sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
08f350782d40a9c51f4412a2dabc9fcf0c1e56d69c8c2fd7eecc54ccce9af510 | Shell | 578 | 35 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=100G
#$ -l mem_free=25G
#$ -l h_vmem=25G
#$ -l h_rt=96:00:00
#$ -o ./logs
module load samtools
# Inputs
dir=$1
outdir=$2
mkdir -p $outdir
# Main
samplepath=$(find $dir -maxdepth 1 -name "*.bam" | \
sort -u | \
head -n $SGE_TASK_ID | \
tail -n 1)
sample=$(basenam... |
6f7890123d03ff7aa5c21300335ce68e3599db51ff6c6b5f18f28292d1ac24c7 | Shell | 581 | 15 | #!/bin/bash -e
#SBATCH slurm/HPC parameters here
#SBATCH --mem=200G
module add FastTree/2.1.11
cd /path/to/file
/path/to/installs/.local/bin/magus -i /path/to/file/subsample_duf3494.fasta -o /path/to/file/subsample_duf3494_backbone.fasta --recurse false
FastTreeMP /path/to/file/subsample_duf3494_align.fasta > /path... |
297bfbf5c39ad1c2b243457f8230d136e4c21f072fdb1889a0d5b00fb9b25510 | Shell | 584 | 16 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
for i in {1..4}
do
python perturb.py bonemarrow-subset-sampling-sampler-subset \
--head-trainer.train-num-steps 5000 \
--pert.perturbation-num $i \
--pert.subset-sampling.lr 1e-3 \
... |
c2a47577b8f901fb40c7c46539cde4acadaddd35964b0bfc2983a0bc53e3a516 | Shell | 586 | 30 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -R y
#$ -l mem_free=50G ## this is high RAM. Observed 30.2G in AHN
#$ -l h_vmem=50G
#$ -l h_fsize=100G
#$ -l h_rt=24:00:00
#$ -M aannapr1@jhmi.edu
#$ -t 40-41
module load conda_R/4.0.x
CWD=$PWD
beddir="../bed"
fragdir="../granges"
mkdir -p $fragdir $beddir
cd $beddir
input=$(ls -1v *... |
50805cc424e13099c50035427c9f5eee4b4378209716688cb1694af605a21fdd | Shell | 593 | 13 | export OUTPUT_PATH=outputs/experiment/perturb/bonemarrow/cxplain
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py bonemarrow-cxplain-inverse-loss \
--pert.perturbation-num 10 \
--pert.cxplain.batch-size 128 \
--pert.cxplain.trainer-config.batch-size 128 \
--pert.cxplain.trainer-config.epoch 500 \
... |
70e62bcfbb8340d48395ddf4b3ebb672e45891e9acbd9db1c333b5da5c124e35 | Shell | 594 | 19 | #!/usr/bin/env bash
## Shell script for extracting the first session of all subjects of ADNI
## and exporting them into a list
set -ux
ADNI="${ADNI_PREPROC_DIR:?path of the preprocessed ADNI data}"
BASE_DIR="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
LIST=${BASE_DIR}/lists/adni_bas... |
4233e85c01a4fcfdea286513c6db29786fc9085c2bbb0d74cb1eaff09e63d0a8 | Shell | 595 | 24 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
set -x
PARTITION=$1
JOB_NAME=$2
CONFIG=$3
CHECKPOINT=$4
GPUS=${GPUS:-8}
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
CPUS_PER_TASK=${CPUS_PER_TASK:-5}
SRUN_ARGS=${SRUN_ARGS:-""}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
srun -p ${PARTITION} \
--job-name=... |
effcc7f1936db522b7a874f6a6097d5b29b9bb5f0217332f81a8e59e45df3c6f | Shell | 597 | 13 | export OUTPUT_PATH=outputs/experiment/perturb/dentategyrus/cxplain
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py dentategyrus-cxplain-inverse-loss \
--pert.perturbation-num 10 \
--pert.cxplain.batch-size 128 \
--pert.cxplain.trainer-config.batch-size 128 \
--pert.cxplain.trainer-config.epoch 500... |
4b0a26eb58651bf950c5d97fa6676d082224bd04f0a74e03b2a378b351de2f37 | Shell | 602 | 20 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Support invoking test script outside the script directory
cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../ || exit 1
# Common setup steps shared by Python test jobs
source ./ci/test_python_c... |
fd5f8f012108fdf39f235dd663c9747dead253e4520e373d62bec1ea5b835ee5 | Shell | 603 | 24 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
set -x
PARTITION=$1
JOB_NAME=$2
CONFIG=$3
WORK_DIR=$4
GPUS=${GPUS:-8}
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
CPUS_PER_TASK=${CPUS_PER_TASK:-5}
SRUN_ARGS=${SRUN_ARGS:-""}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
srun -p ${PARTITION} \
--job-name=${... |
789eac41c904ae33b3849795c0394a423ee8bce4f5ae654419a070fe14fdeb48 | Shell | 614 | 25 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
set -x
PARTITION=$1
JOB_NAME=$2
CONFIG=$3
CHECKPOINT=$4
GPUS=${GPUS:-8}
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
CPUS_PER_TASK=${CPUS_PER_TASK:-5}
PY_ARGS=${@:5}
SRUN_ARGS=${SRUN_ARGS:-""}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
srun -p ${PARTITION} \
... |
03c8b30dbfe4bb2b007ac76b763f33d6b8fe154d00e299439932215184f55cb5 | Shell | 619 | 24 | #!/bin/bash -l
# Set SCC project
# Request 4 CPUs
#$ -pe omp 4
#$ -m ea
# Request 1 GPU
#$ -l gpus=1
#$ -l gpu_memory=80G
#$ -l h_rt=12:00:00
conda activate adrd
module load python3/3.8.10
module load pytorch/1.13.1
data_path="/projectnb/ivc-ml/dlteif/NACC_raw"
path="/projectnb/ivc-ml/dlteif/pretrained_models"
#... |
b92d9f06c5583e4d2504228e58393d46d83ead1a5401a343cd63180bacb01269 | Shell | 620 | 24 | #!/usr/bin/env bash
set -x
PARTITION=$1
JOB_NAME=$2
CONFIG=$3
CHECKPOINT=$4
GPUS=${GPUS:-8}
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
CPUS_PER_TASK=${CPUS_PER_TASK:-5}
PY_ARGS=${@:5} # Arguments starting from the fifth one are captured
SRUN_ARGS=${SRUN_ARGS:-""}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
srun -p ${PARTITI... |
13c40132b5c581c8eabc951e796d4b70c6ea2c5bf1bc268a3e85a707c9aec2d4 | Shell | 622 | 25 | #!/usr/bin/env bash
