Installation

Requirements

In order to install PyNAO, the following packages must be installed

  • git

  • gcc

  • gfortran

  • build-essential

  • liblapack-dev

  • libfftw3-dev

  • make

  • cmake

  • zlib1g-dev

  • python3

  • python3-pip

In addition, you will need to install a BLAS library. The choice of the BLAS library is crucial in order to achieve optimal performances. We recommand you to use openBLAS or Intel MKL.

PyNAO will need also the following python libraries

  • wheel (for installation)

  • pytest (for testing)

  • pytest-cov (for testing)

  • numpy

  • scipy

  • h5py

  • numba

  • pyscf

Some of the examples make use of the ASE package, therefore its installation is recommended. ASE can be handy to launch Siesta calculations from Python script and to setup the molecular system.

PyNAO uses DFT results from Siesta or PySCF. PySCF is a requirement as shown above, but one may use DFT Siesta outputs as inputs for PyNAO, therefore, you may want to install Siesta as well.

Note

Currently, Siesta 5.0 or above is not supported. We advise you to install Siesta 4.1. Can be done using conda

conda install -c conda-forge “siesta=4.1.5=*openmpi*”

PyNAO Installation

Conda package

If you are running on Linux x86 64 platform (other system are not supported) the easiest way to install PyNAO is to use the available conda package. Just create a new conda environment using the package

>>> conda create -n pynao mbarbry::pynao

Activate the environment and you are good to go

>>> conda activate pynao && python -c "import pynao;print(pynao.__version__)"

Compilation from source with pip

On Ubuntu and similar OS, PyNAO can easily be instaaled using the Blas and Lapack libraries.

>>> apt-get update
>>> apt-get install git gcc gfortran build-essential libopenblas-dev liblapack-dev libfftw3-dev make cmake zlib1g-dev python3 python3-pip
>>> pip3 install -r requirements.txt
>>> export CC=gcc && export FC=gfortran && export CXX=g++
>>> python3 setup.py bdist_wheel
>>> cd dist && pip3 install pynao-0.1.2-py3-none-any.whl

Note

By default, the installation will use the lib/cmake_user_inc_examples/cmake.user.inc-gnu-openmp cmake.arch.inc file. For optimal performances, you may want to edit yourself the cmake.arch.inc file. See section Architecture file cmake.arch.inc for more details.

Compilation from source with conda

Install conda and run

>>> apt-get update
>>> apt-get install git gcc gfortran build-essential libopenblas-dev liblapack-dev libfftw3-dev make cmake zlib1g-dev
>>> conda env create -f environment.yml
>>> conda activate pynao
>>> conda install -c pyscf pyscf
>>> pip install ase
>>> export CC=gcc && export FC=gfortran && export CXX=g++
>>> python3 setup.py bdist_wheel
>>> cd dist && pip3 install pynao-0.1.2-py3-none-any.whl

Compilation from source with Anaconda and MKL

For optimal performances, we advise you to use Anaconda Python environment. This for three reasons

  • First, in Anaconda, NumPy and SciPy libraries are compiled with MKL Blas library (offering in general better performances).

  • You can easily use the MKL library included in Anaconda to compile PyNAO (even if it is still possible to use your custom MKL installation).

In the following, we assume Anaconda is installed under ${HOME}/anaconda3. To properly compile PyNAO with Anaconda, perform the following steps

>>> apt-get update
>>> apt-get install git gcc gfortran build-essential liblapack-dev libfftw3-dev make cmake zlib1g-dev python3 python3-pip
>>> mkdir -p ${HOME}/anaconda3/lib/mkl/lib
>>> ln -s ${HOME}/anaconda3/lib/libmkl* ${HOME}/anaconda3/lib/mkl/lib/
>>> export CC=gcc && export FC=gfortran && export CXX=g++
>>> pip install ase
>>> conda install -c pyscf pyscf

Now, go to pynao root folder

>>> cp lib/cmake_user_inc_examples/cmake.user.inc-singularity.anaconda.gnu.mkl lib/cmake.arch.inc

Edit the file lib/cmake.arch.inc to indicate the proper path to MKL library. You should replace the line set(MKLROOT "/opt/conda/lib/mkl/lib") by set(MKLROOT "${HOME}/anaconda3/lib/mkl/lib")

then you can finish the installation

>>> python setup.py bdist_wheel
>>> pip install dist/pynao-0.1.2-py3-none-any.whl

Architecture file cmake.arch.inc

For optimal performances, it is advised to edit the cmake.arch.inc file. This file contains platform specific instruction such as BLAS and LAPACK libraries definition (with MKL for example).

You can find examples in the folder lib/cmake_user_inc_examples

Docker container

If Docker is installed on your system, you can easily use PyNAO by simply

docker run -p 8888:8888 -it mbarbry/pynao:latest

The container starts an jupyter notebook. You can connect to it with your web browser via the address http://127.0.0.1:8888/lab

The PyNAO tutorials are presents in the tutorial-pynao folder.

You can as well simply run a Python script via

docker run -p 8888:8888 -v $(pwd):/usr/src/app -it mbarbry/pynao:latest /usr/bin/python3 yourScript.py

The docker image contains all the require dependency as well as a serial compilation of Siesta. More optimal Docker containers of Siesta can be found in the Atomic-scale Simulation Environments.

Singularity container

We provide in share/singularity-img a collection of Singularity recipes that are optimized. If possible, we advise you to use these recipes to use PyNAO. To build the container

>>> sudo singularity build path2pynao/share/singularity-img/pynao-sing.mkl.gpu pynao-image.img

and to run PyNAO script

>>> singularity run pynao-image.img pynao_script.py

GPU support

Some part of the code can be accelerated using Nvidia GPUs via the cuBlas library. For this you need to install the cupy library

>>> pip install cupy

or via conda

>>> conda install cupy

You can then pass the argument GPU=True in order to use GPU for your calculations. GPU will mainly impact performances for large systems.