From 526b93eea12495da00f19b81414b91eeca7e1313 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 3 Oct 2019 16:08:47 -0400
Subject: [PATCH] fixes
---
.../GENERATE.WITH_HPC_CLUSTER.sh | 123 +--------------
.../data_mass_spec_benchmark/GENERATE.sh | 147 ++++++++++++++----
...er_submit_script_header.mssm_hpc.nogpu.lsf | 4 +-
.../run_predictors.py | 6 +-
4 files changed, 125 insertions(+), 155 deletions(-)
diff --git a/downloads-generation/data_mass_spec_benchmark/GENERATE.WITH_HPC_CLUSTER.sh b/downloads-generation/data_mass_spec_benchmark/GENERATE.WITH_HPC_CLUSTER.sh
index 5f73e767..20127e13 100755
--- a/downloads-generation/data_mass_spec_benchmark/GENERATE.WITH_HPC_CLUSTER.sh
+++ b/downloads-generation/data_mass_spec_benchmark/GENERATE.WITH_HPC_CLUSTER.sh
@@ -1,122 +1 @@
-#!/bin/bash
-#
-#
-set -e
-set -x
-
-DOWNLOAD_NAME=data_mass_spec_benchmark
-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")
-export PYTHONUNBUFFERED=1
-
-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
-date
-pip freeze
-git status
-
-cd $SCRATCH_DIR/$DOWNLOAD_NAME
-
-cp $SCRIPT_DIR/write_proteome_peptides.py .
-cp $SCRIPT_DIR/run_mhcflurry.py .
-cp $SCRIPT_DIR/run_thirdparty_predictors.py .
-cp $SCRIPT_DIR/write_allele_list.py .
-
-PEPTIDES=$(mhcflurry-downloads path data_mass_spec_annotated)/annotated_ms.csv.bz2
-REFERENCES_DIR=$(mhcflurry-downloads path data_references)
-
-python write_allele_list.py "$PEPTIDES" --out alleles.txt
-mkdir predictions
-
-# First just chr1 peptides
-python write_proteome_peptides.py \
- "$PEPTIDES" \
- "${REFERENCES_DIR}/uniprot_proteins.csv.bz2" \
- --chromosome 1 \
- --out proteome_peptides.chr1.csv
-
-#for kind in with_mass_spec no_mass_spec
-#do
-# python run_mhcflurry.py \
-# proteome_peptides.chr1.csv \
-# --chunk-size 100000 \
-# --batch-size 65536 \
-# --models-dir "$(mhcflurry-downloads path models_class1_pan)/models.$kind" \
-# --allele $(cat alleles.txt) \
-# --out "predictions/chr1.mhcflurry.$kind" \
-# --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
-#done
-
-python run_thirdparty_predictors.py \
- proteome_peptides.chr1.csv \
- --predictor netmhcpan4 \
- --chunk-size 10000 \
- --allele $(cat alleles.txt) \
- --out "predictions/chr1.netmhcpan4" \
- --worker-log-dir "$SCRATCH_DIR/$DOWNLOAD_NAME" \
- --cluster-parallelism \
- --cluster-max-retries 3 \
- --cluster-submit-command bsub \
- --cluster-results-workdir ~/mhcflurry-scratch \
- --cluster-script-prefix-path $SCRIPT_DIR/cluster_submit_script_header.mssm_hpc.nogpu.lsf
-
-# Now all peptides
-python write_proteome_peptides.py \
- "$PEPTIDES" \
- "${REFERENCES_DIR}/uniprot_proteins.csv.bz2" \
- --out proteome_peptides.all.csv
-
-#for kind in with_mass_spec no_mass_spec
-#do
-# python run_mhcflurry.py \
-# proteome_peptides.all.csv \
-# --chunk-size 500000 \
-# --batch-size 65536 \
-# --models-dir "$(mhcflurry-downloads path models_class1_pan)/models.$kind" \
-# --allele $(cat alleles.txt) \
-# --out "predictions/all.mhcflurry.$kind" \
-# --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
-#done
-
-python run_thirdparty_predictors.py \
- proteome_peptides.all.csv \
- --predictor netmhcpan4 \
- --chunk-size 10000 \
- --allele $(cat alleles.txt) \
- --out "predictions/all.netmhcpan4" \
- --worker-log-dir "$SCRATCH_DIR/$DOWNLOAD_NAME" \
- --cluster-parallelism \
- --cluster-max-retries 3 \
- --cluster-submit-command bsub \
- --cluster-results-workdir ~/mhcflurry-scratch \
- --cluster-script-prefix-path cluster_submit_script_header.mssm_hpc.nogpu.lsf
-
-
-bzip2 proteome_peptides.chr1.csv
-bzip2 proteome_peptides.all.csv
-
-cp $SCRIPT_ABSOLUTE_PATH .
