docs: add resource prep section and sync docker/bash helpers

- Rewrite myread.md with proper markdown and four download sources:
  code, data, evalscope-complete-py312 image, execution images.
- Add bash/collect_results.py, perf_backup.py, pull_swe_bench_images.py
  for result aggregation, breakpoint perf recovery, and SWE-bench
  image pre-pulling.
- Update bash/run.py with multi-run suites, perf backup/restore, and
  whitelist-based summary.
- Update config/dpv4-int8_nothinking.yaml benchmark parameters.
- Ignore /docker_images and /results in .gitignore.

.gitignore

@@ -19,7 +19,8 @@ env/
 /outputs/
 /output/
 /output*
-
+/docker_images
+/results
 /tau2-bench
 /log
 /docker

bash/collect_results.py

@@ -0,0 +1,631 @@
+#!/usr/bin/env python3
+"""
+Collect benchmark results from an EvalScope output directory and write a summary
+Excel/CSV similar to:
+
+  P800模型能力评测结果 - DS4-Flash-INT8-NO-Thinking-2.0-FULL.csv
+
+Rules:
+- Scores are averaged across seeds / multi-runs.
+- Perf metrics (latency, TTFT, TPOT, TPS, tokens) are recomputed from raw
+  predictions across all seeds / multi-runs, so resuming from a checkpoint
+  no longer resets cumulative statistics.
+"""
+
+import argparse
+import json
+import statistics
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+# Map benchmark -> capability domain (same as the reference CSV)
+BENCHMARK_DOMAIN = {
+    'bigcodebench': '代码与工程',
+    'humaneval': '代码与工程',
+    'live_code_bench': '代码与工程',
+    'aime24': '推理与数学',
+    'aime25': '推理与数学',
+    'aime26': '推理与数学',
+    'hmmt26': '推理与数学',
+    'imo_answerbench': '推理与数学',
+    'gsm8k': '推理与数学',
+    'competition_math': '推理与数学',
+    'bbh': '推理与数学',
+    'drop': '推理与数学',
+    'gpqa_diamond': '知识与语言理解',
+    'hle': '知识与语言理解',
+    'hle': '知识与语言理解',
+    'hle_low': '知识与语言理解',
+    'mmlu_pro': '知识与语言理解',
+    'simple_qa': '知识与语言理解',
+    'super_gpqa': '知识与语言理解',
+    'mmlu': '知识与语言理解',
+    'cmmlu': '知识与语言理解',
+    'arc': '知识与语言理解',
+    'hellaswag': '知识与语言理解',
+    'trivia_qa': '知识与语言理解',
+    'winogrande': '知识与语言理解',
+    'longbench_v2': '长上下文',
+    'openai_mrcr': '长上下文',
+    'tau2_bench': '智能体与工具',
+    'general_fc': '智能体与工具',
+    'bfcl_v3': '智能体与工具',
+}
+
+# Column order matching the reference CSV
+OUTPUT_COLUMNS = [
+    '分类',
+    'Benchmark',
+    '得分',
+    '实测时间(h)',
+    '总样本数',
+    '延迟_mean(s)',
+    '输出TPS',
+    '请求QPS',
+    '输入tokens_mean',
+    '输出tokens_mean',
+    '累计总tokens',
+    'TTFT_mean(s)',
+    'TTFT P90',
+    'TTFT P99',
+    'TPOT_mean(s)',
+    'TPOT P90',
+    'TPOT P99',
+]
+
+# Some evalscope output directory names do not match the canonical benchmark
+# name (e.g. the hle benchmark writes to the `hle_low` directory because of
+# subset naming). Map them to the canonical name here so the summary uses a
+# consistent label.
+BENCHMARK_NAME_ALIAS = {
+    'hle_low': 'hle',
+}
+
+
+def percentile(values, q):
+    """Return the q-th percentile using numpy's linear interpolation."""
+    if not values:
+        return np.nan
+    return float(np.percentile(values, q))
+
+
+def read_predictions(pred_file: Path):
+    """Yield perf_metrics dicts from a predictions JSONL file."""
+    for obj in read_predictions_with_index(pred_file):
+        if obj['perf_metrics'] is not None:
+            yield obj['perf_metrics']
+
+
+def read_predictions_with_index(pred_file: Path):
+    """Yield ``{'index', 'perf_metrics'}`` dicts from a predictions JSONL file."""
+    if not pred_file.exists():
+        return
+    with open(pred_file, 'r', encoding='utf-8') as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                obj = json.loads(line)
+            except json.JSONDecodeError:
+                continue
+            model_output = obj.get('model_output', {})
+            pm = model_output.get('perf_metrics')
+            if not pm and 'choices' in model_output:
+                choices = model_output['choices']
+                if choices and 'message' in choices[0]:
+                    pm = choices[0]['message'].get('perf_metrics')
+            yield {'index': obj.get('index'), 'perf_metrics': pm}
+
+
+def load_backup_summary(output_dir: Path, benchmark: str, model_name: str):
+    """Load the durable ``perf_stats_backup/<benchmark>__<model>.json``.
+
+    Returns the ``summary`` dict (with ``latency`` / ``ttft`` / ``tpot`` /
+    ``throughput`` / ``usage`` sub-dicts) or ``None`` if no backup exists.
+    """
+    try:
+        from perf_backup import get_backup_paths
+    except ImportError:
+        return None
+    perf_path, _ = get_backup_paths(Path(output_dir), benchmark, model_name)
+    if not perf_path.exists():
+        return None
+    try:
+        payload = json.loads(perf_path.read_text(encoding='utf-8'))
+        return payload.get('summary')
+    except Exception:
+        return None
+
+
+def find_report(output_dir: Path, benchmark: str, model_name: str):
+    """Find the first report JSON for a benchmark/model under output_dir."""
+    bench_dir = output_dir / benchmark
+    if not bench_dir.exists():
+        return None
+    for seed_dir in sorted(bench_dir.iterdir()):
+        if not seed_dir.is_dir():
+            continue
+        for candidate in find_report_candidates(seed_dir, benchmark, model_name):
+            if candidate.exists():
+                return candidate
+    return None
+
+
+def find_report_candidates(seed_dir: Path, benchmark: str, model_name: str):
+    """Return candidate report paths, accepting variant naming patterns."""
+    # The actual report filename may not match the directory name exactly
+    # (e.g. hle_low directory holds hle.json). Try all .json files under
+    # the model directory.
+    candidates = [
+        seed_dir / 'reports' / model_name / f'{benchmark}.json',
+        seed_dir / 'reports' / f'{benchmark}.json',
+    ]
+    for p in (seed_dir / 'reports' / model_name).glob('*.json'):
+        candidates.append(p)
+    for p in (seed_dir / 'reports').glob('*.json'):
+        candidates.append(p)
+    return candidates
+
+
+def find_all_reports(output_dir: Path, benchmark: str, model_name: str):
+    """Find all report JSONs for a benchmark/model (across seeds/runs)."""
+    bench_dir = output_dir / benchmark
+    reports = []
+    if not bench_dir.exists():
+        return reports
+    for seed_dir in sorted(bench_dir.iterdir()):
+        if not seed_dir.is_dir():
+            continue
+        for candidate in find_report_candidates(seed_dir, benchmark, model_name):
+            if candidate.exists() and candidate not in reports:
+                reports.append(candidate)
+                break
+    return reports
+
+
+def find_archive_predictions(output_dir: Path, benchmark: str, model_name: str):
+    """Return the durable per-sample archive maintained by ``perf_backup.py``.
+
+    The archive contains every sample ever produced across all runs and
+    breakpoints for this benchmark/model (deduplicated by ``index``). When
+    present, the summary aggregator should prefer it over the raw
+    ``predictions/*.jsonl`` files because the latter can be overwritten on
+    restart.
+    """
+    try:
+        from perf_backup import get_backup_paths
+    except ImportError:
+        return []
+    _, archive_path = get_backup_paths(output_dir, benchmark, model_name)
+    if archive_path.exists():
+        return [archive_path]
+    return []
+
+
+def find_all_predictions(output_dir: Path, benchmark: str, model_name: str):
+    """Find all predictions JSONL files for a benchmark/model.
+
+    The durable ``predictions_archive/<benchmark>__<model>.jsonl`` (if
+    present) is returned **first** so its deduplicated per-sample records
+    dominate any smaller predictions that a fresh run may have written.
+    """
+    files = list(find_archive_predictions(output_dir, benchmark, model_name))
+    bench_dir = output_dir / benchmark
+    if not bench_dir.exists():
+        return files
+    for seed_dir in sorted(bench_dir.iterdir()):
+        if not seed_dir.is_dir():
+            continue
+        pred_dir = seed_dir / 'predictions' / model_name
+        if pred_dir.exists():
+            files.extend(sorted(pred_dir.rglob('*.jsonl')))
+    return files
+
+
+def parse_log_duration(log_file: Path):
+    """Parse first and last timestamp from eval_log.log and return duration in hours."""
+    if not log_file.exists():
+        return np.nan
+    from datetime import datetime
+    first_dt = None
+    last_dt = None
+    fmt = '%Y-%m-%d %H:%M:%S'
+    with open(log_file, 'r', encoding='utf-8') as f:
+        for line in f:
+            line = line.strip()
+            if len(line) < 19:
+                continue
+            try:
+                dt = datetime.strptime(line[:19], fmt)
+            except ValueError:
+                continue
+            if first_dt is None:
+                first_dt = dt
+            last_dt = dt
+    if first_dt is None or last_dt is None or last_dt <= first_dt:
+        return np.nan
+    return (last_dt - first_dt).total_seconds() / 3600.0
+
+
+def extract_score(report_data: dict) -> float:
+    """Extract the top-level score from a report JSON."""
+    score = report_data.get('score')
+    if score is not None:
+        return float(score)
+    metrics = report_data.get('metrics', [])
+    for m in metrics:
+        if m.get('name') == 'mean_acc':
+            return float(m.get('score', m.get('macro_score', 0.0)))
+    return 0.0
+
+
+def collect_benchmark(output_dir: Path, benchmark: str, model_name: str):
+    """Collect aggregated results for one benchmark."""
+    reports = find_all_reports(output_dir, benchmark, model_name)
+    if not reports:
+        return None
+
+    scores = []
+    summary0 = None
+    n_samples_unique = 0
+    for report in reports:
+        try:
+            data = json.loads(report.read_text(encoding='utf-8'))
+            scores.append(extract_score(data))
+            if summary0 is None:
+                summary0 = data.get('perf_metrics', {}).get('summary', {})
+                n_samples_unique = summary0.get('n_samples', 0)
+        except Exception:
+            continue
+
+    avg_score = float(np.mean(scores)) if scores else 0.0
+
+    # Aggregate raw prediction perf metrics across all seeds/runs.
+    # This fixes the breakpoint-resume issue where cumulative stats are reset.
+    # Predictions are deduplicated by sample `index` so the archive (which
+    # spans every run) and the latest ``predictions/*.jsonl`` don't double
+    # count the same sample.
+    pred_files = find_all_predictions(output_dir, benchmark, model_name)
+    seen_indexes = set()
+    latencies = []
+    ttfts = []
+    tpots = []
+    input_tokens = []
+    output_tokens = []
+    sample_indexes = []
+    for pf in pred_files:
+        for obj in read_predictions_with_index(pf):
+            idx = obj['index']
+            if idx is None or idx in seen_indexes:
+                # Still keep perf data even when we lack an index, so older
+                # benchmark files without `index` don't get dropped.
+                pm = obj['perf_metrics']
+            else:
+                seen_indexes.add(idx)
+                sample_indexes.append(idx)
+                pm = obj['perf_metrics']
+            if pm is None:
+                continue
+            if 'latency' in pm:
+                latencies.append(float(pm['latency']))
+            if 'ttft' in pm:
+                ttfts.append(float(pm['ttft']))
+            if 'tpot' in pm:
+                tpots.append(float(pm['tpot']))
+
+            itok = pm.get('input_tokens')
+            otok = pm.get('output_tokens')
+            if itok is None and 'usage' in pm:
+                itok = pm['usage'].get('input_tokens')
+                otok = pm['usage'].get('output_tokens')
+            if itok is not None:
+                input_tokens.append(int(itok))
+            if otok is not None:
+                output_tokens.append(int(otok))
+
+    # If we don't have raw predictions but have a perf_stats backup, that
+    # represents a known-good summary captured right after a clean run —
+    # preferable to summary0 (which may be the just-reset run).
+    backup_summary = None
+    if not latencies:
+        backup_summary = load_backup_summary(output_dir, benchmark, model_name)
+
+    if latencies:
+        latency_mean = float(np.mean(latencies))
+        total_compute_time = float(np.sum(latencies))
+        total_output_tokens = sum(output_tokens)
+        avg_output_tps = total_output_tokens / total_compute_time if total_compute_time > 0 else np.nan
+        avg_req_ps = len(latencies) / total_compute_time if total_compute_time > 0 else np.nan
+        input_tok_mean = float(np.mean(input_tokens)) if input_tokens else np.nan
+        output_tok_mean = float(np.mean(output_tokens)) if output_tokens else np.nan
+        total_tokens = sum(input_tokens) + sum(output_tokens)
+        ttft_mean = float(np.mean(ttfts)) if ttfts else np.nan
+        ttft_p90 = percentile(ttfts, 90)
+        ttft_p99 = percentile(ttfts, 99)
+        tpot_mean = float(np.mean(tpots)) if tpots else np.nan
+        tpot_p90 = percentile(tpots, 90)
+        tpot_p99 = percentile(tpots, 99)
+        # The actual sample count we just rebuilt from raw predictions is more
+        # reliable than the (possibly reset) report summary's n_samples.
+        if sample_indexes:
+            n_samples_unique = len(sample_indexes)
+    elif summary0:
+        # Fallback to report summary if raw predictions are unavailable
+        latency_mean = summary0.get('latency', {}).get('mean', np.nan)
+        avg_output_tps = summary0.get('throughput', {}).get('avg_output_tps', np.nan)
+        avg_req_ps = summary0.get('throughput', {}).get('avg_req_ps', np.nan)
+        input_tok_mean = summary0.get('tokens', {}).get('input_tokens', {}).get('mean', np.nan)
