sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
7c0fe87020bb4a1dc0502a7ea94c434c160e604b8b11a512947dad738a8dc01e | Shell | 516 | 15 | #!/bin/bash
#SBATCH --job-name=evaluate_model # create a short name for your job
#SBATCH --nodes=1 # node count
#SBATCH --ntasks-per-node=1 # number of tasks to run per node
#SBATCH --cpus-per-task=5 # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --gres=gpu:1 # number of gpus per node
#SBATCH -o inference_lo... |
524160a2dfb2253445303aa0cc13372d63928388315c5c98428737f64561078a | Shell | 517 | 12 | while getopts s:k: flag
do
case "${flag}" in
s) GCP_SECRET_NAME=${OPTARG};;
k) SECRET_VAR_KEY=${OPTARG};;
esac
done
#Extract value of a specific key from all key value pairs in gcp secret
SECRET_VAR_VALUE=$(gcloud secrets versions access latest --secret $GCP_SECRET_NAME --format "json" | jq -r ... |
b913ba694af76b7bb8af0542f4e3021743f0903eebacc10f3c8f96dd96f4d502 | Shell | 521 | 8 | uncalled4 align \
--ref /private/groups/brookslab/gabai/tools/ref/yst/sacCer3.fa \
--reads /private/groups/brookslab/gabai/projects/Add-seq/data/ctrl/pod5/220308_ang_0.pod5 \
--bam-in /private/groups/brookslab/gabai/projects/Add-seq/data/ctrl/pod5/220308_ang_0.sorted.bam \
-p 8 \
--eventalign-out /p... |
6b4ef5cb541e0939ed3d98fae2548a3d742106262c7ef02fcb945bde801030c0 | Shell | 524 | 17 | #!/usr/bin/env sh
set -e
TOOLS=./build/tools
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_full_solver.prototxt $@
# reduce learning rate by factor of 10
$TOOLS/caffe train \
--solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
--snapshot=examples/cifar10/cifar10_full_iter_60000.solverst... |
3dae0fb7f539ccfe1685afebff844465e9b55f62e519ce66ce5a160a8f9687d4 | Shell | 526 | 39 | #!/bin/bash
set -e
set -x
tox_args='--recreate -e py3-unit-functional-style'
if [ "${os}" = "cscsviz" ]
then
. /opt/rh/python27/enable
elif [ "${os}" = "Ubuntu-18.04" ]
then
tox_args="${tox_args}"
fi
which python
python --version
cd $WORKSPACE
#########
# Virtualenv
#########
if [ ! -d "${WORKSPACE}/env" ]; th... |
af409c844406f26c23399b427d7e3ae8fd0a79dd04fc431052f9f0e001e22040 | Shell | 526 | 29 | #!/bin/bash -ev
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Run this script at project root by "./linter.sh" before you commit.
{
black --version | grep -E "24.2.0" > /dev/null
} || {
echo "Linter requires 'black==24.2.0' !"
exit 1
}
echo "Running isort..."
isort -y -sp .
echo "Ru... |
bfbc67c38374ed87c4cca87de088ac18951b8900f69009b3acdf5de01235853d | Shell | 527 | 8 | uncalled4 align \
--ref /private/groups/brookslab/gabai/tools/ref/yst/sacCer3.fa \
--reads /private/groups/brookslab/gabai/projects/Add-seq/data/ctrl/pod5/220308_ang_500.pod5 \
--bam-in /private/groups/brookslab/gabai/projects/Add-seq/data/ctrl/pod5/220308_ang_500.sorted.bam \
-p 8 \
--eventalign-ou... |
c5f45cf12834fb69919e526fd6bbab194dc8cf9e3aa3e53faeeb39ac8274e323 | Shell | 531 | 9 | #!/bin/sh
LIBVER_MAJOR_SCRIPT=`sed -n '/define ZSTD_VERSION_MAJOR/s/.*[[:blank:]]\([0-9][0-9]*\).*/\1/p' < ../../lib/zstd.h`
LIBVER_MINOR_SCRIPT=`sed -n '/define ZSTD_VERSION_MINOR/s/.*[[:blank:]]\([0-9][0-9]*\).*/\1/p' < ../../lib/zstd.h`
LIBVER_PATCH_SCRIPT=`sed -n '/define ZSTD_VERSION_RELEASE/s/.*[[:blank:]]\([0-9... |
57e9179fc5d97ef4d2e1fcf3cd3f76657f74ca1971d964571a77928f6b5ec610 | Shell | 541 | 21 | #!/bin/bash
set -x
set -v
# listen to shiny app
cd $proj_dir
eval "$(cat mypipe)" &
SINGULARITYENV_port_num=$port_num \
SINGULARITYENV_hostfilepath=$filepath \
SINGULARITYENV_max_nsamples=$max_nsamples \
SINGULARITYENV_img_dir=$img_dir \
singularity exec \
--bind $proj_dir:/mnt \
--bind $... |
78556b5c5141c69f1dacc26c59228a3ddc9d65d0b9d70cfa473e376271e945f7 | Shell | 542 | 21 | #!/bin/bash -l
# Build the package for Developer purpose
# Four steps:
# 1. Build the package and wheel
# 2. Install the wheel to the current environment
# 3. Install from source distribution as a test
# 4. Switch back to editable mode
# Build the python package and wheel, then install the wheel to the current enviro... |
b5aa78ea0f7f47742da20d59fb1e7fb6479c9a6cd51875693cd8f264aa735720 | Shell | 545 | 26 | #!/bin/bash
## Build libxgboost4j.dylib targeting MacOS (Intel)
set -euox pipefail
# Display system info
echo "--- Display system information"
set -x
system_profiler SPSoftwareDataType
sysctl -n machdep.cpu.brand_string
uname -m
set +x
brew install ninja libomp
# Build XGBoost4J binary
echo "--- Build libxgboost4j.... |
0b7fdd377d2e52e64f124cafd6411f87d228fa01bff97b5b79f67789b86272d7 | Shell | 547 | 25 | #!/bin/bash
#SBATCH --job-name=ID_63_refine_para
#SBATCH --partition=CPU_Compute
#SBATCH --ntasks=8
#SBATCH --nodes=1
##SBATCH --tasks-per-node=1
##SBATCH --mem-per-cpu=32GB
#SBATCH --mem=0
#
## Suggested batch arguments
##SBATCH --mail-type=ALL
##SBATCH --mail-user=thomas.lavigne@ensam.eu
#
## Logging arguments (IMPO... |
bc8250443dc0108faf6238da7406e6c5edea9d5a50152134fefe7939324db95d | Shell | 549 | 10 | #!/bin/sh -e
[ -z "$MMSEQS" ] && echo "Please set the environment variable \$MMSEQS to your MMSEQS binary." && exit 1;
[ "$#" -ne 4 ] && echo "Please provide <queryDB> <targetDB> <outDB> <tmp>" && exit 1;
[ ! -f "$1.dbtype" ] && echo "$1.dbtype not found!" && exit 1;
[ ! -f "$2.dbtype" ] && echo "$2.dbtype not found!" ... |
bff96ac8008d64d441fdbf3ab8d262c383695eee2a66c08f2088adb08a1e3718 | Shell | 552 | 19 | python manage.py test \
mlcube.tests.test_ \
mlcube.tests.test_pk \
dataset.tests.test_ \
dataset.tests.test_pk \
benchmark.tests.test_ \
benchmark.tests.test_pk \
benchmark.tests.test_pk_datasets \
benchmark.tests.test_pk_models \
dataset.tests.test_benchmarks \
dataset.tests.te... |
9e4c59daf545a7ef2198faf23d77ad858f7ee97c5e4af79ed6223f96058581e9 | Shell | 557 | 17 | soccertrack find_calibration_parameters \
--checkerboard_files="/Users/atom/Github/SoccerTrack/x_ignore/checkerboard_crf28.MP4" \
--output 'parameters' \
--fps=1 \
--scale=10 \
--calibration_method="fisheye" \
# --points_to_use=100
LOG_LEVEL="DEBUG" soccertrack calibrate_from_npz \
--input... |
1758eaf9bfb5d989ab28d6afa172eba050abb7136e1f6a7be8bd982c477d5258 | Shell | 559 | 23 | #!/bin/bash
