Commit 33658f2e authored by gsell's avatar gsell

Merge branch 'master' of

parents a4f050be 91f4b522
#!/usr/bin/env modbuild
pbuild::add_to_group 'EM'
pbuild::prep() {
echo "prepping"
mkdir -p "$SRC_DIR"
curl -fsSLo "$SRC_DIR/" ''
curl -fsSLo "$SRC_DIR/cryoloBM.tgz" ''
curl -fsSLo "$SRC_DIR/cryolo.tgz" ''
pbuild::configure() {
pbuild::compile() {
pbuild::install() {
mkdir -p $PREFIX
# Install conda
bash "$SRC_DIR/" -b -p $PREFIX/conda
# Create environment
$PREFIX/conda/bin/conda create -y --name crYOLO anaconda python=3.6 pyqt=5 cudnn=7.1.2 numpy
# Activate
source $PREFIX/conda/bin/activate crYOLO
# Install
pip install $SRC_DIR/cryolo.tgz
pip install $SRC_DIR/cryoloBM.tgz
# Deactivate
source deactivate
crYOLO/1.2.3 unstable cuda/9.0.176
module-whatis "crYOLO is a fast and accurate particle picking procedure for electron microscopy"
module-url ""
module-license "SPHIRE-crYOLO Complimentary Science Software License ( Non-commercial academic and research purposes only"
module-maintainer "Spencer Bliven <>"
module-help "
CrYOLO is a fast and accurate particle picking procedure. It's based on
convolutional neural networks and utilizes the popular You Only Look Once
(YOLO) object detection system.
* crYOLO makes picking fast – On a modern GPU it will pick your particles at
up to 6 micrographs per second.
* crYOLO makes picking smart – The network learns the context of particles
(e.g. not to pick particles on carbon or within ice contamination )
* crYOLO makes training easy – You might use a general network model and skip
training completely. However, if the general model doesn't give you
satisfactory results or if you would like to improve them, you might want
to train a specialized model specific for your data set by selecting
particles (no selection of negative examples necessary) on a small number
of micrographs.
# Check for supported shell types
set shelltype [module-info shelltype]
switch -- $shelltype {
"sh" {
default {
puts stderr "Shells of type '$shelltype' are NOT supported!"
#puts stderr "Switching crYOLO mode to [module-info mode]"
#puts stdout "{ date; echo Switching crYOLO mode to [module-info mode] ; } >> /gpfs/home/bliven_s/cryolo_mod.log;\n"
switch [module-info mode] {
"load" {
#puts stderr "source $PREFIX/conda/bin/activate crYOLO"
puts stdout "source $PREFIX/conda/bin/activate crYOLO;\n"
"unload" -
"remove" {
#puts stderr "source deactivate"
puts stdout "source $PREFIX/conda/bin/deactivate;\n"
puts stdout {unset $(set|sed -rn 's/^(_?conda[a-z_]*).*$/\1/pI');}
# The Mellanox MXM communication library
## Overview
The Mellanox MXM communication library provides support for the Mellanox MXM interface for InfiniBand.
## Installation
For the module we use a RPM distributed by HP.
1. Create new directory `/opt/psi/System/mxm/VERSION_merlin`
1. Download RPM from
1. Unpack RPM with `rpm2cpio RPM | cpio -i --make-dirs` somewhere
1. copy all files from `opt/mellanox/mxm` to the module directory
1. adapt directories in `lib/pkg-config/mxm.pc`
1. add new variant to `files/variants`
1. run the build-script to install the modulefile and to set the release
> **Note:** The shared library ``provided by the RPMs for RHEL 6 cannot be used to compile other software.
They requiry GLIBC >= 2.14, but on RHEL 6 only 2.12 is installed!
\ No newline at end of file
#!/usr/bin/env modbuild
pbuild::prep() {
pbuild::configure() {
pbuild::compile() {
pbuild::install() {
mkdir -p "$PREFIX/bin"
/usr/bin/curl -o "$PREFIX/bin/datasetIngestor"
chmod +x "$PREFIX/bin/datasetIngestor"
datacatalog/1.1.15 removed
datacatalog/1.1.3 deprecated
datacatalog/1.1.4 stable
module-whatis "SciCat datacatalog related tools"
module-url ""
module-license "GPL-V3"
module-maintainer "Stephan Egli <>"
module-help "
Data catalog ingest and retrieve tools.
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