Output:
Exit code: 0
Wall time: 0 seconds
Output:
                    lease_duration_sec=lease_duration_sec
                )
            except Exception as e:
                worker_logger.warning(f"Supabase claim_next_video failed: {e}")
                time.sleep(poll_interval_sec)
                continue

            if not video:
                consecutive_empty += 1
                if consecutive_empty >= max_consecutive_empty:
                    worker_logger.info("No more pending videos, stopping worker")
                    break
                worker_logger.debug(f"No pending videos, sleeping {poll_interval_sec}s...")
                time.sleep(poll_interval_sec)
                continue

            consecutive_empty = 0
            video_id = video['youtube_id']
            video_url = video['youtube_url']

            # Report video pick to analytics (non-blocking)
            analytics.pick(video_id)

            # Fetch full video record to get chapters
            try:
                full_video = supabase.get_video(video_id)
            except Exception as e:
                worker_logger.warning(f"Supabase get_video failed for {video_id}: {e}")
                full_video = None
            if not full_video:
                full_video = video  # Fallback to claim result

            worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
            has_chapters = full_video.get('has_chapters', False)
            if has_chapters:
                worker_logger.info(f"   Chapters: {full_video.get('chapter_count', 0)} from Supabase")
            start_time = time.time()

            try:
                # Process video with Supabase metadata (chapters, title, etc.)
                result = process_single_video(video_url, config, supabase_metadata=full_video)
                process_time = time.time() - start_time

                # Extract results for DB update
                quality_stats = result.get('quality_stats', {}) or {}
                segment_summary = result.get('segment_summary', {}) or {}
                download_meta = result.get('download_meta', {}) or {}

                # Update Supabase with success
                update_data = {
                    'num_speakers': result.get('num_speakers', 0),
                    'total_segments': result.get('total_segments', 0),
                    'usable_percentage': quality_stats.get('usable_percentage', 0),
                    'usable_segments': quality_stats.get('usable_segments', 0),
                    'usable_duration_sec': quality_stats.get('usable_duration', 0),
                    # Store the fully-qualified key (includes R2_PREFIX) so it
                    # can be located in R2 without guessing prefixes.
                    'r2_tar_key': ((result.get('export', {}) or {}).get('r2_upload_full_key')
                                   or result.get('r2_upload_key', '')),
                    'r2_tar_size_bytes': (result.get('export', {}) or {}).get('tar_size_bytes'),
                    'pipeline_version': result.get('pipeline_version', ''),
                    'audio_native_sample_rate': download_meta.get('native_sample_rate'),
                    'audio_channels': download_meta.get('channels'),
                    'audio_duration_sec': result.get('processed_duration', 0),
                    'needs_demucs_count': quality_stats.get('music_detection', {}).get('segments_needs_demucs', 0),
                    'heavy_music_count': quality_stats.get('music_detection', {}).get('segments_heavy_contamination', 0),
                    'processing_meta': {
                        'worker_id': supabase_worker_id,
                        'machine_id': machine_id,
                        'gpu_id': gpu_id,
                        'provider': os.environ.get('WORKER_PROVIDER') or ('vast' if os.environ.get('CONTAINER_ID') else None),
                        'provider_instance_id': os.environ.get('CONTAINER_ID') or os.environ.get('RUNPOD_POD_ID'),
                        'public_ip': os.environ.get('PUBLIC_IPADDR'),
                        'timing_total': result.get('timing_total', 0),
                        'completed_at': datetime.now().isoformat(),
                        'pipeline_version': result.get('pipeline_version', ''),
                    },
                    'quality_stats': quality_stats,
                    'segment_summary': segment_summary,
                    'download_meta': download_meta,
                }

                supabase.update_status(video_id, STATUS_COMPLETED, update_data)

                worker_logger.info(
                    f"Completed: {video_id} | "
                    f"{process_time:.1f}s | "
                    f"{result.get('num_speakers', 0)} speakers | "
                    f"{quality_stats.get('usable_percentage', 0):.1f}% usable"
                )

                # Report success to analytics (non-blocking)
                analytics.done(
                    video_id=video_id,
                    total_time=result.get('timing_total', process_time),
                    audio_min=result.get('processed_duration', 0) / 60.0,
                    speakers=result.get('num_speakers', 0),
                    usable_pct=quality_stats.get('usable_percentage', 0)
                )

                # Put result in queue for stats collection
                video_result = VideoResult(
                    video_url=video_url,
                    video_id=video_id,
                    success=True,
                    duration_sec=result.get('processed_duration', 0),
                    process_time_sec=process_time,
                    pipeline_time_sec=result.get('timing_total', 0),
                    rtf=(result.get('processed_duration', 0) / result.get('timing_total', 1)) if result.get('timing_total') else 0,
                    num_speakers=result.get('num_speakers', 0),
                    usable_percentage=quality_stats.get('usable_percentage', 0),
                    output_path=str(Path(output_dir) / video_id),
                    stage_timings={k: float(v) for k, v in (result.get('timing', {}) or {}).items()},
                    source_type=result.get('source_type', ''),
                    r2_upload_key=result.get('r2_upload_key', ''),
                    gpu_id=gpu_id,
                    worker_id=worker_id
                )

            except Exception as e:
                process_time = time.time() - start_time
                error_msg = str(e)
                error_type = type(e).__name__
                worker_logger.error(f"Failed: {video_id} - {error_msg}")
                traceback.print_exc()

                # Report failure to analytics (non-blocking)
                analytics.fail(
                    video_id=video_id,
                    stage="processing",
                    error_type=error_type,
                    message=error_msg,
                    stack_trace=traceback.format_exc()
                )

                # Update Supabase with failure
                supabase.update_status(video_id, STATUS_FAILED, {
                    'last_error': error_msg[:1000],  # Truncate long errors
                    'last_error_type': type(e).__name__,
                    'processing_meta': {
                        'worker_id': supabase_worker_id,
                        'gpu_id': gpu_id,
                        'failed_at': datetime.now().isoformat(),
                    },
                })

                video_result = VideoResult(
                    video_url=video_url,
                    video_id=video_id,
                    success=False,
                    error=error_msg,
                    process_time_sec=process_time,
                    gpu_id=gpu_id,
                    worker_id=worker_id
                )

            result_queue.put(video_result)
            videos_processed += 1

            # Clear GPU cache between videos
            MODELS.clear_cache(aggressive=True)


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Analytics Reporter for Maya3 Pipeline

Lightweight, non-blocking analytics that reports worker status to Supabase.