## Test XGBoost Python wheel on MacOS
set -euox pipefail
brew install ninja
mkdir build
pushd build
# Set prefix, to use OpenMP library from Conda env
# See https://github.com/dmlc/xgboost/issues/7039#issuecomment-1025038228
# to learn why we don't use libomp from Homebrew.
cmake .. -GNinja -DCMAKE_PREFI... |
27a5cb921979e35dffd01f483b12d2c8080a2dc6b8c5e15bd2f2199f7be66370 | Shell | 560 | 27 | #!/bin/bash
## Build libxgboost4j.dylib targeting MacOS (Apple Silicon)
set -euox pipefail
# Display system info
echo "--- Display system information"
set -x
system_profiler SPSoftwareDataType
sysctl -n machdep.cpu.brand_string
uname -m
set +x
brew install ninja libomp
# Build XGBoost4J binary
echo "--- Build libxg... |
9a256132e7c4cfc13734c57718c588709eadb77fb9835a1aaee257626322195a | Shell | 561 | 10 | python clip_finetune_flickr.py --batch_size 512 \
--num_gpus 1 \
--num_workers 20 \
--train_filename /shared_space/ccnl/mm_data/Flickr30k-CNA/train/flickr30k_cna_train.txt \
--val_filename /shared_space/ccnl/mm_data/Flickr30k-CNA/val/flickr30k_cna_val.txt \
--test_filename /shared_space/ccnl/mm_data/Flickr30k-CNA/test/... |
1356417426842f997d69ffdc6b24db1ee6801d100f331f6a8ee8571602733a5f | Shell | 563 | 14 | #!/bin/bash
set -euo pipefail
## Install basic tools
echo 'debconf debconf/frontend select Noninteractive' | sudo debconf-set-selections
sudo apt-get update
sudo apt-get install -y cmake git build-essential wget ca-certificates curl unzip
## Install CUDA Toolkit 12.6 (Driver will be installed later)
wget -nv https://... |
2358f80747058acd75cbcc324abaf2965a7c0db692e84fbf44ff318213e9b924 | Shell | 566 | 27 | #!/bin/bash
usage() { echo "Usage: $0 [-e <conda_environment>] [-m <mix2_binary>] <docker_image_tag>" 1>&2; exit 1; }
conda_env=./environment.yml
mix2_bin=./mix-square
while getopts ":e:m:" o; do
case "${o}" in
e)
conda_env=${OPTARG}
;;
m)
mix2_bin=${OPTARG}
... |
ce5f2e3a0d147b197d6ff23eefbfed9f7cb6c17d63ce8e89efe8461612494930 | Shell | 571 | 19 | #!/usr/bin/env bash
ENVDIR=${ENVDIR:-~/pkgenv}
# If CentOS server, set C++ compiler manually
if [ -f /etc/redhat-release ]; then
export CC=/opt/ohpc/pub/compiler/gcc/8.3.0/bin/gcc
export CXX=/opt/ohpc/pub/compiler/gcc/8.3.0/bin/g++
fi
mkdir -p build
pushd build
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
... |
bcd0e27817f1624b07b4dc4a9a4c5bac46fe72f40243f8775e31c4dc07d33f48 | Shell | 574 | 19 | #!/bin/bash
# Get list of subjects and runs from list_subject_runs_ALL.txt
sub_runs=$(cat list_subject_runs_ALL.txt)
# sub_runs='Sub01_run01'
# Get current directory
prj_dir=$(pwd)
LBL='4D_clust'
WIN=1
CSZ=10
THR=0
# Loop through all subjects and runs
for SBJID in ${sub_runs}
do
cd "${prj_dir}"/"${SBJID%%_*}"/"$... |
0bc88677a23e7d492a97d6caabac94b048fda67a61118b8af442d70cf4a7c3ae | Shell | 575 | 14 | #!/bin/bash
# --model_name_or_path specifies the original huggingface model
# --lora_model_path specifies the model difference introduced by finetuning,
# i.e. the one saved by ./scripts/run_finetune_with_lora.sh
CUDA_VISIBLE_DEVICES=0 \
deepspeed examples/evaluation.py \
--answer_type math \
--model_nam... |
e74ee88fdc4649e6afd73cf6b6f4bdfcb5bbf692f9eb50baf5d542c6852cc8f7 | Shell | 580 | 14 | #!/bin/bash
# --model_name_or_path specifies the original huggingface model
# --lora_model_path specifies the model difference introduced by finetuning,
# i.e. the one saved by ./scripts/run_finetune_with_lora.sh
CUDA_VISIBLE_DEVICES=0 \
deepspeed examples/evaluate.py \
--answer_type math \
--model_name_... |
c635988384b1dd83189834a72789ee514cab6f1eeef6d1c4939b7775ed713570 | Shell | 583 | 18 | #!/bin/bash
# Copyright 2013-2023, Derrick Wood <dwood@cs.jhu.edu>
#
# This file is part of the Kraken 2 taxonomic sequence classification system.
# Removes intermediate files from a database directory,
# such as reference library FASTA files and taxonomy data from NCBI.
set -u # Protect against uninitialized vars.... |
ae17513643e23b18e199e1dd162113038a14cd42f4a453162671f95820b31107 | Shell | 585 | 24 | #!/bin/bash
# config.sh - Configuration for GliODIL program execution
# CUDA device configuration
export CUDA_VISIBLE_DEVICES="0"
# Optimization and program settings
export OPTIMIZER="adamn"
export POSTFIX=""
export Nt="192"
export Nx="48"
export Ny="48"
export Nz="48"
export DAYS="100"
export HISTORY_EVERY="1000"
ex... |
73e9b70aeb0942fd7ebeb69192ab8176c78646d7a849e3f947c54c7d3567a05d | Shell | 587 | 21 | #!/usr/bin/env sh