# Copyright (c) OpenMMLab. All rights reserved.
set -x
PARTITION=$1
JOB_NAME=$2
CONFIG=$3
WORK_DIR=$4
GPUS=${GPUS:-8}
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
CPUS_PER_TASK=${CPUS_PER_TASK:-5}
SRUN_ARGS=${SRUN_ARGS:-""}
PY_ARGS=${@:5}
PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
srun -p ${PARTITION} \
... |
d642e2b1feda9649c225bc887a754109f1c60f41af57b902a69d205e7b525ce6 | Shell | 622 | 7 | #!/bin/bash
wget -P data/raw/alphafolddb/tarfiles https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000006548_3702_ARATH_v4.tar
wget -P data/raw/alphafolddb/tarfiles https://ftp.ebi.ac.uk/pub/databases/alphafold/latest/UP000000625_83333_ECOLI_v4.tar
wget -P data/raw/alphafolddb/tarfiles https://ftp.ebi.ac.uk/pub/d... |
58ed93baacbf4a787f3c998ede204c06eaa8fc692df4dd7a2c7f60f6842ffa09 | Shell | 627 | 26 | #!/bin/bash
#SBATCH --partition=gpu4_dev,gpu4_short
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=30G
#SBATCH --job-name=TCGA_04
#SBATCH --output=log_TCGA_04_%A_%a.out
#SBATCH --error=log_TCGA_04_%A_%a.err
module load pathganplus/3.6
python3 ./utilities/h5_handling/nc_create_metadata_h5.py \
--... |
69b667a5a8740601fced571f75c3badc78115d80b36b4a811a5677f1879d460f | Shell | 634 | 10 | #!/bin/bash
# convert OR reads to bed
for i in or_reads*; do bedtools bamtobed -split -i $i > bed_$i; done
# print read name for reads that map to more than one OR
bedtools intersect -wo -a $1 -b bed_* | awk -F "\t" '{print $4,$17}' | sort | uniq | awk '{print $2}' | sort | uniq -d > multimapped_and_gap_readnames
# ge... |
b82c24dd3f3aafbaf0428e9db6ea3f3222d6107bd9b075c7b22182619ad7ec0a | Shell | 636 | 27 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -l h_fsize=1000G
#$ -l mem_free=65G
#$ -l h_vmem=65G
#$ -l h_rt=96:00:00
#$ -o ./logs
#$ -t 1-1287
#$ -pe local 4
name=$(find ./bed_type -maxdepth 1 -name "*.bed" | \
sort -u | \
head -n $SGE_TASK_ID | \
tail -n 1)
name=$(basename $name)
sample=${name//.bed}
echo $sample
mk... |
2dff4bfb528f03181d0ba45a052785e87150adad4cb3a207fd413a4851908867 | Shell | 639 | 14 | export OUTPUT_PATH=outputs/experiment/perturb/pancreas/cxplain
export WANDB_API_KEY=YOUR_WANDB_KEY
python perturb.py pancreas-cxplain-inverse-loss \
--pert.perturbation-num 10 \
--pert.cxplain.batch-size 128 \
--pert.cxplain.trainer-config.batch-size 128 \
--pert.cxplain.trainer-config.epoch 500 \
-... |
1b153f8d3c4952d8ecfdf3134a53becf8ad239a9ca2bc8b45117cc0da217ad3d | Shell | 644 | 24 | #!/usr/bin/env bash
export MASTER_PORT=$((12000 + $RANDOM % 20000))
set -x
PARTITION=$1
JOB_NAME=$2
CONFIG=$3
GPUS=${GPUS:-8}
GPUS_PER_NODE=${GPUS_PER_NODE:-8}
CPUS_PER_TASK=${CPUS_PER_TASK:-5}
SRUN_ARGS=${SRUN_ARGS:-""}
PY_ARGS=${@:4} # Any arguments from the forth one are captured by this
PYTHONPATH="$(dirname $0... |
099e3656b8ccd4057794266c65ea5a03a2841882791f21ec7ed974938b2ee052 | Shell | 647 | 7 | #!/bin/bash
# python main_vif.py --mode training --dataset_path ~/AUTOAIF_DATA/loos_model/ --save_checkpoint_path ~/AUTOAIF_DATA/weights/spatial30% --loss_weights 0.3 0.7 0 --epochs 200 --batch_size 1
# python main_vif.py --mode training --dataset_path ~/AUTOAIF_DATA/loos_model/ --save_checkpoint_path ~/AUTO... |
a4437101e3f718f7b1d49ad3f7e12e4250d69c62c4606edf615f59519a528d95 | Shell | 648 | 20 | #!/bin/bash
# run this script from adrd_tool/
conda activate adrd
# install the package
# cd adrd_tool
pip install .
# define the variables
prefix="/home/skowshik/ADRD_repo/pipeline_v1_main/adrd_tool"
data_path="${prefix}/data/train_vld_test_split_updated/merged_train.csv"
ckpt_path="/home/skowshik/publication_ADRD... |
dec4e7ca30e4b2e1bc9c988846743c2244654f868d9aff0187435cdac8536fe0 | Shell | 651 | 17 | Subjlist="M126 M128 M129 M131 M132" #Space delimited list of subject IDs
Subjlist="M132" #Space delimited list of subject IDs
StudyFolder="/media/myelin/brainmappers/Connectome_Project/InVivoMacaques" #Location of Subject folders (named by subjectID)
FunctionalNames="rfMRI_REST"
FunctionalNames="rfMRI_REST_iso"
HighP... |
89e0e1929403ab84db64a890f9cdbba2fe803a9482d8751a63d8cad9d8428782 | Shell | 658 | 25 | #! /usr/bin/env bash
## Apply CNN ensemble models to subjects from validation datasets
## Need to load hvr_validation environment
HERE="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
for dataset in jens_adni jens_icbm
do
datadir=${HERE}/data/validation/${dataset}/t1
outdir=${HERE}/data... |
fd7841d871a320e86f78efec1ceb2ea63477357a5cfb76de6f55b4793ffb47fc | Shell | 666 | 31 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -R y
#$ -l mem_free=100G
#$ -l h_vmem=100G
#$ -l h_fsize=100G
#$ -l h_rt=24:00:00
#$ -o ./logs
rlib="${HOME}/Library/R/3.12-bioc-release-conda"
module load conda_R/4.0.x
CWD=$PWD
fragdir=$1
bindir=$2
binfile=$3
target=$4
mkdir -p $bindir
samplepath=$(find $fragdir -maxdepth 1 -name ... |
4d88f4653b63c6068038408a307edae4c20e24e8bd8a1f2b1f42266b4a45875d | Shell | 674 | 30 | #!/bin/bash
cd ../..