Design principles:
- Never blocks main processing thread (background queue + thread)
- Batches writes to reduce Supabase calls (~2-3 per video, not 10+)
- Uses upserts for heartbeats (single row per worker)
- Gracefully handles failures (analytics never crashes worker)
- Local counters remain accurate even if Supabase is down
"""

import threading
import queue
import time
import socket
import hashlib
import os
import traceback
from typing import Optional, Dict, Any
from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class AnalyticsEvent:
    """Event to be sent to Supabase."""
    event_type: str  # 'heartbeat', 'done', 'fail'
    data: Dict[str, Any]
    timestamp: datetime = field(default_factory=datetime.utcnow)

def get_machine_id() -> str:
    """
    Generate a unique, readable machine ID.
    Format: hostname-xxxx (where xxxx is a short hash)
    """
    # Prefer cloud/provider instance identifiers when available so analytics can
    # be mapped back to the actual machine/contract in the provider UI.
    vast_id = os.environ.get("CONTAINER_ID")  # Vast.ai predefined env var
    if vast_id:
        return f"vast-{vast_id}"

    runpod_id = os.environ.get("RUNPOD_POD_ID")
    if runpod_id:
        return f"runpod-{runpod_id}"

    hostname = socket.gethostname()[:20]  # Truncate long hostnames
    # Create short hash from hostname + MAC-like identifier
    unique_str = f"{hostname}-{socket.getfqdn()}"
    short_hash = hashlib.md5(unique_str.encode()).hexdigest()[:4]
    return f"{hostname}-{short_hash}"

class AnalyticsReporter:
    """
    Non-blocking analytics reporter with background flushing.

    Usage:
        reporter = AnalyticsReporter(gpu_id=0, supabase_url=..., supabase_key=...)
        reporter.session_start()

        # For each video:
        reporter.pick(video_id)
        reporter.stage_update(video_id, 3, "Diarization")  # Optional stage updates
        reporter.done(video_id, total_time, audio_min, speakers, usable_pct)
        # or
        reporter.fail(video_id, "Download", "NetworkError", "Connection timeout", traceback_str)

        # On shutdown:
        reporter.shutdown()
    """

    def __init__(
        self,
        gpu_id: int,
        supabase_url: str,
        supabase_key: str,
        machine_id: Optional[str] = None,
        flush_interval: float = 5.0,
        batch_size: int = 10
    ):
        self.gpu_id = gpu_id
        self.machine_id = machine_id or get_machine_id()
        self.worker_id = f"{self.machine_id}_gpu{gpu_id}"

        # Background queue for non-blocking writes
        self._queue: queue.Queue = queue.Queue()
        self._stop_event = threading.Event()

        # Configuration
        self._flush_interval = flush_interval
        self._batch_size = batch_size

        # Local counters (always accurate, even if Supabase fails)
        self.session_videos_done = 0
        self.session_videos_failed = 0
        self.session_audio_minutes = 0.0
        self.session_usable_minutes = 0.0
        self.session_start_time = datetime.utcnow()

        # Supabase client (lazy init in background thread)
        self._supabase_url = supabase_url
        self._supabase_key = supabase_key
        self._client = None
        self._client_lock = threading.Lock()

        # Start background flusher
        self._flush_thread = threading.Thread(target=self._flush_loop, daemon=True)
        self._flush_thread.start()

    def _get_client(self):
        """Lazy init Supabase client in background thread (thread-safe)."""
        if self._client is None:
            with self._client_lock:
                if self._client is None:  # Double-check after acquiring lock
                    try:
                        from supabase import create_client
                        self._client = create_client(self._supabase_url, self._supabase_key)
                    except Exception:
                        pass  # Silently fail - analytics should never crash worker
        return self._client

    def _flush_loop(self):
        """Background thread that flushes events to Supabase."""
        events_buffer = []
        last_flush = time.time()

        while not self._stop_event.is_set():
            try:
                # Collect events (non-blocking with timeout)
                try:
                    event = self._queue.get(timeout=1.0)
                    events_buffer.append(event)
                except queue.Empty:
                    pass

                # Flush conditions: batch_size+ events OR flush_interval passed
                should_flush = (
                    len(events_buffer) >= self._batch_size or
                    (events_buffer and time.time() - last_flush >= self._flush_interval)
                )

                if should_flush and events_buffer:
                    self._flush_events(events_buffer)
                    events_buffer = []
                    last_flush = time.time()

            except Exception:
                # Never crash the flush thread
                pass

        # Final flush on shutdown
        if events_buffer:
            self._flush_events(events_buffer)

    def _flush_events(self, events: list):
        """Batch write events to Supabase."""
        client = self._get_client()
        if not client:
            return

        try:
            # Separate by type
            heartbeats = [e for e in events if e.event_type == 'heartbeat']
            done_events = [e for e in events if e.event_type == 'done']
            fail_events = [e for e in events if e.event_type == 'fail']

            # Upsert latest heartbeat (only 1 per worker, use most recent)
            if heartbeats:
                latest = heartbeats[-1]
                client.table('worker_heartbeats').upsert(
                    latest.data,
                    on_conflict='worker_id'
                ).execute()

            # Batch insert done events
            if done_events:
                rows = [e.data for e in done_events]
                client.table('processing_events').insert(rows).execute()

            # Batch insert fail events + error_logs
            if fail_events:
                event_rows = [e.data for e in fail_events]
                client.table('processing_events').insert(event_rows).execute()

                # Also log to error_logs table with full details
                error_rows = [{
                    'worker_id': e.data['worker_id'],
                    'video_id': e.data['video_id'],
                    'stage': e.data.get('stage_name'),
                    'error_type': e.data.get('error_type'),
                    'error_message': e.data.get('error_message'),
                    'stack_trace': e.data.get('stack_trace')
                } for e in fail_events]
                client.table('error_logs').insert(error_rows).execute()

        except Exception:
            # Silently fail - analytics should never crash worker
            pass

    # =========================================================================
    # Public API (all non-blocking)
    # =========================================================================

    def session_start(self):
        """Register worker on startup."""
        self.session_start_time = datetime.utcnow()
        self._queue.put(AnalyticsEvent('heartbeat', {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'status': 'idle',
            'current_video_id': None,
            'current_stage': None,
            'current_stage_name': None,
            'last_heartbeat': datetime.utcnow().isoformat(),
            'session_start': self.session_start_time.isoformat(),
            'session_videos_done': 0,
            'session_videos_failed': 0,
            'session_audio_minutes': 0,
            'session_usable_minutes': 0
        }))

    def pick(self, video_id: str):
        """Worker claimed a video."""
        self._queue.put(AnalyticsEvent('heartbeat', {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'status': 'processing',
            'current_video_id': video_id,
            'current_stage': 1,
            'current_stage_name': 'Download',
            'last_heartbeat': datetime.utcnow().isoformat(),
            'session_start': self.session_start_time.isoformat(),
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes
        }))

    def stage_update(self, video_id: str, stage_num: int, stage_name: str):
        """
        Update current stage (lightweight heartbeat).
        Call this optionally for long-running stages.
        """
        self._queue.put(AnalyticsEvent('heartbeat', {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'status': 'processing',
            'current_video_id': video_id,
            'current_stage': stage_num,
            'current_stage_name': stage_name,
            'last_heartbeat': datetime.utcnow().isoformat(),
            'session_start': self.session_start_time.isoformat(),
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes
        }))

    def done(
        self,
        video_id: str,
        total_time: float,
        audio_min: float,
        speakers: int,
        usable_pct: float
    ):
        """Video processed successfully."""
        # Update local counters
        self.session_videos_done += 1
        self.session_audio_minutes += audio_min
        usable_min = audio_min * (usable_pct / 100.0)
        self.session_usable_minutes += usable_min

        # Queue done event (for processing_events table)
        self._queue.put(AnalyticsEvent('done', {
            'worker_id': self.worker_id,
            'video_id': video_id,
            'event_type': 'done',
            'duration_seconds': total_time,
            'audio_minutes': audio_min,
            'speakers': speakers,
            'usable_pct': usable_pct
        }))