+        output_tok_mean = summary0.get('tokens', {}).get('output_tokens', {}).get('mean', np.nan)
+        total_tokens = summary0.get('tokens', {}).get('total_tokens_count', np.nan)
+        ttft_mean = summary0.get('ttft', {}).get('mean', np.nan)
+        ttft_p90 = summary0.get('ttft', {}).get('90%', np.nan)
+        ttft_p99 = summary0.get('ttft', {}).get('99%', np.nan)
+        tpot_mean = summary0.get('tpot', {}).get('mean', np.nan)
+        tpot_p90 = summary0.get('tpot', {}).get('90%', np.nan)
+        tpot_p99 = summary0.get('tpot', {}).get('99%', np.nan)
+        # If the just-read report looks like a freshly-reset run (smaller
+        # n_samples than the durable backup), prefer the backup's summary so
+        # the cumulative numbers are not lost.
+        try:
+            from perf_backup import get_backup_paths
+            backup_path, _ = get_backup_paths(output_dir, benchmark, model_name)
+            if backup_path.exists():
+                payload = json.loads(backup_path.read_text(encoding='utf-8'))
+                if (payload.get('n_samples') or 0) > (summary0.get('n_samples') or 0):
+                    backup_summary = payload.get('summary') or {}
+                    latency_mean = backup_summary.get('latency', {}).get('mean', latency_mean)
+                    avg_output_tps = backup_summary.get('throughput', {}).get('avg_output_tps', avg_output_tps)
+                    avg_req_ps = backup_summary.get('throughput', {}).get('avg_req_ps', avg_req_ps)
+                    input_tok_mean = backup_summary.get('usage', {}).get('input_tokens', {}).get('mean', input_tok_mean)
+                    output_tok_mean = backup_summary.get('usage', {}).get('output_tokens', {}).get('mean', output_tok_mean)
+                    total_tokens = backup_summary.get('usage', {}).get('total_tokens_count', total_tokens)
+                    ttft_mean = backup_summary.get('ttft', {}).get('mean', ttft_mean)
+                    ttft_p90 = backup_summary.get('ttft', {}).get('90%', ttft_p90)
+                    ttft_p99 = backup_summary.get('ttft', {}).get('99%', ttft_p99)
+                    tpot_mean = backup_summary.get('tpot', {}).get('mean', tpot_mean)
+                    tpot_p90 = backup_summary.get('tpot', {}).get('90%', tpot_p90)
+                    tpot_p99 = backup_summary.get('tpot', {}).get('99%', tpot_p99)
+                    n_samples_unique = payload.get('n_samples') or n_samples_unique
+        except Exception:
+            pass
+    elif backup_summary:
+        latency_mean = backup_summary.get('latency', {}).get('mean', np.nan)
+        avg_output_tps = backup_summary.get('throughput', {}).get('avg_output_tps', np.nan)
+        avg_req_ps = backup_summary.get('throughput', {}).get('avg_req_ps', np.nan)
+        input_tok_mean = backup_summary.get('usage', {}).get('input_tokens', {}).get('mean', np.nan)
+        output_tok_mean = backup_summary.get('usage', {}).get('output_tokens', {}).get('mean', np.nan)
+        total_tokens = backup_summary.get('usage', {}).get('total_tokens_count', np.nan)
+        ttft_mean = backup_summary.get('ttft', {}).get('mean', np.nan)
+        ttft_p90 = backup_summary.get('ttft', {}).get('90%', np.nan)
+        ttft_p99 = backup_summary.get('ttft', {}).get('99%', np.nan)
+        tpot_mean = backup_summary.get('tpot', {}).get('mean', np.nan)
+        tpot_p90 = backup_summary.get('tpot', {}).get('90%', np.nan)
+        tpot_p99 = backup_summary.get('tpot', {}).get('99%', np.nan)
+    else:
+        return None
+
+    # Duration: prefer the durable active timer maintained by perf_backup.py,
+    # which only counts time when run_task() is actually executing. This avoids
+    # counting idle gaps caused by manual interruption/resume.
+    # If no active timer exists, fall back to log duration or latency estimate.
+    active_time_hours = np.nan
+    try:
+        from perf_backup import load_active_time
+        active_seconds = load_active_time(output_dir, benchmark, model_name)
+        if active_seconds > 0:
+            active_time_hours = active_seconds / 3600.0
+    except Exception:
+        pass
+
+    if not np.isnan(active_time_hours):
+        duration_hours = active_time_hours
+    else:
+        # Legacy fallback: sum of wall-clock durations from eval_log.log.
+        # For sandbox benchmarks the main log may not cover sandbox execution,
+        # so we also estimate wall time as compute_time/batch_size and take max.
+        duration_hours = 0.0
+        batch_size = 4
+        for report in reports:
+            log_file = report.parent.parent.parent / 'logs' / 'eval_log.log'
+            d = parse_log_duration(log_file)
+            if not np.isnan(d):
+                duration_hours += d
+            cfg_file = report.parent.parent.parent / 'configs' / 'task_config.yaml'
+            if cfg_file.exists():
+                try:
+                    import yaml
+                    cfg = yaml.safe_load(cfg_file.read_text(encoding='utf-8'))
+                    batch_size = int(cfg.get('eval_batch_size', batch_size))
+                except Exception:
+                    pass
+
+        compute_wall_estimate = (total_compute_time / batch_size / 3600.0) if latencies and batch_size > 0 else np.nan
+        if duration_hours <= 0:
+            duration_hours = compute_wall_estimate
+        else:
+            duration_hours = max(duration_hours, compute_wall_estimate)
+
+    return {
+        '分类': BENCHMARK_DOMAIN.get(benchmark, '其他'),
+        'Benchmark': BENCHMARK_NAME_ALIAS.get(benchmark, benchmark),
+        '得分': round(avg_score, 4),
+        '实测时间(h)': round(duration_hours, 4) if not np.isnan(duration_hours) else np.nan,
+        '总样本数': n_samples_unique,
+        '延迟_mean(s)': round(latency_mean, 5) if not np.isnan(latency_mean) else np.nan,
+        '输出TPS': round(avg_output_tps, 2) if not np.isnan(avg_output_tps) else np.nan,
+        '请求QPS': round(avg_req_ps, 4) if not np.isnan(avg_req_ps) else np.nan,
+        '输入tokens_mean': round(input_tok_mean, 2) if not np.isnan(input_tok_mean) else np.nan,
+        '输出tokens_mean': round(output_tok_mean, 2) if not np.isnan(output_tok_mean) else np.nan,
+        '累计总tokens': total_tokens if not np.isnan(total_tokens) else np.nan,
+        'TTFT_mean(s)': round(ttft_mean, 5) if not np.isnan(ttft_mean) else np.nan,
+        'TTFT P90': round(ttft_p90, 5) if not np.isnan(ttft_p90) else np.nan,
+        'TTFT P99': round(ttft_p99, 5) if not np.isnan(ttft_p99) else np.nan,
+        'TPOT_mean(s)': round(tpot_mean, 5) if not np.isnan(tpot_mean) else np.nan,
+        'TPOT P90': round(tpot_p90, 5) if not np.isnan(tpot_p90) else np.nan,
+        'TPOT P99': round(tpot_p99, 5) if not np.isnan(tpot_p99) else np.nan,
+    }
+
+
+def eval_benchmark(benchmark_names: list, output_dir: Path, model_name: str,
+                   out_name: str = None, excel_output_dir: Path = None):
+    """Collect results for a specific list of benchmarks.
+
+    This is the public entry point intended for use by ``run.py`` and other
+    scripts that already know which benchmarks they ran.
+
+    Args:
+        benchmark_names: List of canonical benchmark names to aggregate (e.g.
+            ``['aime24', 'gsm8k', 'arc']``). Only benchmarks with reports on
+            disk will appear in the summary.
+        output_dir: EvalScope output root directory.
+        model_name: Model name to look up reports/predictions for.
+        out_name: Output file name (without extension). Defaults to the safe
+            model name.
+        excel_output_dir: Optional separate directory for the Excel summary.
+            If provided, the Excel file is written here while the CSV stays
+            under ``output_dir/results/<model>/``.
+
+    Returns:
+        (csv_path, xlsx_path) tuple.
+    """
+    return collect_all(output_dir, model_name, out_name,
+                       include_benchmarks=benchmark_names,
+                       excel_output_dir=excel_output_dir)
+
+
+def collect_all(output_dir: Path, model_name: str, out_name: str = None,
+                include_benchmarks: list = None,
+                excel_output_dir: Path = None):
+    """Collect benchmarks under ``output_dir`` and write summary Excel/CSV."""
+    if not output_dir.exists():
+        raise FileNotFoundError(f'Output directory not found: {output_dir}')
+
+    safe_model = model_name.replace('/', '_').replace('\\', '_').replace(' ', '_')
+
+    # Discover benchmarks
+    if include_benchmarks is not None:
+        # Whitelist mode: only aggregate benchmarks that were actually run in
+        # this invocation. Directory names may differ from canonical names
+        # (e.g. evalscope writes the hle benchmark to the hle_low directory),
+        # so we canonicalize each directory name via BENCHMARK_NAME_ALIAS and
+        # check membership in the caller's list. Order follows the caller's
+        # include_benchmarks list.
+        include_set = set(include_benchmarks)
+        discovered = []  # list of (dir_name, canonical_name)
+        for bench_dir in output_dir.iterdir():
+            if not bench_dir.is_dir() or bench_dir.name == safe_model:
+                continue
+            if not find_all_reports(output_dir, bench_dir.name, model_name):
+                continue
+            canonical = BENCHMARK_NAME_ALIAS.get(bench_dir.name, bench_dir.name)
+            if canonical in include_set or bench_dir.name in include_set:
+                discovered.append((bench_dir.name, canonical))
+
+        order = {name: idx for idx, name in enumerate(include_benchmarks)}
+        discovered.sort(key=lambda x: order.get(x[1], len(include_benchmarks)))
+
+        benchmarks = [dir_name for dir_name, _ in discovered]
+    else:
+        # Legacy behaviour: scan everything on disk.
+        benchmarks = []
+        for bench_dir in sorted(output_dir.iterdir()):
+            if not bench_dir.is_dir() or bench_dir.name == safe_model:
+                continue
+            reports = find_all_reports(output_dir, bench_dir.name, model_name)
+            if reports:
+                benchmarks.append(bench_dir.name)
+
+    rows = []
+    for benchmark in benchmarks:
+        row = collect_benchmark(output_dir, benchmark, model_name)
+        if row:
+            rows.append(row)
+
+    if not rows:
+        print(f'No results found for model {model_name} under {output_dir}')
+        return None, None
+
+    df = pd.DataFrame(rows, columns=OUTPUT_COLUMNS)
+    df = df.drop_duplicates(subset=['Benchmark'], keep='first')
+
+    # Add total row
+    total_score = df['得分'].mean()
+    total_time = df['实测时间(h)'].sum()
+    total_row = {
+        '分类': '总计',
+        'Benchmark': '',
+        '得分': round(total_score, 4),
+        '实测时间(h)': round(total_time, 4),
+    }
+    for col in OUTPUT_COLUMNS:
+        if col not in total_row:
+            total_row[col] = np.nan
+    df = pd.concat([df, pd.DataFrame([total_row], columns=OUTPUT_COLUMNS)], ignore_index=True)
+
+    summary_dir = output_dir / 'results' / safe_model
+    summary_dir.mkdir(parents=True, exist_ok=True)
+
+    if out_name is None:
+        out_name = safe_model
+
+    csv_path = summary_dir / f'{out_name}.csv'
+
+    # Excel can optionally be written to a separate project-level results dir
+    # so that the latest summary per model is easy to find independently of
+    # the per-run output directory.
+    if excel_output_dir is not None:
+        xlsx_dir = Path(excel_output_dir)
+        xlsx_dir.mkdir(parents=True, exist_ok=True)
+        xlsx_path = xlsx_dir / f'{out_name}.xlsx'
+    else:
+        xlsx_path = summary_dir / f'{out_name}.xlsx'
+
+    df.to_csv(csv_path, index=False, encoding='utf-8-sig')
+    df.to_excel(xlsx_path, index=False)
+
+    print(f'Summary written to:')
+    print(f'  CSV:  {csv_path}')
+    print(f'  Excel: {xlsx_path}')
+    return csv_path, xlsx_path
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Collect EvalScope benchmark results into Excel/CSV')
+    parser.add_argument('--output-dir', required=True, help='EvalScope output directory')
+    parser.add_argument('--model', default='DeepSeek-V4-Flash-Int8', help='Model name used in reports')
+    parser.add_argument('--out-name', default=None, help='Output file name (without extension); defaults to safe model name')
+    parser.add_argument('--include', default=None,
+                        help='Comma-separated benchmark whitelist (default: all on disk)')
+    parser.add_argument('--benchmarks', '--benchmarks', dest='benchmarks', default=None,
+                        help='Alias for --include')
+    parser.add_argument('--excel-output-dir', default=None,
+                        help='Separate directory for the Excel summary; defaults to <project_root>/results')
+    args = parser.parse_args()
+
+    # Default Excel output dir to project-level results/ so the latest summary
+    # per model is easy to find independently of the per-run output directory.
+    if args.excel_output_dir is None:
+        excel_output_dir = Path(__file__).parent.parent / 'results'
+    else:
+        excel_output_dir = Path(args.excel_output_dir)
+
+    include_benchmarks = None
+    if args.include:
+        include_benchmarks = [b.strip() for b in args.include.split(',') if b.strip()]
+    elif args.benchmarks:
+        include_benchmarks = [b.strip() for b in args.benchmarks.split(',') if b.strip()]
+
+    if include_benchmarks is not None:
+        eval_benchmark(include_benchmarks, Path(args.output_dir), args.model, args.out_name,
+                        excel_output_dir=excel_output_dir)
+    else:
+        collect_all(Path(args.output_dir), args.model, args.out_name,
+                    excel_output_dir=excel_output_dir)
+
+
+if __name__ == '__main__':
+    main()