# custom config
DATA=/path/to/datasets
TRAINER=CoCoOp
# TRAINER=CoOp
DATASET=imagenet
SEED=$1
CFG=vit_b16_c4_ep10_batch1_ctxv1
# CFG=vit_b16_ep50_ctxv1 # uncomment this when TRAINER=CoOp and DATASET=imagenet
SHOTS=16
DIR=output/${DATASET}/${TRAINER}/${CFG}_${SHOTS}shots/seed${SEED}
if [ -d "... |
6a978332bc77c20ffcfedb0c95eca43282129a13ca1b29e9bc8e55cc797b2a0b | Shell | 676 | 22 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Support invoking test_python_singlegpu.sh outside the script directory
cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../ || exit
# Common setup steps shared by Python test jobs
source ./ci/te... |
71d19d6960b5ca6c5254319761a1859790ef781a705f02a09a165351cbb6bfe4 | Shell | 678 | 20 | #!/bin/bash
##################################### Match sample base on genotype and update annotation file #####################
if [ -v $1 ]
then
echo 'to run this script you need R with the following packages installed: argparse, circlize, ComplexHeatmap, RColorBrewer'
echo 'this script should be run like this:'
e... |
a214dc6d61ac292dbe5e72d7ca9a70b5d585c025d6df3b69ece00e22a2ffab68 | Shell | 693 | 26 | #! /usr/bin/env sh
HERE="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
orig_list=${HERE}/lists/adni_baseline.lst
new_list=${HERE}/lists/adni-bl_scanner.lst
[[ -e $new_list ]] && rm $new_list
while read -r line
do
sub=$(printf $line | cut -d, -f1)
sess=$(printf $line | cut -d, -f2)
... |
a4d7c9050f240a501171330d092d69fc1ff19f71266c91b6c69b16ec25400989 | Shell | 693 | 21 | #!/bin/bash
#
# Trains a DeepSequence VAE model.
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=vae
#SBATCH --output=logs/vae.out
#SBATCH --gres=gpu:1 # Number of GPU(s) per node.
#SBATCH --cpus-per-task=1 # CPU cores/thread... |
86107c43b92e5837a6f9ef6342181533386b899903506b771a02385b2e753fde | Shell | 694 | 11 | #!/bin/bash
mkdir backup
conda env export -n subpred_deeplearning | sed -e 's/^.*subpred==5.0.0.*$/# &/' -e 's/^.*pip:*$/# &/' -e '/^prefix/d' > environment_full.yml
conda env export -n dnn_cpu | sed -e 's/^.*subpred==5.0.0.*$/# &/' -e 's/^.*pip:*$/# &/' -e '/^prefix/d' > environment_dnn_cpu_full.yml
tar --exclude=".g... |
2e0b129c64328938464982740fd94575c18a21222eee63a6a0af70376d5f3749 | Shell | 705 | 28 | #!/usr/bin/env bash
### Obtain brain-mask QC images from the preprocessed ADNI directory
set -xue
BASE_DIR="${HVR_ADNI_DIR:?path of the hvr_adni project}"
ADNI_DIR="${ADNI_PREPROC_DIR:?path of the preprocessed ADNI data}"
LIST=${BASE_DIR}/lists/adni_baseline.lst
OUT_DIR=${BASE_DIR}/plots/qc_adni/skull_masks
[[ -d $... |
74a294a73933edf890eafe2589cf675bb6c7b0bbc4d8f7fcf137a9235d9a3d3e | Shell | 705 | 13 | #!/usr/bin/env bash
pdf2scanlist.py /Users/Shared/10_Connectivity/raw_data/sub-2025/sub-2025_CNDA.pdf -p /Users/mcavoy/repo/NRL-misc/10_Connectivity_protocol.csv
OGREdcm2niix.sh /Users/Shared/10_Connectivity/raw_data/sub-2025/sub-2025_scanlist.csv -i /Volumes/NRLbackup/10_Connectivity/dicom/sub-2025
export OGREDIR=/... |
f871c27d88a88dffff858fdfd975a53804aa3874639701c13826272f0e10c499 | Shell | 705 | 34 | #!/bin/bash
cat task_list.txt | shuf > order.txt
IFS=$'\r\n' GLOBIGNORE='*' command eval 'TASKS=($(cat order.txt))'
declare -a lr=( "1e-5" "1e-6" "1e-4" "1e-3" )
prefix="cfg['initial_learning_rate'] = "
echo $prefix
root="/home/ubuntu/task-taxonomy-331b/experiments/aws_batch"
for i in "${TASKS[@]}"
do
for j in... |
bf1c24db5ffea9d04be46075f01abc1465eb60511dc519e2477731ed51508086 | Shell | 708 | 15 | #!/bin/bash
# echo Run 1
# python main_vif.py --mode training --dataset_path /media/network_mriphysics/USC-PPG/AI_training/loos_model \
# --save_checkpoint_path /media/network_mriphysics/USC-PPG/AI_training/weights/run1_fullVOL.h5 \
# --loss_weights 0 0 1
# echo Run 2
# python main_vif.py --mode training --d... |
598b116744eb72e85d831cefde5d44461546d37f33ff6f3a6f0534259320d7ca | Shell | 709 | 21 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2020-2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
EXITCODE=0
for nb in "$@"; do
NBFILENAME=$nb
shift
echo --------------------------------------------------------------------------------
echo STARTING: "${NBFILENAME}"
echo ---... |
1392034cf077d44eea96f373450986ed2833bf945a40176b1078f4b7ebb3be7d | Shell | 719 | 25 | #!/bin/bash
##################################### Plot detected CNV for a Sample in a single analysis (only QC CNV) #####################
if [ -v $1 ]
then
echo 'to run this script you need R with the following packages: argparse, ggplot2, grid, gridExtra'
echo 'this script should be run like this:'
echo './Cnv_Plo... |
66460ee73ba4fa7e0fbe1fd5fc4be9bf13371a67480f9812e3f9cec4dcd4f765 | Shell | 719 | 21 | #!/bin/bash
#SBATCH --partition=gpu4_dev
#SBATCH --job-name=Sort_tr
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --output=rq_sort_tr_%A_%a.out
#SBATCH --error=rq_sort_tr_%A_%a.err
#SBATCH --mem=20GB
###SBATCH --time=3:00:00
module load anaconda3/gpu/5.2.0
conda activate /gpfs/data/coudraylab/NN/env/env_deepPat... |
91aa54f36c9912b994259abede846bfaeea58c2c846c17900b595ecf4a302090 | Shell | 721 | 35 |
# Command to download dataset:
# bash script_download_molecules.sh
DIR=molecules/
cd $DIR
FILE=ZINC.pkl
if test -f "$FILE"; then
echo -e "$FILE already downloaded."