        # Update heartbeat with new counters (status back to idle)
        self._queue.put(AnalyticsEvent('heartbeat', {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'status': 'idle',
            'current_video_id': None,
            'current_stage': None,
            'current_stage_name': None,
            'last_heartbeat': datetime.utcnow().isoformat(),
            'session_start': self.session_start_time.isoformat(),
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes
        }))

    def fail(
        self,
        video_id: str,
        stage: str,
        error_type: str,
        message: str,
        stack_trace: Optional[str] = None
    ):
        """Video processing failed."""
        # Update local counters
        self.session_videos_failed += 1

        # Queue fail event (for processing_events + error_logs tables)
        self._queue.put(AnalyticsEvent('fail', {
            'worker_id': self.worker_id,
            'video_id': video_id,
            'event_type': 'fail',
            'stage_name': stage,
            'error_type': error_type,
            'error_message': message[:500] if message else None,  # Truncate
            'stack_trace': stack_trace[:2000] if stack_trace else None  # Truncate
        }))

        # Update heartbeat (status back to idle)
        self._queue.put(AnalyticsEvent('heartbeat', {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'status': 'idle',
            'current_video_id': None,
            'current_stage': None,
            'current_stage_name': None,
            'last_heartbeat': datetime.utcnow().isoformat(),
            'session_start': self.session_start_time.isoformat(),
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes
        }))

    def shutdown(self):
        """Graceful shutdown - flush remaining events."""
        # Send final heartbeat marking as offline
        self._queue.put(AnalyticsEvent('heartbeat', {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'status': 'offline',
            'current_video_id': None,
            'current_stage': None,
            'current_stage_name': None,
            'last_heartbeat': datetime.utcnow().isoformat(),
            'session_start': self.session_start_time.isoformat(),
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes
        }))

        # Signal stop and wait for flush
        self._stop_event.set()
        self._flush_thread.join(timeout=5.0)

    def get_local_stats(self) -> Dict[str, Any]:
        """Get local session statistics (always accurate)."""
        return {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes,
            'session_duration_minutes': (datetime.utcnow() - self.session_start_time).total_seconds() / 60.0
        }

class NoOpAnalyticsReporter:
    """
    No-op analytics reporter for when analytics is disabled.
    Same interface as AnalyticsReporter but does nothing.
    """

    def __init__(self, gpu_id: int = 0, **kwargs):
        self.gpu_id = gpu_id
        self.worker_id = f"local_gpu{gpu_id}"
        self.session_videos_done = 0
        self.session_videos_failed = 0
        self.session_audio_minutes = 0.0
        self.session_usable_minutes = 0.0

    def session_start(self): pass
    def pick(self, video_id: str): pass
    def stage_update(self, video_id: str, stage_num: int, stage_name: str): pass

    def done(self, video_id: str, total_time: float, audio_min: float,
             speakers: int, usable_pct: float):
        self.session_videos_done += 1
        self.session_audio_minutes += audio_min
        self.session_usable_minutes += audio_min * (usable_pct / 100.0)

    def fail(self, video_id: str, stage: str, error_type: str,
             message: str, stack_trace: str = None):
        self.session_videos_failed += 1

    def shutdown(self): pass

    def get_local_stats(self) -> Dict[str, Any]:
        return {
            'worker_id': self.worker_id,
            'gpu_id': self.gpu_id,
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes
        }

def create_reporter(
    gpu_id: int,
    supabase_url: Optional[str] = None,
    supabase_key: Optional[str] = None,
    machine_id: Optional[str] = None,
    enabled: bool = True
) -> AnalyticsReporter:
    """
    Factory function to create appropriate reporter.
    Returns NoOpAnalyticsReporter if disabled or credentials missing.
    """
    if not enabled or not supabase_url or not supabase_key:
        return NoOpAnalyticsReporter(gpu_id=gpu_id)

    return AnalyticsReporter(
        gpu_id=gpu_id,
        supabase_url=supabase_url,
        supabase_key=supabase_key,
        machine_id=machine_id
    )


**Diagnosing processing issues**

It looks like we have the stage_update function available, but it's not being utilized right now. We can implement a periodic heartbeat to keep track of things while processing, although that needs some changes. The user's request makes me think they might be stuck since the CPU and GPU seem idle, indicating a potential hang. I’ll need to run SSH commands, check logs, and confirm our suspicions about the process getting stuck after the download stage and before the VAD stage. Let’s locate the exact point of the hang.


**Inspecting audio download paths**

I’m trying to understand how the audio downloads are being processed. It seems like the `download_audio` function writes to /dev/shm, but the configuration suggests the output directory is ./output. Let me check how the `src/download` script uses the `output_dir` parameter. It’s likely temporary files are stored in /dev/shm. I’ll look for recently modified files there and also see if the trimmed file path is returning correctly. I need to be cautious with using torchaudio.load since it might hang, so I’ll apply a timeout and check file sizes.


**Investigating potential deadlock**

I’m considering if there might be a deadlock related to ffmpeg, even though the download appears finished. Since the `_download_with_ffmpeg_dual_output` function uses subprocess.run with check=True, it shouldn’t be running still. Maybe torchaudio.load is hanging due to the `sox` backend waiting on something. It’s possible the file is corrupted or slow to parse, prompting the hang. I'll run `lsof` to check for open files on the worker process, while keeping an eye on processes that might be sleeping. Additionally, I’ll review `dmesg` for any OOM killer messages, given our memory situation.


**Examining container access and processes**

I’m wondering if I might be facing access restrictions inside the container, even though I'm root. When I use SSH, I enter the vast container environment where I can run commands. I should gather some information: the last 10 lines of /var/log/onstart.log suggest the process hung after the download. I’ll check if worker processes are alive by using `pgrep` and see resource usage as well. It’s also crucial to verify if /dev/shm or other disks are full, as that could cause torchaudio.load to hang, especially with our changes to the VAD setup.


**Analyzing potential hang issues**

I’m examining the PID limit set in Docker, which is at 31232, so that seems fine. But I'm concerned the system uses `fork` for ProcessPool, which is now switched to spawn. This could complicate things if it's running into resource constraints, potentially causing hangs, especially during ProcessPoolExecutor creation. I suspect it might be stuck while reading the file to numpy in `AudioBuffer.from_file`. I should check the implementation of audio_buffer to see if那里 might be any issues. It seems the hang occurs before the VAD stage starts. Let's investigate!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Shared Audio Buffer - Load Once, Use Everywhere

=== OPTIMIZATION (v6.2) ===
Previously: Each stage (VAD, diarization, embeddings, quality) called torchaudio.load()
Now: Load audio ONCE, pass numpy array/slices to all stages

Benefits:
- Eliminates 4-5 redundant file reads
- Reduces disk I/O by ~80%
- Faster processing (no decode overhead)
- Memory-efficient (single copy)
"""

import logging
import numpy as np
import torch
import torchaudio
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple
import atexit
import shutil

logger = logging.getLogger("FastPipelineV6.AudioBuffer")

@dataclass
class AudioBuffer:
    """
    Holds loaded audio waveform and provides slicing utilities.

