- Build a Linux based Singularity container.
- First build a writable sandbox with essential elements.
- Inspect the container.
- Install additional software.
- Convert the sandbox to a read-only SquashFS container image.
- Install software & packages from multiple sources.
- Using
apt-get
package management system. - Compiling from source code.
- Using
Python pip
. - Using
install.packages()
function in R.
- Using
- Software highlight.
- Jupyter notebook.
- Tensorflow GPU version.
- OpenMPI.
- Popular datascience packages in Python and R.
- Chemistry/chemoinformatics software: RDkit, OpenBabel, Pybel, & Mordred.
- Test the container.
- Test the GPU version of Tensorflow.
First we will build a writable Singularity sandbox with the essential software, languages, and developmental libraries. To build a writable sandbox copy the recipe below to a container.def
text file and then execute:
sudo singularity build --sandbox container/ container.def
Recipe/Definition File
BootStrap: docker
From: ubuntu:bionic
%labels
APPLICATION_NAME Data Science and Chemistry
AUTHOR_NAME Rohit Farmer
AUTHOR_EMAIL rohit.farmer@gmail.com
YEAR 2021
%help
Container for data science and chemistry with packages from Python 3 & R 3.6.
It also includes CUDA and MPI for Tensorflow GPU and parallel processing respectively.
%environment
# Set system locale
PATH=/bin:/sbin:/usr/bin:/usr/sbin:/usr/local/bin:/usr/local/sbin
RDBASE=/usr/local/share/rdkit
CUDA=/usr/local/cuda/lib64:/usr/local/cuda-10.1/lib64:/usr/local/cuda-10.2/lib64
LD_LIBRARY_PATH=/.singularity.d/libs:$RDBASE/lib:$CUDA
PYTHONPATH=modules:$RDBASE:/usr/local/share/rdkit/rdkit:/usr/local/lib/python3.6/dist-packages/
LANG=C.UTF-8 LC_ALL=C.UTF-8
%post
# Change to tmp directory to download temporary files.
cd /tmp
# Install essential software, languages and libraries.
apt-get -qq -y update
export DEBIAN_FRONTEND=noninteractive
apt-get -qq install -y --no-install-recommends tzdata apt-utils
ln -fs /usr/share/zoneinfo/America/New_York /etc/localtime
dpkg-reconfigure --frontend noninteractive tzdata
apt-get -qq -y update
apt-get -qq install -y --no-install-recommends \
autoconf \
automake \
build-essential \
bzip2 \
ca-certificates \
cmake \
gcc \
g++ \
gfortran \
git \
gnupg2 \
libtool \
libjpeg-dev \
libpng-dev \
libtiff-dev \
libatlas-base-dev \
libxml2-dev \
zlib1g-dev \
libcairo2-dev \
libeigen3-dev \
libcupti-dev \
libpcre3-dev \
libssl-dev \
libcurl4-openssl-dev \
libboost-all-dev \
libboost-dev \
libboost-system-dev \
libboost-thread-dev \
libboost-serialization-dev \
libboost-regex-dev \
libgtk2.0-dev \
libreadline-dev \
libbz2-dev \
liblzma-dev \
libpcre++-dev \
libpango1.0-dev \
libmariadb-client-lgpl-dev \
libopenblas-dev \
liblapack-dev \
libxt-dev \
neovim \
openjdk-8-jdk \
python \
python-pip \
python-dev \
python3-dev \
python3-pip \
python3-wheel \
swig \
texlive \
texlive-fonts-extra \
texinfo \
vim \
wget \
xvfb \
xauth \
xfonts-base \
zip
export LANG=C.UTF-8 LC_ALL=C.UTF-8
# Add NVIDIA package repositories.
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
dpkg -i cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
apt-get update
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
apt-get -qq install -y --no-install-recommends ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