else
echo -e "\ndownloading $FILE..."
curl https://data.dgl.ai/dataset/benchmarking-gnns/ZINC.pkl -o ZINC.pkl -J -L -k
fi
FILE=ZINC-full.pkl
i... |
dd3381f7ece2282f33c07c2b97319ea69122196252009345a2f93e3c1b05ca30 | Shell | 721 | 37 | ##!/usr/bin/env bash
CURRDIR=$(pwd)
BASEDIR=$(dirname "$0")
TASKS="autoencoder \
class_1000 \
class_places \
colorization \
curvature \
denoise \
edge2d \
edge3d \
inpainting_whole \
jigsaw \
keypoint2d \
keypoint3d \
reshade \
rgb2depth \
rgb2mist \
rgb2sfnorm \
room_layout \
segment25d \
segment2d \
segmentsemantic... |
f43a2ce8abb9fe63f1b339121c927aa8dddd97b10674672a0d66f268e49e38f2 | Shell | 730 | 22 | #!/bin/bash
#
# Infers log likelihoods from UniRep LSTM models
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=unirep_inf
#SBATCH --output=logs/unirep_inf.out
#SBATCH --gres=gpu:0 # Number of GPU(s) per node.
#SBATCH --cpus-per-task=... |
0ced83f33a3cb5cda6207d339c20e70f003b431fb7b4fdf789e32a4eb0bb8b73 | Shell | 736 | 21 | #!/bin/bash -e
#SBATCH slurm/HPC parameters
#SBATCH --mem=128G
module add hmmer/3.3
cd /path/to/folder
line=$(sed "${SLURM_ARRAY_TASK_ID}q;d" /path/to/folder/pf_annotate_md.txt)
sample_id=$(echo "$line")
sample_dir=$(echo "$sample_id" | awk -F'/' '{print $9}')
domain1=$(echo "$sample_id" | awk -F'm_' '{print $2}')
dom... |
ec68763ad2a7cfc88f3a731b27a2a7795d889740d9135d4f1c5bf7feba79f9d0 | Shell | 739 | 29 | #!/bin/bash
# custom config
DATA=/path/to/datasets
TRAINER=CoOp
SHOTS=16
NCTX=16
CSC=False
CTP=end
DATASET=$1
CFG=$2
for SEED in 1 2 3
do
python train.py \
--root ${DATA} \
--seed ${SEED} \
--trainer ${TRAINER} \
--dataset-config-file configs/datasets/${DATASET}.yaml \
--config-file configs/t... |
70b870f5c9aefaaf070b2d573632789f450b3e5d3d918dc8e0f3b1681f022c92 | Shell | 744 | 30 | #!/bin/bash
# Copyright (c) OpenMMLab. All rights reserved.
# Download pre-compiled files
wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0-linux-x86_64-cxx11abi.tar.gz
# Unzip files
tar -xzvf mmdeploy-1.0.0-linux-x86_64-cxx11abi.tar.gz
# Go to the sdk folder
cd mmdeploy-1.0.0-linux... |
d37e60937c0aaf67f6747fb16aa7bd3fcf716911a67960aa1092534c1968e241 | Shell | 745 | 27 | #!/bin/bash
##################################### For each considered sample create file with SentrixBarcode_SentrixPosition ID #####################
if [ -v $1 ]
then
echo 'to run this script you need R with the following packages installed: argparse'
echo 'this script should be run like this:'
echo './Create_Samp... |
80569b19f83fc35b45926136d048b5e57e4c4b82818d7e2a2a2bf1baf679bb73 | Shell | 747 | 22 | #!/bin/bash
#
# Infers average ELBO values from DeepSequence VAE models
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=vae_inf
#SBATCH --gres=gpu:1 # Number of GPU(s) per node.
#SBATCH --cpus-per-task=2 # CPU cores/threads
#... |
7a7609cf06d458194c7ccd07ae06548424eee4d8d9c88d766b7ef11f58b5f1e0 | Shell | 756 | 21 | #!/bin/bash
set -e
echo -e "\n START: SurfaceSmoothing"
NameOffMRI="$1"
Subject="$2"
DownSampleFolder="$3"
LowResMesh="$4"
SmoothingFWHM="$5"
Sigma=`echo "$SmoothingFWHM / ( 2 * ( sqrt ( 2 * l ( 2 ) ) ) )" | bc -l`
for Hemisphere in L R ; do
${CARET7DIR}/wb_command -metric-smoothing "$DownSampleFolder"/"$Subject... |
839f51a99864c622d5df20fe61613d97363e0edd710311d75b0b7e0eca4aa3fa | Shell | 761 | 39 | #!/usr/bin/env bash
set -xu
HERE="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
OUTDIRS=${HERE}/tmp/nlpb_hcvc
for dir in $OUTDIRS/*
do
id=$(basename $dir)
labels=$(ls $dir/stx_${id}_*_HVR.mnc)
cleaned=${labels/HVR/hcvc}
minclookup \
-discrete \
-float \
-clobber \
-lut "1... |
a37cf4b0c6226f534b65fc4efe600491823d4590a9fc8c83a1259cbbf4b1e67b | Shell | 764 | 32 | #!/bin/bash
cd ../..