    Usage:
        buffer = AudioBuffer.from_file("audio.wav")
        vad_result = run_vad(buffer)
        embeddings = extract_embeddings(buffer, segments)
    """
    waveform_np: np.ndarray  # Shape: (samples,) - mono
    sample_rate: int
    audio_path: str  # Original path for reference
    duration: float

    @classmethod
    def from_file(cls, audio_path: str) -> 'AudioBuffer':
        """Load audio file into buffer."""
        logger.info(f"📥 Loading audio into shared buffer: {Path(audio_path).name}")
        waveform, sr = torchaudio.load(audio_path)
        waveform_np = waveform.squeeze(0).numpy()  # Convert to mono numpy
        duration = len(waveform_np) / sr
        logger.info(f"   Loaded: {duration:.1f}s @ {sr}Hz ({len(waveform_np):,} samples)")
        return cls(
            waveform_np=waveform_np,
            sample_rate=sr,
            audio_path=audio_path,
            duration=duration
        )

    def get_slice(self, start_sec: float, end_sec: float) -> np.ndarray:
        """Get audio slice by time range (seconds)."""
        start_sample = int(start_sec * self.sample_rate)
        end_sample = int(end_sec * self.sample_rate)
        return self.waveform_np[start_sample:end_sample]

    def get_slice_samples(self, start_sample: int, end_sample: int) -> np.ndarray:
        """Get audio slice by sample indices."""
        return self.waveform_np[start_sample:end_sample]

    def get_torch_tensor(self, start_sec: float = 0, end_sec: float = None) -> torch.Tensor:
        """Get slice as torch tensor for GPU processing."""
        if end_sec is None:
            end_sec = self.duration
        chunk = self.get_slice(start_sec, end_sec)
        return torch.from_numpy(chunk).float()

    def get_full_waveform_2d(self) -> Tuple[torch.Tensor, int]:
        """Get full waveform as 2D tensor (1, samples) for torchaudio compatibility."""
        return torch.from_numpy(self.waveform_np).unsqueeze(0), self.sample_rate

class TempDirManager:
    """
    Failure-safe temporary directory manager.

    === OPTIMIZATION (v6.2) ===
    Registers atexit handler to clean up temp files even on crash.
    Prevents disk quota exhaustion from orphaned chunk files.
    """
    _instance = None
    _temp_dirs: list = []

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
            cls._instance._temp_dirs = []
            # Register cleanup on exit (normal or exception)
            atexit.register(cls._instance.cleanup_all)
        return cls._instance

    def register(self, path: Path):
        """Register a temp directory for cleanup on exit."""
        self._temp_dirs.append(path)

    def unregister(self, path: Path):
        """Unregister a temp directory (when successfully cleaned)."""
        if path in self._temp_dirs:
            self._temp_dirs.remove(path)

    def cleanup_all(self):
        """Clean up all registered temp directories."""
        for path in self._temp_dirs[:]:  # Copy list to avoid modification during iteration
            try:
                if path.exists():
                    shutil.rmtree(path, ignore_errors=True)
                    logger.debug(f"   Cleaned up temp dir: {path}")
                self._temp_dirs.remove(path)
            except Exception as e:
                logger.warning(f"   Failed to cleanup {path}: {e}")

    def cleanup_for_audio(self, audio_path: str):
        """Clean up temp dirs associated with an audio file."""
        chunk_dir = Path(audio_path).parent / "chunks"
        if chunk_dir.exists():
            try:
                shutil.rmtree(chunk_dir)
                self.unregister(chunk_dir)
                logger.debug(f"   Cleaned up chunks: {chunk_dir}")
            except Exception as e:
                logger.warning(f"   Failed to cleanup chunks: {e}")

# Global instance
TEMP_MANAGER = TempDirManager()

def get_vram_info() -> Tuple[float, float, float]:
    """
    Get GPU memory info for memory-aware batching.

    === OPTIMIZATION (v6.2) ===
    Use torch.cuda.mem_get_info() to query actual VRAM availability.
    Scale batch sizes dynamically based on available memory.

    Returns:
        (free_gb, total_gb, utilization_pct)
    """
    if not torch.cuda.is_available():
        return (0.0, 0.0, 0.0)

    try:
        free, total = torch.cuda.mem_get_info()
        free_gb = free / (1024**3)
        total_gb = total / (1024**3)
        utilization = (1 - free / total) * 100
        return (free_gb, total_gb, utilization)
    except Exception:
        # Fallback for older PyTorch
        total = torch.cuda.get_device_properties(0).total_memory
        reserved = torch.cuda.memory_reserved(0)
        free = total - reserved
        return (free / (1024**3), total / (1024**3), (reserved / total) * 100)

def compute_optimal_batch_size(
    num_items: int,
    avg_item_samples: int,
    base_batch: int = 8,
    min_batch: int = 2,
    max_batch: int = 64,
    vram_headroom_gb: float = 2.0
) -> int:
    """
    Compute optimal batch size based on available VRAM.

    === OPTIMIZATION (v6.2) ===
    Instead of fixed batch sizes, dynamically scale based on:
    - Available VRAM (torch.cuda.mem_get_info)
    - Average item size (samples)
    - Safety headroom for GPU operations

    Args:
        num_items: Total items to process
        avg_item_samples: Average samples per item
        base_batch: Default batch size
        min_batch: Minimum batch size
        max_batch: Maximum batch size
        vram_headroom_gb: Reserved VRAM headroom

    Returns:
        Optimal batch size
    """
    free_gb, total_gb, _ = get_vram_info()

    if free_gb == 0:  # CPU-only
        return min(base_batch, num_items)

    # Available VRAM for batching (minus headroom)
    available_gb = max(0.5, free_gb - vram_headroom_gb)

    # Estimate memory per batch item (float32 audio + embedding overhead)
    # ~4 bytes per sample + ~2x overhead for model activations
    bytes_per_item = avg_item_samples * 4 * 3  # Conservative 3x multiplier
    gb_per_item = bytes_per_item / (1024**3)

    # Calculate max batch that fits in available VRAM
    if gb_per_item > 0:
        vram_limited_batch = int(available_gb / gb_per_item)
    else:
        vram_limited_batch = max_batch

    # Clamp to reasonable range
    optimal = max(min_batch, min(vram_limited_batch, max_batch, num_items))

    logger.debug(f"   VRAM-aware batch: {optimal} (free: {free_gb:.1f}GB, "
                f"per-item: {gb_per_item*1000:.1f}MB)")

    return optimal


rg -n "nvidia-smi|pynvml|nvml|subprocess\.run\(\[\'nvidia-smi\'" -S src/compute.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
400:                        import pynvml
401:                        pynvml.nvmlInit()
402:                        handle = pynvml.nvmlDeviceGetHandleByIndex(0)
403:                        util = pynvml.nvmlDeviceGetUtilizationRates(handle)
404:                        mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
406:                        pynvml.nvmlShutdown()


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            metrics.end_time = time.time()
            metrics.duration = metrics.end_time - metrics.start_time