#
# N.B. This does not download the ilsvrcC12 data set, as it is gargantuan.
# This script downloads the imagenet example auxiliary files including:
# - the ilsvrc12 image mean, binaryproto
# - synset ids and words
# - Python pickle-format data of ImageNet graph structure and relative infogain
# - the... |
b9162c9bcf5a73b2ad14fb9884a61dd4eac9eec04752f4938c8b50c074c70fdf | Shell | 588 | 19 | #!/bin/bash
#BSUB -J bamCoverage
#BSUB -o logs/bamCoverage_bw.%J.out
#BSUB -e logs/bamCoverage_bw.%J.err
#BSUB -n 12
#BSUB -R rusage[mem=50]
mkdir -p logs
mkdir -p merged_bigwig
. /usr/share/Modules/init/bash
module load modules modules-init
module load python
pip install deeptools
# generate BigWig files from merg... |
1cd2b48cb59276788a0203b0481d9fc7392f8ebb3c72da7d202afba153beb0f8 | Shell | 592 | 23 | #!/bin/bash -l
#SBATCH --job-name=physio01
#SBATCH --nodes=1
#SBATCH --task=1
#SBATCH --mem-per-cpu=8gb
#SBATCH --time=00:15:00
#SBATCH -o ./log/physio01_%A_%a.o
#SBATCH -e ./log/physio01_%A_%a.e
#SBATCH --account=DBIC
#SBATCH --partition=standard
conda activate physio
INPUT_DIR="/dartfs-hpc/rc/lab/C/CANlab/labdata/d... |
33d8b6fe23d434b95a5b932bf3685514a719b5c1949054b4c7caadffd42c3744 | Shell | 594 | 16 | #! /bin/bash
for VARIABLE in {0..17}
do
echo "python infer_chir.py --config ./configs/molnet_non_train.yaml --csp_no $VARIABLE \
--resume_path ./check_point1203/molnet_chirality_cls_etkdg_csp$VARIABLE-tl.pt \
--result_path ./results1203/molnet_cmrt_cls_etkdg_csp$VARIABLE-ena.csv \
--device 0"
python ... |
6bbbbf32070a303ccc162aafcce63620cb7ac0a871095050d4a0723e92144def | Shell | 599 | 20 | #!/usr/bin/env bash
ENVDIR=${ENVDIR:-~/pkgenv}
# If CentOS server, set C++ compiler manually
if [ -f /etc/redhat-release ]; then
export CC=/opt/ohpc/pub/compiler/gcc/8.3.0/bin/gcc
export CXX=/opt/ohpc/pub/compiler/gcc/8.3.0/bin/g++
fi
mkdir -p build
pushd build
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
... |
05003951fa31a16a70bc2cabca35d0b1edfeeb90d309a0385164532e72705a9c | Shell | 600 | 19 | #!/bin/bash
# Get list of subjects and runs from list_subject_runs_ALL.txt
sub_runs=$(cat list_subject_runs_ALL.txt)
# sub_runs='Sub01_run01'
# Get current directory
prj_dir=$(pwd)
LBL='4D_clust'
WIN=1
CSZ=10
THR=0
# Loop through all subjects and runs
for SBJID in ${sub_runs}
do
cd "${prj_dir}"/"${SBJID%%_*}"/"$... |
49c49ac7853f6f2120003f2957af1c0d3ac69ce70d4216cddf204cb5e9408499 | Shell | 600 | 22 | #!/bin/bash
#BSUB -o logs/sambamba.%J.out
#BSUB -e logs/sambamba.%J.err
#BSUB -n 12
#BSUB -R rusage[mem=50]
mkdir -p logs
. /usr/share/Modules/init/bash
module load modules modules-init
module load sambamba
#go through each .bam file in the current directory
for file in *.bam; do
#make new output file ... |
94e4aa73916f5c1bb029996c5223772165b7dafe67da230067c1102eb8e933d6 | Shell | 601 | 27 | #!/bin/bash
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
set -ex
for PV in 3.6 3.7 3.8
do
PYTHON_VERSION=$PV bash packaging/build_conda.sh
done
ls -Rl packaging
for version in 36 37 38
do
(cd packaging/out && conda convert -p win-64 linux-64/fvcore-*-py$version.tar.bz2)
(cd pack... |
a017d6aa410bc46ae48c85bed82b4f1d028a6d661cd6f9fa4706f1da3710dea7 | Shell | 601 | 23 | #!/bin/bash
## Test if Python XGBoost can be configured to use libxgboost.so from the system prefix
set -euox pipefail
sudo apt-get update && sudo apt-get install -y ninja-build
mkdir build
pushd build
cmake .. -GNinja
ninja
popd
# Copy libxgboost.so to system prefix
cp -v lib/* "$(python -c 'import sys; print(sys.... |
b2e844434bd0dc429a1ac61d901601d31f709fa12b7f717395839c13c218fb36 | Shell | 604 | 20 | python3 ../../src/train.py \
--exp_id ang_test_r10 \
--neg_data ../output/ang_0-sigalign.parquet \
--pos_data ../output/ang_500-sigalign.parquet \
--batch_size 256 \
--seq_len 400 \
--model_type resnet \
--outpath ../output/ \
--save_test \
--epochs 5 \
--steps_per_epoch 20 \
... |
fd2cca0d74938465cc23ef0e64f5bf31cdc46d52eb599739c86819b60e71b030 | Shell | 613 | 15 | #!/bin/sh
if ! [ -S /var/run/docker.sock ] && [ -z "$DOCKER_HOST" ]; then
>&2 echo 'ERROR: cwltool cannot work inside a container without access to docker'
>&2 echo 'Launch the container with the option -v /var/run/docker.sock:/var/run/docker.sock'
# shellcheck disable=SC2016
>&2 echo 'or launch the container w... |
559a42b5ed3b157f972bc9bd135dfa03890b4e64f2ca3d354d9b6910854127f1 | Shell | 616 | 19 | # we should frequently check to ensure that the postgres version
# matches the one we use in production
# NOTE: postgresql docker images show vulnerabilities, but we are using it for dev.
# Also the vulnerabilities don't affect how the container is primarily used.
while getopts n: flag; do
case "${flag}" in
... |
c526baa33e120bbb29cea553bc78762cde2fd27e6a51eb6bcf583d4f67e65908 | Shell | 616 | 17 | #!/bin/bash
# map feature using indicator encoding, also produce featmap.txt
python mapfeat.py
# split train and test
python mknfold.py agaricus.txt 1
XGBOOST=../../../xgboost
# training and output the models
$XGBOOST mushroom.conf
# output prediction task=pred
$XGBOOST mushroom.conf task=pred model_in=0002.model
# p... |
db013eded3696d21e417e0d42af64d9533264a6d4aab48ea5013fda2d49dac46 | Shell | 617 | 22 | #!/usr/bin/env sh
# This script converts the mnist data into leveldb format.
set -e
EXAMPLES=./build/examples/siamese
DATA=./data/mnist
echo "Creating leveldb..."
