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DeepHealth/train_key_models_multiseed_linux.sh

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#!/usr/bin/env bash
#
# Re-run the key DeepHealth experiments with additional random seeds.
#
# The selected configurations form a compact evidence chain:
# 1. FFN + next_token + absolute + exponential
# Delphi2M reproduction baseline.
# 2. FFN + all_future + absolute + exponential
# Isolates the target change from next_token to all_future.
# 3. TrajMixer + all_future + absolute + exponential
# Isolates the architecture change from FFN to TrajMixer.
# 4. TrajMixer + all_future + relative + exponential
# Isolates the time-mode change and is the current disease-best candidate.
# 5. TrajMixer + all_future + relative + Weibull
# Unified disease/death candidate.
# 6. FFN + all_future + relative + Weibull
# Current mortality-best control and matched architecture comparison for (5).
#
# Each task uses one GPU. Tasks assigned to the same GPU run sequentially,
# while different GPUs run in parallel.
#
# Examples:
# bash train_key_models_multiseed_linux.sh --gpus 0,1,2,3
# bash train_key_models_multiseed_linux.sh --gpus 0,1 --seeds 43,44
#
set -uo pipefail
SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
cd "$SCRIPT_DIR"
GPU_CSV=""
SEED_CSV="43,44,45"
NUM_WORKERS=4
PYTHON_BIN="${PYTHON_BIN:-python}"
CAMPAIGN_NAME="key_models_multiseed_smoking_alcohol_bmi"
DRY_RUN=0
BATCH_SIZE=256
EXTRA_INFO_TYPES_FILE="$SCRIPT_DIR/extra_info_types_smoking_alcohol_bmi.txt"
usage() {
cat <<'EOF'
Usage:
bash train_key_models_multiseed_linux.sh --gpus GPU_LIST [options]
Required:
--gpus LIST Comma-separated GPU ids, for example 0,1,2,3.
Options:
--seeds LIST Additional seeds (default: 43,44,45).
Use 43,44 for two additional seeds.
--num-workers N DataLoader workers per task (default: 4).
--python PATH Python executable (default: $PYTHON_BIN or python).
--campaign NAME Output campaign name.
--dry-run Print all commands without running them.
-h, --help Show this help message.
Fixed experiment settings:
batch_size 256
extra_info_types extra_info_types_smoking_alcohol_bmi.txt
continuous scaling required train-split RobustScale
model size Defaults from the training entrypoints
tasks per seed 6
Outputs:
runs/<campaign>/seed_<seed>/<architecture>/...
batch_logs/<campaign>/seed_<seed>/<job>.log
EOF
}
while (($# > 0)); do
case "$1" in
--gpus)
[[ $# -ge 2 ]] || {
echo "ERROR: --gpus requires a value." >&2
exit 2
}
GPU_CSV="$2"
shift 2
;;
--seeds)
[[ $# -ge 2 ]] || {
echo "ERROR: --seeds requires a value." >&2
exit 2
}
SEED_CSV="$2"
shift 2
;;
--num-workers)
[[ $# -ge 2 ]] || {
echo "ERROR: --num-workers requires a value." >&2
exit 2
}
NUM_WORKERS="$2"
shift 2
;;
--python)
[[ $# -ge 2 ]] || {
echo "ERROR: --python requires a value." >&2
exit 2
}
PYTHON_BIN="$2"
shift 2
;;
--campaign)
[[ $# -ge 2 ]] || {
echo "ERROR: --campaign requires a value." >&2
exit 2
}
CAMPAIGN_NAME="$2"
shift 2
;;
--dry-run)
DRY_RUN=1
shift
;;
-h|--help)
usage
exit 0
;;
*)
echo "ERROR: unknown argument: $1" >&2
usage >&2
exit 2
;;
esac
done
[[ -n "$GPU_CSV" ]] || {
echo "ERROR: --gpus is required." >&2
usage >&2
exit 2
}
[[ -n "$SEED_CSV" ]] || {
echo "ERROR: --seeds must not be empty." >&2
exit 2
}
[[ "$NUM_WORKERS" =~ ^[0-9]+$ ]] || {
echo "ERROR: --num-workers must be a non-negative integer." >&2
exit 2
}
[[ "$CAMPAIGN_NAME" =~ ^[A-Za-z0-9._-]+$ ]] || {
echo "ERROR: --campaign may contain only letters, numbers, ., _, and -." >&2
exit 2
}
[[ -f "$EXTRA_INFO_TYPES_FILE" ]] || {
echo "ERROR: missing extra-info file: $EXTRA_INFO_TYPES_FILE" >&2
exit 2
}
command -v "$PYTHON_BIN" >/dev/null 2>&1 || {
echo "ERROR: Python executable not found: $PYTHON_BIN" >&2
exit 2
}
IFS=',' read -r -a GPU_IDS <<< "$GPU_CSV"
declare -A SEEN_GPUS=()
for gpu in "${GPU_IDS[@]}"; do
[[ -n "$gpu" && "$gpu" =~ ^[A-Za-z0-9._:-]+$ ]] || {
echo "ERROR: invalid GPU id: $gpu" >&2
exit 2
}
[[ -z "${SEEN_GPUS[$gpu]+x}" ]] || {
echo "ERROR: duplicate GPU id: $gpu" >&2
exit 2
}