            # Compute averages
            if self._cpu_samples:
                metrics.cpu_percent_avg = sum(self._cpu_samples) / len(self._cpu_samples)
                metrics.cpu_percent_max = max(self._cpu_samples)

            if self._gpu_samples:
                gpu_utils = [s[0] for s in self._gpu_samples]
                gpu_mems = [s[1] for s in self._gpu_samples]
                metrics.gpu_percent_avg = sum(gpu_utils) / len(gpu_utils) if gpu_utils else 0
                metrics.gpu_percent_max = max(gpu_utils) if gpu_utils else 0
                metrics.gpu_memory_used_gb = max(gpu_mems) if gpu_mems else 0

            if items > 0 and metrics.duration > 0:
                metrics.throughput = items / metrics.duration

            self.stage_metrics[stage_name] = metrics
            self._current_stage = None

            # Log stage summary
            self._log_stage_summary(metrics)

    def _start_monitoring(self):
        """Start background utilization monitoring."""
        self._monitoring = True
        self._monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True)
        self._monitor_thread.start()

    def _stop_monitoring(self):
        """Stop background monitoring."""
        self._monitoring = False
        if self._monitor_thread:
            self._monitor_thread.join(timeout=1.0)

    def _monitor_loop(self):
        """Background thread that samples CPU/GPU utilization."""
        try:
            import psutil
            has_psutil = True
        except ImportError:
            has_psutil = False

        try:
            import torch
            has_torch = torch.cuda.is_available()
        except ImportError:
            has_torch = False

        while self._monitoring:
            try:
                # Sample CPU
                if has_psutil:
                    cpu = psutil.cpu_percent(interval=0.1)
                    self._cpu_samples.append(cpu)

                # Sample GPU
                if has_torch:
                    try:
                        import pynvml
                        pynvml.nvmlInit()
                        handle = pynvml.nvmlDeviceGetHandleByIndex(0)
                        util = pynvml.nvmlDeviceGetUtilizationRates(handle)
                        mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
                        self._gpu_samples.append((util.gpu, mem.used / (1024**3)))
                        pynvml.nvmlShutdown()
                    except Exception:
                        # Fallback: just get memory from torch
                        import torch
                        mem = torch.cuda.memory_allocated() / (1024**3)
                        self._gpu_samples.append((0, mem))

                time.sleep(0.5)  # Sample every 500ms
            except Exception:
                time.sleep(0.5)

    def _log_stage_summary(self, metrics: StageMetrics):
        """Log summary for a completed stage."""
        cpu_status = "✅ HIGH" if metrics.cpu_percent_avg > 70 else "⚠ LOW" if metrics.cpu_percent_avg < 30 else "➖ OK"
        gpu_status = "✅ HIGH" if metrics.gpu_percent_avg > 70 else "⚠ LOW" if metrics.gpu_percent_avg < 30 else "➖ OK"

        logger.info(f"📊 {metrics.stage_name}: {metrics.duration:.1f}s | "
                   f"CPU: {metrics.cpu_percent_avg:.0f}% ({cpu_status}) | "
                   f"GPU: {metrics.gpu_percent_avg:.0f}% ({gpu_status}) | "
                   f"VRAM: {metrics.gpu_memory_used_gb:.1f}GB")

        # Optimization hints
        if metrics.cpu_percent_avg < 30 and metrics.duration > 5:
            logger.info(f"   💡 HINT: CPU underutilized - consider more workers for {metrics.stage_name}")
        if metrics.gpu_percent_avg > 0 and metrics.gpu_percent_avg < 30 and metrics.duration > 5:
            logger.info(f"   💡 HINT: GPU underutilized - could run CPU work in parallel")

    def summary(self) -> str:
        """Return full pipeline summary."""
        lines = [
            "\n" + "=" * 70,
            "📊 COMPUTE UTILIZATION SUMMARY",
            "=" * 70
        ]

        total_time = 0
        cpu_weighted = 0
        gpu_weighted = 0

        for name, m in self.stage_metrics.items():
            lines.append(f"   {name:20s}: {m.duration:6.1f}s | CPU: {m.cpu_percent_avg:5.1f}% | GPU: {m.gpu_percent_avg:5.1f}%")
            total_time += m.duration
            cpu_weighted += m.cpu_percent_avg * m.duration
            gpu_weighted += m.gpu_percent_avg * m.duration

        if total_time > 0:
            avg_cpu = cpu_weighted / total_time
            avg_gpu = gpu_weighted / total_time
            lines.append("-" * 70)
            lines.append(f"   {'TOTAL':20s}: {total_time:6.1f}s | CPU: {avg_cpu:5.1f}% | GPU: {avg_gpu:5.1f}%")

            # Efficiency rating
            efficiency = (avg_cpu + avg_gpu) / 2
            if efficiency > 60:
                rating = "🟢 EXCELLENT"


**Assessing monitor issues**

I'm looking into how the compute monitor interacts with pynvml, where it's running init and shutdown in a loop. This could get heavy. Calling nvmlInit from multiple threads may lead to hanging issues. Each of the eight GPU worker processes runs a monitoring thread every 0.5 seconds, which might overwhelm the NVML library and potentially cause deadlocks. I'm curious about whether torch.cuda.is_available is being called before torch is fully imported in these worker processes. This could complicate things further.


**Investigating potential deadlocks**

I'm thinking about what happens if `nvmlInit` leads to a deadlock in the monitoring thread. This could cause a hang, which shouldn't typically block main processing unless it holds a global lock in the NVML shared library. I’m pondering if this could interfere with other NVML calls, like torch CUDA initialization. This might explain why GPU and CPU utilization are reported as zero if the monitor thread gets stuck early. If `nvmlInit` holds the GIL, it could block all Python threads, including the main thread, resulting in a hang.


**Analyzing monitor thread hang-ups**

I'm considering the implications of a deadlock in the monitor thread. If it holds the GIL without releasing it, it could block the entire Python interpreter. I suspect that pynvml might not release the GIL during `nvmlInit`, putting all Python processes in that context at a standstill. This would affect the main thread, preventing it from proceeding beyond the initial stage. If the log message "Loading audio..." needs the GIL, it would stop altogether once the monitoring thread blocks, impacting all workers without showing any errors. That seems quite plausible!


**Proposing fixes for monitoring**

I’m identifying fixes for the compute monitoring issue. One straightforward solution is to disable compute monitoring (`monitor_compute=False`) in multi-worker mode or adjust the ComputeMonitor to sample GPU utilization only through torch, avoiding repeated init and shutdown. It might be easiest to set `monitor_compute=False` by default in supabase queue mode, preventing the monitoring thread from starting. However, since the current setup always triggers the monitoring stage, I’d need to modify pipeline.py to only use it if `monitor_compute` is true. Reducing hangs could be an effective outcome!


**Planning NVML mitigation steps**

For safety, I consider setting the environment variable `MAYA3_DISABLE_NVML=1`, but I’d rather simplify things by removing pynvml from ComputeMonitor’s monitoring loop. The idea is to check GPU utilization with `torch.cuda.utilization`, but it’s not available, so I could explore using `nvidia-smi` instead. Avoiding NVML should help resolve hanging issues. We need immediate remediation—perhaps a SIGINT to restart processes? However, they can't change code on existing images. I’ll also check if any processes are deadlocked by analyzing their states and CPU usage. My plan involves running specific commands to explore the output files and system status!


