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Python on Arm64

Python is an interpreted, high-level, general-purpose programming language, with interpreters available for many operating systems and architectures, including Arm64. Read more on Wikipedia

Installing Python packages

When pip (the standard package installer for Python) is used, it pulls the packages from Python Package Index and other indexes.

In the case pip could not find a pre-compiled package, it automatically downloads, compiles, and builds the package from source code. Normally it may take a few more minutes to install the package from source code than from pre-built. For some large packages (i.e. pandas) it may take up to 20 minutes. In some cases, compilation may fail due to missing dependencies.

Prerequisites for installing Python packages from source

For installing common Python packages from source code, we need to install the following development tools:

On RedHat:

sudo yum install "@Development tools" python3-pip python3-devel blas-devel gcc-gfortran lapack-devel
python3 -m pip install --user --upgrade pip

On Debian/Ubuntu:

sudo apt update
sudo apt-get install build-essential python3-pip python3-dev libblas-dev gfortran liblapack-dev
python3 -m pip install --user --upgrade pip

On all distributions, additional compile time dependencies might be needed depending on the Python modules you are trying to install.

Recommended versions

When adopting Arm64, it is recommended to use recent software versions as much as possible, and Python is no exception.

Python 2.7 is EOL since January the 1st 2020, so it is definitely recommended to upgrade to a Python 3.x version before moving to Arm64.

Python 3.6 will reach EOL in December, 2021, so when starting to port an application to Arm64, it is recommended to target at least Python 3.7.

Python on Centos 8 and RHEL 8

Centos 8 and RHEL 8 distribute Python 3.6 which is scheduled for end of life in December, 2021. However as of May 2021, some package maintainers have already begun dropping support for Python 3.6 by omitting prebuilt wheels published to pypi.org. For some packages, it is still possible to use Python 3.6 by using the distribution from the package manager. For example numpy no longer publishes Python 3.6 wheels, but can be installed from the package manager: yum install python3-numpy.

Another option is to use Python 3.8 instead of the default Python package. You can install Python 3.8 with pip: yum install python38-pip. Then use pip to install the latest versions of packages: pip3 install numpy.

Some common Python packages that are distributed by the package manager are:

  • python3-numpy
  • python3-markupsafe
  • python3-pillow

To see a full list run yum search python3

Scientific and numerical applications (NumPy, SciPy, BLAS, etc)

Python relies on native code to achieve high performance. For scientific and numerical applications, NumPy and SciPy provide an interface to high performance computing libraries such as ATLAS, BLAS, BLIS, OpenBLAS, etc. These libraries contain code tuned for Arm64 processors, and especially the Arm Neoverse-N1 core which powers the Ampere Altra CPU in the NVIDIA Arm HPC Developer Kit.

It is recommended to use the latest software versions as much as possible. If the latest version migration is not feasible, please ensure that it is at least the minimum version recommended below. Multiple fixes related to data precision and correctness on Arm64 went into OpenBLAS between v0.3.9 and v0.3.17 and the below SciPy and NumPy versions upgraded OpenBLAS from v0.3.9 to OpenBLAS v0.3.17.

  • OpenBLAS: >= v0.3.17
  • SciPy: >= v1.7.2
  • NumPy: >= 1.21.1

BLIS may be a faster BLAS

The default SciPy and NumPy binary installations with pip3 install numpy scipy are configured to use OpenBLAS. The default installations of SciPy and NumPy are easy to setup and well tested.

Some workloads will benefit from using BLIS. Benchmarking SciPy and NumPy workloads with BLIS might allow to identify additional performance improvement.

Install NumPy and SciPy with BLIS on Ubuntu and Debian

On Ubuntu and Debian apt install python3-numpy python3-scipy will install NumPy and SciPy with BLAS and LAPACK libraries. To install SciPy and NumPy with BLIS and OpenBLAS on Ubuntu and Debian:

sudo apt -y install python3-scipy python3-numpy libopenblas-dev libblis-dev
sudo update-alternatives --set libblas.so.3-aarch64-linux-gnu \
    /usr/lib/aarch64-linux-gnu/blis-openmp/libblas.so.3

To switch between available alternatives:

sudo update-alternatives --config libblas.so.3-aarch64-linux-gnu
sudo update-alternatives --config liblapack.so.3-aarch64-linux-gnu

Install NumPy and SciPy with BLIS on RedHat

As of June 20th, 2020, NumPy now provides binaries for Arm64.

