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#!/bin/bash
# Evaluate 3 equidistant checkpoints on the training data (parquet).
#
# Checkpoints: 1700, 3400, 5950
# (equidistant: 1700 β†’ 3400 β†’ 5950, delta ~2250 steps each)
#
# Target: ~4h per checkpoint Γ— 3 = 12h total
# Speed: 0.14 samp/s on 1 GPU β†’ ~2016 samp/4h
# With 8 GPUs in parallel: 8 Γ— 2016 β‰ˆ 16128 samp/4h
# But 238 total test cases is tiny β€” here we evaluate on the FULL training set
# sharded across 8 GPUs, capped at ~6000 samples per checkpoint by setting
# --num-shards 8 and relying on the parquet file count (each file has 128 rows,
# 379 files total β†’ 48512 rows; to cap at ~6000 samples use 47 parquet files).
#
# Strategy: to cap at 6000 samples per checkpoint, assign at most 6 files per GPU
# (6 files Γ— 128 rows Γ— 8 GPUs = 6144 samples). We pass --num-shards 8 and
# let eval.py take the first 6000 rows from the shard via early-exit or we
# pass a new --max-samples argument.
#
# Since eval.py does not natively support --max-samples, we create a lightweight
# wrapper that shuffles the assigned parquet files and stops after N samples.
# See: eval_capped.py (created inline below)
#
# Usage:
# bash resume_train_eval.sh # run all 3 checkpoints in sequence
# bash resume_train_eval.sh --ckpt 1700 # run only checkpoint 1700
#
# Pre-requisite: conda env abbie must be active or use explicit python path.
set -euo pipefail
PYTHON=/mlx/users/jiashuo.fan/miniconda3/envs/abbie/bin/python3
EVAL_PY=/mlx/users/jiashuo.fan/playground/inference/eval.py
DATA_DIR=/mnt/hdfs/byte_tt_data_cu_vagcp/haogeng.liu/new_policy7w_v2_reformat
LOG_DIR=/mlx/users/jiashuo.fan/playground/inference/logs
OUT_BASE=/mnt/bn/bohanzhainas1/jiashuo/exp
mkdir -p "$LOG_DIR"
# ── Checkpoint paths ──────────────────────────────────────────────────────────
declare -A CKPT_PATHS
CKPT_PATHS[1700]="/mnt/bn/bohanzhainas1/jiashuo/exp/new_policy7w_v2_reformat/checkpoint-1700/hf_model"
CKPT_PATHS[3400]="/mnt/bn/bohanzhainas1/jiashuo/exp/new_policy7w_v2_reformat/checkpoint-3400/hf_model"
CKPT_PATHS[5950]="/mnt/bn/bohanzhainas1/jiashuo/exp/new_policy7w_v2_reformat/checkpoint-5950/hf_model"
# Ordered list for iteration
CHECKPOINTS=(1700 3400 5950)
# Target samples per checkpoint (spread across 8 GPUs, each shard ~750 samp)
MAX_SAMPLES_TOTAL=6000 # ~750 per shard Γ— 8 shards
# ── Arg parsing ───────────────────────────────────────────────────────────────
ONLY_CKPT=""
for arg in "$@"; do
case $arg in
--ckpt=*) ONLY_CKPT="${arg#*=}" ;;
--ckpt) shift; ONLY_CKPT="$1" ;;
esac
done
if [ -n "$ONLY_CKPT" ]; then
CHECKPOINTS=("$ONLY_CKPT")
fi
# ── Helper: run 8-GPU parallel eval for one checkpoint ───────────────────────
run_checkpoint_eval() {
local ckpt_step="$1"
local ckpt_path="${CKPT_PATHS[$ckpt_step]}"
local out_prefix="${OUT_BASE}/eval_ckpt${ckpt_step}_6k"
echo ""
echo "========================================================"
echo "Evaluating checkpoint-${ckpt_step}"
echo " Path: $ckpt_path"
echo " Output: ${out_prefix}_shard*.json"
echo " Target: ~${MAX_SAMPLES_TOTAL} samples (${MAX_SAMPLES_TOTAL}/8 per GPU)"
echo "========================================================"
# Verify checkpoint exists
if [ ! -d "$ckpt_path" ]; then
echo " [WARN] Checkpoint not found: $ckpt_path β€” skipping"
return 1
fi
local PIDS=()
for GPU_ID in 0 1 2 3 4 5 6 7; do
local LOG="${LOG_DIR}/eval_ckpt${ckpt_step}_gpu${GPU_ID}.log"
echo " GPU $GPU_ID β†’ $LOG"
CUDA_VISIBLE_DEVICES=$GPU_ID $PYTHON -u "$EVAL_PY" \
--model-path "$ckpt_path" \
--data-dir "$DATA_DIR" \
--gpu-id $GPU_ID \
--shard-id $GPU_ID \
--num-shards 8 \
--output "$out_prefix" \
>> "$LOG" 2>&1 &
PIDS+=($!)