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14080 root@ssh1.vast.ai 'echo "==top"; top -b -n 1 | head -n 35; echo "==ps"; ps -eo pid,ppid,pcpu,pmem,stat,wchan:24,cmd --sort=-pcpu | head -n 25; echo "==df"; df -h /dev/shm /workspace/maya3_data | sed -n "1,5p"; echo "==out"; ls -la /workspace/maya3_data/output | head -n 25'


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14084 root@ssh5.vast.ai 'echo "==top"; top -b -n 1 | head -n 35; echo "==ps"; ps -eo pid,ppid,pcpu,pmem,stat,wchan:24,cmd --sort=-pcpu | head -n 25; echo "==df"; df -h /dev/shm /workspace/maya3_data | sed -n "1,5p"; echo "==out"; ls -la /workspace/maya3_data/output | head -n 25'


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14080 root@ssh1.vast.ai 'for p in 505 513 514 516 519 521 524 527 530; do if [ -e /proc/$p/stat ]; then echo "-- $p"; ps -p $p -o pid,pcpu,stat,wchan:24,etime,cmd; fi; done'


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14084 root@ssh5.vast.ai 'for p in 506 514 515 516 518 520 523 525 528; do if [ -e /proc/$p/stat ]; then echo "-- $p"; ps -p $p -o pid,pcpu,stat,wchan:24,etime,cmd; fi; done'


set -a; source .env; set +a; python3 - <<'PY'
import os
from supabase import create_client
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
for mid in ['vast-29774081','vast-29774085']:
    rows=(sb.table('worker_heartbeats')
        .select('worker_id,gpu_id,status,current_video_id,current_stage,current_stage_name,last_heartbeat,session_videos_done,session_videos_failed')
        .eq('machine_id', mid)
        .order('gpu_id')
        .execute().data or [])
    print('===', mid)
    for r in rows:
        print(r['worker_id'], 'stage', r.get('current_stage_name'), 'last', r.get('last_heartbeat'), 'done', r.get('session_videos_done'), 'fail', r.get('session_videos_failed'))
PY


set -a; source .env; set +a; python3 - <<'PY'
import os
from supabase import create_client
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
# Are their claimed videos' leases expired?
vids=['rZDMixhTObE','JFWxCoqpMY4','GKNDyoHj2kw','y2YKR_ZCwK0','sSw-yz7q3_4','HuhVewv__7Q','bB5IxXjy6M8','DFp9GO7sK7E',
      'hrKFmtUj8qA','DYsZzk13J2o','Bk311xupJyE','L1Scbtvkgao','0zdWbSRNVOg','uHPs6TT9ATs','emG8RXetZZM','KioyTUQcSJA']
rows=(sb.table('videos').select('youtube_id,status,claimed_by,claimed_at,lease_expires_at').in_('youtube_id', vids).execute().data or [])
rows=sorted(rows, key=lambda r:r['youtube_id'])
for r in rows:
    print(r['youtube_id'], r['status'], r.get('claimed_by'), r.get('lease_expires_at'))
PY


Output:
Exit code: 0
Wall time: 1.7 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
==top
top - 14:49:30 up 7 days, 23:00,  0 users,  load average: 0.23, 0.30, 0.38
Tasks:  21 total,   1 running,  20 sleeping,   0 stopped,   0 zombie
%Cpu(s):  0.0 us,  0.1 sy,  0.0 ni, 99.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st
MiB Mem : 483665.3 total, 406658.8 free,   9853.3 used,  67153.2 buff/cache
MiB Swap:  81920.0 total,  81919.8 free,      0.2 used. 470563.3 avail Mem

    PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND
      1 root      20   0    2892   1008    912 S   0.0   0.0   0:00.04 sh
    108 root      20   0    4364   3448   3140 S   0.0   0.0   0:00.00 bash
    424 root      20   0    4364   1764   1472 S   0.0   0.0   0:00.00 bash
    428 root      20   0    4364   3216   2944 S   0.0   0.0   0:00.00 bash
    429 root      20   0    2796    996    904 S   0.0   0.0   0:00.00 tee
    430 root      20   0   32416  27616  11292 S   0.0   0.0   0:00.12 python
    431 root      20   0   15432   5532   3908 S   0.0   0.0   0:00.00 sshd
    437 root      20   0   11444   8344   6544 S   0.0   0.0   0:00.04 ssh
    505 root      20   0  769896  21776   5400 S   0.0   0.0   0:00.07 python
    513 root      20   0   14.2g   1.0g 407828 S   0.0   0.2   0:17.86 python
    514 root      20   0   14.4g   1.1g 407712 S   0.0   0.2   0:17.87 python
    516 root      20   0   16.8g   1.1g 412860 S   0.0   0.2   0:17.40 python
    519 root      20   0   14.2g   1.0g 407756 S   0.0   0.2   0:16.97 python
    521 root      20   0   14.3g   1.1g 407576 S   0.0   0.2   0:16.81 python
    524 root      20   0   14.3g   1.1g 407676 S   0.0   0.2   0:17.57 python
    527 root      20   0   14.3g   1.1g 407828 S   0.0   0.2   0:20.29 python
    530 root      20   0   16.8g   1.1g 412808 S   0.0   0.2   0:18.49 python
   4559 root      20   0   16680  10600   8520 S   0.0   0.0   0:00.01 sshd
   4570 root      20   0    4364   3052   2788 S   0.0   0.0   0:00.00 bash
   4571 root      20   0    7376   2984   2628 R   0.0   0.0   0:00.00 top
   4572 root      20   0    2804   1024    932 S   0.0   0.0   0:00.00 head
==ps
    PID    PPID %CPU %MEM STAT WCHAN                    CMD
    527     430  2.8  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    513     430  2.5  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    514     430  2.5  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    530     430  2.5  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    516     430  2.4  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    524     430  2.4  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    519     430  2.3  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    521     430  2.3  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
   4559     431  1.0  0.0 Ss   do_poll.constprop.0      sshd: root@notty
      1       0  0.0  0.0 Ss   do_wait                  /bin/sh -c while [ ! -e /.launch ]; do sleep 1; done; bash /.launch
    108       1  0.0  0.0 S    do_wait                  bash /.launch
    424       1  0.0  0.0 S    do_wait                  bash /.launch
    428     424  0.0  0.0 S    do_wait                  bash /root/onstart.sh
    429     424  0.0  0.0 S    pipe_read                tee -a /var/log/onstart.log
    430     428  0.0  0.0 S    unix_stream_data_wait    /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    431       1  0.0  0.0 Ss   do_poll.constprop.0      sshd: /usr/sbin/sshd -E /proc/1/fd/1 [listener] 0 of 10-100 startups
    437     108  0.0  0.0 S    do_poll.constprop.0      ssh -i /etc/forward_key -R *:14080:localhost:22 -R *:14081:localhost:8080 -N -o TCPKeepAlive=yes -o ServerAliveInterval=10 -o ServerAliveCountMax=2 ubuntu@ssh1.vast.ai -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no -o ConnectTimeout=10 -o RequestTTY=no -o ExitOnForwardFailure=yes
    505     430  0.0  0.0 Sl   futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
   4570    4559  0.0  0.0 Ss   do_wait                  bash -c echo "==top"; top -b -n 1 | head -n 35; echo "==ps"; ps -eo pid,ppid,pcpu,pmem,stat,wchan:24,cmd --sort=-pcpu | head -n 25; echo "==df"; df -h /dev/shm /workspace/maya3_data | sed -n "1,5p"; echo "==out"; ls -la /workspace/maya3_data/output | head -n 25
   4573    4570  0.0  0.0 R    -                        ps -eo pid,ppid,pcpu,pmem,stat,wchan:24,cmd --sort=-pcpu
   4574    4570  0.0  0.0 S    pipe_read                head -n 25
==df
Filesystem      Size  Used Avail Use% Mounted on
shm             236G  8.0K  236G   1% /dev/shm
overlay         400G  797M  400G   1% /
==out
total 4
drwxr-xr-x 1 root root 4096 Jan  8 14:37 .
drwxr-xr-x 1 root root   71 Jan  8 14:37 ..
drwxr-xr-x 2 root root   82 Jan  8 14:37 DFp9GO7sK7E
drwxr-xr-x 2 root root   82 Jan  8 14:37 GKNDyoHj2kw
drwxr-xr-x 2 root root   82 Jan  8 14:38 HuhVewv__7Q
drwxr-xr-x 2 root root   82 Jan  8 14:37 JFWxCoqpMY4
drwxr-xr-x 2 root root   82 Jan  8 14:38 bB5IxXjy6M8
drwxr-xr-x 2 root root   82 Jan  8 14:37 rZDMixhTObE
drwxr-xr-x 2 root root   82 Jan  8 14:37 sSw-yz7q3_4
drwxr-xr-x 2 root root   82 Jan  8 14:37 y2YKR_ZCwK0