# custom config
DATA=/path/to/datasets
TRAINER=CoCoOp
# TRAINER=CoOp
DATASET=$1
SEED=$2
CFG=vit_b16_c4_ep10_batch1_ctxv1
# CFG=vit_b16_ep50_ctxv1 # uncomment this when TRAINER=CoOp and DATASET=imagenet
SHOTS=16
DIR=output/evaluation/${TRAINER}/${CFG}_${SHOTS}shots/${DATASET}/seed${SEED}
if [... |
87a6b416ae02a92e905ab48a5aebe825eb622c6ea6d328592ab6c58ff3450af0 | Shell | 765 | 30 | #!/bin/bash
# adapted from code found on https://www.biostars.org/p/13452/ by Frédéric Mahé
NAMES_FILE="/path/to/names.dmp"
NODES_FILE="/path/to/nodes.dmp"
TAXID="${1}"
# Read the names.dmp file into an associative array
declare -A NAMES
while IFS=$'\t' read -r -a fields; do
TAXID=${fields[0]}
NAME=${fields[1... |
b26fc0519b749d6ae260173ec17b3c72704e1b45316d1c7a414819d7dfedafe8 | Shell | 770 | 40 | #!/bin/sh
bit_len=$1
prefix=$2
tmp=tmp.$$
tmp64=tmp64.$$
exps="607 1279 2281 4253 11213 19937 44497 86243 132049 216091"
for mexp in $exps; do
if [ $bit_len = "64" ]; then
./test-std-M${mexp} -b64 > $tmp64
compare=$tmp64
else
compare=SFMT.${mexp}.out.txt
fi
command=${prefix}-M${mexp}
if ./$comman... |
6d99fc9689f7a9a1881d841538f536800774fe118b000da720c55b63f3772a0b | Shell | 772 | 28 | conda create -n prompt python=3.7
conda activate prompt
pip install -q tensorflow
# specifying tfds versions is important to reproduce our results
pip install tfds-nightly==4.4.0.dev202201080107
pip install opencv-python
pip install tensorflow-addons
pip install mock
conda install pytorch==1.7.1 torchvision==0.8.2 t... |
28896987b13e28f0e243168bbce612b1ffe17cee4b6e893c580eacd3c7b2564c | Shell | 775 | 33 | #!/bin/bash
#SBATCH --partition=gpu4_dev,gpu8_short
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=120G
#SBATCH --job-name=06_RepTiles
#SBATCH --output=rq_06_RepTiles_%A_%a.out
#SBATCH --error=rq_06_RepTiles_%A_%a.err
unset PYTHONPATH
module load condaenvs/gpu/pathgan_SSL37
python3 ./report_represent... |
674079d23da3e8f1221cbd40c6ca48036e95c7cd769cead903018d9c955faa5f | Shell | 777 | 36 | #!/bin/bash
##################################### Detect CNV in the pairwise comparison #####################
if [ -v $1 ]
then
echo 'to run this script you need bcftools globally installed'
echo 'this script should be run like this:'
echo './Cnv_Analysis.sh <path folder> <path .txt file with SentrixBarcode_SentrixP... |
f6745cb52a8e1c5d3b6a6469cc9c98e3c2a0d3156a868d6da0542961c8d5eea4 | Shell | 781 | 23 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# This script runs scikit-learn tests with the cuml.accel plugin.
# Any arguments passed to this script are forwarded directly to pytest.
#
# Example usage:
# ./run-tests.sh # Run a... |
85e88251ff0ea889efec3fe575190b0089f46fdc41f96e1ae72a45deb22edfbf | Shell | 782 | 32 | #!/bin/bash
cd ../..
# custom config
DATA=/path/to/datasets
TRAINER=CoCoOp
# TRAINER=CoOp
DATASET=$1
SEED=$2
CFG=vit_b16_c4_ep10_batch1_ctxv1
# CFG=vit_b16_ctxv1 # uncomment this when TRAINER=CoOp
# CFG=vit_b16_ep50_ctxv1 # uncomment this when TRAINER=CoOp and DATASET=imagenet
SHOTS=16
DIR=output/base2new/train... |
d500ee9c5f65f92631e6033ec377a566cf169a538d2ceca068304e60ad34e4a4 | Shell | 786 | 23 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# This script runs the hdbscan tests with the cuml.accel plugin.
# Any arguments passed to this script are forwarded directly to pytest.
#
# Example usage:
# ./run-tests.sh # Run al... |
6434e50d23705b140d030b0f97a9cca6429a7f6dc810197f23647bf1253671ea | Shell | 791 | 23 | #!/bin/bash
#
# Infers log-likelihoods from an HMM and writes results to a CSV file.
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=hmminf
#SBATCH --output=logs/hmminf.out
#SBATCH --gres=gpu:0 # Number of GPU(s) per node.
#SBAT... |
e4f52742eeee5fc729b6b5a0f5e2548e8113e7e8e7636b47ab4f9310f3944c79 | Shell | 800 | 31 | #! /usr/bin/env bash
## Link adni stx2 files listed in subject list
WORK_PATH="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
# Default list vs input
# ID,SESS
if [ $# -eq 1 ]
then
LIST=$1
else
LIST=${WORK_PATH}/lists/adni_baseline.lst
fi
mapfile -t IDS < $LIST
[ ${#IDS[@]} -eq 0 ] &... |
74b859583967b38e6710ff86a19f14b679c7cfbd522b2899ebc9dd8127827a82 | Shell | 802 | 24 | #!/bin/bash
#
# Fine-tunes the UniRep model on evolutionary data.
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=evotune_unirep
#SBATCH --output=logs/uni_tune.out
#SBATCH --gres=gpu:1 # Number of GPU(s) per node.
#SBATCH --cpus-per-... |
456642d988d77722f6eccd3050d02e6558782b4b29d3d925f60cfc5672b57375 | Shell | 805 | 26 | #!/bin/bash
##################################### Quality control for the CNV and plot the delition and duplication detected #####################
if [ -v $1 ]
then
echo 'to run this script you need R with the following packages: argparse, ggplot2, grid, gridExtra'
echo 'this script should be run like this:'
echo '... |
69fc513154e2a34626bd8248267351a20e5ba249c39db87811a692867d95587d | Shell | 805 | 23 | #!/bin/bash
#
# Infers pseudo log likelihood approximations from ESM Transformer models
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=esm_inf
#SBATCH --gres=gpu:1 # Number of GPU(s) per node.
