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#!/usr/bin/env bash
# It may be necessary to start the ray server manually.
set -e
for cfg_name in {9,21}-player; do
cd repo/
# Run training
rm -rf log_dir
python ../helper.py $cfg_name
# Move generic log dir to properly named one (the code does not support
# naming the log dir natively).
log_dir=log_dir-$cfg_name
rm -rf $log_dir
mv log_dir $log_dir
# Find the last checkpoint
ray_dir=$log_dir/ray_results/APPO/*/
checkpoint=$(ls $ray_dir | grep '^checkpoint' | sort -t_ -nk2 | tail -n1)
if [[ -z "$checkpoint" ]]; then
echo Could not find checkpoint in $ray_dir.
exit 1
fi
ckpt_iter=$(echo $checkpoint | cut -d_ -f2)
# Run the corpus generation code. If a second argument is given to
# helper, assume that we are doing corpus genernation.
python ../helper.py $cfg_name $ray_dir/$checkpoint/checkpoint-$ckpt_iter
cd ..
# Make data dir
data_dir=../data/$cfg_name
mkdir -p $data_dir
out_metadata=$data_dir/metadata.json
# Copy corpora into data dir
cp repo/log_dir/eval/*.jsonl $data_dir
# Extract metrics from output
system_metrics=$(
tail -n100 repo/$ray_dir/result.json |\
jq "
select(.iterations_since_restore == $ckpt_iter).custom_metrics |
pick(.accord_mean,.win_vil_mean)
"
)
if [[ -z "$system_metrics" ]]; then
echo Failed to extract system metrics
exit 1
fi
# Update corpus metadata
# TODO Put this in common file
if ! [[ -s $out_metadata ]]; then
echo '{}' >$out_metadata
fi
jq ".metrics.system=$system_metrics" \
<$out_metadata >$out_metadata.tmp
mv $out_metadata{.tmp,}
done
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