-bzip2 LOG.txt
-RESULT="$SCRATCH_DIR/${DOWNLOAD_NAME}.$(date +%Y%m%d).tar.bz2"
-tar -cjf "$RESULT" *
-echo "Created archive: $RESULT"
+bash GENERATE.sh cluster reuse-predictions
\ No newline at end of file
diff --git a/downloads-generation/data_mass_spec_benchmark/GENERATE.sh b/downloads-generation/data_mass_spec_benchmark/GENERATE.sh
index 1818ddbf..b1bc0029 100755
--- a/downloads-generation/data_mass_spec_benchmark/GENERATE.sh
+++ b/downloads-generation/data_mass_spec_benchmark/GENERATE.sh
@@ -1,5 +1,6 @@
#!/bin/bash
#
+# GENERATE.sh <local|cluster> <reuse-all|reuse-none|reuse-predictions>
#
set -e
set -x
@@ -15,8 +16,8 @@ rm -rf "$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/$DOWNLOAD_NAME/LOG.txt" >&2)
+exec > >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt")
+exec 2> >(tee -ia "$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.txt" >&2)
# Log some environment info
date
@@ -26,48 +27,123 @@ git status
cd $SCRATCH_DIR/$DOWNLOAD_NAME
cp $SCRIPT_DIR/write_proteome_peptides.py .
-cp $SCRIPT_DIR/run_mhcflurry.py .
cp $SCRIPT_DIR/write_allele_list.py .
+cp $SCRIPT_DIR/run_predictors.py .
-GPUS=$(nvidia-smi -L 2> /dev/null | wc -l) || GPUS=0
-echo "Detected GPUS: $GPUS"
+if [ "$1" != "cluster" ]
+then
-PROCESSORS=$(getconf _NPROCESSORS_ONLN)
-echo "Detected processors: $PROCESSORS"
+ GPUS=$(nvidia-smi -L 2> /dev/null | wc -l) || GPUS=0
+ echo "Detected GPUS: $GPUS"
-if [ "$GPUS" -eq "0" ]; then
- NUM_JOBS=${NUM_JOBS-1}
+ PROCESSORS=$(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 jobs: $NUM_JOBS"
+ EXTRA_ARGS+=" --num-jobs $NUM_JOBS --max-tasks-per-worker 1 --gpus $GPUS --max-workers-per-gpu 1"
else
- NUM_JOBS=${NUM_JOBS-$GPUS}
+ EXTRA_ARGS+=" --cluster-parallelism --cluster-max-retries 3 --cluster-submit-command bsub --cluster-results-workdir ~/mhcflurry-scratch"
fi
-echo "Num jobs: $NUM_JOBS"
-
PEPTIDES=$(mhcflurry-downloads path data_mass_spec_annotated)/annotated_ms.csv.bz2
REFERENCES_DIR=$(mhcflurry-downloads path data_references)
-#python write_proteome_peptides.py \
-# "$PEPTIDES" \
-# "${REFERENCES_DIR}/uniprot_proteins.csv.bz2" \
-# --out proteome_peptides.csv
-#ls -lh proteome_peptides.csv
-#bzip2 proteome_peptides.csv
-ln -s ~/Dropbox/sinai/projects/201808-mhcflurry-pan/20190622-models/proteome_peptides.csv.bz2 proteome_peptides.csv.bz2
-
-python write_allele_list.py "$PEPTIDES" --out alleles.txt
+if [ "${2:-reuse-none}" != "reuse-none" ]
+then
+ EXISTING_DATA=$(mhcflurry-downloads path $DOWNLOAD_NAME)
+ echo "Will reuse data from $REFERENCES_DIR"
+else
+ EXISTING_DATA=""
+ echo "Will NOT reuse any data"
+fi
mkdir predictions
-for kind in with_mass_spec no_mass_spec
+# Write out alleles
+if [ "$2" == "reuse-all" ]
+then
+ echo "Reusing allele list"
+ cp "$EXISTING_DATA/alleles.txt" .