#SBATCH --cpus-per-task=2 # CPU... |
2bd318366c8ec25f05cc6b1ccdc69ff960d914924d88436c9a3fbd0a62743870 | Shell | 808 | 26 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2023-2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
set -euo pipefail
rapids-logger "Create clang_tidy conda environment"
. /opt/conda/etc/profile.d/conda.sh
rapids-logger "Configuring conda strict channel priority"
conda config --set channel_prior... |
59204af36421090ddd56d93a4828fbf27ace8f51c5dacb6c9bc6af1574ffd05a | Shell | 811 | 31 | #!/usr/bin/env bash
## Shell script for extracting all sessions of all subjects of ADNI
## and exporting them into a list
## Include MAG_strength
set -ux
ADNI="${ADNI_PREPROC_DIR:?path of the preprocessed ADNI data}"
HERE="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
LIST=${HERE}/lis... |
4be9481bdee8d1a6ba436c92de7d827958ddc9464eee6865c2004abe7d7320a9 | Shell | 816 | 31 | #!/bin/sh
# Master version, for use in tarballs or non-git source copies
VERSION=1.9
# If we have a git clone, then check against the current tag
if [ -e .git ]
then
# If we ever get to 10.x this will need to be more liberal
VERSION=`git describe --match '[0-9].[0-9]*' --dirty`
fi
# Numeric version is for us... |
bdb33a1e9cc4c74a08fa319ddb336212c1df5a66fc7372a457b775c4204faee5 | Shell | 816 | 22 | #!/bin/bash
cwd=`pwd`
# LyX --> LaTeX
find . -type f -name \*.lyx -exec "$1" --export latex {} \;
# LaTeX & BibTeX --> XML
latexml --destination=references.bib.xml references.bib
find . -type f -name \*.tex -exec latexml --destination={}.xml {} \;
# XML --> HTML & PNG
find . -type f -name \*.tex.xml -exec latexmlpo... |
dbf8dbb0e56812370d0ab4cc4f284d258f8f96ee9298545d509be92bec6efdee | Shell | 817 | 27 | #!/bin/bash
#
# Evaluates the predictive performance of a predictor
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=evaluate
#SBATCH --output=logs/evaluate.out
#SBATCH --gres=gpu:0 # Number of GPU(s) per node.
#SBATCH --cpus-per-task... |
3f4cd5d13da7b607a9aaab19cb8d2390ce9c9b6ade9bba1acadf143014f153b4 | Shell | 818 | 28 | #!/bin/env bash
#SBATCH --job-name=Pope_Meta
#SBATCH --account=
#SBATCH --time=12
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=6
#SBATCH --mem-per-cpu=6G
#SBATCH --mail-user=
#SBATCH --mail-type=FAIL
#SBATCH --output=slurmout/job-%j.out
# NOTE: Above settings may need to be adjusted, especially if running the null mode... |
5d7a4af5f0683d7962e47bcb80a266587211dd2e6dbb2a3b1ac77ec65dc26a61 | Shell | 824 | 32 | #!/usr/bin/env bash
## Create directories of qc images to be loaded on QRATER
## Fill them with subjects from lists
set -xu
BASE_DIR="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
# Read PTIDS lists into Arrays
mapfile -t REGIS_FAILS < ${BASE_DIR}/lists/adni-bl_qc_lin-reg_ids.lst
mapf... |
98aac670e3a9fb92551fbce0173fc87853e05b8c3335e05314b3db13edb5944e | Shell | 828 | 23 | #!/bin/bash -e
#SBATCH #HPC/slurm parameters here
#SBATCH --mem=450G
#sort kraken files into prokaryotic, eukaryotic, and unclassified
module add R/4.3.1
module add python/anaconda/2020.11/3.8
cd /path/to/file
line=$(sed "${SLURM_ARRAY_TASK_ID}q;d" /path/to/metadata/files/sort_kraken_md.txt) # filepaths, if running a... |
6ddc7dc786476b6bf6972016131fb35b2a69f2e9196806481e373fd1955d21fe | Shell | 839 | 35 | #!/bin/bash
##################################### Plot LRR and BAF for single analysis #####################
if [ -v $1 ]
then
echo 'to run this script you need R with the following packages installed: ggplot2, argparse, grid, gridExtra'
echo 'this script should be run like this:'
echo './Cnv_Plot_LRR-BAF_single.sh ... |
1727af88a1fb56f97c86261f1406ce1ef3e090691f30a99eaf9a44bd0f81250e | Shell | 841 | 35 | #!/bin/bash
#SBATCH --partition=cpu_medium,cpu_long
#SBATCH --time=4-20:00:00
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=50G
unset PYTHONPATH
x=$(printf %.2f $1)
echo $x
module load singularity/3.9.8
singularity shell --bind /gpfs/data/coudraylab/NN/Head_Neck/carucci/Histomorphological-Phenotype-L... |
cfb9431413daec46b886516622421d553f2346f667cbf3317e0c1921a4b5d0a6 | Shell | 844 | 32 | # Before VCF files can be used they need to be compressed using bgzip and indexed with a tabix
# Install tabix:
# Conda activate YOUR_ENV
# conda install -c bioconda tabix
# Use tabix: tabix -p vcf human.YRI.hg38.all.AF.gencode.vcf.gz
# Reference: https://github.com/single-cell-genetics/limix_qtl/wiki/Inputs... |
d54c87446fa1a981bf71e7a13fbeaef8c0bf0da7645e3baca0fc8957fedf8c4e | Shell | 849 | 33 | #!/bin/bash
##################################### Plot detected CNV for a Sample in different comparisons (only QC CNV) #####################
if [ -v $1 ]
then
echo 'to run this script you need R with the following packages: argparse, ggplot2, grid, gridExtra'
echo 'this script should be run like this:'
echo './Cnv... |
2243626f5c9669f65c0d4a1d81c9068109fe9e1736f7aeee661179d3cdc01fbc | Shell | 853 | 36 | #!/usr/bin/env bash
## Shell script for extracting the ICC and SCALE_factor
## from all preprocessed subjects of ADNI
## and exporting them into a list
set -xu
HERE="${HVR_VALIDATION_DIR:-$(git -C "$(dirname "$0")" rev-parse --show-toplevel)}"
LIST=${HERE}/lists/adni_preproc.lst
VOLUMES=${HERE}/data/derivatives/adni... |
51597162c08e163066975147ca77d4b0f8d3792e0ebe69c3da2011945fbb71d6 | Shell | 857 | 33 | #!/bin/bash
##################################### Plot detected CNV for a Sample in different comparisons (only QC CNV) #####################
if [ -v $1 ]
then
echo 'to run this script you need R with the following packages: argparse, ggplot2, grid, gridExtra'
echo 'this script should be run like this:'
echo './Cnv... |
5e8125c005846e09650f3c799683d8f24583cd849a854ca69fca09eacaf221bb | Shell | 869 | 28 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2022-2026, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
# Support invoking test_python_dask.sh outside the script directory
cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../ || exit
# Common setup steps shared by Python test jobs
export DEPENDENCY_F... |
5ac772f35f161196b768e11149787cf2bd51511747bfa5cae03901fcfd1be444 | Shell | 875 | 38 | #!/bin/bash
cd ../..
