Anaconda compiler tools

Anaconda 5.0 switched from OS-provided compiler tools to our own toolsets. This allows improved compiler capabilities, including better security and performance. This page describes how to use these tools and enable these benefits.

Compiler packages

Before Anaconda 5.0, compilers were installed using system tools such as XCode or yum install gcc. Now there are conda packages for Linux and macOS compilers. Unlike the previous gcc 4.8.5 packages that included gcc, g++ and gfortran all in the same package, these conda packages are split into separate compilers:


  • gcc_linux-64
  • gxx_linux-64
  • gfortran_linux-64


  • clang_osx-64
  • clangxx_osx-64
  • gfortran_osx-64

A compiler’s “build platform” is the platform where the compiler runs and builds the code.

A compiler’s “host platform” is the platform where the built code will finally be hosted and run.

Notice that all of these package names end in a platform identifier which specifies the host platform. All compiler packages are specific to both the build platform and the host platform.

Using the compiler packages

The compiler packages can be installed with conda. Because they are designed with (pseudo) cross-compiling in mind, all of the executables in a compiler package are “prefixed.” Instead of gcc, the executable name of the compiler you use will be something like x86_64-conda_cos6-linux-gnu-gcc. These full compiler names are shown in the build logs, recording the host platform and helping prevent the common mistake of using the wrong compiler.

Many build tools such as make and cmake search by default for a compiler named simply gcc, so we set environment variables to point these tools to the correct compiler.

We set these variables in conda activate.d scripts, so any environment in which you will use the compilers must first be activated so the scripts will run. Conda-build does this activation for you using activation hooks installed with the compiler packages in CONDA_PREFIX/etc/conda/activate.d, so no additional effort is necessary.

You can activate the root environment with the command source activate root.


The macOS compilers require the macOS 10.9 SDK. The SDK license prevents it from being bundled in the conda package.

We know of two current sources for the macOS 10.9 SDK:

We usually install this SDK at /opt/MacOSX10.9.sdk but you may install it anywhere.

Edit your conda_build_config.yaml file to point to it, like this:

  - /opt/MacOSX10.9.sdk        # [osx]

At Anaconda we have this configuration setting in a centralized conda_build_config.yaml at the root of our recipe repository. Since we run build commands from that location, the file and the setting are used for all recipes.

The conda_build_config.yaml search order is described further at Creating conda-build variant config files.

Backward compatibility

Some users want to use the latest Anaconda packages but do not yet want to use the Anaconda compilers. To enable this, the latest Python package builds have a default _sysconfigdata file. This file sets the compilers provided by the system, such as gcc and g++, as the default compilers. This way legacy recipes will keep working.

Python packages also include an alternative _sysconfigdata file that sets the Anaconda compilers as the default compilers. The Anaconda Python executable itself is made with these Anaconda compilers.

The compiler packages set the environment variable _PYTHON_SYSCONFIGDATA_NAME, which tells Python which _sysconfigdata file to use. This variable is set at activation time using the activation hooks described above.

The new _sysconfigdata customization system is only present in recent versions of the Python package. Conda-build automatically tries to use the latest Python version available in the currently configured channels, which normally gets the latest from the default channel. If you’re using something other than conda-build while working with the new compilers, conda does not automatically update Python, so make sure you have the correct _sysconfigdata files by updating your Python package manually.

Anaconda compilers and conda-build 3

The Anaconda 5.0 compilers and conda-build 3 are designed to work together.

Conda-build 3 defines a special jinja2 function, compiler(), to make it easy to specify compiler packages dynamically on many platforms. The compiler function takes at least one argument, the language of the compiler to use:

    - {{ compiler('c') }}

“Cross-capable” recipes can be used to make packages with a host platform different than the build platform where conda-build runs. To write cross-capable recipes you may also need to use the “host” section in the requirements section. In this example we set “host” to “zlib” to tell conda-build to use the zlib in the conda environment and not the system zlib. This makes sure conda-build uses the zlib for the host platform and not the zlib for the build platform.

    - {{ compiler('c') }}
    - zlib

Generally the build section should include compilers and other build tools, and the host section should include everything else, including shared libraries, Python, and Python libraries.

Customizing the compilers

The compiler packages listed above are small packages that only include the activation scripts and list most of the software they provide as runtime dependencies.

This design is intended to make it easy for you to customize your own compiler packages by copying these recipes and changing the flags. You can then edit the conda_build_config.yaml file to specify your own packages.

We have been careful to select good, general purpose, secure and fast flags. We have also used them for all packages in Anaconda Distribution 5.0.0, except for some minor customizations in a few recipes. When changing these flags, remember that choosing the wrong flags can reduce security, reduce performance and cause incompatibilities.

With that warning in mind, let’s look at good ways to customize clang.

  1. Download or fork the code from . The clang package recipe is in the clang folder. The main material is in the llvm-compilers-feedstock folder.

  2. Edit clang/recipe/meta.yaml:

      name: clang_{{ target_platform }}
      version: {{ version }}

    The name here does not matter but the output names below do. Conda-build expects any compiler to follow the BASENAME_PLATFORMNAME pattern, so it is important to keep the {{target_platform}} part of the name.