rm -rf ./examples/siamese/mnist_siamese_train_leveldb
rm -rf ./examples/siamese/mnist_siamese_test_leveldb
$EXAMPLES/convert_mnist_siamese_data.bin \
... |
235327c4692082631adf07481f3fcad60c9210b58e535da8cbcf3996f165a3cd | Shell | 619 | 17 | #!/usr/bin/env bash
# Simple Bash wrapper script to launch MagellanMapper without relying on
# the python binary specified in the run script shebang line
# Author: David Young, 2020
# assumes run.py is in current directory
if command -v python &> /dev/null; then
# launch run script directly from python, allowing it ... |
42f047dc9cef1b1d4b8f240a750e932d2a797da2e66bc2c341cf5f028791d55d | Shell | 619 | 16 | #download from https://myersgroup.github.io/relate/#Binaries. we used RELATE 1.8
mkdir -p ancestral
cd ancestral
wget https://ftp.ensembl.org/pub/release-105/fasta/ancestral_alleles/homo_sapiens_ancestor_GRCh38.tar.gz -nc
tar -xzvf homo_sapiens_ancestor_GRCh38.tar.gz
seq 22| awk '{system (" mv homo_sapiens_ancestor_GRC... |
2a1123bc78d883d94af2a015ae9377411617ce69fb54ac7e062fd4593607b52b | Shell | 626 | 14 | #! /bin/bash
for SBJ in Sub01 Sub03 Sub04 Sub05 Sub06 Sub07 Sub09 Sub10 Sub11 Sub12 Sub13 Sub14 Sub15 Sub16 Sub17 Sub18 Sub19 Sub20 Sub21 Sub22; do
for run in run01; do
cwd=/mnt/h/Experiments/Experiment2-Blink_Tic/SPFM/feat_preproc/${SBJ}_${run}_echo01.feat/reg
cd "${cwd}"
... |
18bae96e565271fd3a32e900f2c9954a31f5b6e3a8093c343a51fd6a7ec2e4e5 | Shell | 628 | 32 | #!/bin/sh
if [ $# -ne 3 ]
then
echo "Usage: $0 <DATABASE-NAME> <DATABASE-USER> <DATABASE-PASWORD>"
exit 1
fi
CATMAID_DATABASE="$1"
CATMAID_USER="$2"
CATMAID_PASSWORD="$(echo $3 | sed -e "s/\\\\/\\\\\\\/g" -e "s/'/\\\'/g")"
cat <<EOSQL
DO
\$body\$
BEGIN
IF NOT EXISTS (
SELECT *
FROM pg_catalog.p... |
5b23704daf2dad8c612cd2fde6290da62cb6a13bbd6b5c9c8253ddaebc11f0a2 | Shell | 628 | 33 | #!/usr/bin/bash
#
# set max wallclock time hh:mm:ss
#SBATCH --time=12:00:00
#
# set number of tasks
#SBATCH --ntasks=1
#
# set number of cores per node
#SBATCH --cpus-per-task=12
#
# set memory
#SBATCH --mem=64G
#
# set output filename
#SBATCH -o slurm-%j.out-%N
#
# mail all alerts (start, end and abortion)
#SBATCH --m... |
85fcc46f83db224ff4238f3d2fc448ce1f7ea56a05f43d74fe06ad1dec702bab | Shell | 630 | 33 | #!/usr/bin/bash
#
# set max wallclock time hh:mm:ss
#SBATCH --time=12:00:00
#
# set number of tasks
#SBATCH --ntasks=1
#
# set number of cores per node
#SBATCH --cpus-per-task=12
#
# set memory
#SBATCH --mem=64G
#
# set output filename
#SBATCH -o slurm-%j.out-%N
#
# mail all alerts (start, end and abortion)
#SBATCH --m... |
a7f476f06770307554af983c14c04b6a6e05c917149ddcc3e1a0be42a6603ff3 | Shell | 634 | 22 | #!/usr/bin/env sh
# This script converts the mnist data into lmdb/leveldb format,
# depending on the value assigned to $BACKEND.
set -e
EXAMPLE=examples/mnist
DATA=data/mnist
BUILD=build/examples/mnist
BACKEND="lmdb"
echo "Creating ${BACKEND}..."
rm -rf $EXAMPLE/mnist_train_${BACKEND}
rm -rf $EXAMPLE/mnist_test_${B... |
f69b9b6c58dd948c5fdcfbf07b05b0ca5d805ec457f33a9d205a444cdd907e65 | Shell | 634 | 24 | #!/bin/bash
#SBATCH -J CENTaUR2
#SBATCH -p gpu_p
#SBATCH --qos gpu_normal
#SBATCH --gres=gpu:1
#SBATCH --mem=80G
#SBATCH -t 48:00:00
#SBATCH --constraint=a100_80gb
#SBATCH --nice=10000
#SBATCH --cpus-per-task=20
source activate unsloth_env2
cd ..
python test_adapter.py --model unsloth/Meta-Llama-3.1-8B-bnb-4bit
pyth... |
65b2d442c8cbb65a9104d495ac9f8c92e085f638e87171aaf287bcda4e24ce74 | Shell | 637 | 16 | #!/bin/bash -l
experiments=("ERN" "LRP" "MMN" "N170" "N2pc" "N400" "P3")
for experiment in ${experiments[@]}; do
sbatch --nodes=1 \
--ntasks-per-node=1 \
--cpus-per-task=72 \
--time=1:00:00 \
--mem=256G \
--job-name=tr_group_${experiment} \
--output=/u/kroma/m4d/logs... |
bd5e76a6a81eadc678a94787aec844a7b3639a6497c0d7d28c226cdc6c9dca12 | Shell | 639 | 24 | #!/bin/bash
#SBATCH -J CENTaUR2
#SBATCH -p gpu_p
#SBATCH --qos gpu_normal
#SBATCH --gres=gpu:1
#SBATCH --mem=80G
#SBATCH -t 48:00:00
#SBATCH --constraint=a100_80gb
#SBATCH --nice=10000
#SBATCH --cpus-per-task=20
source activate unsloth_env2
cd ..