apt-get update
# Install NVIDIA driver (optional)
# apt-get install --no-install-recommends nvidia-driver-430
# Install development and runtime libraries.
apt-get install -y --no-install-recommends \
cuda-10-1 \
libcudnn7=7.6.4.38-1+cuda10.1 \
libcudnn7-dev=7.6.4.38-1+cuda10.1
# Install TensorRT. Requires that libcudnn7 is installed above.
apt-get install -y --no-install-recommends libnvinfer6=6.0.1-1+cuda10.1 \
libnvinfer-dev=6.0.1-1+cuda10.1 \
libnvinfer-plugin6=6.0.1-1+cuda10.1
# Update python pip.
python3 -m pip --no-cache-dir install --upgrade pip
python3 -m pip --no-cache-dir install setuptools --upgrade
python -m pip --no-cache-dir install setuptools --upgrade
# Install R 3.6.
echo "deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/" >> /etc/apt/sources.list
apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
apt-get update
apt-get install -y --no-install-recommends r-base
apt-get install -y --no-install-recommends r-base-dev
# Install Jupyter notebook with Python and R support.
python3 -m pip --no-cache-dir install jupyter
R --quiet --slave -e 'install.packages(c("IRkernel"), repos="https://cloud.r-project.org/")'
# Install MPI (match the version with the cluster).
mkdir -p /tmp/mpi
cd /tmp/mpi
wget -O openmpi-2.1.0.tar.bz2 https://www.open-mpi.org/software/ompi/v2.1/downloads/openmpi-2.1.0.tar.bz2
tar -xjf openmpi-2.1.0.tar.bz2
cd openmpi-2.1.0
./configure --prefix=/usr/local --with-cuda
make -j $(nproc)
make install
ldconfig
# Cleanup
apt-get -qq clean
rm -rf /var/lib/apt/lists/*
rm -rf /tmp/mpi
To get a list of the labels defined for the container singularity inspect --labels container/
To print the container's help section singularity inspect --helpfile container/
To show container’s environment singularity inspect --environment container/
To retrieve the definition file used to build the container singularity inspect --deffile container/
Once the core writable sandbox is built we will install the additional data science and chemistry packages.
To do that execute:
sudo singularity shell --writable container/
Then execute the following lines in the shell environment.
# Install Python packages.
python3 -m pip --no-cache-dir install numpy pandas h5py pyarrow sklearn statsmodels matplotlib seaborn plotly
# Install Tensorflow.
python3 -m pip --no-cache-dir install tensorflow==2.2.0
# Install R packages.
R --quiet --slave -e 'install.packages("tidyverse", version = "1.3.0", repos="https://cloud.r-project.org/")'
R --quiet --slave -e 'install.packages("tidymodels", version = "0.1.0", repos="https://cloud.r-project.org/")'
R --quiet --slave -e 'install.packages(c("lme4", "glmnet", "yaml", "jsonlite", "rlang"), repos="https://cloud.r-project.org/")'
# Install RDKit
export RDBASE=/usr/local/share/rdkit
export LD_LIBRARY_PATH="$RDBASE/lib:$LD_LIBRARY_PATH"
export PYTHONPATH="$RDBASE:$PYTHONPATH"
mkdir -p /tmp/rdkit
cd /tmp/rdkit
wget https://github.com/rdkit/rdkit/archive/2020_03_3.tar.gz
tar zxf 2020_03_3.tar.gz
mv rdkit-2020_03_3 $RDBASE
mkdir $RDBASE/build
cd $RDBASE/build
cmake -DPYTHON_EXECUTABLE=/usr/bin/python3 ..
make -j $(nproc)
make install
ln -s /usr/local/share/rdkit/rdkit /usr/local/lib/python3.6/dist-packages/
# Install OpenBabel.
apt-get -qq -y update
apt-get -qq install -y --no-install-recommends openbabel python-openbabel
# Install Mordred Molecular Descriptor Calculator.
python3 -m pip --no-cache-dir install mordred
# Cleanup
rm -rf /tmp/rdkit
Once you are satisfied that you have installed all the required packages you can convert the writable sandbox to a read only squashfs filesystem. Squashfs is a compressed read-only file system for Linux.
sudo singularity build container.sif container/
Kernel specs are installed from outside the container in the host's home environment.
singularity exec container.sif R --quiet --slave -e 'IRkernel::installspec()'
NOTE: You only have to do it once per host to install kernelspec
.
import tensorflow as tf
tf.debugging.set_log_device_placement(True)
gpus = tf.config.list_physical_devices('GPU')
if gpus:
with tf.device('/GPU:0'):
tf.random.set_seed(123)
a = tf.random.normal([10000,20000], 0, 1, tf.float32, seed=1)
b = tf.random.normal([20000,10000], 0, 1, tf.float32, seed=1)
c = tf.matmul(a, b)
print(c)
else:
print("No GPUs found.")
print("Num GPUs:", len(gpus))
To execute the script singularity exec --nv container.sif python3 tf_gpu.py
To monitor NVIDIA GPU usage nvidia-smi