    {{ version }} is left as an intentionally undefined jinja2 variable. It is set later in conda_build_config.yaml.

  3. Before any packaging is done, run the script:

    In this recipe, values are changed here. Those values are inserted into the activate scripts that are installed later.

    FINAL_CPPFLAGS="-D_FORTIFY_SOURCE=2 -mmacosx-version-min=${macos_min_version}"
    FINAL_CFLAGS="-march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O2 -pipe"
    FINAL_CXXFLAGS="-march=core2 -mtune=haswell -mssse3 -ftree-vectorize -fPIC -fPIE -fstack-protector-strong -O2 -pipe -stdlib=libc++ -fvisibility-inlines-hidden -std=c++14 -fmessage-length=0"
    # These are the LDFLAGS for when the linker is being called directly, without "-Wl,"
    FINAL_LDFLAGS="-pie -headerpad_max_install_names"
    # These are the LDFLAGS for when the linker is being driven by a compiler, with "-Wl,"
    FINAL_LDFLAGS_CC="-Wl,-pie -Wl,-headerpad_max_install_names"
    FINAL_DEBUG_CFLAGS="-Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments"
    FINAL_DEBUG_CXXFLAGS="-Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments"
    FINAL_DEBUG_FFLAGS="-Og -g -Wall -Wextra -fcheck=all -fbacktrace -fimplicit-none -fvar-tracking-assignments"
    find "${RECIPE_DIR}" -name "*activate*.sh" -exec cp {} . \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${CHOST}|g" "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_CPPFLAGS}|g"             "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_CFLAGS}|g"                 "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_DEBUG_CFLAGS}|g"     "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_CXXFLAGS}|g"             "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_DEBUG_CXXFLAGS}|g" "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_DEBUG_CXXFLAGS}|g" "{}" \;
    # find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_FFLAGS}|g"                 "{}" \;
    # find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_DEBUG_FFLAGS}|g"     "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_LDFLAGS}|g"               "{}" \;
    find . -name "*activate*.sh" -exec sed -i.bak "s|@[email protected]|${FINAL_LDFLAGS_CC}|g"         "{}" \;
    find . -name "*activate*.sh.bak" -exec rm "{}" \;
  4. With those changes to the activate scripts in place, it’s time to move on to installing things. Look back at the clang folder’s meta.yaml. Here’s where we change the package name. Notice what comes before the {{ target_platform }}.

      - name: super_duper_clang_{{ target_platform }}
          - clang {{ version }}

    The script reference here is another place you might add customization. You’ll either change the contents of those install scripts, or change the scripts that those install scripts are installing.

    Note that we make the package clang in the main material agree in version with our output version. This is implicitly the same as the top-level recipe. The clang package sets no environment variables at all, so it may be difficult to use directly.

  5. Let’s examine the script

    set -e -x
    mkdir -p "${PREFIX}"/etc/conda/{de,}activate.d/
    cp "${SRC_DIR}"/ "${PREFIX}"/etc/conda/activate.d/activate_"${PKG_NAME}".sh
    cp "${SRC_DIR}"/ "${PREFIX}"/etc/conda/deactivate.d/deactivate_"${PKG_NAME}".sh
    pushd "${PREFIX}"/bin
      ln -s clang ${CHOST}-clang

    Nothing here is too unusual.

    Activate scripts are named according to our package name so they won’t conflict with other activate scripts.

    The symlink for clang is a clang implementation detail that sets the host platform.

    We define macos_machine in aggregate’s conda_build_config.yaml:

    The activate scripts that are being installed are where we actually set the environment variables. Remember that these have been modified by

  6. With any of your desired changes in place, go ahead and build the recipe.

    You should end up with a super_duper_clang_osx-64 package. Or, if you’re not on macOS and are modifying a different recipe, you should end up with an equivalent package for your platform.

Using your customized compiler package with conda-build 3

Remember the Jinja2 function, {{ compiler('c') }}? Here’s where that comes in. Specific keys in conda_build_config.yaml are named for the language argument to that jinja2 function. In your conda_build_config.yaml, add this:

  - super_duper_clang

Note that we’re not adding the target_platform part, which is separate. You can define that key, too:

  - super_duper_clang
  - win-64

With those two keys defined, conda-build will try to use a compiler package named super_duper_clang_win-64. That package needs to exist for your native platform. For example, if you’re on macOS, your native platform is osx-64.

The package subdirectory for your native platform is the build platform. The build platform and the target_platform can be the same, and they are the same by default, but they can also be different. When they are different, you’re cross-compiling.

If you ever needed a different compiler key for the same language, remember that the language key is arbitrary. For example, we might want different compilers for Python and for R within one ecosystem. On Windows the Python ecosystem uses the Microsoft Visual C compilers, while the R ecosystem uses the Mingw compilers.

Let’s start in conda_build_config.yaml:

  - vs2015
  - m2w64-gcc
  - win-64

In Python recipes, you’d have:

    - {{ compiler('python_c') }}

In R recipes, you’d have:

    - {{ compiler('r_c') }}

This example is a little contrived, because the m2w64-gcc_win-64 package is not available. You’d need to create a metapackage m2w64-gcc_win-64 to point at the m2w64-gcc package, which does exist on the msys2 channel on .