Tips & tricks¶
Using multiple channels¶
It is quite common to install a package from conda-forge and, when trying to use it, see an error like (OS X example):
ImportError: dlopen(.../site-packages/rpy2/rinterface/_rinterface.so, 2): Library not loaded: @rpath/libicuuc.54.dylib Referenced from: .../site-packages/rpy2/rinterface/_rinterface.so Reason: image not found
That happens because either the correct version of
or any other package in the error,
is not present or the package is missing altogether.
You can confirm this by issuing the command
conda list and searching for the package in question.
Why does that happen?¶
defaults are not 100% compatible.
In the example above it is known that
icu 54.* while
conda-forge relies on
that mismatch can lead to errors when the install environment is mixing packages from multiple channels.
All of conda-forge software pinning can be found at: https://github.com/conda-forge/conda-forge-pinning-feedstock/blob/master/recipe/conda_build_config.yaml
How to fix it?¶
conda versions (>=4.6) introduced a strict channel priority feature.
conda config --describe channel_priority for more information.
The solution is to add the
conda-forge channel on top of
defaults in your
.condarc file when using
and activate the strict channel priority with:
$ conda config --set channel_priority strict
This will ensure that all the dependencies come from the
conda-forge channel unless they exist only on
Here is how a
.condarc file would look like:
$ cat .condarc channel_priority: strict channels: - conda-forge - defaults
In addition to the channel priority, we recommend always installing your packages inside a new environment instead of the
base environment from anaconda/miniconda.
Using envs make it easier to debug problems with packages and ensure the stability of your root env.
In the past
conda-forge used to vendorize some of
defaults dependencies that were not built in our infrastructure,
like compilers run-times, to avoid the mixing channel problem.
However, with the
strict option, we no longer have to vendorize those (this led to its own set of problems),
instead, we removed everything that is not built in
conda-forge and let
strict pull those from
TL;DR if you are experiencing missing compilers run-times like
that is probably because you removed
just re-add it and activate
strict for a smooth and stable experience when installing packages.
Using External Message Passing Interface (MPI) Libraries¶
On some high-performance computing (HPC) systems, users are expected to use the
MPI binaries that are available on the system as opposed to those built by
These binaries are typically specialized for the system and interface properly with job
schedulers, etc. However, this practice creates issues for
conda-forge users. When you install
a package from
conda-forge that relies on MPI,
conda will install the MPI binaries
conda-forge and the package will link to those binaries. This setup often either
does not work at all or functions in unexpected ways on HPC systems.
To solve these issues,
conda-forge has created special dummy builds of the
libraries that are simply shell packages with no contents. These packages allow the
conda solver to produce
correct environments while avoiding installing MPI binaries from
conda-forge. You can install the
dummy package with the following command
$ conda install "mpich=x.y.z=external_*" $ conda install "openmpi=x.y.z=external_*"
As long as you have the local copies of the
openmpi library in your linking paths and
the local version matches the
conda version within the proper ABI range, then this procedure should
work. At runtime, the
conda-forge package that depends on MPI should find the
local copy of
openmpi and link to it.
Installing Apple Intel packages on Apple Silicon¶
Using Rosetta 2, you can install packages originally compiled for Mac computers with Intel processors on Mac computers with Apple silicon processors.
This can be enabled per environment using the following commands:
CONDA_SUBDIR=osx-64 conda create -n your_environment_name python # Create a new environment called your_environment_name with intel packages. conda activate your_environment_name python -c "import platform;print(platform.machine())" # Confirm that the correct values are being used. conda config --env --set subdir osx-64 # Make sure that conda commands in this environment use intel packages.
To verify that the correct platform is being used, run the following commands after the environment has been activated:
python -c "import platform;print(platform.machine())" # Should print "x86_64" echo "CONDA_SUBDIR: $CONDA_SUBDIR" # Should print "CONDA_SUBDIR: osx-64"
Installing CUDA-enabled packages like TensorFlow and PyTorch¶
In conda-forge, some packages are available with GPU support. These packages not only take significantly longer to compile and build, but they also result in rather large binaries that users then download. As an effort to maximize accessibility for users with lower connection and/or storage bandwidth, there is an ongoing effort to limit installing packages compiled for GPUs unnecessarily on CPU-only machines by default. This is accomplished by adding a run dependency,
__cuda, that detects if the local machine has a GPU. However, this introduces challenges to users who may prefer to still download and use GPU-enabled packages even on a non-GPU machine. For example, login nodes on HPCs often do not have GPUs and their compute counterparts with GPUs often do not have internet access. In this case, a user can override the default setting via the environment variable
CONDA_OVERRIDE_CUDA to install GPU packages on the login node to be used later on the compute node. At the time of writing (February 2022), we have concluded this safe default behavior is best for most of conda-forge users, with an easy override option available and documented. Please let us know if you have thoughts on or issues with this.
In order to override the default behavior, a user can set the environment variable
CONDA_OVERRIDE_CUDA like below to install TensorFlow with GPU support even on a machine with CPU only.
CONDA_OVERRIDE_CUDA="11.2" conda install "tensorflow==2.7.0=cuda112*" -c conda-forge # OR CONDA_OVERRIDE_CUDA="11.2" mamba install "tensorflow==2.7.0=cuda112*" -c conda-forge
You should select the cudatoolkit version most appropriate for your GPU; currently, we have “10.2”, “11.0”, “11.1”, and “11.2” builds available, where the “11.2” builds are compatible with all cudatoolkits>=11.2. At the time of writing (Mar 2022), there seems to be a bug in how the CUDA builds are resolved by
mamba, defaulting to
cudatoolkit==10.2; thus, it is prudent to be as explicit as possible like above or by adding
cudatoolkit>=11.2 or similar to the line above.
For context, installing the TensorFlow 2.7.0 CUDA-enabled variant,
tensorflow==2.7.0=cuda*, results in approximately 2 GB of packages to download while the CPU variant,
tensorflow=2.7.0=cpu*, results in approximately 200 MB to download. That is a significant bandwidth and storage wasted if one only needs the CPU only variant!
Using PyPy as an interpreter¶
conda-forge channel supports creating and installing packages into
environments using the PyPy interpreter. Many packages are already
available. You need to enable the
conda-forge channel and use
pypy identifier when creating your environment:
$ conda create -c conda-forge -n my-pypy-env pypy python=3.8 $ conda activate my-pypy-env
Currently supported python versions are 3.8 and 3.9. Support for
has been dropped. While you can still create a python 3.7 environment, you
you will not be getting updates as new package versions are released (including
As of March 8 2020, if you are using defaults as a low priority channel, then you need to use strict channel priority as the metadata in defaults has not been patched yet which allows cpython extension packages to be installed alongside pypy.
$ conda config --set channel_priority strict