bash/perf_backup.py

@@ -0,0 +1,241 @@
+#!/usr/bin/env python3
+"""
+Perf-stats backup helpers.
+
+EvalScope writes the per-benchmark cumulative perf summary into
+``report.json`` under ``perf_metrics.summary`` and the raw per-sample records
+into ``predictions/*.jsonl``. Both are overwritten on every run, so a
+checkpoint restart resets cumulative stats and loses prior samples.
+
+This module maintains **two durable files** under ``<output_dir>/`` so the
+breakpoint-resume issue can be reconstructed:
+
+  1. ``perf_stats_backup/<benchmark>__<model>.json``
+     - The latest complete ``perf_metrics.summary`` captured right after
+       each ``run_task()`` finishes.
+  2. ``predictions_archive/<benchmark>__<model>.jsonl``
+     - All per-sample records ever seen, deduplicated by sample ``index``.
+
+``bash.collect_results`` prefers these archive files when aggregating metrics.
+"""
+
+import json
+import shutil
+from datetime import datetime
+from pathlib import Path
+
+
+PERF_STATS_BACKUP_DIRNAME = 'perf_stats_backup'
+PREDICTIONS_ARCHIVE_DIRNAME = 'predictions_archive'
+ACTIVE_TIME_DIRNAME = 'active_time'
+
+
+def _safe(s: str) -> str:
+    return s.replace('/', '_').replace('\\', '_').replace(' ', '_')
+
+
+def get_backup_paths(output_dir: Path, benchmark: str, model_name: str):
+    """Return the (perf_stats_backup, predictions_archive) paths for a benchmark/model."""
+    safe = _safe(model_name)
+    backup_dir = Path(output_dir) / PERF_STATS_BACKUP_DIRNAME
+    archive_dir = Path(output_dir) / PREDICTIONS_ARCHIVE_DIRNAME
+    perf_stats_path = backup_dir / f'{_safe(benchmark)}__{safe}.json'
+    predictions_path = archive_dir / f'{_safe(benchmark)}__{safe}.jsonl'
+    return perf_stats_path, predictions_path
+
+
+def backup_perf_stats(output_dir: Path, benchmark: str, model_name: str,
+                       report_json: Path) -> Path:
+    """Copy ``perf_metrics.summary`` from a finished report to the durable backup.
+
+    Only writes if ``report_json`` exists and contains a ``perf_metrics.summary``.
+    Returns the backup path.
+    """
+    if not report_json or not report_json.exists():
+        return None
+    try:
+        data = json.loads(report_json.read_text(encoding='utf-8'))
+    except Exception:
+        return None
+    summary = (data.get('perf_metrics') or {}).get('summary')
+    if not summary:
+        return None
+
+    perf_stats_path, _ = get_backup_paths(Path(output_dir), benchmark, model_name)
+    perf_stats_path.parent.mkdir(parents=True, exist_ok=True)
+    payload = {
+        'benchmark': benchmark,
+        'model': model_name,
+        'updated_at': datetime.now().isoformat(timespec='seconds'),
+        'n_samples': summary.get('n_samples'),
+        'summary': summary,
+    }
+    # Don't overwrite a strictly larger backup with a smaller one — that
+    # would be the symptom of a reset run and we want to keep the larger.
+    if perf_stats_path.exists():
+        try:
+            old = json.loads(perf_stats_path.read_text(encoding='utf-8'))
+            old_n = old.get('n_samples') or 0
+            new_n = summary.get('n_samples') or 0
+            if new_n < old_n:
+                # Don't clobber the larger historical backup.
+                return perf_stats_path
+        except Exception:
+            pass
+    perf_stats_path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding='utf-8')
+    return perf_stats_path
+
+
+def archive_predictions(output_dir: Path, benchmark: str, model_name: str,
+                         predictions_dir: Path) -> Path:
+    """Append the latest predictions into the durable archive.
+
+    Reads every ``*.jsonl`` under ``predictions_dir``, parses each line, and
+    appends each sample to the archive file (deduplicated by ``index``). If
+    the same ``index`` was already archived with a different perf payload,
+    the newer record wins.
+
+    Returns the archive path.
+    """
+    _, archive_path = get_backup_paths(Path(output_dir), benchmark, model_name)
+    archive_path.parent.mkdir(parents=True, exist_ok=True)
+
+    if not predictions_dir or not predictions_dir.exists():
+        return archive_path
+
+    # Load existing archive (index -> record)
+    existing = {}
+    if archive_path.exists():
+        with archive_path.open('r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    obj = json.loads(line)
+                except json.JSONDecodeError:
+                    continue
+                idx = obj.get('index')
+                if idx is not None:
+                    existing[idx] = obj
+
+    # Add new predictions
+    new_count = 0
+    updated_count = 0
+    for jsonl in sorted(predictions_dir.rglob('*.jsonl')):
+        with jsonl.open('r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    obj = json.loads(line)
+                except json.JSONDecodeError:
+                    continue
+                idx = obj.get('index')
+                if idx is None:
+                    continue
+                if idx in existing:
+                    updated_count += 1
+                else:
+                    new_count += 1
+                existing[idx] = obj
+
+    # Write back atomically
+    tmp_path = archive_path.with_suffix('.jsonl.tmp')
+    with tmp_path.open('w', encoding='utf-8') as f:
+        for idx in sorted(existing.keys()):
+            f.write(json.dumps(existing[idx], ensure_ascii=False))
+            f.write('\n')
+    shutil.move(str(tmp_path), str(archive_path))
+
+    return archive_path
+
+
+def restore_from_backup(output_dir: Path, benchmark: str, model_name: str,
+                        target_predictions_dir: Path, target_report_json: Path):
+    """If both ``perf_stats_backup`` and ``predictions_archive`` exist for a
+    benchmark/model, materialise them into ``target_predictions_dir`` and
+    ``target_report_json`` before the next run starts, so evalscope resumes
+    from the larger historical state instead of overwriting it.
+
+    Returns ``True`` if a backup was restored, ``False`` otherwise.
+    """
+    perf_stats_path, archive_path = get_backup_paths(Path(output_dir), benchmark, model_name)
+    if not perf_stats_path.exists() and not archive_path.exists():
+        return False
+
+    # Restore the archive into the predictions directory so evalscope's
+    # use_cache mechanism sees the previously-completed samples.
+    if archive_path.exists():
+        target_predictions_dir.mkdir(parents=True, exist_ok=True)
+        target_jsonl = target_predictions_dir / archive_path.name
+        shutil.copy2(archive_path, target_jsonl)
+
+    # Restore the perf summary into the report file so the cumulative stats
+    # survive. We do NOT overwrite score/metrics — only the perf section.
+    if perf_stats_path.exists() and target_report_json.exists():
+        try:
+            payload = json.loads(perf_stats_path.read_text(encoding='utf-8'))
+            summary = payload.get('summary')
+            if not summary:
+                return True
+            report = json.loads(target_report_json.read_text(encoding='utf-8'))
+            report.setdefault('perf_metrics', {})['summary'] = summary
+            target_report_json.write_text(
+                json.dumps(report, ensure_ascii=False, indent=2), encoding='utf-8'
+            )
+        except Exception:
+            pass
+
+    return True
+
+
+# ============================================================
+# Active run-time tracking (excludes idle gaps between resumes)
+# ============================================================
+
+
+def get_active_time_path(output_dir: Path, benchmark: str, model_name: str) -> Path:
+    """Return the path to the durable active-time record for a benchmark/model."""
+    safe = _safe(model_name)
+    active_dir = Path(output_dir) / ACTIVE_TIME_DIRNAME
+    return active_dir / f'{_safe(benchmark)}__{safe}.json'
+
+
+def record_active_time(output_dir: Path, benchmark: str, model_name: str,
+                       seconds: float) -> Path:
+    """Accumulate ``seconds`` of active run time for a benchmark/model.
+
+    This is intended to measure only the time ``run_task()`` was actually
+    executing, excluding idle gaps caused by manual interruption/resume.
+    """
+    path = get_active_time_path(Path(output_dir), benchmark, model_name)
+    path.parent.mkdir(parents=True, exist_ok=True)
+
+    data = {'total_seconds': 0.0}
+    if path.exists():
+        try:
+            data = json.loads(path.read_text(encoding='utf-8'))
+        except Exception:
+            pass
+
+    data['total_seconds'] = float(data.get('total_seconds', 0.0)) + float(seconds)
+    data['updated_at'] = datetime.now().isoformat(timespec='seconds')
+    data['benchmark'] = benchmark
+    data['model'] = model_name
+
+    path.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding='utf-8')
+    return path
+
+
+def load_active_time(output_dir: Path, benchmark: str, model_name: str) -> float:
+    """Return accumulated active run time in seconds, or 0.0 if no record."""
+    path = get_active_time_path(Path(output_dir), benchmark, model_name)
+    if not path.exists():
+        return 0.0
+    try:
+        data = json.loads(path.read_text(encoding='utf-8'))
+        return float(data.get('total_seconds', 0.0))
+    except Exception:
+        return 0.0

bash/pull_swe_bench_images.py

@@ -0,0 +1,329 @@
+#!/usr/bin/env python3
+"""
+预拉取 SWE-bench 评测所需的 Docker 镜像，支持断点续传。
+
+用法:
+    # 默认从上次进度继续
+    python bash/pull_swe_bench_images.py --dataset swe_bench_verified --max-workers 4
+
+    # 强制重新开始（删除进度文件）
+    rm -f output/.swe_bench_pull_state.json
+    python bash/pull_swe_bench_images.py --dataset swe_bench_verified --max-workers 4
+
+    # 测试前 3 个镜像
+    python bash/pull_swe_bench_images.py --dry-run 3 --max-workers 1
+
+进度文件:
+    output/.swe_bench_pull_state.json
+    记录已拉取/跳过/失败的镜像，Ctrl+C 后下次自动跳过。
+"""
+
+import json
+import os
+import sys
+import threading
+import subprocess
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+# 国内网络加速（必须在导入 evalscope/huggingface 之前设置）
+os.environ.setdefault('USE_MODELSCOPE_HUB', '1')
+os.environ.setdefault('HF_ENDPOINT', 'https://hf-mirror.com')
+os.environ.setdefault('PYTHONUNBUFFERED', '1')
+
+from tqdm import tqdm
+
+# 让 evalscope 可导入
+PROJECT_ROOT = Path(__file__).parent.parent.resolve()
+sys.path.insert(0, str(PROJECT_ROOT / 'evalscope'))
+
+from evalscope.api.dataset import RemoteDataLoader, FieldSpec
+
+
+DATASET_IDS = {
+    'swe_bench_verified': 'princeton-nlp/SWE-bench_Verified',
+    # 'swe_bench_lite': 'princeton-nlp/SWE-bench_Lite',
+    # 'swe_bench_verified_mini': 'evalscope/swe-bench-verified-mini',
+    # 'swe_bench_pro': 'evalscope/swe-bench-pro',
+}
+
+DEFAULT_OUTPUT_DIR = PROJECT_ROOT / 'output'
+STATE_FILENAME = '.swe_bench_pull_state.json'
+
+
+def get_state_path(output_dir: Path) -> Path:
+    return Path(output_dir) / STATE_FILENAME
+
+
+def load_state(output_dir: Path) -> dict:
+    """Load previous pull state; return empty state if none exists."""
+    state_path = get_state_path(output_dir)
+    if not state_path.exists():
+        return {'done': [], 'skipped': [], 'failed': []}
+    try:
+        with state_path.open('r', encoding='utf-8') as f:
+            state = json.load(f)
+        # Ensure required keys exist
+        for key in ('done', 'skipped', 'failed'):
+            state.setdefault(key, [])
+        return state
+    except Exception as e:
+        print(f'WARNING: failed to load state file {state_path}: {e}')
+        return {'done': [], 'skipped': [], 'failed': []}
+
+
+def save_state(output_dir: Path, state: dict):
+    """Atomically save current pull state."""
+    state_path = get_state_path(output_dir)
+    state_path.parent.mkdir(parents=True, exist_ok=True)
+    tmp_path = state_path.with_suffix('.tmp')
+    try:
+        with tmp_path.open('w', encoding='utf-8') as f:
+            json.dump(state, f, ensure_ascii=False, indent=2)
+        tmp_path.replace(state_path)
+    except Exception as e:
+        print(f'WARNING: failed to save state file {state_path}: {e}')
+
+
+def load_swe_samples(dataset_id: str):
+    """Load SWE-bench samples and wrap them into the format expected by build_images."""
+    print(f'正在加载数据集元数据: {dataset_id} ...')
+    loader = RemoteDataLoader(
+        data_id_or_path=dataset_id,
+        split='test',
+        sample_fields=FieldSpec(
+            input='problem_statement',
+            metadata=[
+                'instance_id',
+                'repo',
+                'base_commit',
+                'patch',
+                'PASS_TO_PASS',
+                'FAIL_TO_PASS',
+                'test_patch',
+                'version',
+                'environment_setup_commit',
+                'hints_text',
+                'created_at',
+            ],
+        ),
+    )
+    return loader.load()
+
+
+def get_image_name(instance, namespace: str, arch: str) -> str:
+    """根据 swebench 规则生成远程镜像名称。"""
+    if isinstance(instance, dict):
+        metadata = instance
+    else:
+        metadata = getattr(instance, 'metadata', {}) or {}
+
+    instance_id = metadata.get('instance_id', '')
+    if not instance_id and not isinstance(instance, dict):
+        instance_id = getattr(instance, 'instance_id', '')
+
+    if not instance_id:
+        return None
+
+    image_name = f'{namespace}/sweb.eval.{arch}.{instance_id.lower()}:latest'
+    image_name = image_name.replace('__', '_1776_')
+    return image_name
+
+
+def get_local_images() -> set:
+    """Return a set of all locally available Docker image names."""
+    result = subprocess.run(
+        ['docker', 'images', '--format', '{{.Repository}}:{{.Tag}}'],
+        capture_output=True, text=True
+    )
+    if result.returncode != 0:
+        return set()
+    return {line.strip() for line in result.stdout.splitlines() if line.strip()}
+
+
+_print_lock = threading.Lock()
+
+
+def _log(msg: str):
+    with _print_lock:
+        print(msg, flush=True)
+
+
+def _stream_pull(image_name: str) -> tuple:
+    """Run docker pull and stream output in real time.
+
+    Returns (returncode, last_lines_of_output).
+    """
+    process = subprocess.Popen(
+        ['docker', 'pull', image_name],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+        bufsize=1,
+    )
+
+    lines = []
+    if process.stdout:
+        for line in process.stdout:
+            line = line.rstrip('\n')
+            lines.append(line)
+            _log(f'   {line}')
+            # Keep only the last 50 lines for error reporting
+            if len(lines) > 50:
+                lines.pop(0)
+
+    process.wait()
+    return process.returncode, '\n'.join(lines)
+
+
+def pull_one_image(args_tuple, state: dict, output_dir: Path, local_images: set):
+    """Pull a single image and update shared state.
+
+    Returns (idx, image_name, status, message).
+    """
+    idx, total, instance, namespace, arch = args_tuple
+    image_name = get_image_name(instance, namespace, arch)
+    if not image_name:
+        return idx, None, 'skip', 'missing instance_id'
+
+    # Resume: skip if already recorded in state
+    if image_name in state['done'] or image_name in state['skipped']:
+        return idx, image_name, 'skip', 'already recorded in state'
+
+    # Also check local docker images (in case state was deleted)
+    if image_name in local_images:
+        state['skipped'].append(image_name)
+        save_state(output_dir, state)
+        return idx, image_name, 'skip', 'already exists locally'
+
+    _log(f'⬇️  [{idx+1}/{total}] START  {image_name}')
+
+    rc, output = _stream_pull(image_name)
+
+    if rc == 0:
+        state['done'].append(image_name)
+        save_state(output_dir, state)
+        return idx, image_name, 'success', ''
+    else:
+        msg = output.strip()[-500:] if output.strip() else 'docker pull failed'
+        state['failed'].append(image_name)
+        save_state(output_dir, state)
+        return idx, image_name, 'fail', msg
+
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description='Pre-pull SWE-bench Docker images with resume support')
+    parser.add_argument(
+        '--dataset',
+        default='swe_bench_verified',
+        choices=list(DATASET_IDS.keys()),
+        help='SWE-bench dataset variant',
+    )
+    parser.add_argument(
+        '--max-workers',
+        type=int,
+        default=4,
+        help='Max concurrent docker pulls (default: 4)',
+    )
+    parser.add_argument(
+        '--force-arch',
+        default='x86_64',
+        choices=['', 'arm64', 'x86_64'],
+        help='Image architecture (default: x86_64)',
+    )
+    parser.add_argument(
+        '--dockerhub-username',
+        default='swebench',
+        help='DockerHub namespace for remote images (default: swebench)',
+    )
+    parser.add_argument(
+        '--output-dir',
+        default=str(DEFAULT_OUTPUT_DIR),
+        help='Directory to store progress state (default: output/)',
+    )
+    parser.add_argument(
+        '--dry-run',
+        type=int,
+        default=0,
+        help='Only test the first N images',
+    )
+    parser.add_argument(
+        '--retry-failed',
+        action='store_true',
+        help='Retry images that failed in previous runs',
+    )
+    args = parser.parse_args()
+
+    output_dir = Path(args.output_dir)
+    state = load_state(output_dir)
+
+    # Optionally retry previously failed images
+    if args.retry_failed:
+        failed = state['failed']
+        state['failed'] = []
+        print(f'🔄 重试 {len(failed)} 个之前失败的镜像')
+
+    dataset_id = DATASET_IDS[args.dataset]
+    samples = load_swe_samples(dataset_id)
+
+    if args.dry_run > 0:
+        samples = samples[:args.dry_run]
+        print(f'⚠️  测试模式：仅处理前 {args.dry_run} 个样本以验证镜像名和网络...')
+    else:
+        print(f'✅ 成功加载 {len(samples)} 个样本。')
+
+    already_done = len(state['done']) + len(state['skipped'])
+    print(f'📂 进度文件: {get_state_path(output_dir)}')
+    print(f'   已下载/已存在: {len(state["done"])} | 已跳过: {len(state["skipped"])} | 历史失败: {len(state["failed"])}')
+
+    # Cache local image names once to avoid 500+ `docker images -q` calls.
+    print('🔄 正在扫描本地镜像...')
+    local_images = get_local_images()
+    print(f'   本地已有 {len(local_images)} 个镜像')
+    print('-' * 60)
+
+    tasks = [
+        (idx, len(samples), sample, args.dockerhub_username, args.force_arch)
+        for idx, sample in enumerate(samples)
+    ]
+
+    success_count = 0
+    skip_count = 0
+    fail_count = 0
+
+    with ThreadPoolExecutor(max_workers=args.max_workers) as executor:
+        futures = {executor.submit(pull_one_image, t, state, output_dir, local_images): t[0] for t in tasks}
+        for future in tqdm(as_completed(futures), total=len(futures), desc='Pulling images', unit='img', mininterval=1.0):
+            idx = futures[future]
+            try:
+                _, image_name, status, msg = future.result()
+            except Exception as e:
+                image_name, status, msg = None, 'fail', str(e)
+
+            if status == 'success':
+                success_count += 1
+                _log(f'✅ [{idx+1}/{len(samples)}] DONE   {image_name}')
+            elif status == 'skip':
+                skip_count += 1
+                _log(f'⏭️  [{idx+1}/{len(samples)}] SKIP   {image_name or msg}')
+            else:
+                fail_count += 1
+                _log(f'❌ [{idx+1}/{len(samples)}] FAILED {image_name}')
+                if msg:
+                    _log(f'   {msg}')
+
+    print('=' * 60)
+    print('🎉 预拉取任务结束！')
+    print(f'   总计: {len(samples)} | 已存在/跳过: {skip_count} | 本次成功: {success_count} | 本次失败: {fail_count}')
+    print(f'   进度文件: {get_state_path(output_dir)}')
+    print('=' * 60)
+
+
+if __name__ == '__main__':
+    try:
+        main()
+    except KeyboardInterrupt:
+        print('\n\n⚠️  用户手动中断。进度已保存，下次运行会自动跳过已完成的镜像。')
+        sys.exit(0)
+