Output:
Exit code: 0
Wall time: 1.9 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
==top
top - 14:49:33 up 121 days, 21:57,  0 users,  load average: 8.73, 3.60, 2.99
Tasks:  21 total,   1 running,  20 sleeping,   0 stopped,   0 zombie
%Cpu(s):  0.5 us,  0.3 sy,  0.0 ni, 99.2 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st
MiB Mem : 515836.9 total, 148262.0 free,  57781.8 used, 309793.1 buff/cache
MiB Swap:   8192.0 total,   7526.1 free,    665.9 used. 454497.7 avail Mem

    PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND
      1 root      20   0    2884   1004    908 S   0.0   0.0   0:00.05 sh
    110 root      20   0    4356   3188   2880 S   0.0   0.0   0:00.01 bash
    419 root      20   0    4356   1520   1224 S   0.0   0.0   0:00.00 bash
    423 root      20   0    4356   3224   2956 S   0.0   0.0   0:00.00 bash
    424 root      20   0    2788   1044    952 S   0.0   0.0   0:00.00 tee
    425 root      20   0   32408  27524  11220 S   0.0   0.0   0:00.15 python
    426 root      20   0   15424   5580   3956 S   0.0   0.0   0:00.00 sshd
    432 root      20   0   11432   8156   6364 S   0.0   0.0   0:00.04 ssh
    506 root      20   0  769888  21836   5456 S   0.0   0.0   0:00.06 python
    514 root      20   0   14.6g 986.0m 400948 S   0.0   0.2   0:16.71 python
    515 root      20   0   14.7g   1.0g 410824 S   0.0   0.2   0:17.13 python
    516 root      20   0   14.8g   1.1g 402708 S   0.0   0.2   0:15.28 python
    518 root      20   0   14.7g   1.0g 404548 S   0.0   0.2   0:16.79 python
    520 root      20   0   14.7g   1.0g 404376 S   0.0   0.2   0:16.49 python
    523 root      20   0   17.2g   1.1g 416352 S   0.0   0.2   0:15.92 python
    525 root      20   0   14.7g   1.1g 405552 S   0.0   0.2   0:18.67 python
    528 root      20   0   14.7g   1.0g 402996 S   0.0   0.2   0:17.28 python
   9063 root      20   0   16672  10516   8432 S   0.0   0.0   0:00.02 sshd
   9074 root      20   0    4356   3160   2900 S   0.0   0.0   0:00.00 bash
   9075 root      20   0    7360   3060   2696 R   0.0   0.0   0:00.00 top
   9076 root      20   0    2796   1040    948 S   0.0   0.0   0:00.00 head
==ps
    PID    PPID %CPU %MEM STAT WCHAN                    CMD
    525     425  3.0  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    515     425  2.7  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    518     425  2.7  0.1 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    528     425  2.7  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    514     425  2.6  0.1 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    520     425  2.6  0.1 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    523     425  2.5  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    516     425  2.4  0.2 SLl  futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
   9063     426  1.0  0.0 Ss   poll_schedule_timeout.co sshd: root@notty
      1       0  0.0  0.0 Ss   do_wait                  /bin/sh -c while [ ! -e /.launch ]; do sleep 1; done; bash /.launch
    110       1  0.0  0.0 S    do_wait                  bash /.launch
    419       1  0.0  0.0 S    do_wait                  bash /.launch
    423     419  0.0  0.0 S    do_wait                  bash /root/onstart.sh
    424     419  0.0  0.0 S    pipe_wait                tee -a /var/log/onstart.log
    425     423  0.0  0.0 S    unix_stream_read_generic /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
    426       1  0.0  0.0 Ss   poll_schedule_timeout.co sshd: /usr/sbin/sshd -E /proc/1/fd/1 [listener] 0 of 10-100 startups
    432     110  0.0  0.0 S    poll_schedule_timeout.co ssh -i /etc/forward_key -R *:14084:localhost:22 -R *:14085:localhost:8080 -N -o TCPKeepAlive=yes -o ServerAliveInterval=10 -o ServerAliveCountMax=2 ubuntu@ssh5.vast.ai -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no -o ConnectTimeout=10 -o RequestTTY=no -o ExitOnForwardFailure=yes
    506     425  0.0  0.0 Sl   futex_wait_queue_me      /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
   9074    9063  0.0  0.0 Ss   do_wait                  bash -c echo "==top"; top -b -n 1 | head -n 35; echo "==ps"; ps -eo pid,ppid,pcpu,pmem,stat,wchan:24,cmd --sort=-pcpu | head -n 25; echo "==df"; df -h /dev/shm /workspace/maya3_data | sed -n "1,5p"; echo "==out"; ls -la /workspace/maya3_data/output | head -n 25
   9077    9074  0.0  0.0 R    -                        ps -eo pid,ppid,pcpu,pmem,stat,wchan:24,cmd --sort=-pcpu
   9078    9074  0.0  0.0 S    pipe_wait                head -n 25
==df
Filesystem      Size  Used Avail Use% Mounted on
shm             167G   12K  167G   1% /dev/shm
overlay         400G  748M  400G   1% /
==out
total 0
drwxr-xr-x 1 root root 217 Jan  8 14:39 .
drwxr-xr-x 1 root root  71 Jan  8 14:39 ..
drwxr-xr-x 2 root root  82 Jan  8 14:39 0zdWbSRNVOg
drwxr-xr-x 2 root root  82 Jan  8 14:39 Bk311xupJyE
drwxr-xr-x 2 root root  82 Jan  8 14:39 DYsZzk13J2o
drwxr-xr-x 2 root root   6 Jan  8 14:39 FylCnfugHRY
drwxr-xr-x 2 root root  82 Jan  8 14:39 KioyTUQcSJA
drwxr-xr-x 2 root root  82 Jan  8 14:39 L1Scbtvkgao
drwxr-xr-x 2 root root  70 Jan  8 14:39 emG8RXetZZM
drwxr-xr-x 2 root root  82 Jan  8 14:39 hrKFmtUj8qA
drwxr-xr-x 2 root root  82 Jan  8 14:39 uHPs6TT9ATs