SEEN_GPUS["$gpu"]=1
done
IFS=',' read -r -a SEEDS <<< "$SEED_CSV"
declare -A SEEN_SEEDS=()
for seed in "${SEEDS[@]}"; do
[[ "$seed" =~ ^[0-9]+$ ]] || {
echo "ERROR: invalid seed: $seed" >&2
exit 2
}
[[ -z "${SEEN_SEEDS[$seed]+x}" ]] || {
echo "ERROR: duplicate seed: $seed" >&2
exit 2
}
SEEN_SEEDS["$seed"]=1
done
RUNS_ROOT="$SCRIPT_DIR/runs/$CAMPAIGN_NAME"
LOG_ROOT="$SCRIPT_DIR/batch_logs/$CAMPAIGN_NAME"
if ((!DRY_RUN)); then
mkdir -p "$RUNS_ROOT" "$LOG_ROOT"
fi
declare -a JOB_NAMES=()
declare -a JOB_SEEDS=()
declare -a JOB_ENTRYPOINTS=()
declare -a JOB_ARCHITECTURES=()
declare -a JOB_TIME_MODES=()
declare -a JOB_DIST_MODES=()
add_job() {
JOB_NAMES+=("$1")
JOB_SEEDS+=("$2")
JOB_ENTRYPOINTS+=("$3")
JOB_ARCHITECTURES+=("$4")
JOB_TIME_MODES+=("$5")
JOB_DIST_MODES+=("$6")
}
for seed in "${SEEDS[@]}"; do
add_job \
"ffn_next_token_absolute_exponential" \
"$seed" \
"train_next_step.py" \
"transformer_ffn_v1" \
"absolute" \
"exponential"
add_job \
"ffn_all_future_absolute_exponential" \
"$seed" \
"train_all_future.py" \
"transformer_ffn_v1" \
"absolute" \
"exponential"
add_job \
"traj_mixer_all_future_absolute_exponential" \
"$seed" \
"train_all_future.py" \
"traj_mixer_v5" \
"absolute" \
"exponential"
add_job \
"traj_mixer_all_future_relative_exponential" \
"$seed" \
"train_all_future.py" \
"traj_mixer_v5" \
"relative" \
"exponential"
add_job \
"traj_mixer_all_future_relative_weibull" \
"$seed" \
"train_all_future.py" \
"traj_mixer_v5" \
"relative" \
"weibull"
add_job \
"ffn_all_future_relative_weibull" \
"$seed" \
"train_all_future.py" \
"transformer_ffn_v1" \
"relative" \
"weibull"
done
print_command() {
printf '%q ' "$@"
printf '\n'
}
run_job() {
local job_index="$1"
local gpu="$2"
local job_name="${JOB_NAMES[$job_index]}"
local seed="${JOB_SEEDS[$job_index]}"
local entrypoint="${JOB_ENTRYPOINTS[$job_index]}"
local architecture="${JOB_ARCHITECTURES[$job_index]}"
local time_mode="${JOB_TIME_MODES[$job_index]}"
local dist_mode="${JOB_DIST_MODES[$job_index]}"
local seed_runs_root="$RUNS_ROOT/seed_$seed"
local seed_log_root="$LOG_ROOT/seed_$seed"
local log_file="$seed_log_root/$job_name.log"
local -a command=(
"$PYTHON_BIN"
-u
"$SCRIPT_DIR/$entrypoint"
--runs_root "$seed_runs_root"
--seed "$seed"
--batch_size "$BATCH_SIZE"
--extra_info_types_file "$EXTRA_INFO_TYPES_FILE"
--model_architecture "$architecture"
--num_workers "$NUM_WORKERS"
--device cuda
)
if ((!DRY_RUN)); then
mkdir -p "$seed_runs_root" "$seed_log_root"
fi
if [[ "$entrypoint" == "train_all_future.py" ]]; then
command+=(
--time_mode "$time_mode"
--dist_mode "$dist_mode"
)
fi
echo "[$(date '+%F %T')] START seed=$seed job=$job_name gpu=$gpu"
echo " log=$log_file"
if ((DRY_RUN)); then
printf ' CUDA_VISIBLE_DEVICES=%q ' "$gpu"
print_command "${command[@]}"
return 0
fi
if CUDA_VISIBLE_DEVICES="$gpu" PYTHONUNBUFFERED=1 \
"${command[@]}" >"$log_file" 2>&1; then
echo "[$(date '+%F %T')] DONE seed=$seed job=$job_name gpu=$gpu"
return 0
else
local exit_code=$?
echo "[$(date '+%F %T')] FAIL seed=$seed job=$job_name gpu=$gpu exit=$exit_code" >&2
echo " See: $log_file" >&2
return "$exit_code"
fi
}
worker() {
local slot="$1"
local gpu="${GPU_IDS[$slot]}"
local job_index
local failed=0
for ((job_index = slot; job_index < ${#JOB_NAMES[@]}; job_index += ${#GPU_IDS[@]})); do
run_job "$job_index" "$gpu" || failed=1
done
return "$failed"
}
echo "Campaign: $CAMPAIGN_NAME"
echo "Additional seeds: ${SEEDS[*]}"
echo "GPUs: ${GPU_IDS[*]}"
echo "Configurations per seed: 6"
echo "Total tasks: ${#JOB_NAMES[@]}"
echo "Runs root: $RUNS_ROOT"
echo "Log root: $LOG_ROOT"
echo
declare -a WORKER_PIDS=()
for ((slot = 0; slot < ${#GPU_IDS[@]}; slot++)); do
worker "$slot" &
WORKER_PIDS+=("$!")
done
overall_status=0
for pid in "${WORKER_PIDS[@]}"; do
wait "$pid" || overall_status=1
done
if ((overall_status != 0)); then
echo "One or more training tasks failed. Inspect logs under: $LOG_ROOT" >&2
exit 1
fi
if ((DRY_RUN)); then
echo "Dry run completed successfully."
else
echo "All training tasks completed successfully."
fi