+else
+ echo "Generating allele list"
+ python write_allele_list.py "$PEPTIDES" --out alleles.txt
+fi
+
+# Write out and process peptides.
+# First just chr1 peptides, then all peptides.
+for subset in chr1 all
do
- python run_mhcflurry.py \
- proteome_peptides.csv.bz2 \
- --chunk-size 100000 \
- --models-dir "$(mhcflurry-downloads path models_class1_pan)/models.$kind" \
- --batch-size 65536 \
+ if [ "$2" == "reuse-all" ]
+ then
+ echo "Reusing peptide list"
+ cp "$EXISTING_DATA/proteome_peptides.$subset.csv.bz2" .
+ else
+ echo "Generating peptide list"
+ SUBSET_ARG=""
+ if [ "$subset" == "chr1" ]
+ then
+ SUBSET_ARG="--chromosome 1"
+ fi
+ python write_proteome_peptides.py \
+ "$PEPTIDES" \
+ "${REFERENCES_DIR}/uniprot_proteins.csv.bz2" \
+ --out proteome_peptides.$subset.csv $SUBSET_ARG
+ bzip2 proteome_peptides.$subset.csv
+ fi
+
+ # Run MHCflurry
+ for kind in with_mass_spec no_mass_spec
+ do
+ OUT_DIR=predictions/${subset}.mhcflurry.${kind}
+ REUSE_ARG=""
+ if [ "$subset" == "all" ]
+ then
+ REUSE_ARG="--reuse-predictions predictions/chr1.mhcflurry.${kind}"
+ fi
+ if [ "${2:-reuse-none}" != "reuse-none" ]
+ then
+ REUSE_ARG+="--reuse-predictions" "$EXISTING_DATA/$OUT_DIR"
+ fi
+
+ python run_predictors.py \
+ proteome_peptides.${subset}.csv.bz2 \
+ --predictor mhcflurry \
+ --chunk-size 500000 \
+ --mhcflurry-batch-size 65536 \
+ --mhcflurry-models-dir "$(mhcflurry-downloads path models_class1_pan)/models.$kind" \
+ --allele $(cat alleles.txt) \
+ --out "$OUT_DIR" \
+ --worker-log-dir "$SCRATCH_DIR/$DOWNLOAD_NAME" \
+ --cluster-script-prefix-path $SCRIPT_DIR/cluster_submit_script_header.mssm_hpc.lsf \
+ $REUSE_ARG $EXTRA_ARGS
+ done
+
+ # Run netmhcpan4
+ OUT_DIR=predictions/${subset}.netmhcpan4
+ REUSE_ARG=""
+ if [ "$subset" == "all" ]
+ then
+ REUSE_ARG="--reuse-predictions predictions/chr1.netmhcpan4"
+ fi
+ if [ "${2:-reuse-none}" != "reuse-none" ]
+ then
+ REUSE_ARG+="--reuse-predictions" "$EXISTING_DATA/$OUT_DIR"
+ fi
+
+ python run_predictors.py \
+ proteome_peptides.$subset.csv.bz2 \
+ --predictor netmhcpan4 \
+ --chunk-size 10000 \
--allele $(cat alleles.txt) \
- --out "predictions/mhcflurry.$kind" \
- --num-jobs $NUM_JOBS --max-tasks-per-worker 1 --gpus $GPUS --max-workers-per-gpu 1
+ --out "$OUT_DIR" \
+ --worker-log-dir "$SCRATCH_DIR/$DOWNLOAD_NAME" \
+ --cluster-script-prefix-path $SCRIPT_DIR/cluster_submit_script_header.mssm_hpc.nogpu.lsf \
+ $REUSE_ARG $EXTRA_ARGS
done
cp $SCRIPT_ABSOLUTE_PATH .
