Installation with conda from conda-forge

We provide a package for the [conda] package manager in the conda-forge channel, so you can install TeNPy as:

conda install --channel=conda-forge physics-tenpy

Following the recommondation of conda-forge, you can also make conda-forge the default channel as follows:

conda config --add channels conda-forge
conda config --set channel_priority strict

If you have done this, you don’t need to specify the --channel=conda-forge explicitly.


The numpy package provided by the conda-forge channel by default uses openblas on linux. As outlined in the conda forge docs, you can switch to MKL using:

conda install "libblas=*=*mkl"


If you use the conda-forge channe and don’t pin BLAS to the MKL version as outlined in the above version, but nevertheless have mkl-devel installed during compilation of TeNPy, this can have crazy effects on the number of threads used: numpy will call openblas and open up $OMP_NUM_THREADS - 1 new threads, while MKL called from tenpy will open another $MKL_NUM_THREADS - 1 threads, making it very hard to control the number of threads used!

Moreover, it is actually recommended to create a separate environment. To create a conda environment with the name tenpy, where the TeNPy package (called physics-tenpy) is installed:

conda create --name tenpy --channel=conda-forge physics-tenpy

In that case, you need to activate the environment each time you want to use the package with:

conda activate tenpy

The big advantage of this approach is that it allows multiple version of software to be installed in parallel, e.g., if one of your projects requires python>=3.8 and another one requires an old library which doesn’t support that. Further info can be found in the conda documentation.