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gsell
buildblocks
Commits
3153fc66
Commit
3153fc66
authored
Mar 12, 2019
by
bliven_s
Committed by
gsell
Mar 12, 2019
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Create crYOLO module
parent
28ef97e2
Changes
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EM/crYOLO/build
EM/crYOLO/build
+40
-0
EM/crYOLO/files/variants
EM/crYOLO/files/variants
+2
-0
EM/crYOLO/modulefile
EM/crYOLO/modulefile
+51
-0
No files found.
EM/crYOLO/build
0 → 100755
View file @
3153fc66
#!/usr/bin/env modbuild
pbuild
::
add_to_group
'EM'
pbuild
::
prep
()
{
echo
"prepping"
mkdir
-
p
"$SRC_DIR"
curl
-
fsSLo
"$SRC_DIR/miniconda.sh"
'https://repo.anaconda.com/miniconda/Miniconda2-latest-Linux-x86_64.sh'
curl
-
fsSLo
"$SRC_DIR/cryoloBM.tgz"
'ftp://ftp.gwdg.de/pub/misc/sphire/crYOLO_BM_V1_1_1/cryoloBM-1.1.1.tar.gz'
curl
-
fsSLo
"$SRC_DIR/cryolo.tgz"
'ftp://ftp.gwdg.de/pub/misc/sphire/crYOLO_V1_2_3/cryolo-1.2.3.tar.gz'
:
}
pbuild
::
configure
()
{
:
}
pbuild
::
compile
()
{
:
}
pbuild
::
install
()
{
mkdir
-
p
$
PREFIX
# Install conda
bash
"$SRC_DIR/miniconda.sh"
-
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
}
EM/crYOLO/files/variants
0 → 100644
View file @
3153fc66
crYOLO/1.2.3 unstable cuda/9.0.176
EM/crYOLO/modulefile
0 → 100644
View file @
3153fc66
#%Module
module-whatis "crYOLO is a fast and accurate particle picking procedure for electron microscopy"
module-url "http://sphire.mpg.de/wiki/doku.php?id=pipeline:window:cryolo"
module-license "SPHIRE-crYOLO Complimentary Science Software License (http://sphire.mpg.de/wiki/doku.php?id=cryolo_license). Non-commercial academic and research purposes only"
module-maintainer "Spencer Bliven <spencer.bliven@psi.ch>"
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');}
}
}
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