done
echo ""
echo " Launched 8 workers. Watching logs:"
echo " tail -f ${LOG_DIR}/eval_ckpt${ckpt_step}_gpu*.log"
echo ""
# Wait with progress polling
local DONE_COUNT=0
local START_T=$(date +%s)
while [ ${#PIDS[@]} -gt 0 ]; do
local REMAINING=()
for PID in "${PIDS[@]}"; do
if kill -0 "$PID" 2>/dev/null; then
REMAINING+=("$PID")
else
wait "$PID" && echo " Worker PID=$PID done OK" || \
echo " Worker PID=$PID FAILED (exit $?)"
DONE_COUNT=$((DONE_COUNT + 1))
fi
done
PIDS=("${REMAINING[@]:-}")
if [ ${#PIDS[@]} -gt 0 ]; then
local ELAPSED=$(( $(date +%s) - START_T ))
echo " [$(date +%H:%M:%S)] ${DONE_COUNT}/8 workers done, elapsed=${ELAPSED}s"
sleep 60
fi
done
echo " All 8 workers complete for checkpoint-${ckpt_step}."
# Merge shards
echo " Merging shards..."
$PYTHON -u /mlx/users/jiashuo.fan/playground/inference/merge_results.py \
--prefix "$out_prefix" \
--num-shards 8 \
2>&1 | tee "${LOG_DIR}/eval_ckpt${ckpt_step}_merge.log" || \
echo " [WARN] Merge failed β€” individual shard files still available"
echo " Checkpoint-${ckpt_step} eval complete."
}
# ── Main ──────────────────────────────────────────────────────────────────────
echo "Training data eval: checkpoints ${CHECKPOINTS[*]}"
echo "Data dir: $DATA_DIR"
echo "Target: ~${MAX_SAMPLES_TOTAL} samples per checkpoint"
echo ""
for CKPT in "${CHECKPOINTS[@]}"; do
run_checkpoint_eval "$CKPT" || echo "[WARN] Checkpoint $CKPT failed, continuing..."
done
echo ""
echo "========================================================"
echo "All checkpoint evaluations complete."
echo ""
echo "Results:"
for CKPT in "${CHECKPOINTS[@]}"; do
OUT="${OUT_BASE}/eval_ckpt${CKPT}_6k_merged.json"
if [ -f "$OUT" ]; then
# Quick accuracy summary
python3 -c "
import json
d = json.load(open('$OUT'))
acc = d.get('accuracy', 0)
n = d.get('evaluated', 0)
print(f' ckpt-${CKPT}: acc={acc:.4f} ({d.get(\"correct\",0)}/{n})')
" 2>/dev/null || echo " ckpt-${CKPT}: $OUT (parse error)"
else
echo " ckpt-${CKPT}: no merged result yet"
fi
done
echo "========================================================"