bash/run.py

@@ -31,6 +31,7 @@ Examples:
 import argparse
 import json
 import sys
+import time
 from copy import deepcopy
 from pathlib import Path
 
@@ -43,6 +44,11 @@ from evalscope.config import SandboxTaskConfig
 SCRIPT_DIR = Path(__file__).parent.resolve()
 PROJECT_ROOT = SCRIPT_DIR.parent
 
+# Make collect_results importable
+sys.path.insert(0, str(SCRIPT_DIR))
+import collect_results as collect_results_module
+import perf_backup as perf_backup_module
+
 # ============================================================
 # Default configuration (override via CLI)
 # ============================================================
@@ -231,6 +237,10 @@ def build_parser():
     parser.add_argument('--truncation-tokens', type=int, default=DEFAULT_TRUNCATION_TOKENS,
                         help='Middle-truncation token budget for long-context benchmarks (default: %(default)s)')
 
+    # Result collection
+    parser.add_argument('--no-summary', dest='write_summary', action='store_false',
+                        help='Skip writing summary Excel/CSV after each benchmark')
+
     return parser
 
 
@@ -433,6 +443,95 @@ def build_task_config(
 # Main
 # ============================================================
 
+def write_summary(output_dir: str, model_name: str,
+                  benchmark_names: list = None):
+    """Re-aggregate results for the given benchmarks (or all on disk if None)."""
+    try:
+        # Derive a safe file name from the model name
+        safe_name = model_name.replace('/', '_').replace('\\', '_').replace(' ', '_')
+        excel_output_dir = PROJECT_ROOT / 'results'
+        if benchmark_names is not None:
+            collect_results_module.eval_benchmark(
+                benchmark_names, Path(output_dir), model_name, safe_name,
+                excel_output_dir=excel_output_dir,
+            )
+        else:
+            collect_results_module.collect_all(
+                Path(output_dir), model_name, safe_name,
+                excel_output_dir=excel_output_dir,
+            )
+    except Exception as e:
+        print(f'WARNING: failed to write summary: {e}')
+
+
+def run_and_summarize(task_cfg, write_summary_flag: bool, output_dir: str,
+                       model_name: str, benchmark_names: list):
+    """Run a benchmark task and optionally refresh the summary table.
+
+    ``benchmark_names`` is the running list of canonical benchmark names that
+    were scheduled for this invocation. The summary table will be limited to
+    these benchmarks so it does not pick up older results left in the output
+    directory from previous runs.
+
+    After a successful ``run_task()`` we also snapshot the cumulative
+    ``perf_metrics.summary`` and the per-sample predictions into the durable
+    backup files maintained by ``perf_backup.py`` so checkpoint restarts can
+    recover them.
+    """
+    dataset_name = task_cfg.datasets[0]
+    work_dir = Path(task_cfg.work_dir)
+    restore_before_run(output_dir, dataset_name, model_name, work_dir)
+
+    start_ts = time.monotonic()
+    try:
+        run_task(task_cfg)
+    finally:
+        elapsed = time.monotonic() - start_ts
+        perf_backup_module.record_active_time(output_dir, dataset_name, model_name, elapsed)
+        print(f'Active time for {dataset_name}: {elapsed:.1f}s (total accumulated)')
+
+    backup_after_run(output_dir, dataset_name, model_name, work_dir)
+    if write_summary_flag:
+        write_summary(output_dir, model_name, benchmark_names=benchmark_names)
+
+
+def backup_after_run(output_dir: str, benchmark: str, model_name: str,
+                     work_dir: Path):
+    """Snapshot the just-finished run's perf summary and predictions."""
+    try:
+        report_json = work_dir / 'reports' / model_name / f'{benchmark}.json'
+        perf_backup_module.backup_perf_stats(
+            Path(output_dir), benchmark, model_name, report_json,
+        )
+        predictions_dir = work_dir / 'predictions' / model_name
+        perf_backup_module.archive_predictions(
+            Path(output_dir), benchmark, model_name, predictions_dir,
+        )
+    except Exception as e:
+        print(f'WARNING: perf backup for {benchmark} failed: {e}')
+
+
+def restore_before_run(output_dir: str, benchmark: str, model_name: str,
+                        work_dir: Path) -> bool:
+    """If durable backups exist for a benchmark/model, materialise them into
+    the new ``work_dir`` before evalscope starts so the next run resumes from
+    the larger historical state instead of overwriting it.
+    """
+    try:
+        predictions_dir = work_dir / 'predictions' / model_name
+        report_json = work_dir / 'reports' / model_name / f'{benchmark}.json'
+        restored = perf_backup_module.restore_from_backup(
+            Path(output_dir), benchmark, model_name,
+            predictions_dir, report_json,
+        )
+        if restored:
+            print(f'Restored {benchmark}/{model_name} from backup before run')
+        return restored
+    except Exception as e:
+        print(f'WARNING: perf restore for {benchmark} failed: {e}')
+        return False
+
+
 def main():
     parser = build_parser()
     args = parser.parse_args()
@@ -499,65 +598,60 @@ def main():
     print(f'Multi-run datasets: {multi_run}')
     print(f'Single-run datasets: {single_run}')
     print(f'Agent datasets: {agent}')
+    print(f'Write summary: {args.write_summary}')
     print('=' * 60)
 
+    def run_one(dataset_name, run_idx=0, benchmark_names=None):
+        ds_cfg = dataset_configs[dataset_name]
+        task_cfg = build_task_config(
+            dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
+            args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
+            run_idx=run_idx,
+        )
+        try:
+            run_and_summarize(task_cfg, args.write_summary, args.output_dir, args.model,
+                              benchmark_names=benchmark_names)
+        except Exception as e:
+            print(f'ERROR in {dataset_name} (run {run_idx + 1 if run_idx else 1}): {e}')
+
+    # Keep the ordered list of benchmarks actually run in this invocation so
+    # the summary table only includes them, not stale results from earlier runs.
+    benchmark_names = []
+
     for dataset_name in multi_run:
         if dataset_name not in dataset_configs:
             print(f'WARNING: {dataset_name} not in YAML config, skipping')
             continue
-        ds_cfg = dataset_configs[dataset_name]
+        benchmark_names.append(dataset_name)
         num_runs = MULTI_RUN_CONFIG.get(dataset_name, 1)
         for run_idx in range(num_runs):
             print(f"\n{'='*60}")
             print(f'Running: {dataset_name} (run {run_idx + 1}/{num_runs}, seed={args.seed})')
             print(f"{'='*60}")
-            task_cfg = build_task_config(
+            run_one(dataset_name, run_idx=run_idx, benchmark_names=benchmark_names)
-                dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
-                args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
-                run_idx=run_idx,
-            )
-            try:
-                run_task(task_cfg)
-            except Exception as e:
-                print(f'ERROR in {dataset_name} (run {run_idx + 1}): {e}')
-                continue
 
     for dataset_name in single_run:
         if dataset_name not in dataset_configs:
             print(f'WARNING: {dataset_name} not in YAML config, skipping')
             continue
-        ds_cfg = dataset_configs[dataset_name]
+        benchmark_names.append(dataset_name)
         print(f"\n{'='*60}")
         print(f'Running: {dataset_name} (seed={args.seed})')
         print(f"{'='*60}")
-        task_cfg = build_task_config(
+        run_one(dataset_name, benchmark_names=benchmark_names)
-            dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
-            args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
-        )
-        try:
-            run_task(task_cfg)
-        except Exception as e:
-            print(f'ERROR in {dataset_name}: {e}')
-            continue
 
     for dataset_name in agent:
         if dataset_name not in dataset_configs:
             print(f'WARNING: {dataset_name} not in YAML config, skipping')
             continue
-        ds_cfg = dataset_configs[dataset_name]
+        benchmark_names.append(dataset_name)
         print(f"\n{'='*60}")
         print(f'Running: {dataset_name} (seed={args.seed})')
         print(f"{'='*60}")
-        task_cfg = build_task_config(
+        run_one(dataset_name, benchmark_names=benchmark_names)
-            dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
-            args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
-        )
-        try:
-            run_task(task_cfg)
-        except Exception as e:
-            print(f'ERROR in {dataset_name}: {e}')
-            continue
 
+    if args.write_summary:
+        write_summary(args.output_dir, args.model, benchmark_names=benchmark_names)
     print('\nAll benchmarks done!')
 
 

bash/set.sh

@@ -0,0 +1,9 @@
+docker run -it --rm \
+  --network host \
+  -v /data1/sora/evalscope:/opt/evalscope \
+  -v /data1/sora/models:/opt/models \
+  -v /var/run/docker.sock:/var/run/docker.sock \
+  evalscope-complete-py312:latest \
+  bash
+
+

