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#!/bin/bash
if [[ $# -eq 0 ]] ; then
echo 'WARNING: This script is intended to be called with additional arguments to pass to mhcflurry-class1-allele-specific-cv-and-train'
echo 'At minimum you probably want to pass --dask-scheduler <IP:PORT> as training many models on one node is extremely '
echo 'slow.'
fi
set -e
set -x
DOWNLOAD_NAME=models_class1_allele_specific_single
SCRATCH_DIR=/tmp/mhcflurry-downloads-generation
SCRIPT_ABSOLUTE_PATH="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/$(basename "${BASH_SOURCE[0]}")"
SCRIPT_DIR=$(dirname "$SCRIPT_ABSOLUTE_PATH")
export PYTHONUNBUFFERED=1
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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 rev-parse HEAD
git status
cd $SCRATCH_DIR/$DOWNLOAD_NAME
mkdir models
cp $SCRIPT_DIR/models.py $SCRIPT_DIR/imputer.json .
python models.py > models.json
time mhcflurry-class1-allele-specific-cv-and-train \
--model-architectures models.json \
--imputer-description imputer.json \
--train-data "$(mhcflurry-downloads path data_combined_iedb_kim2014)/combined_human_class1_dataset.csv" \
--min-samples-per-allele 200 \
--out-cv-results cv.csv \
--out-production-results production.csv \
--out-models models \
--verbose \
"$@"
cp $SCRIPT_ABSOLUTE_PATH .
tar -cjf "../${DOWNLOAD_NAME}.tar.bz2" *
echo "Created archive: $SCRATCH_DIR/$DOWNLOAD_NAME.tar.bz2"