python test_adapter.py --model unsloth/Meta-Llama-3.1-70B-bnb-4bit
pyt... |
5772d377fe96d72a85e47808bcd1ee9cc71a993f533582cbfd84662b4c9ed773 | Shell | 641 | 27 | #!/bin/sh
#$ -S /bin/bash
#$ -cwd
# for PCA
# qsub -pe def_slot 10 0_job_pca.sh
ulimit -s unlimited
echo running on `hostname`
echo starting at
date
DATADIR=../../../../data/1_single_strain/gwas/
IND=${DATADIR}/prep/analyzed_inds.txt
SNP=${DATADIR}/prep/analyzed_SNPs.txt
GENO=../../../../data/0_genome/dgrp2
export O... |
8c6b7c34e74429d9c708638d4048c37709f764277670bb525ecfff5e04c9c6ba | Shell | 642 | 27 | #!/bin/sh
#$ -S /bin/bash
#$ -cwd
# for SCORE
# qsub -l medium -l s_vmem=20G -l mem_req=20G 2_job_SCORE.sh
ulimit -s unlimited
echo running on `hostname`
echo starting at
date
DATADIR=../../../../data/1_single_strain/gwas/
QC=../../../../data/0_genome/dgrp2_QC
SCORE_PREP=${DATADIR}/SCORE_prep/
SCORE_RES=${DATADIR}/re... |
0cf22684e5998c4898916271a7cd0bf2699521a02b414ff40e7a96af5ee46d85 | Shell | 644 | 33 | #!/usr/bin/bash
#
# set max wallclock time hh:mm:ss
#SBATCH --time=12:00:00
#
# set number of tasks
#SBATCH --ntasks=1
#
# set number of cores per node
#SBATCH --cpus-per-task=12
#
# set memory
#SBATCH --mem=64G
#
# set output filename
#SBATCH -o slurm-%j.out-%N
#
# mail all alerts (start, end and abortion)
#SBATCH --m... |
abae890a701a78073cfc77ca60673290ef537c52e1734e97ce4c3e130bb65776 | Shell | 645 | 30 | #!/bin/bash
## Test XGBoost Python wheel on the Linux platform
set -euo pipefail
if [[ "$#" -lt 2 ]]
then
echo "Usage: $0 {gpu|mgpu|cpu|cpu-arm64} [image_repo]"
exit 1
fi
suite="$1"
image_repo="$2"
if [[ "$suite" == "gpu" || "$suite" == "mgpu" ]]
then
gpu_option="--use-gpus"
else
gpu_option=""
fi
source ... |
8b2c9b5e4868a4454b8e9a5e6e2c48b5e01f274833cb247af6b82a24adcb089a | Shell | 646 | 8 | python3 ../../src/analyzeKmer.py \
--posbam /private/groups/brookslab/gabai/projects/Add-seq/data/ctrl/pod5/220517_ang_500.sorted.bam \
--negbam /private/groups/brookslab/gabai/projects/Add-seq/data/ctrl/pod5/220308_ang_0.sorted.bam \
--posparq /private/groups/brookslab/gabai/projects/Add-seq/data/ctrl/pod... |
0d9ecfcef2946d073837b6f7bf3ee045a0e92a45f608c995d7865a01dd5fe8dd | Shell | 649 | 7 | python -u train.py kpi anomaly_0 --loader anomaly --repr-dims 320 --max-threads 8 --seed 1 --eval
python -u train.py kpi anomaly_1 --loader anomaly --repr-dims 320 --max-threads 8 --seed 2 --eval
python -u train.py kpi anomaly_2 --loader anomaly --repr-dims 320 --max-threads 8 --seed 3 --eval
python -u train.py kpi an... |
0b6a51e18e1ce9b48fa56cd521febfa18b0b9770fe1e61385a3ecfe7bfa4eb49 | Shell | 652 | 17 | #!/bin/bash
if [ -f YearPredictionMSD.txt ]
then
echo "use existing data to run experiment"
else
echo "getting data from uci, make sure you are connected to internet"
wget https://archive.ics.uci.edu/ml/machine-learning-databases/00203/YearPredictionMSD.txt.zip
unzip YearPredictionMSD.txt.zip
fi
echo "... |
867f13179a4181c60dd0404335a149edf0129d79b640f2a0aef14db0ca5c3ab0 | Shell | 653 | 30 | # /etc/profile: system-wide .profile file for the Bourne shell (sh(1))
# and Bourne compatible shells (bash(1), ksh(1), ash(1), ...).
source /etc/profile.d/modules.sh
export GMX_VERSION="SED_GMX_VERSION"
if [ "${PS1-}" ]; then
if [ "${BASH-}" ] && [ "$BASH" != "/bin/sh" ]; then
# The file bash.bashrc already se... |
e2a74417a4ec8edd4c89105ea03c870acaa5b88435bb27d3e9984ad3803bf344 | Shell | 656 | 31 | #!/bin/bash
# turn on bash's job control
set -m
# bring up sshd
/usr/sbin/sshd
# print uid
id
# start the munge daeomon
service munge start
su -u munge /sbin/munged
munge -n
munge -n | unmunge
remunge
# replace nproc
sed -i "s/REPLACE_IT/CPUs=$(nproc)/g" /etc/slurm-llnl/slurm.conf
# start the slurm daemon
service... |
fc9b62bfec4b2e1f7afeedf0ee11d54ae03f9ba88cbed50e5b2bb2f0042a4b46 | Shell | 656 | 17 | #!/bin/bash
# update-deps.sh
set -e
# Create an environment with the binder dependencies
TUTORIAL_DEPS="ipywidgets bokeh holoviews hvplot"
SIMULATION_DEPS="ngspice umap-learn scikit-learn matplotlib"
BINDER_DEPS="neuprint-python jupyterlab ${TUTORIAL_DEPS} ${SIMULATION_DEPS}"
conda create -y -n neuprint-python -c fly... |
6f8d8353564d5c3e280682da04ee1e60eed6647f11b6e955760973362d340a26 | Shell | 658 | 33 | #!/usr/bin/bash
#
# set max wallclock time hh:mm:ss
#SBATCH --time=12:00:00
#
# set number of tasks
#SBATCH --ntasks=1
#
# set number of cores per node
#SBATCH --cpus-per-task=12
#
# set memory
#SBATCH --mem=64G
#
# set output filename
#SBATCH -o slurm-%j.out-%N
#
# mail all alerts (start, end and abortion)
#SBATCH --m... |
9dbc25334fb12d8c88e2d73bd489731c432016ecebc52eab88a547dbc26b5a79 | Shell | 658 | 17 | #!/bin/bash
set -eu
VER=$(grep -Po '(?<=^__version__ = ).*' ../heudiconv/info.py | sed 's/"//g')
image="kaczmarj/neurodocker:master@sha256:936401fe8f677e0d294f688f352cbb643c9693f8de371475de1d593650e42a66"
docker run --rm $image generate docker -b neurodebian:stretch -p apt \
--dcm2niix version=v1.0.20180622 met... |
d3226a234b27ff549b1f63218c894ed6a5a7c86505909e576fafd24fb94e17d4 | Shell | 659 | 24 | #!/bin/bash
#SBATCH -J CENTaUR2
#SBATCH -p gpu_p
#SBATCH --qos gpu_normal
#SBATCH --gres=gpu:1
#SBATCH --mem=80G
#SBATCH -t 48:00:00
#SBATCH --constraint=a100_80gb
#SBATCH --nice=10000
#SBATCH --cpus-per-task=20
source activate unsloth_env2
cd ..
