Merge pull request #16 from maurerv/master

Docker Container for PowerFit

Dockerfile

@@ -0,0 +1,65 @@
+FROM ubuntu:18.04
+
+ARG POWERFIT_VERSION=v2.1.0
+
+ENV LD_LIBRARY_PATH=/usr/local/lib64
+ENV CLFFT_DIR=/src/clFFT
+ENV CLFFT_LIB_DIRS=/usr/local/lib64
+ENV CLFFT_INCL_DIRS=/src/clFFT/src/include
+ENV CL_INCL_DIRS=/usr/local/include
+ENV LD_LIBRARY_PATH=/usr/local/lib64:/usr/local/lib
+
+RUN apt-get update --fix-missing && \
+    apt-get install -y \
+    python2.7 \
+    python2.7-dev \
+    git \
+    wget \
+    gcc \
+    g++ \
+    libfftw3-dev \
+    libfftw3-doc \
+    libfreetype6-dev \
+    pkg-config \
+    libopenblas-dev \
+    gfortran \
+    python-scipy \
+    python-numpy \
+    time \
+    cmake \
+    make \
+    opencl-headers \
+    ocl-icd-opencl-dev \
+    libboost-all-dev \
+    pocl-opencl-icd && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+WORKDIR /src
+
+RUN wget https://bootstrap.pypa.io/pip/2.7/get-pip.py && \
+    python2 get-pip.py && \
+    python2.7 -mpip install \
+    Cython==0.29.33 pyfftw==0.12.0 pybind11 setuptools \
+    numpy==1.16.6 pyopencl==2020.2 backports.weakref==1.0.post1
+
+RUN git clone https://github.com/clMathLibraries/clFFT.git && \
+    cd clFFT/src && \
+    cmake . && \
+    make && \
+    make install
+
+RUN git clone https://github.com/haddocking/powerfit && \
+    cd powerfit && \
+    git checkout ${POWERFIT_VERSION} && \
+    python2.7 setup.py install
+
+RUN git clone https://github.com/geggo/gpyfft.git && \
+    cd gpyfft && \
+    sed -i \
+    -e "s|CLFFT_DIR = r'/home/gregor/devel/clFFT'|CLFFT_DIR = '/src/clFFT'|" \
+    -e "s|CL_INCL_DIRS = \['/opt/AMDAPPSDK-3.0/include'\]|CL_INCL_DIRS = ['/usr/local/include']|" \
+    setup.py && \
+    python2.7 setup.py install
+
+ENTRYPOINT [ "powerfit" ]

README.md

@@ -17,7 +17,6 @@ further be hardware-accelerated by leveraging multi-core CPU machines out of
 the box or by GPU via the OpenCL framework. PowerFit is Free Software and has
 been succesfully installed and used on Linux and MacOSX machines.
 
-
 ## Requirements
 
 Minimal requirements for the CPU version:
@@ -44,7 +43,6 @@ Recommended for installation
 * git
 * pip
 
-
 ## Installation
 
 If you already have fulfilled the requirements, the installation should be as
@@ -58,24 +56,33 @@ If you are starting from a clean system, follow the instructions for your
 particular operating system as described below, they should get you up and
 running in no time.
 
+### Docker
+
+First install [docker](https://docs.docker.com/engine/install/) by following the
+instructions.
+
+A docker container comprised of PowerFit and its CPU/GPU
+dependencies can be created for your compute platform as follows
 
-### Linux 
+    docker build -t haddocking/powerfit:v2.1.0 -f Dockerfile .
+    docker run haddocking/powerfit:v2.1.0 <map> <resolution> <pdb>
+
+### Linux
 
 Linux systems usually already include a Python2.7 distribution. First make
 sure the Python header files, NumPy, SciPy, and *git* are available by
 opening up a terminal and typing for Debian and Ubuntu systems
 
     sudo apt-get install python-dev python-numpy python-scipy git
 
-If you are working on Fedora, this should be replaced by 
+If you are working on Fedora, this should be replaced by
 
     sudo yum install python-devel numpy scipy git
 
 Sit back and wait till the compilation and installation is finished. Your
 system is now prepared, follow the general instructions above to install
 **PowerFit**.
 
-
 ### MacOSX
 
 First install [*git*](https://git-scm.com/download) by following the
@@ -85,7 +92,7 @@ instructions on their website, or using a package manager such as *brew*
 
 Next install [*pip*](https://pip.pypa.io/en/latest/installing.html), the
 Python package manager, by following the installation instructions on the
-website or open a terminal and type 
+website or open a terminal and type
 
     sudo easy_install pip
 
@@ -101,7 +108,6 @@ Installing pyFFTW for faster CPU version can be done as follows using *brew*
     brew install fftw
     sudo pip install pyfftw
 
-
 ### Windows
 
 First install *git* for Windows, as it comes with a handy bash shell. Go to
@@ -111,7 +117,6 @@ install a Python distribution with NumPy and Scipy included such as
 bash shell shipped with *git* and follow the general instructions written
 above.
 
-
 ## Usage
 
 After installing PowerFit the command line tool *powerfit* should be at your
@@ -126,6 +131,11 @@ cross-correlation score on a single CPU-core. During the search, *powerfit*
 will update you about the progress of the search if you are using it
 interactively in the shell.
 
+Running PowerFit in a docker container named powerfit on data located at
+a hypothetical `/path/to/data` on your machine can be done as follows
+
+    docker run --rm -v /path/to/data:/data powerfit \
+        powerfit /data/<map> <resolution> /data/<pdb> -d /data
 
 ### Options
 
@@ -153,7 +163,6 @@ and write out the top 15 solutions
 Note that all options can be combined except for the `-g` and `-p` flag:
 calculations are either performed on the CPU or GPU.
 
-
 ### Output
 
 When the search is finished, several output files are created
@@ -170,7 +179,6 @@ correlation score found during the rotational search.
 * *powerfit.log*: a log file, including the input parameters with date and
 timing information.
 
-
 ## Creating an image-pyramid
 
 The use of multi-scale image pyramids can signicantly increase the speed of
@@ -190,30 +198,26 @@ To see the other options type
 
     image-pyramid --help
 
-
 ## Licensing
 
 If this software was useful to your research, please cite us
 
-**G.C.P. van Zundert and A.M.J.J. Bonvin**. 
+**G.C.P. van Zundert and A.M.J.J. Bonvin**.
 Fast and sensitive rigid-body fitting into cryo-EM density maps with PowerFit.
 *AIMS Biophysics* 2, 73-87 (2015).
 
-
 For the use of image-pyramids and reliability measures for fitting, please cite
 
-**G.C.P van Zundert and A.M.J.J. Bonvin**. 
+**G.C.P van Zundert and A.M.J.J. Bonvin**.
 Defining the limits and reliability of rigid-body fitting in cryo-EM maps using
-multi-scale image pyramids. 
+multi-scale image pyramids.
 *J. Struct. Biol.* 195, 252-258 (2016).
 
 Apache License Version 2.0
 
 The elements.py module is licensed under MIT License (see header).
 Copyright (c) 2005-2015, Christoph Gohlke
 
-
-
 ## Tested platforms
 
 | Operating System| CPU single | CPU multi | GPU |
@@ -223,5 +227,6 @@ Copyright (c) 2005-2015, Christoph Gohlke
 |Windows          | Yes        | Fail      | No  |
 
 The GPU version has been tested on:
+
 * NVIDIA GeForce GTX 680 and AMD Radeon HD 7730M for Linux
 * NVIDIA GeForce GTX 775M for MacOSX 10.10