Add models_class1_pan_variants download

downloads-generation/models_class1_pan_variants/GENERATE.WITH_HPC_CLUSTER.sh

+bash GENERATE.sh cluster

downloads-generation/models_class1_pan_variants/GENERATE.sh

+#!/bin/bash
+#
+# Uses an HPC cluster (Mount Sinai chimera cluster, which uses lsf job
+# scheduler). This would need to be modified for other sites.
+#
+# Usage: GENERATE.sh <local|cluster> <fresh|continue-incomplete>
+#
+set -e
+set -x
+
+DOWNLOAD_NAME=models_class1_pan_variants
+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")
+
+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=${NUM_JOBS-1}
+    else
+        NUM_JOBS=${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
+    PARALLELISM_ARGS+=" --cluster-parallelism --cluster-max-retries 3 --cluster-submit-command bsub --cluster-results-workdir $HOME/mhcflurry-scratch $SCRIPT_DIR/cluster_submit_script_header.mssm_hpc.gpu.lsf"
+fi
+
+mkdir -p "$SCRATCH_DIR"
+if [ "$2" != "continue-incomplete" ]
+then
+    echo "Fresh run"
+    rm -rf "$SCRATCH_DIR/$DOWNLOAD_NAME"
+    mkdir "$SCRATCH_DIR/$DOWNLOAD_NAME"
+else
+    echo "Continuing incomplete run"
+fi
+
+# Send stdout and stderr to a logfile included with the archive.
+LOG="$SCRATCH_DIR/$DOWNLOAD_NAME/LOG.$(date +%s).txt"
+exec >  >(tee -ia "$LOG")
+exec 2> >(tee -ia "$LOG" >&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
+
+if [ "$2" != "continue-incomplete" ]
+then
+    cp $SCRIPT_DIR/generate_hyperparameters.production.py .
+    cp $SCRIPT_DIR/generate_hyperparameters.py .
+    python generate_hyperparameters.production.py > hyperparameters.production.json
+    python generate_hyperparameters.py hyperparameters.production.json no_pretrain > hyperparameters.no_pretrain.yaml
+    python generate_hyperparameters.py hyperparameters.no_pretrain.yaml single_hidden > hyperparameters.single_hidden_no_pretrain.yaml
+fi
+
+for kind in single_hidden_no_pretrain no_pretrain 34mer_sequence
+do
+    CONTINUE_INCOMPLETE_ARGS=""
+    if [ "$2" == "continue-incomplete" ] && [ -d "models.${kind}" ]
+    then
+        echo "Will continue existing run: $kind"
+        CONTINUE_INCOMPLETE_ARGS="--continue-incomplete"
+    fi
+
+    ALLELE_SEQUENCES="$(mhcflurry-downloads path allele_sequences)/allele_sequences.csv"
+    HYPERPARAMETERS=hyperparameters.$kind.yaml
+    if [ "$kind" == "34mer_sequence" ]
+    then
+        ALLELE_SEQUENCES="$(mhcflurry-downloads path allele_sequences)/class1_pseudosequences.csv"
+        HYPERPARAMETERS=hyperparameters.production.yaml
+    fi
+
+    mhcflurry-class1-train-pan-allele-models \
+        --data "$(mhcflurry-downloads path data_curated)/curated_training_data.with_mass_spec.csv.bz2" \
+        --allele-sequences "$ALLELE_SEQUENCES" \
+        --pretrain-data "$(mhcflurry-downloads path random_peptide_predictions)/predictions.csv.bz2" \
+        --held-out-measurements-per-allele-fraction-and-max 0.25 100 \
+        --num-folds 4 \
+        --hyperparameters "$HYPERPARAMETERS" \
+        --out-models-dir $(pwd)/models.unselected.${kind} \
+        --worker-log-dir "$SCRATCH_DIR/$DOWNLOAD_NAME" \
+        $PARALLELISM_ARGS $CONTINUE_INCOMPLETE_ARGS
+done
+
+echo "Done training. Beginning model selection."
+
+for kind in single_hidden_no_pretrain no_pretrain 34mer_sequence
+do
+    MODELS_DIR="models.unselected.${kind}"
+    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 \
+        $PARALLELISM_ARGS
+    ln -s "$MODELS_DIR/train_data.csv.bz2" "models.${kind}/train_data.csv.bz2"
+done
+
+cp $SCRIPT_ABSOLUTE_PATH .
+bzip2 -f "$LOG"
+for i in $(ls LOG-worker.*.txt) ; do bzip2 -f $i ; done
+RESULT="$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}"*