python test_adapter.py --model marcelbinz/Llama-3.1-Centaur-8B-adapter... |
aff72f3aee25e0602a05b806453d9c914832897f42ac5e2169df4730dbbba950 | Shell | 661 | 7 | python -u train.py yahoo anomaly_0 --loader anomaly --repr-dims 320 --max-threads 8 --seed 1 --eval
python -u train.py yahoo anomaly_1 --loader anomaly --repr-dims 320 --max-threads 8 --seed 2 --eval
python -u train.py yahoo anomaly_2 --loader anomaly --repr-dims 320 --max-threads 8 --seed 3 --eval
python -u train.py ... |
5f5be8486241ade9da9ecd65dc0c03f04c6358d6e18632e29dd95d2630a092c4 | Shell | 664 | 17 | #!/bin/bash
# --model_name_or_path specifies the original huggingface model
# --lora_model_path specifies the model difference introduced by finetuning,
# i.e. the one saved by ./scripts/run_finetune_with_lora.sh
deepspeed_args="--master_port=11000"
CUDA_VISIBLE_DEVICES=0 \
deepspeed ${deepspeed_args} \
examples/... |
94b4a84d5c14f84e5fcadef0b076fce4af4f3dc5b24c2b93fb6e6df946dc321c | Shell | 664 | 24 | #!/bin/bash
#SBATCH -J CENTaUR2
#SBATCH -p gpu_p
#SBATCH --qos gpu_normal
#SBATCH --gres=gpu:1
#SBATCH --mem=80G
#SBATCH -t 48:00:00
#SBATCH --constraint=a100_80gb
#SBATCH --nice=10000
#SBATCH --cpus-per-task=20
source activate unsloth_env2
cd ..
python test_adapter.py --model marcelbinz/Llama-3.1-Centaur-70B-adapte... |
ac5f750745a39c103f498b519e67b828494cbd1c176402035d1e26c0de806f40 | Shell | 666 | 33 | #!/usr/bin/bash
#
# set max wallclock time hh:mm:ss
#SBATCH --time=12:00:00
#
# set number of tasks
#SBATCH --ntasks=1
#
# set number of cores per node
#SBATCH --cpus-per-task=12
#
# set memory
#SBATCH --mem=64G
#
# set output filename
#SBATCH -o slurm-%j.out-%N
#
# mail all alerts (start, end and abortion)
#SBATCH --m... |
61c76c6f22d9b41042c5b73cbf4134b62101eaadd771cbf1e0c3f14275ad9c2f | Shell | 669 | 27 | #!/bin/sh
#SBATCH --job-name=mriqc-group
#SBATCH --mail-user=heejung.jung@colorado.edu
#SBATCH --mail-type=BEGIN,FAIL,END
#SBATCH --qos normal
#SBATCH --output ./log/group%j.out
#SBATCH --error ./log/group.e%j
#SBATCH --nodes 1
#SBATCH -c 12
#SBATCH -t 20:00:00
#SBATCH --exclusive
ml singularity/3.3.0
IMAGE=/projec... |
37f4956e985f835316bf78d1812394c9018f78bfdc12283b7a91d73d139d30db | Shell | 670 | 33 | #!/bin/bash
## Build and test XGBoost with ARM64 CPU
## Companion script for ops/pipeline/build-cpu-arm64.sh
set -euox pipefail
source activate aarch64_test
echo "--- Build libxgboost from the source"
mkdir -p build
pushd build
cmake .. \
-GNinja \
-DCMAKE_PREFIX_PATH="${CONDA_PREFIX}" \
-DUSE_OPENMP=ON \
-... |
eec889836194a8ee5a3ab0107baa35ef3da4141b8ff6a79613cecd237b5399b1 | Shell | 671 | 14 | #!/bin/bash
# Spinal cord analysis pipeline for the SCT course and webpage tutorials:
# https://spinalcordtoolbox.com/user_section/tutorials/analysis-pipelines-with-sct.html
# First, download the manual correction script into a local folder.
sct_download_data -d manual-correction -o manual-correction
# Apply process... |
f5a2cb468193c2c9e8af755e7711a3a4f1f0bfdbae28c705f5fa9ecaeb465949 | Shell | 671 | 21 | python3 ../src/predict.py \
--sigalign pos_sig.tsv \
--seqlen 40 \
--step 20 \
--weight /private/groups/brookslab/gabai/projects/Add-seq/data/train/240510_train_seqlen40/240510_train_addseq_seqlen40_resnet_best_model.pt \
--thread 16 \
--outpath ./output/pred/ \
--prefix pos_sig \
--bat... |
504e9ab3a8360236bf0b43dd580c049fcce675bb8eab3108f1d1fac139028fd1 | Shell | 672 | 7 | DATA_PATH=/tmp/medperf_tests_20260222031259/storage/data/localhost_8000/c86149a1f5cc0af3a78069c80c080147580e1c0b55ee3870e6e5633a95309cde/data
LABELS_PATH=/tmp/medperf_tests_20260222031259/storage/data/localhost_8000/c86149a1f5cc0af3a78069c80c080147580e1c0b55ee3870e6e5633a95309cde/labels
MODEL=/home/hasan/work/medperf_w... |
625d7e6055a3ea1f125bff3899e275c3c8dd8e5edb3a693f3860e80b5ef6c756 | Shell | 675 | 14 | echo "Building random dictionary with in=../../lib/common k=200 out=dict1"
./main in=../../../lib/common k=200 out=dict1
zstd -be3 -D dict1 -r ../../../lib/common -q
echo "Building random dictionary with in=../../lib/common k=500 out=dict2 dictID=100 maxdict=140000"
./main in=../../../lib/common k=500 out=dict2 dictID=... |
680c10fc73f2aeb53d31bf9399f1dc5735324d57ccb0c016d16b8676575425c8 | Shell | 675 | 29 | #!/bin/bash
## Deploy JVM packages to S3 bucket
set -euo pipefail
source ops/pipeline/enforce-ci.sh
source ops/pipeline/get-docker-registry-details.sh
source ops/pipeline/get-image-tag.sh
if [[ "$#" -lt 3 ]]
then
echo "Usage: $0 {cpu,gpu} [image_repo] [scala_version]"
exit 1
fi
variant="$1"
image_repo="$2"
scal... |
0d150e1c156810ae216c59c4042e194114712b77cad3d0cb7fc0d1a2dbe6212c | Shell | 679 | 22 | #!/bin/bash
# Create a workspace
mkdir -p medperf_tutorial
cd medperf_tutorial
# Copy the data preparation container
cp -r ../examples/chestxray_tutorial/data_preparator data_preparator
# Copy the benchmark script container
cp -r ../examples/cc/chestxray/implementation cc_chestxray
# Copy the metrics container
cp -... |
229752e2942fdf29eacd093c6e221e411c58dccb2753be681086a4917118a354 | Shell | 681 | 29 | #!/bin/bash
set -e
SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]:-$0}"; )" &> /dev/null && pwd 2> /dev/null; )";