bash/test.py

@@ -0,0 +1,659 @@
+#!/usr/bin/env python3
+"""
+Unified benchmark runner for EvalScope.
+
+A single entry point for lite / mid / full / group1 / group2 / group3 evaluations.
+All tunable parameters can be controlled via command-line arguments.
+
+Examples:
+    # Full evaluation (all benchmarks, multi-run for stability)
+    python bash/run.py \
+        --model DeepSeek-V4-Flash-Int8 \
+        --api-url http://localhost:30000/v1 \
+        --dataset-dir /data1/sora/evalscope \
+        --output-dir /data1/sora/evalscope/output \
+        --suite full \
+        --limit none
+
+    # Lite smoke test (~5h with full samples)
+    python bash/run.py --suite lite --limit none
+
+    # Run only selected benchmarks
+    python bash/run.py --datasets aime24,gsm8k,arc --limit 20
+
+    # Custom judge model
+    python bash/run.py \
+        --judge-model deepseek-v4-pro \
+        --judge-api-url https://api.deepseek.com/v1 \
+        --judge-api-key sk-xxx
+"""
+
+import argparse
+import json
+import sys
+import time
+from copy import deepcopy
+from pathlib import Path
+
+import yaml
+
+from evalscope import run_task, TaskConfig
+from evalscope.api.agent import NativeAgentConfig
+from evalscope.config import SandboxTaskConfig
+
+SCRIPT_DIR = Path(__file__).parent.resolve()
+PROJECT_ROOT = SCRIPT_DIR.parent
+
+# Make collect_results importable
+sys.path.insert(0, str(SCRIPT_DIR))
+import collect_results as collect_results_module
+import perf_backup as perf_backup_module
+
+# ============================================================
+# Default configuration (override via CLI)
+# ============================================================
+
+DEFAULT_MODEL = 'DeepSeek-V4-Flash-Int8'
+DEFAULT_API_URL = 'http://localhost:30000/v1'
+DEFAULT_DATASET_DIR = str(PROJECT_ROOT)
+DEFAULT_OUTPUT_DIR = str(PROJECT_ROOT / 'output')
+DEFAULT_CONFIG = str(PROJECT_ROOT / 'config' / 'dpv4-int8_nothinking.yaml')
+DEFAULT_TOKENIZER_PATH = '/data1/models/DeepSeek-V4-Flash-INT8'
+DEFAULT_LIMIT = None
+DEFAULT_SEED = 42
+DEFAULT_BATCH_SIZE = 4
+DEFAULT_ENABLE_THINKING = False
+
+DEFAULT_JUDGE_MODEL = 'DeepSeek/DeepSeek-V4-Pro'
+DEFAULT_JUDGE_API_URL = 'https://api.vectron.meta-stone.com/v1'
+DEFAULT_JUDGE_API_KEY = 'sk-dbd8a665f7634081b87ec409c7636500'
+DEFAULT_JUDGE_MAX_TOKENS = 10240
+
+# 长文本 middle-truncation 上限（token 数）。当前默认 128k。
+DEFAULT_TRUNCATION_TOKENS = 32768 * 4
+
+# ============================================================
+# Benchmark suites
+# ============================================================
+
+# 多次采样配置：总样本数控制在 ~400-500
+MULTI_RUN_CONFIG = {
+    'aime24': 12,
+    'aime25': 12,
+    'aime26': 12,
+    'hmmt26': 12,
+    'live_code_bench': 5,
+    'imo_answerbench': 4,
+    'humaneval': 3,
+    'gpqa_diamond': 2,
+}
+
+# 能力域完整列表
+ALL_MULTI_RUN = [
+    # 'humaneval', 'live_code_bench',
+    # 'aime24', 'aime25', 'aime26', 'hmmt26',
+    # 'imo_answerbench', 'gpqa_diamond',
+]
+
+
+ALL_SINGLE_RUN = [
+    # 'bigcodebench', 'bfcl_v3', 'competition_math', 'gsm8k', 'hle', 'super_gpqa',
+    # 'arc', 'bbh', 'cmmlu', 'drop', 'hellaswag', 'mmlu', 'mmlu_pro',
+    # 'simple_qa', 'trivia_qa', 'winogrande',
+    # 'openai_mrcr', 'longbench_v2',
+    'swe_bench_verified', 'swe_bench_pro',
+]
+ALL_AGENT = [
+    # 'tau2_bench', 'general_fc'
+    ]
+
+# 分组基于 CSV 单次时间 + multi-run 后的 wall time 平衡：
+#   Group1: ~61h | Group2: ~62h | Group3: ~55h
+SUITES = {
+    'full': {
+        'multi': ALL_MULTI_RUN,
+        'single': ALL_SINGLE_RUN,
+        'agent': ALL_AGENT,
+    },
+    'lite': {
+        'multi': ['aime24', 'humaneval'],
+        'single': ['gsm8k', 'arc', 'longbench_v2'],
+        'agent': ['general_fc'],
+    },
+    'mid': {
+        'multi': ['aime24', 'humaneval'],
+        'single': [
+            'live_code_bench', 'bigcodebench', 'competition_math', 'gsm8k',
+            'gpqa_diamond', 'mmlu_pro', 'simple_qa', 'longbench_v2', 'openai_mrcr',
+        ],
+        'agent': ['general_fc', 'tau2_bench'],
+    },
+    # 多机组分组，基于 CSV 实测完整时间（已含 multi-run）平衡：
+    #   Group1: ~22.7h | Group2: ~25.6h | Group3: ~27.0h | 合计 ~75.2h
+    'group1': {
+        'multi': ['live_code_bench', 'aime24', 'aime25', 'aime26', 'hmmt26', 'imo_answerbench', 'humaneval'],
+        'single': ['bigcodebench', 'competition_math', 'gsm8k', 'drop', 'arc', 'hellaswag', 'winogrande'],
+        'agent': [],
+    },
+    'group2': {
+        'multi': [],
+        'single': ['hle', 'mmlu_pro', 'trivia_qa'],
+        'agent': [],
+    },
+    'group3': {
+        'multi': ['gpqa_diamond'],
+        'single': ['openai_mrcr', 'longbench_v2', 'bfcl_v3', 'mmlu', 'cmmlu', 'bbh', 'simple_qa'],
+        'agent': ['tau2_bench', 'general_fc'],
+    },
+}
+
+# ============================================================
+# Fixed configuration
+# ============================================================
+
+MATH_DATASETS = {
+    'aime24', 'aime25', 'aime26', 'hmmt26',
+    'gsm8k', 'competition_math', 'imo_answerbench',
+}
+
+MATH_PROMPT_TEMPLATE = (
+    "{question}\n"
+    "Please reason step by step, and put your final answer within \\boxed{{}}."
+)
+
+SANDBOX_DATASETS = {'humaneval', 'bigcodebench', 'swe_bench_verified', 'swe_bench_pro'}
+SANDBOX_CONFIGS = {
+    'bigcodebench': {
+        'image': 'bigcodebench-sandbox:latest',
+        'working_dir': '/tmp',
+        'tools_config': {
+            'shell_executor': {},
+            'python_executor': {}
+        }
+    },
+    'humaneval': {
+        'image': 'python:3.11-slim',
+        'tools_config': {
+            'shell_executor': {},
+            'python_executor': {}
+        }
+    },
+    'swe_bench_verified': {
+        'image': 'swe-bench-sandbox:latest',
+        'working_dir': '/tmp',
+        'tools_config': {
+            'shell_executor': {},
+            'python_executor': {}
+        }
+    },
+    'swe_bench_pro': {
+        'image': 'swe-bench-sandbox:latest',
+        'working_dir': '/tmp',
+        'tools_config': {
+            'shell_executor': {},
+            'python_executor': {}
+        }
+    },
+}
+
+
+# ============================================================
+# CLI parser
+# ============================================================
+
+def build_parser():
+    parser = argparse.ArgumentParser(
+        description='Unified EvalScope benchmark runner',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog='Suites: full, lite, mid, group1, group2, group3',
+    )
+
+    # Model / API
+    parser.add_argument('--model', default=DEFAULT_MODEL,
+                        help='Served model name (default: %(default)s)')
+    parser.add_argument('--api-url', default=DEFAULT_API_URL,
+                        help='OpenAI-compatible API URL (default: %(default)s)')
+
+    # Paths
+    parser.add_argument('--dataset-dir', default=DEFAULT_DATASET_DIR,
+                        help='Parent directory containing datasets/ subdir (default: %(default)s)')
+    parser.add_argument('--output-dir', default=DEFAULT_OUTPUT_DIR,
+                        help='Output root directory (default: %(default)s)')
+    parser.add_argument('--config', default=DEFAULT_CONFIG,
+                        help='YAML config path (default: %(default)s)')
+    parser.add_argument('--tokenizer-path', default=DEFAULT_TOKENIZER_PATH,
+                        help='Local tokenizer path for middle-truncation (default: %(default)s)')
+
+    # Run control
+    parser.add_argument('--suite', default='full', choices=list(SUITES.keys()),
+                        help='Benchmark suite to run (default: %(default)s)')
+    parser.add_argument('--datasets', '--benchmarks', dest='datasets', default=None,
+                        help='Override suite with comma-separated benchmark names, e.g. aime24,gsm8k')
+    parser.add_argument('--exclude', default=None,
+                        help='Comma-separated benchmarks to exclude from the chosen suite')
+    parser.add_argument('--limit', default=None,
+                        help='Max samples per benchmark; "none"/"all" for no limit (default: none)')
+    parser.add_argument('--seed', type=int, default=DEFAULT_SEED,
+                        help='Random seed (default: %(default)s)')
+    parser.add_argument('--batch-size', type=int, default=DEFAULT_BATCH_SIZE,
+                        help='Evaluation batch size (default: %(default)s)')
+
+    # Decoding / thinking
+    parser.add_argument('--thinking', action='store_true', default=None,
+                        help='Enable thinking mode (sglang chat_template_kwargs.thinking=True)')
+    parser.add_argument('--no-thinking', dest='thinking', action='store_false',
+                        help='Disable thinking mode (default)')
+
+    # Judge model
+    parser.add_argument('--judge-model', default=DEFAULT_JUDGE_MODEL,
+                        help='Judge model name (default: %(default)s)')
+    parser.add_argument('--judge-api-url', default=DEFAULT_JUDGE_API_URL,
+                        help='Judge model API URL (default: %(default)s)')
+    parser.add_argument('--judge-api-key', default=DEFAULT_JUDGE_API_KEY,
+                        help='Judge model API key')
+    parser.add_argument('--judge-max-tokens', type=int, default=DEFAULT_JUDGE_MAX_TOKENS,
+                        help='Judge model max_tokens (default: %(default)s)')
+
+    # Truncation
+    parser.add_argument('--truncation-tokens', type=int, default=DEFAULT_TRUNCATION_TOKENS,
+                        help='Middle-truncation token budget for long-context benchmarks (default: %(default)s)')
+
+    # Result collection
+    parser.add_argument('--no-summary', dest='write_summary', action='store_false',
+                        help='Skip writing summary Excel/CSV after each benchmark')
+    parser.add_argument('--summary-name', default=None,
+                        help='Output summary file name (without extension); defaults to safe model name')
+
+    return parser
+
+
+# ============================================================
+# Middle-truncation helpers
+# ============================================================
+
+_TOKENIZER = None
+
+
+def get_tokenizer(tokenizer_path: str):
+    global _TOKENIZER
+    if _TOKENIZER is None:
+        from transformers import AutoTokenizer
+        try:
+            _TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True)
+        except Exception:
+            _TOKENIZER = AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-V4-Flash', trust_remote_code=True)
+    return _TOKENIZER
+
+
+def truncate_middle(text: str, max_tokens: int, tokenizer_path: str) -> str:
+    if max_tokens <= 0:
+        return text
+    tokenizer = get_tokenizer(tokenizer_path)
+    token_ids = tokenizer.encode(text, add_special_tokens=False)
+    if len(token_ids) <= max_tokens:
+        return text
+    keep_head = max_tokens // 2
+    keep_tail = max_tokens - keep_head
+    truncated_ids = token_ids[:keep_head] + token_ids[-keep_tail:]
+    return tokenizer.decode(truncated_ids, skip_special_tokens=True)
+
+
+def _patch_adapters_for_truncation(tokenizer_path: str, truncation_tokens: int):
+    from evalscope.benchmarks.longbench_v2.longbench_v2_adapter import LongBenchV2Adapter
+    from evalscope.benchmarks.openai_mrcr.openai_mrcr_adapter import OpenAIMRCRAdapter
+
+    _orig_longbench_format = LongBenchV2Adapter.format_prompt_template
+
+    def _patched_longbench_format(self, sample):
+        if sample.metadata and 'context' in sample.metadata:
+            sample.metadata['context'] = truncate_middle(sample.metadata['context'], truncation_tokens, tokenizer_path)
+        return _orig_longbench_format(self, sample)
+
+    LongBenchV2Adapter.format_prompt_template = _patched_longbench_format
+
+    _orig_mrcr_record = OpenAIMRCRAdapter.record_to_sample
+
+    def _patched_mrcr_record(self, record):
+        per_msg_max_tok = 8192
+        if 'prompt' in record:
+            try:
+                prompt_data = json.loads(record['prompt'])
+                if not isinstance(prompt_data, list) or len(prompt_data) == 0:
+                    return _orig_mrcr_record(self, record)
+
+                tokenizer = get_tokenizer(tokenizer_path)
+                total_tok = sum(
+                    len(tokenizer.encode(msg.get('content', '') if isinstance(msg, dict) else '', add_special_tokens=False))
+                    for msg in prompt_data
+                )
+                if total_tok <= truncation_tokens:
+                    return _orig_mrcr_record(self, record)
+
+                desired_idx = record.get('desired_msg_index', 0)
+                if not isinstance(desired_idx, int) or desired_idx < 0 or desired_idx >= len(prompt_data):
+                    desired_idx = 0
+
+                n = len(prompt_data)
+                keep = set()
+                keep.update(range(min(2, n)))
+                keep.update(range(max(0, n - 2), n))
+                window = 2
+                keep.update(range(max(0, desired_idx - window), min(n, desired_idx + window + 1)))
+                keep = sorted(keep)
+
+                new_prompt = []
+                for idx in keep:
+                    msg = prompt_data[idx]
+                    if isinstance(msg, dict):
+                        msg = dict(msg)
+                        content = msg.get('content', '')
+                        if len(tokenizer.encode(content, add_special_tokens=False)) > per_msg_max_tok:
+                            msg['content'] = truncate_middle(content, per_msg_max_tok, tokenizer_path)
+                    new_prompt.append(msg)
+
+                record = dict(record)
+                record['prompt'] = json.dumps(new_prompt)
+            except (json.JSONDecodeError, TypeError):
+                pass
+        return _orig_mrcr_record(self, record)
+
+    OpenAIMRCRAdapter.record_to_sample = _patched_mrcr_record
+
+
+# ============================================================
+# Helpers
+# ============================================================
+
+def load_dataset_configs(config_path: str):
+    if not Path(config_path).exists():
+        raise FileNotFoundError(f'Config file not found: {config_path}')
+    with open(config_path, 'r', encoding='utf-8') as f:
+        return yaml.safe_load(f)
+
+
+def configure_thinking(generation_config: dict, enable: bool) -> dict:
+    extra_body = generation_config.get('extra_body', {})
+    chat_template_kwargs = extra_body.get('chat_template_kwargs', {})
+    if enable:
+        chat_template_kwargs['thinking'] = True
+    else:
+        chat_template_kwargs.pop('thinking', None)
+    if chat_template_kwargs:
+        extra_body['chat_template_kwargs'] = chat_template_kwargs
+    if extra_body:
+        generation_config['extra_body'] = extra_body
+    return generation_config
+
+
+def build_agent_config(agent_cfg: dict) -> NativeAgentConfig:
+    agent_cfg = deepcopy(agent_cfg or {})
+    known_fields = {'mode', 'strategy', 'tools', 'max_steps', 'mcp_servers', 'environment', 'environment_extra'}
+    kwargs = agent_cfg.pop('kwargs', {})
+    for key in list(agent_cfg.keys()):
+        if key not in known_fields:
+            kwargs[key] = agent_cfg.pop(key)
+    if kwargs:
+        agent_cfg['kwargs'] = kwargs
+    return NativeAgentConfig(**agent_cfg)
+
+
+def build_task_config(
+    dataset_name: str,
+    ds_cfg: dict,
+    batch_size: int,
+    enable_thinking: bool,
+    seed: int,
+    limit,
+    output_dir: str,
+    model: str,
+    api_url: str,
+    dataset_dir: str,
+    judge_model_args: dict,
+    run_idx: int = 0,
+) -> TaskConfig:
+    if run_idx > 0:
+        work_dir = Path(output_dir) / dataset_name / f'seed_{seed}_run_{run_idx}'
+    else:
+        work_dir = Path(output_dir) / dataset_name / f'seed_{seed}'
+    work_dir.mkdir(parents=True, exist_ok=True)
+    work_dir = str(work_dir)
+
+    generation_config = configure_thinking(deepcopy(ds_cfg['generation_config']), enable_thinking)
+    dataset_args = deepcopy(ds_cfg.get('dataset_args', {}))
+    dataset_args.setdefault('shuffle', True)
+
+    if dataset_name in MATH_DATASETS:
+        dataset_args['prompt_template'] = MATH_PROMPT_TEMPLATE
+
+    dataset_args_dict = {dataset_name: dataset_args}
+
+    agent_config = None
+    if 'agent_config' in ds_cfg:
+        agent_config = build_agent_config(ds_cfg['agent_config'])
+
+    return TaskConfig(
+        model=model,
+        api_url=api_url,
+        eval_type='openai_api',
+        dataset_dir=dataset_dir,
+        judge_model_args=judge_model_args,
+        seed=seed,
+        limit=limit,
+        collect_perf=True,
+        no_timestamp=True,
+        work_dir=work_dir,
+        use_cache=work_dir,
+        datasets=[dataset_name],
+        generation_config=generation_config,
+        dataset_args=dataset_args_dict,
+        agent_config=agent_config,
+        eval_batch_size=batch_size,
+        sandbox=SandboxTaskConfig(
+            enabled=True,
+            engine='docker',
+            default_config=SANDBOX_CONFIGS.get(dataset_name, {
+                'image': 'python:3.11-slim',
+                'tools_config': {
+                    'shell_executor': {},
+                    'python_executor': {}
+                }
+            })
+        ) if dataset_name in SANDBOX_DATASETS else None,
+    )
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def write_summary(output_dir: str, model_name: str, summary_name: str):
+    """Re-aggregate results across all benchmarks under output_dir."""
+    try:
+        excel_output_dir = PROJECT_ROOT / 'results'
+        collect_results_module.collect_all(
+            Path(output_dir), model_name, summary_name,
+            excel_output_dir=excel_output_dir,
+        )
+    except Exception as e:
+        print(f'WARNING: failed to write summary: {e}')
+
+
+def backup_after_run(output_dir: str, benchmark: str, model_name: str,
+                     work_dir: Path):
+    """Snapshot the just-finished run's perf summary and predictions."""
+    try:
+        report_json = work_dir / 'reports' / model_name / f'{benchmark}.json'
+        perf_backup_module.backup_perf_stats(
+            Path(output_dir), benchmark, model_name, report_json,
+        )
+        predictions_dir = work_dir / 'predictions' / model_name
+        perf_backup_module.archive_predictions(
+            Path(output_dir), benchmark, model_name, predictions_dir,
+        )
+    except Exception as e:
+        print(f'WARNING: perf backup for {benchmark} failed: {e}')
+
+
+def restore_before_run(output_dir: str, benchmark: str, model_name: str,
+                        work_dir: Path) -> bool:
+    """If durable backups exist for a benchmark/model, materialise them into
+    the new ``work_dir`` before evalscope starts so the next run resumes from
+    the larger historical state instead of overwriting it.
+    """
+    try:
+        predictions_dir = work_dir / 'predictions' / model_name
+        report_json = work_dir / 'reports' / model_name / f'{benchmark}.json'
+        restored = perf_backup_module.restore_from_backup(
+            Path(output_dir), benchmark, model_name,
+            predictions_dir, report_json,
+        )
+        if restored:
+            print(f'Restored {benchmark}/{model_name} from backup before run')
+        return restored
+    except Exception as e:
+        print(f'WARNING: perf restore for {benchmark} failed: {e}')
+        return False
+
+
+def run_and_summarize(task_cfg, write_summary_flag: bool, output_dir: str, model_name: str, summary_name: str):
+    """Run a benchmark task and optionally refresh the summary table.
+
+    After a successful ``run_task()`` we snapshot the cumulative
+    ``perf_metrics.summary`` and the per-sample predictions into the durable
+    backup files maintained by ``perf_backup.py`` so checkpoint restarts can
+    recover them.
+    """
+    dataset_name = task_cfg.datasets[0]
+    work_dir = Path(task_cfg.work_dir)
+    restore_before_run(output_dir, dataset_name, model_name, work_dir)
+
+    start_ts = time.monotonic()
+    try:
+        run_task(task_cfg)
+    finally:
+        elapsed = time.monotonic() - start_ts
+        perf_backup_module.record_active_time(output_dir, dataset_name, model_name, elapsed)
+        print(f'Active time for {dataset_name}: {elapsed:.1f}s (total accumulated)')
+
+    backup_after_run(output_dir, dataset_name, model_name, work_dir)
+    if write_summary_flag:
+        write_summary(output_dir, model_name, summary_name)
+
+
+def main():
+    parser = build_parser()
+    args = parser.parse_args()
+
+    # Resolve limit
+    limit = args.limit
+    if limit is not None:
+        if str(limit).lower() in ('none', 'all'):
+            limit = None
+        else:
+            limit = int(limit)
+
+    enable_thinking = DEFAULT_ENABLE_THINKING if args.thinking is None else args.thinking
+
+    # Resolve suite or custom datasets
+    if args.datasets:
+        custom = [d.strip() for d in args.datasets.split(',') if d.strip()]
+        multi_run = [d for d in custom if d in MULTI_RUN_CONFIG]
+        single_run = [d for d in custom if d not in MULTI_RUN_CONFIG]
+        agent = [d for d in custom if d in ALL_AGENT]
+        single_run = [d for d in single_run if d not in ALL_AGENT]
+    else:
+        suite = SUITES[args.suite]
+        multi_run = list(suite['multi'])
+        single_run = list(suite['single'])
+        agent = list(suite['agent'])
+
+    # Apply --exclude
+    if args.exclude:
+        exclude = {d.strip() for d in args.exclude.split(',') if d.strip()}
+        multi_run = [d for d in multi_run if d not in exclude]
+        single_run = [d for d in single_run if d not in exclude]
+        agent = [d for d in agent if d not in exclude]
+
+    judge_model_args = {
+        'model_id': args.judge_model,
+        'api_url': args.judge_api_url,
+        'api_key': args.judge_api_key,
+        'eval_type': 'openai_api',
+        'generation_config': {
+            'temperature': 0.0,
+            'max_tokens': args.judge_max_tokens,
+        },
+    }
+
+    truncation_tokens = args.truncation_tokens
+    _patch_adapters_for_truncation(args.tokenizer_path, truncation_tokens)
+
+    dataset_configs = load_dataset_configs(args.config)
+
+    print('=' * 60)
+    print(f'Config: {args.config}')
+    print(f'Model: {args.model}')
+    print(f'API URL: {args.api_url}')
+    print(f'Dataset Dir: {args.dataset_dir}')
+    print(f'Output Dir: {args.output_dir}')
+    print(f'Suite: {args.suite}')
+    print(f'Limit: {limit if limit is not None else "ALL"}')
+    print(f'Thinking: {enable_thinking}')
+    print(f'Seed: {args.seed}')
+    print(f'Batch Size: {args.batch_size}')
+    print(f'Tokenizer Path: {args.tokenizer_path}')
+    print(f'Truncation Tokens: {truncation_tokens}')
+    print(f'Multi-run datasets: {multi_run}')
+    print(f'Single-run datasets: {single_run}')
+    print(f'Agent datasets: {agent}')
+    print(f'Write summary: {args.write_summary}')
+    print('=' * 60)
+
+    def run_one(dataset_name, run_idx=0):
+        ds_cfg = dataset_configs[dataset_name]
+        task_cfg = build_task_config(
+            dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
+            args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
+            run_idx=run_idx,
+        )
+        try:
+            run_and_summarize(task_cfg, args.write_summary, args.output_dir, args.model, args.summary_name)
+        except Exception as e:
+            print(f'ERROR in {dataset_name} (run {run_idx + 1 if run_idx else 1}): {e}')
+
+    for dataset_name in multi_run:
+        if dataset_name not in dataset_configs:
+            print(f'WARNING: {dataset_name} not in YAML config, skipping')
+            continue
+        num_runs = MULTI_RUN_CONFIG.get(dataset_name, 1)
+        for run_idx in range(num_runs):
+            print(f"\n{'='*60}")
+            print(f'Running: {dataset_name} (run {run_idx + 1}/{num_runs}, seed={args.seed})')
+            print(f"{'='*60}")
+            run_one(dataset_name, run_idx=run_idx)
+
+    for dataset_name in single_run:
+        if dataset_name not in dataset_configs:
+            print(f'WARNING: {dataset_name} not in YAML config, skipping')
+            continue
+        print(f"\n{'='*60}")
+        print(f'Running: {dataset_name} (seed={args.seed})')
+        print(f"{'='*60}")
+        run_one(dataset_name)
+
+    for dataset_name in agent:
+        if dataset_name not in dataset_configs:
+            print(f'WARNING: {dataset_name} not in YAML config, skipping')
+            continue
+        print(f"\n{'='*60}")
+        print(f'Running: {dataset_name} (seed={args.seed})')
+        print(f"{'='*60}")
+        run_one(dataset_name)
+
+    if args.write_summary:
+        write_summary(args.output_dir, args.model, args.summary_name)
+    print('\nAll benchmarks done!')
+
+
+if __name__ == '__main__':
+    main()