# custom config
DATA=/path/to/datasets
TRAINER=CoCoOp
# TRAINER=CoOp
DATASET=$1
SEED=$2
CFG=vit_b16_c4_ep10_batch1_ctxv1
# CFG=vit_b16_ctxv1 # uncomment this when TRAINER=CoOp
SHOTS=16
LOADEP=10
SUB=new
COMMON_DIR=${DATASET}/shots_${SHOTS}/${TRAINER}/${CFG}/seed${SEED}
MODEL_DIR=output/base2... |
3a0ff4d7ce49d47ef0f986cf71beb5380c8f6e1e4b4413c5b29a83b89818b91d | Shell | 877 | 33 | #!/bin/sh
# script for execution of deployed applications
#
# Sets up the MATLAB Runtime environment for the current $ARCH and executes
# the specified command.
#
exe_name=$0
exe_dir=`dirname "$0"`
echo "------------------------------------------"
if [ "x$1" = "x" ]; then
echo Usage:
echo $0 \<deployedMCRroot\>... |
afc4bc4cbb9f0237441082411ef7f1cba188eb7d9b948dd97fe68e844f535d32 | Shell | 877 | 21 | #!/bin/bash
#SBATCH --partition=gpu4_dev,gpu4_short,gpu4_medium
#SBATCH --job-name=TCGAl
#SBATCH --ntasks=40
#SBATCH --cpus-per-task=1
#SBATCH --output=rq_TCGA_%A.out
#SBATCH --error=rq_TCGA_%A.err
#SBATCH --mem=70GB
module unload python
module load openmpi/3.1.0-mt
module load python/cpu/3.6.5
mpirun -n 40 python 0... |
d4d1d11c26c3b38ceb953d03a1a405245f574119ae349b3cb5e8208aecc5e554 | Shell | 879 | 33 | #!/bin/sh
# script for execution of deployed applications
#
# Sets up the MATLAB Runtime environment for the current $ARCH and executes
# the specified command.
#
exe_name=$0
exe_dir=`dirname "$0"`
echo "------------------------------------------"
if [ "x$1" = "x" ]; then
echo Usage:
echo $0 \<deployedMCRroot\>... |
ed62af93969cb8fce90d7bce0cc8c23340c42bed5c65049b95a382548ff8c7d8 | Shell | 879 | 33 | #!/bin/sh
# script for execution of deployed applications
#
# Sets up the MATLAB Runtime environment for the current $ARCH and executes
# the specified command.
#
exe_name=$0
exe_dir=`dirname "$0"`
echo "------------------------------------------"
if [ "x$1" = "x" ]; then
echo Usage:
echo $0 \<deployedMCRroot\>... |
9eb7d55febf50364423f4ace5cf972fa549ec76b58c35236e66b80a43e4eadcf | Shell | 881 | 33 | #!/bin/sh
# script for execution of deployed applications
#
# Sets up the MATLAB Runtime environment for the current $ARCH and executes
# the specified command.
#
exe_name=$0
exe_dir=`dirname "$0"`
echo "------------------------------------------"
if [ "x$1" = "x" ]; then
echo Usage:
echo $0 \<deployedMCRroot\>... |
b1ac10b6ae600900cc6cd72cbfb49a55d76c3d31b6b51f361a8e7d5bc92602cf | Shell | 883 | 29 | #!/bin/bash
#
# Estimates couplings model from alignment with plmc package
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=plmc
#SBATCH --output=logs/plmc.out
#SBATCH --gres=gpu:0 # Number of GPU(s) per node.
#SBATCH --cpus-per-task=... |
b7df3cedefe3a2c9e3f2d9512aeb2ac99aa278700da1cdefeef69e2ea2cbc888 | Shell | 883 | 28 | #!/bin/sh
SCRIPTSDIR=$(cd "$(dirname "$0")"; pwd)
BASEDIR="$(dirname "$SCRIPTSDIR")"
cd "$BASEDIR"
CMP_BUILD_DATE="$(date -u +\"%Y-%m-%dT%H:%M:%SZ\")"
echo "$CMP_BUILD_DATE"
VERSION="v$(python get_version.py)"
echo "$VERSION"
VCS_REF="$(git rev-parse --verify HEAD)"
echo "$VCS_REF"
MAIN_DOCKER="sebastientourbier/mi... |
78c19242ee88699b002d6acffe470a2870854738ebba54932c76c0f3fb2e2e32 | Shell | 885 | 25 | #!/bin/bash
#SBATCH --cluster=beef # Don"t change
#SBATCH --partition=long # Don"t change
#SBATCH --account=researcher # Don"t change
#SBATCH --job-name=hmmsearch
#SBATCH --output=hmmsearch.out
#SBATCH --gres=gpu:0 # Number of GPU(s) per node.
#SBATCH --cpus-per-task=8 # CP... |
b6ed1ddcc6cd077adcc7332bd08b813fce8508770e8b212b3e46949d5e961a39 | Shell | 889 | 31 | #!/bin/bash
