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Commit 0464808e authored by Tim O'Donnell's avatar Tim O'Donnell
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update

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downloads-generation/models_class1_pan/GENERATE.WITH_HPC_CLUSTER.sh
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− 103
View file @ 0464808e
#!/bin/bash
#
# Model select pan-allele MHCflurry Class I models and calibrate percentile ranks.
#
# Uses an HPC cluster (Mount Sinai chimera cluster, which uses lsf job
# scheduler). This would need to be modified for other sites.
#
set -e
set -x
DOWNLOAD_NAME=models_class1_pan
SCRATCH_DIR=${TMPDIR-/tmp}/mhcflurry-downloads-generation
SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
mkdir -p "$SCRATCH_DIR"
rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
mkdir "$SCRATCH_DIR/$DOWNLOAD_NAME"
# Send stdout and stderr to a logfile included with the archive.
exec > >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt")
exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
# Log some environment info
echo "Invocation: $0 $@"
date
pip freeze
git status
cd $SCRATCH_DIR/$DOWNLOAD_NAME
export OMP_NUM_THREADS=1
export PYTHONUNBUFFERED=1
cp $SCRIPT_ABSOLUTE_PATH .
cp $SCRIPT_DIR/additional_alleles.txt .
GPUS=$(nvidia-smi -L 2> /dev/null | wc -l) || GPUS=0
echo "Detected GPUS: $GPUS"
PROCESSORS=$(getconf _NPROCESSORS_ONLN)
echo "Detected processors: $PROCESSORS"
if [ "$GPUS" -eq "0" ]; then
NUM_JOBS=${NUM_JOBS-1}
else
NUM_JOBS=${NUM_JOBS-$GPUS}
fi
echo "Num local jobs for model selection: $NUM_JOBS"
UNSELECTED_PATH="$(mhcflurry-downloads path models_class1_pan_unselected)"
# For now we calibrate percentile ranks only for alleles for which there
# is training data. Calibrating all alleles would be too slow.
# This could be improved though.
ALLELE_LIST=$(bzcat "$UNSELECTED_PATH/models.with_mass_spec/train_data.csv.bz2" | cut -f 1 -d , | grep -v allele | uniq | sort | uniq)
ALLELE_LIST+=$(echo " " $(cat additional_alleles.txt | grep -v '#') )
for kind in with_mass_spec no_mass_spec
do
# Model selection is run on the cluster, although for any reasonable
# machine it could be run locally. We run on the cluster because our
# cluster login nodes are often overloaded.
MODELS_DIR="$UNSELECTED_PATH/models.${kind}"
time mhcflurry-class1-select-pan-allele-models \
--data "$MODELS_DIR/train_data.csv.bz2" \
--models-dir "$MODELS_DIR" \
--out-models-dir models.${kind} \
--min-models 2 \
--max-models 8 \
--verbosity 1 \
--worker-log-dir "$SCRATCH_DIR/$DOWNLOAD_NAME" \
--cluster-parallelism \
--cluster-max-retries 15 \
--cluster-submit-command bsub \
--cluster-results-workdir ~/mhcflurry-scratch \
--cluster-script-prefix-path $SCRIPT_DIR/cluster_submit_script_header.mssm_hpc.lsf
cp "$MODELS_DIR/train_data.csv.bz2" "models.${kind}/"
# Percentile rank calibration is run on the cluster.
time mhcflurry-calibrate-percentile-ranks \
--models-dir models.${kind} \
--match-amino-acid-distribution-data "$MODELS_DIR/train_data.csv.bz2" \
--motif-summary \
--num-peptides-per-length 100000 \
--allele $ALLELE_LIST \
--verbosity 1 \
--worker-log-dir "$SCRATCH_DIR/$DOWNLOAD_NAME" \
--prediction-batch-size 524288 \
--cluster-parallelism \
--cluster-max-retries 15 \
--cluster-submit-command bsub \
--cluster-results-workdir ~/mhcflurry-scratch \
--cluster-script-prefix-path $SCRIPT_DIR/cluster_submit_script_header.mssm_hpc.lsf
done
bzip2 LOG.txt
for i in $(ls LOG-worker.*.txt) ; do bzip2 $i ; done
RESULT="$SCRATCH_DIR/${DOWNLOAD_NAME}.$(date +%Y%m%d).tar.bz2"
tar -cjf "$RESULT" *
echo "Created archive: $RESULT"