REPO_DIR="$(dirname ${SCRIPT_DIR})"
echo "Building docs in your local repo"
cd ${REPO_DIR}/docs
GIT_DESC=$(git describe)
make html
TMP_REPO=$(mktemp -d)
echo "Cloning to ${TMP_REPO}/neuprint-pyth... |
9ae9c11549570e36eeb14a818a25e32534456a7f02852b75cc960fa728f28c36 | Shell | 682 | 22 | #!/bin/bash
# ディレクトリの指定
input_dir="../../data/Experiment2_group/1_rawdata/tracks"
output_dir="../../data/Experiment2_group/2_moddata/tracks"
file=$1
# sh 2_2_modify_tracking_group.sh 20250121
# 出力ディレクトリが存在しない場合は作成
mkdir -p "$output_dir"
# 各ファイルを処理
for input_file in "$input_dir"/$file*.tsv; do
# ファイル名の取得と拡張子の削除
... |
f351717c2017b96b72d51633452349ac4d81930504f1510e0bfbadb0d2cb4b50 | Shell | 683 | 24 | BATCH --job-name=mriqc
#SBATCH --mail-user=heejung.jung@colorado.edu
#SBATCH --mail-type=BEGIN,FAIL,END
#SBATCH --qos normal
#SBATCH --output ./log/mriqc%j.out
#SBATCH --error ./log/mriqc.e%j
#SBATCH --nodes 1
#SBATCH -c 12
#SBATCH -t 20:00:00
#SBATCH --exclusive
SUBJ=${1}
IMAGE=/projects/heju9108/container/mriqc-0.... |
1ef52e4c8a06505d25141e9b206d5df310206559557a702daa3b9b0abeb0b653 | Shell | 684 | 25 | # run from (base) environment
SCRIPT_DIR=$(dirname $0)
if [ $# -eq 0 ]; then
echo "No arguments: Provide the conda path of an existing conda environment"
exit 1
fi
CONDA_PREFIX=$1
if [[ ! -d ${CONDA_PREFIX} ]]; then
echo "Path ${CONDA_PREFIX} is not a directory"
exit 1
fi
CONDA_DIR=${CONDA_PREFIX}/etc/cond... |
47f7da563ad59033f7faa60a916485accf7c063bd98d98924df63a5c7171a451 | Shell | 685 | 33 | #!/bin/bash
## Build and test JVM packages.
##
## Note. This script takes in all inputs via environment variables.
INPUT_DOC=$(
cat <<-EOF
Inputs
- SCALA_VERSION: Scala version, either 2.12 or 2.13 (Required)
EOF
)
set -euo pipefail
source ops/pipeline/get-docker-registry-details.sh
source ops/pipeline/get-image-t... |
803db1b1febf98710448c6ffa22e5876d61007078ea615ccb36b96c0085e17fc | Shell | 686 | 22 | #!/bin/bash
# Get the current date and time
timestamp=$(date +"%Y-%m-%d_%H-%M-%S")
# Set the relevant directories
code_directory="INSERT/PATH/TO/PHIMO-MRM/CODE/DIRECTORY"
anaconda_directory="INSERT/PATH/TO/ANACONDA/DIRECTORY"
# Set the output filename with the timestamp
output_filename="$code_directory/iml-dl/result... |
972c6b673783a578565255eb81ecceb36a4ed499aa47c680fa0bc43782b1c63e | Shell | 687 | 26 | #!/bin/bash
#
#SBATCH -J mriqc
#SBATCH --array=1-214
#SBATCH --time=24:00:00
#SBATCH -n 1
#SBATCH --cpus-per-task=16
#SBATCH --mem-per-cpu=4G
#SBATCH -p <partitions>
# Outputs ----------------------------------
#SBATCH -o log-ng/%A-%a.out
#SBATCH -e log-ng/%A-%a.err
#SBATCH --mail-user=<email>
#SBATCH --mail-type=ALL
... |
001564438cc305da176c98a28b9e5110d3ec6897912c651a50585f6955736533 | Shell | 689 | 24 | python3 ../src/train.py \
--exp_id test_r9 \
--neg_data neg_sig.tsv \
--pos_data pos_sig.tsv \
--outpath ./output/ \
--epochs 5 \
--steps_per_epoch 50 \
--val_steps_per_epoch 50 \
--save_test \
--input_dtype sigalign
python3 ../src/src/test.py \
--exp_id test_r9 \
--test_d... |
54f0c429096a133240dbd6e69886ee34387bdc35a077fbf4ea5cf12b4964316f | Shell | 689 | 22 | #!/bin/bash
# Get the current date and time
timestamp=$(date +"%Y-%m-%d_%H-%M-%S")
# Set the relevant directories
code_directory="INSERT/PATH/TO/PHIMO-MRM/CODE/DIRECTORY"
anaconda_directory="INSERT/PATH/TO/ANACONDA/DIRECTORY"
# Set the output filename with the timestamp
output_filename="$code_directory/iml-dl/result... |
cdb994b9c40736eca5474dd245ef86fbc8b82d9dbd05199e0099de556b70914f | Shell | 689 | 21 | #!/bin/bash
set -e
# Simulate 3 conditions
echo -e "\n> Simulating coherent condition\n"
python3 scripts/simulate.py --outdir results --condition coherent --dts 50
echo -e "\n> Simulating incoherent condition\n"
python3 scripts/simulate.py --outdir results --condition incoherent --dts 50
echo -e "\n> Simulating non-a... |
f003653f20aea75d9ef390d32f2082f674950c809f32c1585e537d7c74d83be4 | Shell | 691 | 23 | #!/bin/bash
#PBS -N fmriprep_submit
#PBS -q default
#PBS -l nodes=1:ppn=8
#PBS -l walltime=01:00:00
#PBS -m bea
subjects=("sub-01" "sub-02" "sub-03" "sub-04" "sub-05" \
"sub-06" "sub-07" "sub-08" "sub-09" "sub-10" \
"sub-11" "sub-12" "sub-13" "sub-14" "sub-15" \
"sub-16" "sub-17" "sub-18" "sub-19" "sub-20" \
"sub... |
40f0d4c7c9e3a11e35a3d5ec7e42b3970f1abe8028fd5affa19a0deee49a6ff6 | Shell | 694 | 30 | #!/bin/bash
export FREESURFER_HOME='/usr/local/freesurfer/7.4.1'
source $FREESURFER_HOME/SetUpFreeSurfer.sh
segfiles=('aseg+DKT.stats')
export SUBJECTS_DIR="/media/raid/ibrazug/Dokumente/KindersegV2/Ibra/derivatives/FastSurferVINN"
subs=($(ls -1d $SUBJECTS_DIR/sub*))
cd $SUBJECTS_DIR
#echo ${subs[@]}
stats_dir... |
88984fc8530da97c66678cb5d137840404912d2f5b795ed040efae1d81c636e8 | Shell | 695 | 16 | #!/bin/bash
# map the data to features. For convenience we only use 7 original attributes and encode them as features in a trivial way
python mapfeat.py
# split train and test
python mknfold.py machine.txt 1
# training and output the models
../../xgboost machine.conf
# output predictions of test data
../../xgboost mac... |
5f07db87f554bc4bcaab07bb35e347e28e4a6709b339c1b408b50e998c627682 | Shell | 698 | 16 | #!/bin/bash