# adapted from code found on https://www.biostars.org/p/13452/ by Frédéric Mahé
## Download NCBI's taxonomic data and GI (GenBank ID) taxonomic
## assignation.
## Variables
NCBI="ftp://ftp.ncbi.nlm.nih.gov/pub/taxonomy/"
TAXDUMP="taxdump.tar.gz"
TAXID="gi_taxid_nucl.dmp.gz"
NAMES="names.dmp"
NODES="nodes.d... |
46b90162bf8cf2b7e8cc30fba05362d76d5d4fa4389608ccb4abca32cd830efe | Shell | 891 | 29 | #!/bin/bash
# #SBATCH --partition=fn_medium,fn_long,gpu4_medium,gpu4_long,gpu8_medium,gpu8_long
#SBATCH --partition=fn_short,fn_medium,fn_long
# #SBATCH --partition=gpu4_dev
#SBATCH --cpus-per-task=20
#SBATCH --mem=30GB
# module load python/gpu/3.6.5
#module unload python/gpu/3.6.5
module load anaconda3/gpu/5.2.0
#con... |
db1af853a3fd559a3e5d5ffa773bf74ff93b5a637fdfe28afde781d6b42e773d | Shell | 895 | 24 | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
set -euo pipefail
# Support customizing the ctests' install location
# First, try the installed location (CI/conda environments)
installed_test_location="${INSTALL_PREFIX:-${CONDA_PREFIX:-/usr}}/bi... |
d819b21921e107e082c8a2478603ade88923a98c043f3d17f8f8d26939a01406 | Shell | 912 | 30 | #!/bin/bash
#SBATCH --job-name=Pope_CPM
#SBATCH --account=
#SBATCH --time=12
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=6
#SBATCH --mem-per-cpu=4G
#SBATCH --mail-user=
#SBATCH --mail-type=FAIL
#SBATCH --output=slurmout/job-%j.out
# NOTE: Above settings may need to be adjusted, especially if running the null models
# ... |
a7e6ee3ec4ec7ba321026a0c4e74b7f4475b05d970b4f63a4c368a653d6b74e3 | Shell | 932 | 32 | #!/bin/bash
#$ -cwd
#$ -j y
#$ -R y
#$ -l mem_free=50G
#$ -l h_vmem=50G
#$ -l h_fsize=100G
#$ -l h_rt=24:00:00
#$ -M aannapr1@jhmi.edu
#$ -t 345
# Log output
#$ -o /dcs04/scharpf/data/annapragada/DELFI_pipeline_updates/logs_55
rlib="${HOME}/Library/R/3.12-bioc-release-conda"
module load conda_R/4.0.x
CWD=$PWD
fragdi... |
3419eb4130c042d4b4f655fa32795a2edd4aa8a03be516feee42628dc90e7b89 | Shell | 933 | 37 | datapath=/root/cqy/dataset/MVTec
augpath=/root/cqy/dataset/dtd/images
classes=('carpet' 'grid' 'leather' 'tile' 'wood' 'bottle' 'cable' 'capsule' 'hazelnut' 'metal_nut' 'pill' 'screw' 'toothbrush' 'transistor' 'zipper')
flags=($(for class in "${classes[@]}"; do echo '-d '"${class}"; done))
cd ..
python main.py \
-... |
75ba517c6cc876847341d43cbd04182c38d8b92bc99f0f82caebf9ee3a588609 | Shell | 933 | 51 | #!/bin/bash
#SBATCH --partition=gpu4_medium,gpu8_medium
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --mem=20G
#SBATCH --job-name=07_Cox
#SBATCH --output=rq_07_Cox_%A_%a.out
#SBATCH --error=rq_07_Cox_%A_%a.err
unset PYTHONPATH
module load condaenvs/gpu/pathgan_SSL37
all_ind=os_event_ind
all_data=os_eve... |
3dc8a835fad9d90a613834154e29a258485de50f1bbc61c64f25fcf6cd9b77c2 | Shell | 935 | 22 | export OUTPUT_PATH=outputs/experiment/perturb/synthetic_data/approximation
export WANDB_API_KEY=YOUR_WANDB_KEY
DATASET_NAME=nonlinear_additive
DATA_SIZE=100000
FEATURE_DIM=1000
python perturb_synthetic_data.py nonlinear-additive-approximation-inverse-loss \
--pert.perturbation-num 3 \
--pert.subset-sampling.lr... |
33e284bf48718af3b780b54594e7dab5dde3cc48e595391823d2ffe882b61d06 | Shell | 936 | 22 | export OUTPUT_PATH=outputs/experiment/perturb/synthetic_data/approximation
export WANDB_API_KEY=YOUR_WANDB_KEY
DATASET_NAME=orange_skin_additive
DATA_SIZE=10000
FEATURE_DIM=100
python perturb_synthetic_data.py orange-skin-additive-approximation-inverse-loss \
--pert.perturbation-num 3 \
--pert.subset-sampling.... |
d17246a2102014f2c5c6a06b01914ab1dc55f5e3837c60cf9c8bb98bc6fbda28 | Shell | 938 | 32 | #!/bin/bash
# custom config
DATA=/path/to/datasets
TRAINER=CoOp
DATASET=$1
CFG=$2 # config file
CTP=$3 # class token position (end or middle)
NCTX=$4 # number of context tokens
SHOTS=$5 # number of shots (1, 2, 4, 8, 16)
CSC=$6 # class-specific context (False or True)
for SEED in 1 2 3
do
DIR=output/${DATAS... |
3505fa0067eb7cf9abc833f5bc93cddd4c6d1e619fef1517cb4d094c76665c4c | Shell | 940 | 28 | #!/bin/bash -l
# Set SCC project
#$ -P ivc-ml
# Request 4 CPUs
#$ -pe omp 3
#$ -m ea
# Request 1 GPU
#$ -l gpus=1
#$ -l gpu_memory=48G
#$ -l h_rt=1:00:00
module load python3/3.8.10
module load pytorch/1.13.1
conda activate adrd
arch="ViTAutoEnc"
ps=32
bs=8
path="checkpoints/DINO_NACC_raw_ViTAutoEnc_voxel_size12... |
7ebbbdcfe500b11cf5a6e58078877b1bc2de3c760e8b8916530425359261a5e6 | Shell | 941 | 22 | export OUTPUT_PATH=outputs/experiment/perturb/synthetic_data/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
DATASET_NAME=nonlinear_additive
DATA_SIZE=100000
FEATURE_DIM=1000
python perturb_synthetic_data.py nonlinear-additive-subset-sampling-sampler-subset \
--pert.perturbation-num 5 \
--pert.subset-sampl... |
177b5153c0cb1ea1e1bf1e6328003d08a37b1e5208b122b2caabafcb9f786059 | Shell | 943 | 22 | export OUTPUT_PATH=outputs/experiment/perturb/synthetic_data/subset_sampling
export WANDB_API_KEY=YOUR_WANDB_KEY
DATASET_NAME=orange_skin_additive
DATA_SIZE=10000
FEATURE_DIM=100
python perturb_synthetic_data.py orange-skin-additive-subset-sampling-sampler-subset \
--pert.perturbation-num 3 \
--pert.subset-sam... |
8f96e7d061748cec0823d5dfd03844b3bd179e80244eefba4105f1e9fd4b8fd8 | Shell | 947 | 30 | #!/bin/bash
#
# Finetunes the ESM-1b Transformer model on supervised data.
#
#SBATCH --cluster=<clustername>
#SBATCH --partition=<partitionname>
#SBATCH --account=<accountname>
#SBATCH --job-name=esm_finetune
#SBATCH --gres=gpu:1 # Number of GPU(s) per node.
#SBATCH --cpus-per-task=2 # CPU cores/t... |
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