bash GENERATE.sh cluster
downloads-generation/models_class1_pan/GENERATE.sh
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View file @ 0464808e
#!/bin/bash
# Model select pan-allele MHCflurry Class I models and calibrate percentile ranks.
#
# Usage: GENERATE.sh <local|cluster>
#
set -e
set -x
......@@ -27,18 +29,26 @@ cd $SCRATCH_DIR/$DOWNLOAD_NAME
cp $SCRIPT_ABSOLUTE_PATH .
cp $SCRIPT_DIR/additional_alleles.txt .
GPUS=$(nvidia-smi -L 2> /dev/null | wc -l) || GPUS=0
echo "Detected GPUS: $GPUS"
PROCESSORS=$(getconf _NPROCESSORS_ONLN)
echo "Detected processors: $PROCESSORS"
if [ "$1" != "cluster" ]
then
GPUS=$(nvidia-smi -L 2> /dev/null | wc -l) || GPUS=0
echo "Detected GPUS: $GPUS"
PROCESSORS=$(getconf _NPROCESSORS_ONLN)
echo "Detected processors: $PROCESSORS"
if [ "$GPUS" -eq "0" ]; then
NUM_JOBS=${NUM_JOBS-1}
if [ "$GPUS" -eq "0" ]; then
NUM_JOBS=${NUM_JOBS-1}
else
NUM_JOBS=${NUM_JOBS-$GPUS}
fi
echo "Num jobs: $NUM_JOBS"
PARALLELISM_ARGS+=" --num-jobs $NUM_JOBS --max-tasks-per-worker 1 --gpus $GPUS --max-workers-per-gpu 1"
else
NUM_JOBS=${NUM_JOBS-$GPUS}
PARALLELISM_ARGS+=" --cluster-parallelism --cluster-max-retries 3 --cluster-submit-command bsub --cluster-results-workdir $HOME/mhcflurry-scratch --cluster-script-prefix-path $SCRIPT_DIR/cluster_submit_script_header.mssm_hpc.lsf"
fi
echo "Num jobs: $NUM_JOBS"
export PYTHONUNBUFFERED=1
......@@ -50,7 +60,7 @@ UNSELECTED_PATH="$(mhcflurry-downloads path models_class1_pan_unselected)"
ALLELE_LIST=$(bzcat "$UNSELECTED_PATH/models.with_mass_spec/train_data.csv.bz2" | cut -f 1 -d , | grep -v allele | uniq | sort | uniq)
ALLELE_LIST+=$(echo " " $(cat additional_alleles.txt | grep -v '#') )
for kind in with_mass_spec no_mass_spec
for kind in combined
do
MODELS_DIR="$UNSELECTED_PATH/models.${kind}"
time mhcflurry-class1-select-pan-allele-models \
......@@ -59,8 +69,7 @@ do
--out-models-dir models.${kind} \
--min-models 2 \
--max-models 8 \
--num-jobs 0 \
--num-jobs $NUM_JOBS --max-tasks-per-worker 1 --gpus $GPUS --max-workers-per-gpu 1
$PARALLELISM_ARGS
cp "$MODELS_DIR/train_data.csv.bz2" "models.${kind}/"
......@@ -74,7 +83,7 @@ do
--num-peptides-per-length 100000 \
--allele $ALLELE_LIST \
--verbosity 1 \
--num-jobs $NUM_JOBS --max-tasks-per-worker 1 --gpus $GPUS --max-workers-per-gpu 1
$PARALLELISM_ARGS
done
bzip2 LOG.txt
......
mhcflurry/downloads.yml
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......@@ -26,7 +26,7 @@ releases:
- name: models_class1_pan_unselected
part_urls:
- https://github.com/openvax/mhcflurry/releases/download/pre-1.4.0/models_class1_pan_unselected.20190924.tar.bz2.part.aa
- https://github.com/openvax/mhcflurry/releases/download/1.4.0/models_class1_pan_unselected.20191221.tar.bz2.part.aa
default: false
- name: models_class1_pan_refined
......
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