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#!/usr/bin/env bash
set -euo pipefail
ROOT=/home/yurenh2/sdil
AUTHOR=/scratch/yurenh2/energy-based-learning
PYTHON=/scratch/yurenh2/venvs/burstccn/bin/python
PROFILE="$ROOT/results/physical_bias/p0_state_dependence.json"
OUT="$ROOT/results/ep_bias/centered_r1"
mkdir -p "$OUT"
cd "$AUTHOR"
run_cell() {
local gpu=$1
local tag=$2
local mode=$3
local cadence=$4
local profile=$5
local profile_args=()
if [ "$profile" = released ]; then
profile_args=(--dillavou-profile-json "$PROFILE")
fi
CUBLAS_WORKSPACE_CONFIG=:4096:8 CUDA_VISIBLE_DEVICES="$gpu" "$PYTHON" \
"$ROOT/experiments/rain_ep_bias_train.py" \
--author-root "$AUTHOR" --device cuda \
--adapter dillavou --network-protocol comparative32 \
--beta-policy centered --beta-value 0.25 \
--mode "$mode" --bias-ratio 1 --dillavou-drift-ratio 0 \
"${profile_args[@]}" --predictor-rate 1 \
--dillavou-calibration-steps 1 --neutral-cadence "$cadence" \
--epochs 1 --schedule-epochs 100 \
--train-limit 10000 --test-limit 2000 --batch-size 128 \
--training-iterations 15 --inference-iterations 60 \
--evaluation-split train_holdout --data-seed 6200 \
--seed 1988 --beta-seed 7100 --deterministic \
--output "$OUT/$tag.json" > "$OUT/$tag.log" 2>&1
}
run_cell 0 centered_clean clean 0 constant &
run_cell 1 fixed_raw raw 0 constant &
run_cell 2 fixed_intercept constant 0 constant &
run_cell 3 fixed_sdil innovation 0 constant &
run_cell 4 profile_raw raw 0 released &
run_cell 5 profile_intercept_c10 constant 10 released &
run_cell 6 profile_sdil_c10 innovation 10 released &
run_cell 7 profile_oracle oracle 0 released &
wait
"$PYTHON" - "$OUT" <<'PY'
import json
from pathlib import Path
import sys
root = Path(sys.argv[1])
summary = {}
for path in sorted(root.glob("*.json")):
if path.name == "summary.json":
continue
report = json.loads(path.read_text())
summary[path.stem] = {
"holdout_accuracy": report["final"]["test_accuracy"],
"finite": report["final"]["finite"],
"wall_seconds": report["final"]["wall_seconds"],
}
(root / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
print(json.dumps(summary, indent=2))
PY
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