsung's contract supported development to conda-build that greatly expanded its ability to support explicit variants of recipes. Intel was working on developing their own Python distribution at the time, which they based on Anaconda and added their accelerated math libraries and patches to. Part of the Intel contract was that Continuum would move all of their internal recipes into public-facing GitHub repositories. Rather than putting another set of repositories (another set of changes to merge) in between internal and external sources, such as conda-forge, Michael and Ray pushed for a design where conda-forge would be the reference source of recipes. Continuum would only carry local changes if they were not able to be incorporated into the conda-forge recipe for social, licensing, or technical reasons. The combination of these conda-forge based recipes and the new toolchain are what made up the `main` channel, which was also part of `defaults`. The `main` channel represented a major step forward in keeping conda-forge and Continuum aligned, which equates to smooth operation and happy users.
+Around this point in time, [GCC 5 arrived][gcc-5] with a breaking change in `libstdc++`. These changes, among other compiler updates, began to make the CentOS 5 toolchain troublesome. Cutting edge packages, such as the nascent TensorFlow project, required cumbersome patching to work with the older toolchain, if they worked at all. There was strong pressure from the community to update the ecosystem (i.e. the toolchain, and implicitly everything built with it). There were two prevailing options. One was Red Hat's `devtoolset`. This used an older GCC version which statically linked the newer `libstdc++` parts into binaries, so that `libstdc++` updates were not necessary on end user systems. The other was to build GCC ourselves, and to ship the newer `libstdc++` library as a conda package. This was a community decision, and it was split roughly down the middle. In the end, the community decided to take the latter route, for the sake of greater control over updating to the latest toolchains, instead of having to rely on Red Hat. One major advantage of providing our own toolchain was that we could provide the toolchain as a conda package instead of a system dependency, so we could now express toolchain requirements in our recipes and have better control over compiler flags and behavior.
+
+As more and more conflicts with `free` channel packages occurred, conda-forge gradually added more and more of their own core dependency packages to avoid those breakages. At the same time, Continuum was working on two contracts that would prove revolutionary. Samsung wanted to use conda packages to manage their internal toolchains, and Ray suggested that this was complementary to our own internal needs to update our toolchain. Samsung's contract supported development to `conda-build` that greatly expanded its ability to support explicit variants of recipes. Intel was working on developing their own Python distribution at the time, which they based on Anaconda and added their accelerated math libraries and patches to. Part of the Intel contract was that Continuum would move all of their internal recipes into public-facing GitHub repositories. Rather than putting another set of repositories (another set of changes to merge) in between internal and external sources, such as conda-forge, Michael and Ray pushed for a design where conda-forge would be the reference source of recipes. Continuum would only carry local changes if they were not able to be incorporated into the conda-forge recipe for social, licensing, or technical reasons. The combination of these `conda-forge`-based recipes and the new toolchain is what made up the `main` channel, which was also part of `defaults`. The `main` channel represented a major step forward in keeping conda-forge and Continuum aligned, which equated to smooth operation and happy users.
 
 <!-- miniforge -->
 
@@ -83,3 +89,7 @@ As more and more conflicts with `free` channel packages occurred, conda-forge gr
 [^pythonxy]: https://python-xy.github.io/, 2015.
 [^technical-discovery]: https://technicaldiscovery.blogspot.com/2013/12/why-i-promote-conda.html, 2013.
 [^wheels]: [PEP 427 – The Wheel Binary Package Format 1.0](https://peps.python.org/pep-0427/)
+
+<!-- links -->
+[free-channel]: https://anaconda.org/free
+[gcc-5]: https://gcc.gnu.org/gcc-5/changes.html