Prerequisites to build SciPy and NumPy with BLIS on RedHat:

# Install RedHat prerequisites
sudo yum install "@Development tools" python3-pip python3-devel blas-devel gcc-gfortran

# Install BLIS
git clone https://github.com/flame/blis $HOME/blis
cd $HOME/blis;  
./configure --enable-threading=openmp --enable-cblas --prefix=/usr cortexa57
make -j;  sudo make install

# Install OpenBLAS
git clone https://github.com/xianyi/OpenBLAS.git $HOME/OpenBLAS
cd $HOME/OpenBLAS
make -j BINARY=64 FC=gfortran USE_OPENMP=1
sudo make PREFIX=/usr install

To build and install NumPy and SciPy with BLIS and OpenBLAS:

git clone https://github.com/numpy/numpy/ $HOME/numpy
cd $HOME/numpy
pip3 install .

git clone https://github.com/scipy/scipy/ $HOME/scipy
cd $HOME/scipy
pip3 install .

When NumPy and SciPy detect the presence of the BLIS library at build time, they will use BLIS in priority over the same functionality from BLAS and OpenBLAS. OpenBLAS or LAPACK libraries need to be installed along BLIS to provide LAPACK functionality. To change the library dependencies, one can set environment variables NPY_BLAS_ORDER and NPY_LAPACK_ORDER before building numpy and scipy. The default is: NPY_BLAS_ORDER=mkl,blis,openblas,atlas,accelerate,blas and NPY_LAPACK_ORDER=mkl,openblas,libflame,atlas,accelerate,lapack.

Testing NumPy and SciPy installation

To test that the installed NumPy and SciPy are built with BLIS and OpenBLAS, the following commands will print native library dependencies:

python3 -c "import numpy as np; np.__config__.show()"
python3 -c "import scipy as sp; sp.__config__.show()"

In the case of Ubuntu and Debian these commands will print blas and lapack which are symbolic links managed by update-alternatives.

Improving BLIS and OpenBLAS performance with multi-threading

When OpenBLAS is built with USE_OPENMP=1 it will use OpenMP to parallelize the computations. The environment variable OMP_NUM_THREADS can be set to specify the maximum number of threads. If this variable is not set, the default is to use a single thread.

To enable parallelism with BLIS, one needs to both configure with --enable-threading=openmp and set the environment variable BLIS_NUM_THREADS to the number of threads to use, the default is to use a single thread.

Arm64 support in Conda / Anaconda

Anaconda is a distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment.

Anaconda has announced support for Arm64 via AWS Graviton 2 on May 14, 2021. The Ampere Altra CPU found in the NVIDIA Arm HPC DevKit is based on the same Arm Neoverse-N1 core as the AWS Gravition 2, so Anaconda also supports the Ampere Altra. Instructions to install the full Anaconda package installer can be found at https://docs.anaconda.com/anaconda/install/graviton2/.

Anaconda also offers a lightweight version called Miniconda which is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others.

See the Anaconda, Miniconda, Conda, Mamba example for details.

Other common Python packages

Pillow

Pillow 8.x or later have a binary wheel for all Arm64 platforms, included OSes with 64kB pages like Redhat/CentOS8.

pip3 install --user pillow

should work across all platforms we tested.

Machine Learning Python packages

PyTorch

PyTorch wheels for nightly builds (cpu builds) are are available for Arm64 since PyTorch 1.8.

pip install numpy
pip install --pre torch torchvision  -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html

DGL

Install PyTorch as described above, then follow the install from source instructions.

Sentencepiece

Install PyTorch as described above. Then:

# git the source and build/install the libraries.
git clone https://github.com/google/sentencepiece
cd sentencepiece
mkdir build
cd build
cmake ..
make -j
sudo make install
sudo ldconfig -v

cd ../python
vi make_py_wheel.sh
# change the manylinux1_{$1} to manylinux2014_{$1}

sudo python3 setup.py install

With the above steps, the wheel should be installed.

Important: Before calling any python script or starting python, one of the libraries need to be set as preload for python.

export LD_PRELOAD=/lib/aarch64-linux-gnu/libtcmalloc_minimal.so.4:/$LD_PRELOAD
python3

Morfeusz

# download the source
wget http://download.sgjp.pl/morfeusz/20200913/morfeusz-src-20200913.tar.gz
tar -xf morfeusz-src-20200913.tar.gz
cd Morfeusz/
sudo apt install cmake zip build-essential autotools-dev \
    python3-stdeb python3-pip python3-all-dev python3-pyparsing devscripts \
    libcppunit-dev acl  default-jdk swig python3-all-dev python3-stdeb
export JAVA_TOOL_OPTIONS=-Dfile.encoding=UTF8
mkdir build
cd build
cmake ..
sudo make install
sudo ldconfig -v
sudo PYTHONPATH=/usr/local/lib/python make install-builder

If you run into issue with the last command (make install-builder), please try:

sudo PYTHONPATH=`which python3` make install-builder