bash/test_e2e.py

@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+"""End-to-end offline test for bash/run.py + collect_results + perf_backup.
+
+This script does NOT call any model API. It uses an existing benchmark
+directory (aime24/seed_42) and walks through every code path that the real
+``run.py`` would touch:
+
+1. backup_perf_stats — confirm a perf summary snapshot is written
+2. archive_predictions — confirm per-sample records are deduplicated into
+   the archive
+3. restore_from_backup — confirm a fresh work_dir can be rehydrated from
+   the backup files
+4. collect_results aggregation — confirm correct metrics are produced
+5. Whitelist mode — confirm only the requested benchmarks appear in the
+   summary
+6. hle_low alias — confirm hle_low directory maps to canonical "hle"
+7. Reset recovery — confirm that even after wiping the predictions file
+   and the report summary, the archive+backup still recover the metrics
+
+Run from the project root:
+
+    python bash/test_e2e.py
+"""
+
+import json
+import shutil
+import sys
+import tempfile
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+import collect_results as cr  # noqa: E402
+import perf_backup as pb  # noqa: E402
+
+
+PROJECT_ROOT = Path('/data1/sora/evalscope')
+SOURCE_OUTPUT = PROJECT_ROOT / 'output'
+
+
+def banner(msg):
+    print('\n' + '=' * 60)
+    print(f' {msg}')
+    print('=' * 60)
+
+
+def assert_close(actual, expected, name, tol=1e-3):
+    if abs(actual - expected) > tol:
+        raise AssertionError(f'{name}: expected ~{expected}, got {actual}')
+    print(f'  OK  {name}={actual:.4f}')
+
+
+def main():
+    banner('Setting up isolated test output dir')
+    test_root = PROJECT_ROOT / 'output_e2e_test'
+    if test_root.exists():
+        shutil.rmtree(test_root)
+    test_root.mkdir(parents=True)
+
+    # Copy the canonical aime24 data into the isolated dir
+    src = SOURCE_OUTPUT / 'aime24' / 'seed_42'
+    dst = test_root / 'aime24' / 'seed_42'
+    shutil.copytree(src, dst)
+
+    # And copy hle_low as hle_low under the test dir (so we exercise the
+    # alias code path).
+    hle_src = SOURCE_OUTPUT / 'hle_low' / 'seed_42'
+    hle_dst = test_root / 'hle_low' / 'seed_42'
+    if hle_src.exists():
+        shutil.copytree(hle_src, hle_dst)
+
+    benchmark = 'aime24'
+    model_name = 'DeepSeek-V4-Flash-Int8'
+
+    banner('1) backup_perf_stats — snapshot summary')
+    report_json = dst / 'reports' / model_name / f'{benchmark}.json'
+    backup_path = pb.backup_perf_stats(test_root, benchmark, model_name, report_json)
+    assert backup_path.exists(), 'backup file was not created'
+    payload = json.loads(backup_path.read_text())
+    assert payload['n_samples'] == 30, f'expected 30 samples, got {payload["n_samples"]}'
+    print(f'  OK  backup written: {backup_path}, n_samples={payload["n_samples"]}')
+
+    banner('2) archive_predictions — deduplicate into archive')
+    preds = dst / 'predictions' / model_name
+    archive_path = pb.archive_predictions(test_root, benchmark, model_name, preds)
+    assert archive_path.exists(), 'archive file was not created'
+    n_lines = sum(1 for line in archive_path.read_text().splitlines() if line.strip())
+    assert n_lines == 30, f'expected 30 archived samples, got {n_lines}'
+    print(f'  OK  archive written: {archive_path}, samples={n_lines}')
+
+    banner('3) restore_from_backup — rehydrate a fresh work_dir')
+    fresh_dir = test_root / 'fresh_work' / 'seed_42'
+    fresh_report = fresh_dir / 'reports' / model_name / f'{benchmark}.json'
+    fresh_preds = fresh_dir / 'predictions' / model_name
+    # Pre-create empty structure to mimic what eval_task would do
+    fresh_report.parent.mkdir(parents=True, exist_ok=True)
+    fresh_report.write_text(json.dumps({
+        'name': f'{model_name}@{benchmark}',
+        'perf_metrics': {'summary': {'n_samples': 1}},
+    }))
+    restored = pb.restore_from_backup(
+        test_root, benchmark, model_name, fresh_preds, fresh_report,
+    )
+    assert restored, 'restore_from_backup returned False'
+    restored_report = json.loads(fresh_report.read_text())
+    assert restored_report['perf_metrics']['summary']['n_samples'] == 30, \
+        f'restored n_samples={restored_report["perf_metrics"]["summary"]["n_samples"]}'
+    print('  OK  restored report n_samples=30 (matches backup)')
+
+    banner('4) collect_results — aggregate metrics')
+    csv, xlsx = cr.eval_benchmark([benchmark], test_root, model_name)
+    import pandas as pd
+    df = pd.read_csv(csv)
+    aime24 = df[df['Benchmark'] == 'aime24'].iloc[0]
+    # Single seed_42 run, so values match the report directly (no multi-run
+    # averaging). The full-summary CSV averages across run_1..run_11.
+    assert_close(aime24['得分'], 0.6333, 'aime24 score (single seed)')
+    assert_close(aime24['实测时间(h)'], 0.1469, 'aime24 duration (h)')
+    assert_close(aime24['总样本数'], 30, 'aime24 sample count')
+    assert_close(aime24['延迟_mean(s)'], 35.24971, 'aime24 latency mean')
+    assert_close(aime24['TTFT_mean(s)'], 0.26301, 'aime24 TTFT mean')
+    assert_close(aime24['TPOT_mean(s)'], 0.02165, 'aime24 TPOT mean')
+    assert_close(aime24['输入tokens_mean'], 119.33, 'aime24 input_tokens_mean')
+    assert_close(aime24['输出tokens_mean'], 1609.8, 'aime24 output_tokens_mean')
+
+    banner('5) Whitelist mode — only the requested benchmarks appear')
+    csv2, _ = cr.eval_benchmark([benchmark], test_root, model_name)
+    df2 = pd.read_csv(csv2)
+    assert df2['Benchmark'].dropna().tolist() == ['aime24'], \
+        f'whitelist did not limit benchmarks: {df2["Benchmark"].tolist()}'
+    print('  OK  summary only contains aime24 + total')
+
+    banner('6) hle_low → hle alias')
+    if hle_src.exists():
+        csv3, _ = cr.eval_benchmark(['hle'], test_root, model_name)
+        df3 = pd.read_csv(csv3)
+        assert 'hle' in df3['Benchmark'].tolist(), \
+            f'hle canonical name missing from: {df3["Benchmark"].tolist()}'
+        assert 'hle_low' not in df3['Benchmark'].tolist(), \
+            f'hle_low should be aliased away: {df3["Benchmark"].tolist()}'
+        print('  OK  hle_low directory maps to canonical "hle"')
+
+    banner('7) Reset recovery — wipe report+predictions, expect archive to restore')
+    # Wipe the current predictions
+    shutil.rmtree(preds)
+    # Reset report.json
+    data = json.loads(report_json.read_text())
+    data['perf_metrics']['summary']['n_samples'] = 1
+    data['perf_metrics']['summary']['latency']['mean'] = 0.0
+    report_json.write_text(json.dumps(data, indent=2))
+
+    csv4, _ = cr.eval_benchmark([benchmark], test_root, model_name)
+    df4 = pd.read_csv(csv4)
+    aime24_after = df4[df4['Benchmark'] == 'aime24'].iloc[0]
+    assert_close(aime24_after['得分'], 0.6333, 'aime24 score (post-reset)')
+    assert_close(aime24_after['总样本数'], 30, 'aime24 sample count (post-reset)')
+    assert_close(aime24_after['延迟_mean(s)'], 35.24971, 'aime24 latency mean (post-reset)')
+    assert_close(aime24_after['TTFT_mean(s)'], 0.26301, 'aime24 TTFT mean (post-reset)')
+    print('  OK  archive fully recovered metrics after predictions wipe')
+
+    banner('8) Backup monotonicity — new backup must not overwrite a larger one')
+    # We have a backup with n_samples=30. Now write a fresh report with
+    # n_samples=2 and call backup_perf_stats again — the backup should NOT
+    # be overwritten.
+    data = json.loads(report_json.read_text())
+    data['perf_metrics']['summary']['n_samples'] = 2
+    report_json.write_text(json.dumps(data, indent=2))
+    pb.backup_perf_stats(test_root, benchmark, model_name, report_json)
+    payload2 = json.loads(backup_path.read_text())
+    assert payload2['n_samples'] == 30, \
+        f'backup was clobbered: n_samples now {payload2["n_samples"]}'
+    print('  OK  backup preserved n_samples=30 even after a reset run')
+
+    banner('All checks passed')
+    print(f'Test artifacts under {test_root} (kept for inspection)')
+
+
+if __name__ == '__main__':
+    main()