@@ -75,3 +151,16 @@ bzip2 LOG.txt
RESULT="$SCRATCH_DIR/${DOWNLOAD_NAME}.$(date +%Y%m%d).tar.bz2"
tar -cjf "$RESULT" *
echo "Created archive: $RESULT"
+
+# Split into <2GB chunks for GitHub
+PARTS="${RESULT}.part."
+# Check for pre-existing part files and rename them.
+for i in $(ls "${PARTS}"* )
+do
+ DEST="${i}.OLD.$(date +%s)"
+ echo "WARNING: already exists: $i . Moving to $DEST"
+ mv $i $DEST
+done
+split -b 2000M "$RESULT" "$PARTS"
+echo "Split into parts:"
+ls -lh "${PARTS}"*
diff --git a/downloads-generation/data_mass_spec_benchmark/cluster_submit_script_header.mssm_hpc.nogpu.lsf b/downloads-generation/data_mass_spec_benchmark/cluster_submit_script_header.mssm_hpc.nogpu.lsf
index 275d650b..444d2c11 100644
--- a/downloads-generation/data_mass_spec_benchmark/cluster_submit_script_header.mssm_hpc.nogpu.lsf
+++ b/downloads-generation/data_mass_spec_benchmark/cluster_submit_script_header.mssm_hpc.nogpu.lsf
@@ -1,11 +1,11 @@
#!/bin/bash
#BSUB -J MHCf-{work_item_num} # Job name
#BSUB -P acc_nkcancer # allocation account or Unix group
-#BSUB -q express # queue
+#BSUB -q premium # queue
#BSUB -R span[hosts=1] # one node
#BSUB -n 1 # number of compute cores
#BSUB -W 12:00 # walltime in HH:MM
-#BSUB -R rusage[mem=20000] # mb memory requested
+#BSUB -R rusage[mem=4000] # mb memory requested
#BSUB -o {work_dir}/%J.stdout # output log (%J : JobID)
#BSUB -eo {work_dir}/STDERR # error log
#BSUB -L /bin/bash # Initialize the execution environment
diff --git a/downloads-generation/data_mass_spec_benchmark/run_predictors.py b/downloads-generation/data_mass_spec_benchmark/run_predictors.py
index fd871ff6..a86ce0da 100644
--- a/downloads-generation/data_mass_spec_benchmark/run_predictors.py
+++ b/downloads-generation/data_mass_spec_benchmark/run_predictors.py
@@ -75,7 +75,7 @@ parser.add_argument(
parser.add_argument(
"--reuse-predictions",
metavar="DIR",
- nargs="+",
+ action="append",
help="Take predictions from indicated DIR instead of re-running them")
add_local_parallelism_args(parser)
@@ -385,7 +385,9 @@ def do_predictions_mhcflurry(work_item_num, peptides, alleles, constant_data=Non
peptides=peptides,
allele=allele,
throw=False,
- model_kwargs={'batch_size': args.batch_size}).astype('float32')
+ model_kwargs={
+ 'batch_size': args.mhcflurry_batch_size,
+ }).astype('float32')
print("Done predicting in", time.time() - start, "sec")
return (work_item_num, results)
--
GitLab