downloads-generation/models_class1_pan_variants/cluster_submit_script_header.mssm_hpc.gpu.lsf

+#!/bin/bash
+#BSUB -J MHCf-{work_item_num} # Job name
+#BSUB -P acc_nkcancer # allocation account or Unix group
+#BSUB -q gpu # queue
+#BSUB -R rusage[ngpus_excl_p=1]  # 1 exclusive GPU
+#BSUB -R span[hosts=1] # one node
+#BSUB -n 1 # number of compute cores
+#BSUB -W 46:00 # walltime in HH:MM
+#BSUB -R rusage[mem=30000] # 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
+#
+
+set -e
+set -x
+
+echo "Subsequent stderr output redirected to stdout" >&2
+exec 2>&1
+
+export TMPDIR=/local/JOBS/mhcflurry-{work_item_num}
+export PATH=$HOME/.conda/envs/py36b/bin/:$PATH
+export PYTHONUNBUFFERED=1
+export KMP_SETTINGS=1
+export NETMHC_BUNDLE_HOME=$HOME/sinai/git/netmhc-bundle
+export NETMHC_BUNDLE_TMPDIR=/local/JOBS/netmhctmp-{work_item_num}
+export PATH=$NETMHC_BUNDLE_HOME/bin:$PATH
+
+free -m
+
+module add cuda/10.0.130 cudnn/7.1.1
+module list
+
+# python -c 'import tensorflow as tf ; print("GPU AVAILABLE" if tf.test.is_gpu_available() else "GPU NOT AVAILABLE")'
+
+env
+
+cd {work_dir}
+

downloads-generation/models_class1_pan_variants/generate_hyperparameters.production.py

+../models_class1_pan_unselected/generate_hyperparameters.py
\ No newline at end of file

downloads-generation/models_class1_pan_variants/generate_hyperparameters.py

+"""
+Generate grid of hyperparameters
+"""
+
+from sys import stdout, argv
+from copy import deepcopy
+from yaml import dump, load
+import argparse
+
+parser = argparse.ArgumentParser(usage=__doc__)
+parser.add_argument(
+    "production_hyperparameters",
+    metavar="data.json",
+    help="Production (i.e. standard) hyperparameters grid.")
+parser.add_argument(
+    "kind",
+    choices=('single_hidden', 'no_pretrain'),
+    help="Hyperameters variant to output")
+
+args = parser.parse_args(argv[1:])
+
+with open(args.production_hyperparameters) as fd:
+    production_hyperparameters_list = load(fd)
+
+
+def transform_to_single_hidden(hyperparameters):
+    result = []
+    for size in [64, 128, 256, 1024]:
+        hyperparameters['layer_sizes'] = [size]
+        result.append(deepcopy(hyperparameters))
+    return result
+
+
+def transform_to_no_pretrain(hyperparameters):
+    result = deepcopy(hyperparameters)
+    result['train_data']['pretrain'] = False
+    return [result]
+
+
+TRANSFORMS={
+    "single_hidden": transform_to_single_hidden,
+    "no_pretrain": transform_to_no_pretrain,
+}
+
+transform = TRANSFORMS[args.kind]
+
+result_list = []
+for item in production_hyperparameters_list:
+    for result_item in transform(item):
+        if result_item not in result_list:
+            result_list.append(item)
+
+dump(result_list, stdout)