# This script is only made for running XGBoost tests on official CI where we have access
# to a 4-GPU cluster, the discovery command is for running tests on a local machine where
# the driver and the GPU worker might be the same machine for the ease of development.
if ! command -v nvidia-smi &> /dev/null
... |
3820c00c17ca6faba2915039ca360c19395a0ac73bc72a5a9984a05927f3988d | Shell | 707 | 22 | #!/bin/bash
# Get the current date and time
timestamp=$(date +"%Y-%m-%d_%H-%M-%S")
# Set the relevant directories
code_directory="INSERT/PATH/TO/PHIMO-MRM/CODE/DIRECTORY"
anaconda_directory="INSERT/PATH/TO/ANACONDA/DIRECTORY"
# Set the output filename with the timestamp
output_filename="$code_directory/iml-dl/result... |
2bdc397d3238b344117ba1e4f1c2e22084f1e5e31de8f2495d7022966f645e3c | Shell | 715 | 22 | #!/bin/bash
# ディレクトリの指定
input_dir="../../../../data/6_spiderACI/Experiment1_single/rawdata/tracks"
output_dir="../../../../data/6_spiderACI/Experiment1_single/moddata/tracks"
file=$1
# sh 2_modify_tracking.sh 20241102
# 出力ディレクトリが存在しない場合は作成
mkdir -p "$output_dir"
# 各ファイルを処理
for input_file in "$input_dir"/$file*.tsv; ... |
11b25a8bbd93711cb36a97f14b7a87bcb037be0ec21574a5b773a260804e1588 | Shell | 716 | 29 | #!/bin/bash
#SBATCH -A MST109178
#SBATCH -J YARN
#SBATCH -p ngs186G
#SBATCH -c 28
#SBATCH --mem=186g
#SBATCH -o YARN_out.txt
#SBATCH -e YARN_err.txt
# Step1. Summary counts in a table.
./01_summaryCounts.R -c ../counts/ -o ../meta/raw_counts_table.csv
./01_summaryCounts.R -x -c ../TH_counts/ -o ../meta/TERRA_counts_t... |
7c8edbdbeaf9f8f3616cb0c4363be1d78c2ed1d277bfb6b43ca8d54ba2172e43 | Shell | 717 | 29 | #!/bin/bash
#BSUB -J homer
#BSUB -o logs/homer.findMotifs.%J.out
#BSUB -e logs/homer.findMotifs.%J.err
#BSUB -n 12
#BSUB -R rusage[mem=50]
mkdir -p logs
. /usr/share/Modules/init/bash
module load modules modules-init
module load homer
#Paths to BED files and genome FASTA file
BED1="diffbind_output/CTRL_e16... |
18716ac371b95e33b1be765dc0076f3aee1a2bb3acc01e1bf18cce0b8039630f | Shell | 718 | 22 | #!/bin/sh
# Note, sashimi_plot is originally from MISO, and developed
# under Python2, thus we include it in BRIE-kit as a folder.
# It can be imported from its path, but not directly from
# briekit package.
ANNO_DIR=~/annotation
GFF_FILE=$ANNO_DIR/mouse/AS_events/SE.filtered.gff3
GFF_DIR=$ANNO_DIR/mouse/AS_events/... |
3183306e3c44cf59fb3aae5e006ba1d8f565f0c1eae6a5ab9b0d742e2ec42712 | Shell | 720 | 9 | # multivar
python -u train.py ETTh1 forecast_multivar --loader forecast_csv --repr-dims 320 --max-threads 8 --seed 42 --eval
python -u train.py ETTh2 forecast_multivar --loader forecast_csv --repr-dims 320 --max-threads 8 --seed 42 --eval
python -u train.py ETTm1 forecast_multivar --loader forecast_csv --repr-dims 320 ... |
2bc97c394d81b319763108938df5af3cf2e0d6a929c8ca932aa8884b60b444ec | Shell | 723 | 34 | #!/bin/bash
## Build libxgboost4j.so with CUDA
set -euo pipefail
source ops/pipeline/classify-git-branch.sh
source ops/pipeline/get-docker-registry-details.sh
source ops/pipeline/get-image-tag.sh
IMAGE_URI=${DOCKER_REGISTRY_URL}/xgb-ci.jvm_gpu_build:${IMAGE_TAG}
echo "--- Build libxgboost4j.so with CUDA"
if [[ ($i... |
726deb117e72ee977ea0100e22fe72299cfc99541f2252110ac24b410ab1e331 | Shell | 724 | 23 | #! /bin/bash
ANNO_DIR=~/annotation
cd $ANNO_DIR
wget https://assets.thermofisher.com/TFS-Assets/LSG/manuals/ERCC92.zip -P $ANNO_DIR
wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M17/GRCm38.p6.genome.fa.gz -P $ANNO_DIR/mouse
wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_mouse/release_M1... |
159850fafddf3e08786c82c148b3e7e32943ae467f4c3ddcfac82beb0fcc5def | Shell | 726 | 18 | #!/bin/bash
# Updated script to run the SAM-Plus-VNC container in the foreground using exec
PORT=8502
# Remove any existing container with the same name
if docker ps -a -q --filter "name=sam-plus-vnc-container" | grep -q .; then
echo "Removing existing SAM-Plus-VNC container..."
docker rm -f sam-plus-vnc-containe... |
26cfbc9c4af24f5b75b5b9a43b247e90d5f1faa2b40d6ece2784d7f89785ad88 | Shell | 729 | 19 | #set -e
#cargo clean
# CARGO_TARGET_WASM32_WASI_RUNNER="wasmer run --native --llvm --enable-simd --"
# CARGO_TARGET_WASM32_WASI_RUNNER="wavm run --enable simd"
CARGO_TARGET_WASM32_WASI_RUNNER="wasmtime --wasm-features simd --" cargo bench --target=wasm32-wasi --features simd_wasm -- --nocapture "$@"
#RUSTFLAGS="-C t... |
ad15598d167d1ec13ef01df569ba8ca5f34190e5f664b8caf79c2bb8399f31b3 | Shell | 729 | 14 | #!/bin/bash
# Move asegstats2table to the correct FreeSurfer directories
mv /fastsurfer/kinderseg/scripts/stats/asegstats2table/bin/asegstats2table /opt/freesurfer/bin/asegstats2table
mv /fastsurfer/kinderseg/scripts/stats/asegstats2table/python/scripts/asegstats2table /opt/freesurfer/python/scripts/asegstats2table
mv... |
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