config/dpv4-int8_nothinking.yaml

@@ -191,3 +191,15 @@ bfcl_v3:
     extra_params:
       is_fc_model: true
       underscore_to_dot: true
+swe_bench_verified:
+  generation_config:
+    temperature: 0.0
+    top_p: 1.0
+    stream: true
+    max_tokens: 32768
+swe_bench_pro:
+  generation_config:
+    temperature: 0.0
+    top_p: 1.0
+    stream: true
+    max_tokens: 32768

myread.md

@@ -0,0 +1,629 @@
+# EvalScope 评测手册（DeepSeek-V4-Flash-INT8 no-thinking）
+
+本手册记录基于 EvalScope 对 `DeepSeek-V4-Flash-INT8`（no-thinking 模式）进行能力评测的完整流程，包括代码/数据/镜像下载、环境搭建、benchmark 选择、参数配置、Docker 一键评测和结果收集。
+
+---
+
+## 1. 项目结构
+
+```text
+/data1/sora/evalscope/
+├── bash/                          # Python 评测入口脚本
+│   ├── run.py                     # 唯一主入口
+│   ├── collect_results.py         # 结果汇总成 Excel/CSV
+│   ├── perf_backup.py             # 断点续跑的 perf 持久化备份
+│   └── pull_swe_bench_images.py   # 预拉 SWE-bench 实例镜像
+├── scripts/                       # Shell 辅助脚本
+│   ├── run_docker_full.sh
+│   ├── run_docker_lite.sh
+│   └── run_docker_group.sh
+├── config/                        # 评测参数配置文件
+│   └── dpv4-int8_nothinking.yaml
+├── evalscope/                     # EvalScope 源码（pip install -e .）
+├── datasets/                      # 评测数据缓存
+├── output/                        # 默认输出目录
+├── docker/                        # Docker 镜像/数据根目录
+│   ├── images/                    # Docker data-root
+│   └── containerd/                # containerd root
+├── scritps -> bash/               # 兼容旧路径的软链接
+└── tools/docker/                  # Docker 镜像构建相关
+```
+
+### 目录职责
+
+| 目录 | 作用 |
+|------|------|
+| `bash/` | 可直接执行的 Python 入口脚本。`run.py` 是统一评测入口，`collect_results.py` 用于结果汇总，`perf_backup.py` 维护断点续跑的持久化备份，`pull_swe_bench_images.py` 用于预拉 SWE-bench 镜像。 |
+| `scripts/` | Shell 辅助脚本，例如宿主机一键启动 Docker 容器。 |
+| `config/` | YAML 配置文件，集中管理各 benchmark 的 `max_tokens`、`temperature` 等参数。 |
+| `evalscope/` | EvalScope 源码，通过 `pip install -e .` 安装。 |
+| `datasets/` | 评测数据缓存目录。 |
+| `output/` | 默认评测结果输出目录。 |
+| `docker/images/` | Docker `data-root`，存放所有拉取的镜像层。 |
+| `docker/containerd/` | containerd `root`，存放镜像元数据。 |
+
+> **注意**：`scritps` 是历史遗留的目录名（拼写错误），已通过软链接指向 `bash/`。请统一使用 `bash/run.py`。
+
+---
+
+## 2. 资源准备
+
+评测前需要准备四类资源：**代码**、**数据**、**Docker 环境镜像**、**Docker 执行镜像**。下面分别说明下载/加载方式。
+
+### 2.1 Code（评测入口与配置）
+
+- 仓库地址：`https://git.meta-stone.net/sora/evalstone.git`
+- 推荐版本：`798c3d06ab298d7d6b7ed69915d9b01736653bc7`
+- 本地路径：`/data1/sora/evalscope`
+
+```bash
+# 若已克隆，更新到指定 commit
+cd /data1/sora/evalscope
+git fetch origin
+git checkout 798c3d06ab298d7d6b7ed69915d9b01736653bc7
+
+# 若首次克隆
+git clone https://git.meta-stone.net/sora/evalstone.git /data1/sora/evalscope
+cd /data1/sora/evalscope
+git checkout 798c3d06ab298d7d6b7ed69915d9b01736653bc7
+```
+
+> 该 commit 之后可能有新提交。建议先用 `git log --oneline -5` 查看最新状态；如无特殊说明，默认使用上述版本。
+
+### 2.2 Data（评测数据集）
+
+数据集托管在 ModelScope：`SoraAmami/evalscope-datasets`
+
+```bash
+pip install modelscope
+modelscope login --token ms-3d554a39-6e07-496d-8022-0b0ee64a6389
+
+python3 -c "
+from modelscope.hub.snapshot_download import snapshot_download
+snapshot_download(
+    'SoraAmami/evalscope-datasets',
+    repo_type='dataset',
+    cache_dir='/data1/sora/evalscope',
+    local_dir='/data1/sora/evalscope'
+)
+"
+```
+
+下载完成后目录结构：
+
+```text
+/data1/sora/evalscope/
+├── datasets/                    # benchmark 数据
+│   ├── AI-ModelScope_gsm8k-*/
+│   └── ...
+└── ...
+```
+
+> **不要把 `dataset_dir` 指向已经包含 `datasets/` 子目录的路径**（例如 `/data1/sora/evalscope/datasets`），否则 evalscope 会到 `datasets/datasets/` 下重新查找/下载，造成数据重复。应指向父目录 `/data1/sora/evalscope`。
+>
+> 也不要直接用 `MsDataset.load()` 加载整个数据集，因为不同 benchmark 的 schema 不同，会报 `CastError: column names don't match`。
+
+### 2.3 Docker 环境镜像（evalscope-complete-py312）
+
+该镜像包含 Python 3.12 + EvalScope 全部依赖，是主要运行环境。
+
+**方式一：从本地 tar.gz 加载（离线/已拷贝）**
+
+```bash
+docker load -i /data1/sora/evalscope/docker/evalscope-complete-py312.tar.gz
+docker images | grep evalscope-complete-py312
+```
+
+**方式二：从 ModelScope 下载**
+
+```bash
+pip install modelscope
+modelscope login --token ms-3d554a39-6e07-496d-8022-0b0ee64a6389
+
+python3 -c "
+from modelscope.hub.file_download import model_file_download
+model_file_download(
+    model_id='SoraAmami/evalscope-docker',
+    file_path='evalscope-complete-py312.tar.gz',
+    local_dir='/data1/sora/evalscope/docker'
+)
+"
+
+docker load -i /data1/sora/evalscope/docker/evalscope-complete-py312.tar.gz
+docker images | grep evalscope-complete-py312
+```
+
+**方式三：离线 tar.gz 拷贝**
+
+如果目标机器无法访问 ModelScope：
+
+```bash
+# 在源机器导出
+# docker save -o evalscope-complete-py312.tar.gz evalscope-complete-py312:latest
+
+# 在目标机器加载
+docker load -i /path/to/docker/evalscope-complete-py312.tar.gz
+docker images | grep evalscope-complete-py312
+```
+
+> 此方式只拷贝 evalscope 运行环境，数据集仍需单独拷贝或下载。
+
+### 2.4 Docker 执行镜像（代码 sandbox 与 SWE-bench）
+
+这些镜像用于在容器内执行被测代码，主要包括：
+
+| 镜像 | 用途 |
+|------|------|
+| `bigcodebench/bigcodebench-evaluate:latest` | `bigcodebench` / `humaneval` 官方 sandbox |
+| `bigcodebench-sandbox:latest` | 基于官方镜像构建的守护态 sandbox |
+| `python:3.11-slim` | 通用代码执行环境 |
+| `swebench/sweb.eval.x86_64.*` | SWE-bench 每个样本一个实例镜像 |
+
+#### 2.4.1 配置 Docker（国内镜像加速 + 大容量存储路径）
+
+SWE-bench 镜像数量多、体积大，建议把 Docker `data-root` 和 containerd `root` 都迁移到 `/data1` 大容量盘：
+
+```bash
+# 1. 停止 Docker
+systemctl stop docker.socket
+systemctl stop docker
+systemctl stop containerd
+
+# 2. 创建新的数据目录
+mkdir -p /data1/sora/evalscope/docker/images
+mkdir -p /data1/sora/evalscope/docker/containerd
+
+# 3. 配置国内镜像加速（多填几个，自动轮询）
+cat > /etc/docker/daemon.json <<'EOF'
+{
+  "data-root": "/data1/sora/evalscope/docker/images",
+  "registry-mirrors": [
+    "https://docker.m.daocloud.io",
+    "https://docker.1ms.run",
+    "https://docker.1panel.live",
+    "https://hub.rat.dev",
+    "https://hub-mirror.c.163.com",
+    "https://mirror.baidubce.com",
+    "https://dockerproxy.com",
+    "https://docker.mirrors.aliyun.com",
+    "https://docker.mirrors.sjtug.sjtu.edu.cn",
+    "https://docker.nju.edu.cn",
+    "https://docker.mirrors.ustc.edu.cn",
+    "https://docker.mirrors.tuna.tsinghua.edu.cn"
+  ]
+}
+EOF
+
+# 4. 配置 containerd 数据目录
+cat > /etc/containerd/config.toml <<'EOF'
+root = "/data1/sora/evalscope/docker/containerd"
+state = "/run/containerd"
+EOF
+
+# 5. 重启
+systemctl reset-failed docker.service
+systemctl start containerd
+systemctl start docker
+```
+
+#### 2.4.2 拉取/构建代码执行镜像
+
+```bash
+# bigcodebench 官方镜像
+docker pull bigcodebench/bigcodebench-evaluate:latest
+
+# 构建守护态 sandbox
+docker build -t bigcodebench-sandbox:latest -f - . <<'EOF'
+FROM bigcodebench/bigcodebench-evaluate:latest
+ENTRYPOINT []
+CMD ["tail", "-f", "/dev/null"]
+EOF
+
+# 通用 Python 执行环境
+docker pull python:3.11-slim
+```
+
+验证：
+
+```bash
+docker images | grep -E 'evalscope-complete-py312|bigcodebench|python:3.11-slim'
+```
+
+#### 2.4.3 预拉 SWE-bench 实例镜像（可选，强烈建议）
+
+SWE-bench 每个样本一个独立 Docker 镜像，评测时再拉会极慢，且容易触发限流。建议提前批量预拉：
+
+```bash
+cd /data1/sora/evalscope
+export USE_MODELSCOPE_HUB=1
+export HF_ENDPOINT=https://hf-mirror.com
+export PYTHONUNBUFFERED=1
+
+# 推荐单 worker，避免触发限流
+python bash/pull_swe_bench_images.py \
+  --dataset swe_bench_verified \
+  --max-workers 1
+```
+
+脚本特性：
+
+- 断点续传：状态文件 `output/.swe_bench_pull_state.json`，中断后再次运行会自动跳过已成功的镜像。
+- 本地已有镜像自动跳过。
+- 单 worker 模式稳定，减少限流概率；若网络非常好，可适当提高 `--max-workers`。
+
+镜像会存到 Docker `data-root` 配置的路径：
+
+```text
+/data1/sora/evalscope/docker/images/
+```
+
+> 如果拉取过程中频繁失败，可尝试：
+> 1. 换一个 registry-mirror；
+> 2. 降低 `--max-workers` 到 1；
+> 3. 使用 `tmux` 挂后台运行，避免 SSH 断连导致中断。
+
+---
+
+## 3. 环境准备
+
+### 3.1 方式一：本地 Conda 环境
+
+```bash
+conda create -n evalscope python=3.12 -y
+conda activate evalscope
+cd /data1/sora/evalscope
+cd evalscope && pip install -e . && cd ..
+
+# 关键依赖
+pip install 'evalscope[terminal_bench,swe_bench,openai_mrcr]'
+pip install git+https://github.com/sierra-research/tau2-bench@v0.2.0
+pip install openpyxl  # 用于结果汇总
+```
+
+### 3.2 方式二：Docker 一键环境（推荐多机复用）
+
+除 `swe_bench` 系列外，其他 benchmark 均可直接运行。
+
+**运行容器：**
+
+```bash
+mkdir -p /data1/sora/evalscope/output
+
+docker run -it --rm \
+  --network host \
+  -v /data1/sora/evalscope:/opt/evalscope \
+  -v /data1/models:/opt/models \
+  -v /var/run/docker.sock:/var/run/docker.sock \
+  evalscope-complete-py312:latest \
+  bash
+
+# 容器内
+cd /opt/evalscope
+python bash/run.py --help
+```
+
+> **数据集路径注意**：evalscope 会在 `--dataset-dir` 后自动拼接 `datasets/` 子目录查找数据。若数据在 `/data1/sora/evalscope/datasets/`，则 `--dataset-dir` 应填 `/data1/sora/evalscope`，不要填 `/data1/sora/evalscope/datasets`。
+
+---
+
+## 4. 统一入口：`bash/run.py`
+
+所有评测都通过这一个脚本启动，全部参数均可通过命令行控制。
+
+### 4.1 常用示例
+
+```bash
+cd /data1/sora/evalscope
+
+# 1) Full 全量评测（约 75h，CSV 实测完整时间，已含 multi-run）
+python bash/run.py \
+  --model DeepSeek-V4-Flash-Int8 \
+  --api-url http://localhost:30000/v1 \
+  --dataset-dir /data1/sora/evalscope \
+  --output-dir /data1/sora/evalscope/output \
+  --suite full \
+  --limit none
+
+# 2) Lite 快速冒烟（约 5h）
+python bash/run.py --suite lite --limit none
+
+# 3) Lite 自己选 benchmark
+python bash/run.py \
+  --datasets humaneval,aime24,gsm8k,arc,longbench_v2,general_fc \
+  --limit 20
+
+# 4) 只跑某几个 benchmark
+python bash/run.py --datasets aime24,gsm8k --limit none
+
+# 5) 多机分组：group1 / group2 / group3
+python bash/run.py --suite group1 --limit none
+python bash/run.py --suite group2 --limit none
+python bash/run.py --suite group3 --limit none
+
+# 6) 从某个 suite 中排除特定 benchmark
+python bash/run.py --suite full --exclude hle,trivia_qa --limit none
+
+# 7) 开启 thinking 模式
+python bash/run.py --suite lite --thinking
+
+# 8) 换模型或 API 地址
+python bash/run.py --model MyModel --api-url http://10.0.0.5:30000/v1
+```
+
+### 4.2 参数说明
+
+| 参数 | 默认值 | 说明 |
+|------|--------|------|
+| `--model` | `DeepSeek-V4-Flash-Int8` | 模型名，会传给 OpenAI API 的 `model` 字段 |
+| `--api-url` | `http://localhost:30000/v1` | 模型服务地址 |
+| `--dataset-dir` | `/data1/sora/evalscope` | 数据集父目录，evalscope 会自动找其下的 `datasets/` |
+| `--output-dir` | `/data1/sora/evalscope/output` | 评测输出根目录 |
+| `--config` | `config/dpv4-int8_nothinking.yaml` | 各 benchmark 的 `max_tokens` / `temperature` 等配置 |
+| `--tokenizer-path` | `/data1/models/DeepSeek-V4-Flash-INT8` | 本地 tokenizer 路径，用于长文本 middle-truncation |
+| `--suite` | `full` | 预置套件：`full` / `lite` / `mid` / `group1` / `group2` / `group3` |
+| `--datasets` / `--benchmarks` | `None` | 逗号分隔自定义 benchmark 列表，会覆盖 `--suite` |
+| `--exclude` | `None` | 从当前 suite 中排除的 benchmark |
+| `--limit` | `None` | 每数据集最多跑几条；`none` / `all` 表示全量 |
+| `--seed` | `42` | 随机种子 |
+| `--batch-size` | `4` | 评测并发 |
+| `--thinking` | `False` | 开启 sglang thinking 模式 |
+| `--no-thinking` | `False` | 关闭 thinking 模式（默认） |
+| `--judge-model` | `DeepSeek/DeepSeek-V4-Pro` | 裁判模型名（LLM-as-judge） |
+| `--judge-api-url` | Vectron 地址 | 裁判模型 API 地址 |
+| `--judge-api-key` | 内置 key | 裁判模型 API key |
+| `--judge-max-tokens` | `10240` | 裁判模型最大输出长度 |
+| `--truncation-tokens` | `131072` | 长文本 middle-truncation token 上限 |
+| `--no-summary` | `False` | 禁止每次 benchmark 后自动写汇总表 |
+
+> 汇总表文件名由模型名自动派生，例如 `--model DeepSeek-V4-Flash-Int8` → `<safe_model_name>.csv/.xlsx`。
+
+### 4.3 套件详情
+
+| 套件 | 覆盖 benchmark | 预计时间 | 说明 |
+|------|---------------|----------|------|
+| `full` | 全部 | 约 **75h** | CSV 实测完整时间，已含 multi-run |
+| `lite` | 各领域代表作 | 约 **5h** | 快速冒烟，覆盖代码/数学/知识/长文本/Agent |
+| `mid` | 较全子集 | 约 **25h** | 平衡速度与覆盖 |
+| `group1` | 代码 + 数学/推理 | 约 **22h** | 多机组 1 |
+| `group2` | 重知识 | 约 **26h** | 多机组 2 |
+| `group3` | 长文本 + Agent + 知识 | 约 **27h** | 多机组 3 |
+
+> **分组时间说明**
+>
+> CSV 中记录的是**完整评测时间**（已包含 multi-run），总约 75h。因此分组直接按 CSV 实测时间平衡：Group1 ~22h、Group2 ~26h、Group3 ~27h，合计 ~75h。
+
+### 4.4 Multi-run 配置
+
+以下 benchmark 会重复跑多次，再取平均：
+
+| Benchmark | 次数 | 目的 |
+|-----------|------|------|
+| `aime24` / `aime25` / `aime26` / `hmmt26` | 12 | 样本少（各 30 题），多次采样稳定结果 |
+| `live_code_bench` | 5 | 约 100 样本，多次采样 |
+| `imo_answerbench` | 4 | 约 120 样本 |
+| `humaneval` | 3 | 约 164 样本 |
+| `gpqa_diamond` | 2 | 约 198 样本 |
+
+multi-run 次数在 `bash/run.py` 的 `MULTI_RUN_CONFIG` 中固定，暂不提供命令行覆盖。
+
+---
+
+## 5. 配置文件
+
+路径：`config/dpv4-int8_nothinking.yaml`
+
+每个 benchmark 可独立配置：
+
+```yaml
+aime24:
+  generation_config:
+    temperature: 1.0     # 多次采样用 1.0
+    top_p: 1.0
+    stream: true
+    max_tokens: 32768
+
+longbench_v2:
+  generation_config:
+    temperature: 0.0     # 长文本贪心
+    top_p: 1.0
+    stream: true
+    max_tokens: 8192
+  dataset_args:
+    subset_list:
+      - short
+      - medium
+      - long
+```
+
+如需换模型，通常只需改 `bash/run.py` 的命令行参数，不需要改 YAML。
+
+---
+
+## 6. 长文本截断
+
+`longbench_v2` 和 `openai_mrcr` 默认启用 **middle-truncation**，上限由 `--truncation-tokens` 控制（默认 128k）。
+
+- 对 `longbench_v2`：保留 context 头部和尾部，截断中间。
+- 对 `openai_mrcr`：保留对话头尾及 needle 附近窗口，并限制单条消息长度。
+
+如需关闭截断：
+
+```bash
+python bash/run.py --suite full --truncation-tokens 0
+```
+
+---
+
+## 7. 结果汇总：`bash/collect_results.py`
+
+跑完 benchmark 后，自动或手动汇总分数到 Excel/CSV。
+
+### 7.1 自动汇总（run.py 每次跑完都会更新）
+
+`run.py` 会在每个 benchmark 结束后自动调用 `collect_results.py`，在按模型名分目录的位置生成：
+
+```text
+<output-dir>/
+└── <safe_model_name>/                 # 按模型名分目录，目录和文件名均由 --model 自动派生
+    ├── <safe_model_name>.xlsx
+    └── <safe_model_name>.csv
+```
+
+例：`--model DeepSeek-V4-Flash-Int8` → `/data1/sora/evalscope/output/DeepSeek-V4-Flash-Int8/DeepSeek-V4-Flash-Int8.csv`。
+
+### 7.2 手动汇总
+
+```bash
+# 汇总所有已跑完的 benchmark（默认行为）
+python bash/collect_results.py \
+  --output-dir /data1/sora/evalscope/output \
+  --model DeepSeek-V4-Flash-Int8
+
+# 只汇总指定 benchmark（推荐，跟 run.py 自动汇总行为一致）
+python bash/collect_results.py \
+  --output-dir /data1/sora/evalscope/output \
+  --model DeepSeek-V4-Flash-Int8 \
+  --include aime24,gsm8k,arc,longbench_v2
+
+# 可选：--out-name 自定义文件名（默认就是 safe model name）
+```
+
+### 7.3 汇总规则
+
+- **只统计本次实际跑的 benchmark**：`run.py` 会维护一个白名单，只把当前这次运行涉及的 benchmark 传给汇总脚本。因此，如果本次只跑了 `aime24,gsm8k,arc`，汇总表里也只会出现这 3 个，不会把 output 目录里以前跑的其它 benchmark 一起拉进来。
+- 对同一个 benchmark 的多个 seed/run，**分数取平均**。
+- 性能指标（latency、TTFT、TPOT、TPS 等）**从所有 run 的原始 `predictions/*.jsonl` 重新聚合**，避免断点续跑时从 0 重置。
+- 输出格式与 `/data1/sora/evalscope/P800模型能力评测结果 - DS4-Flash-INT8-NO-Thinking-2.0-FULL.csv` 保持一致。
+
+### 7.4 断点续跑统计修复
+
+**问题**：`evalscope` 在运行时会把所有样本的累计统计写入 `reports/*.json` 里的 `perf_metrics.summary`（旧版本会写到独立的 `perf_stats.json`），并在 `predictions/*.jsonl` 里记录每条样本的原始 perf 数据。如果一次 benchmark 需要跑很久，中间断点续跑时这个累计 summary 会被**重新初始化**（只反映本次重启之后的样本），导致汇总表里 latency / TTFT / TPOT / 总 tokens / 总耗时全部**从 0 开始重新计算**，出现严重低估。
+
+**修复思路**：保留 `report.json` 里的累计 summary，但**额外维护两份独立于 evalscope 的持久化文件**：
+
+| 文件 | 内容 | 用途 |
+|------|------|------|
+| `perf_stats_backup/<benchmark>__<model>.json` | 最近一次完整跑完的 `perf_metrics.summary` | 累计 summary 的稳定副本，避免被重启覆盖 |
+| `predictions_archive/<benchmark>__<model>.jsonl` | 历次 run 所有样本的原始记录，按 `index` 去重 | 重建 latency / TTFT / TPOT 的真实分布（含 P90/P99） |
+
+每次 `run_task()` 完成后，`run.py` 会自动调用 `bash/perf_backup.py`：
+
+1. 把当前 `reports/<model>/<benchmark>.json` 里的 `perf_metrics.summary` 复制到 `perf_stats_backup/<benchmark>__<model>.json`。如果 backup 的 `n_samples` 已经大于新 summary（即检测到 reset），则保留旧 backup，避免覆盖。
+2. 把当前 `predictions/<model>/*.jsonl` 的每条样本按 `index` 去重追加到 `predictions_archive/<benchmark>__<model>.jsonl`，支持多次断点的样本累积。
+
+每次下一个 run 开始前，`run.py` 会调用 `restore_from_backup()`：如果 backup 存在，就把 archive 还原回 `predictions/<model>/` 并把 summary 还原回 `report.json` 的 `perf_metrics.summary`，让 evalscope 的 `use_cache` resume 机制能从更大的历史状态继续。
+
+`collect_results.py` 读取汇总时的优先级：
+
+1. **优先**读 `predictions_archive/<benchmark>__<model>.jsonl`，按 `index` 去重得到所有历史样本，从原始 perf_metrics 重新计算 latency / TTFT / TPOT 的 mean、P90、P99、QPS、TPS、总 tokens。
+2. 没有 archive 时，读 `predictions/<model>/*.jsonl`（当前 predictions）。
+3. 都没有时，回退到 `perf_stats_backup/<benchmark>__<model>.json` 的 summary。
+4. summary 里 `n_samples` 比 backup 大就用 summary，否则用 backup 里的累计值。
+
+### 7.5 验证断点续跑确实能恢复
+
+```python
+# 1. 模拟 report.json 被重置（n_samples 改成 2）
+# 2. 不动 predictions 和 archive
+# 3. 跑 collect_results
+# 期望：汇总表仍显示 30 个样本的真实指标
+```
+
+实际验证结果：把 `aime24/report.json` 的 `n_samples` 改成 2 后再汇总，得分仍是 **0.6167**、耗时 **1.22h**、**TTFT/TPOT 正常值**，证明 archive 修复确实生效。
+
+---
+
+## 8. 输出目录结构
+
+```text
+<output-dir>/
+├── <benchmark_name>/
+│   ├── seed_<seed>/
+│   │   ├── reports/<model_name>/<benchmark_name>.json
+│   │   ├── predictions/<model_name>/<benchmark_name>.jsonl
+│   │   └── logs/eval_log.log
+│   └── seed_<seed>_run_<idx>/       # multi-run 额外目录
+├── perf_stats_backup/                # 累计 summary 持久化（断点恢复）
+│   └── <benchmark>__<model>.json
+├── predictions_archive/              # 原始每条样本归档（断点恢复）
+│   └── <benchmark>__<model>.jsonl
+└── <safe_model_name>/                # 汇总表（按模型名）
+    ├── <safe_model_name>.xlsx
+    └── <safe_model_name>.csv
+```
+
+### 查看分数
+
+```bash
+# 单个 benchmark
+cat output/aime24/seed_42/reports/DeepSeek-V4-Flash-Int8/aime24.json
+
+# 汇总所有分数
+python3 -c "
+import json, glob
+for f in sorted(glob.glob('output/*/seed_*/reports/*/*.json')):
+    data = json.load(open(f))
+    score = data.get('score', data.get('mean_acc', 'N/A'))
+    print(f'{f}: {score}')
+"
+```
+
+---
+
+## 9. 最终 Benchmark 集合
+
+| 能力域 | Benchmark |
+|--------|-----------|
+| 代码生成 | `humaneval`, `live_code_bench`, `bigcodebench` |
+| 推理/数学 | `aime24`, `aime25`, `aime26`, `hmmt26`, `imo_answerbench`, `gsm8k`, `competition_math`, `bbh`, `drop` |
+| 知识 | `gpqa_diamond`, `hle`, `mmlu_pro`, `simple_qa`, `super_gpqa`, `mmlu`, `cmmlu`, `arc`, `hellaswag`, `trivia_qa`, `winogrande` |
+| 长上下文 | `longbench_v2`, `openai_mrcr` |
+| 智能体/工具 | `tau2_bench`, `general_fc`, `bfcl_v3` |
+
+未纳入标准流程的：
+
+- `swe_bench_pro` / `swe_bench_verified` / `swe_bench_multilingual_agentic`：每个样本需独立 Docker 镜像，建议按 2.4.3 预拉后单独运行。
+- `browsecomp` / `mcp_atlas`：需要外部 MCP 搜索工具。
+- `terminal_bench_v2`：镜像兼容性问题。
+
+---
+
+## 10. 常见问题
+
+### Q1: Docker 里找不到 datasets？
+
+确保 `--dataset-dir` 指向包含 `datasets/` 的**父目录**，而不是 `datasets/` 本身。
+
+### Q2: `bigcodebench` 或 `humaneval` 报 sandbox 错误？
+
+检查是否已准备镜像：
+
+```bash
+docker images | grep -E 'bigcodebench-sandbox|python:3.11-slim'
+```
+
+### Q3: 只想跑某个 benchmark 怎么办？
+
+```bash
+python bash/run.py --datasets aime24 --limit none
+```
+
+### Q4: 想换 judge model？
+
+```bash
+python bash/run.py \
+  --judge-model deepseek-v4-pro \
+  --judge-api-url https://api.deepseek.com/v1 \
+  --judge-api-key sk-xxx
+```
+
+### Q5: 断点续跑后 perf 指标被重置？
+
+`run.py` 会在每次 benchmark 后把 `perf_metrics.summary` 和 `predictions` 备份到 `output/perf_stats_backup/` 和 `output/predictions_archive/`；汇总时优先从 archive 重新计算，因此断点重启不会导致指标被低估。同时汇总表只包含**本次运行实际跑的 benchmark**。
+
+### Q6: SWE-bench 镜像拉取慢/被限流？
+
+1. 按 2.4.1 配置多 mirror；
+2. 使用 `--max-workers 1` 单 worker 拉取；
+3. 用 `tmux` 或 `nohup` 挂后台，避免 SSH 断连；
+4. 若某个 mirror 完全不可用，从 `daemon.json` 中移除，避免无效重试。