Output:
Exit code: 0
Wall time: 1.6 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
-- 505
    PID %CPU STAT WCHAN                        ELAPSED CMD
    505  0.0 Sl   futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 513
    PID %CPU STAT WCHAN                        ELAPSED CMD
    513  2.4 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 514
    PID %CPU STAT WCHAN                        ELAPSED CMD
    514  2.4 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 516
    PID %CPU STAT WCHAN                        ELAPSED CMD
    516  2.4 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 519
    PID %CPU STAT WCHAN                        ELAPSED CMD
    519  2.3 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 521
    PID %CPU STAT WCHAN                        ELAPSED CMD
    521  2.3 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 524
    PID %CPU STAT WCHAN                        ELAPSED CMD
    524  2.4 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 527
    PID %CPU STAT WCHAN                        ELAPSED CMD
    527  2.8 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 530
    PID %CPU STAT WCHAN                        ELAPSED CMD
    530  2.5 SLl  futex_wait_queue_me            11:57 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output


Output:
Exit code: 0
Wall time: 1.8 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
-- 506
    PID %CPU STAT WCHAN                        ELAPSED CMD
    506  0.0 Sl   futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 514
    PID %CPU STAT WCHAN                        ELAPSED CMD
    514  2.6 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 515
    PID %CPU STAT WCHAN                        ELAPSED CMD
    515  2.7 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 516
    PID %CPU STAT WCHAN                        ELAPSED CMD
    516  2.4 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 518
    PID %CPU STAT WCHAN                        ELAPSED CMD
    518  2.6 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 520
    PID %CPU STAT WCHAN                        ELAPSED CMD
    520  2.6 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 523
    PID %CPU STAT WCHAN                        ELAPSED CMD
    523  2.5 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 525
    PID %CPU STAT WCHAN                        ELAPSED CMD
    525  2.9 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output
-- 528
    PID %CPU STAT WCHAN                        ELAPSED CMD
    528  2.7 SLl  futex_wait_queue_me            10:24 /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue --workers-per-gpu 1 --r2-upload --output ./output


Output:
Exit code: 0
Wall time: 1.1 seconds
Output:
=== vast-29774081
vast-29774081_gpu0 stage Download last 2026-01-08T14:37:52.287046+00:00 done 0 fail 0
vast-29774081_gpu1 stage Download last 2026-01-08T14:37:52.226285+00:00 done 0 fail 0
vast-29774081_gpu2 stage Download last 2026-01-08T14:37:52.287705+00:00 done 0 fail 0
vast-29774081_gpu3 stage Download last 2026-01-08T14:37:52.264671+00:00 done 0 fail 0
vast-29774081_gpu4 stage Download last 2026-01-08T14:37:52.263259+00:00 done 0 fail 0
vast-29774081_gpu5 stage Download last 2026-01-08T14:37:52.21771+00:00 done 0 fail 0
vast-29774081_gpu6 stage Download last 2026-01-08T14:37:52.265599+00:00 done 0 fail 0
vast-29774081_gpu7 stage Download last 2026-01-08T14:37:52.214107+00:00 done 0 fail 0
=== vast-29774085
vast-29774085_gpu0 stage Download last 2026-01-08T14:39:23.554443+00:00 done 0 fail 0
vast-29774085_gpu1 stage Download last 2026-01-08T14:39:23.564534+00:00 done 0 fail 0
vast-29774085_gpu2 stage Download last 2026-01-08T14:39:23.562861+00:00 done 0 fail 0
vast-29774085_gpu3 stage Download last 2026-01-08T14:39:23.106972+00:00 done 0 fail 0
vast-29774085_gpu4 stage Download last 2026-01-08T14:39:23.515922+00:00 done 0 fail 0
vast-29774085_gpu5 stage Download last 2026-01-08T14:39:23.106543+00:00 done 0 fail 0
vast-29774085_gpu6 stage Download last 2026-01-08T14:39:23.718664+00:00 done 0 fail 0
vast-29774085_gpu7 stage Download last 2026-01-08T14:39:32.014749+00:00 done 0 fail 1


Output:
Exit code: 0
Wall time: 1.1 seconds
Output:
0zdWbSRNVOg CLAIMED vast-29774085_gpu6 2026-01-08T14:54:23.589932+00:00
Bk311xupJyE CLAIMED vast-29774085_gpu0 2026-01-08T14:54:23.427849+00:00
DFp9GO7sK7E CLAIMED vast-29774081_gpu2 2026-01-08T14:52:52.160447+00:00
DYsZzk13J2o CLAIMED vast-29774085_gpu4 2026-01-08T14:54:23.392813+00:00
GKNDyoHj2kw CLAIMED vast-29774081_gpu3 2026-01-08T14:52:52.160215+00:00
HuhVewv__7Q CLAIMED vast-29774081_gpu5 2026-01-08T14:52:52.114015+00:00
JFWxCoqpMY4 CLAIMED vast-29774081_gpu1 2026-01-08T14:52:52.102882+00:00
KioyTUQcSJA CLAIMED vast-29774085_gpu1 2026-01-08T14:54:23.438308+00:00
L1Scbtvkgao CLAIMED vast-29774085_gpu3 2026-01-08T14:54:22.955849+00:00
bB5IxXjy6M8 CLAIMED vast-29774081_gpu6 2026-01-08T14:52:52.149246+00:00
emG8RXetZZM CLAIMED vast-29774085_gpu2 2026-01-08T14:54:23.442482+00:00
hrKFmtUj8qA CLAIMED vast-29774085_gpu5 2026-01-08T14:54:22.98179+00:00
rZDMixhTObE CLAIMED vast-29774081_gpu7 2026-01-08T14:52:52.100522+00:00
sSw-yz7q3_4 CLAIMED vast-29774081_gpu0 2026-01-08T14:52:52.173383+00:00
uHPs6TT9ATs CLAIMED vast-29774085_gpu7 2026-01-08T14:54:31.894726+00:00
y2YKR_ZCwK0 CLAIMED vast-29774081_gpu4 2026-01-08T14:52:52.149236+00:00


Summary: auto


**Analyzing video claims**

I'm thinking about whether this is related to the uninterruptible disk or if `wchan` would be different. It might show `futex_wait_queue_me`, as libsndfile could involve pthread condition waiting. All the workers seem blocked; maybe they're waiting for a global lock during torch initialization. I noticed that the lease expiration times indicate recent claims. It seems like something reset the videos to pending before they were claimed again, possibly by the supabase reclaiming service.


**Investigating worker claims**

I'm realizing the stuck workers wouldn’t be making new claims if they're blocked; they'd still be attached to their original picks from earlier times, with expiration matching. The lease duration is 900 seconds, so claims from 14:37:52 would expire at 14:52:52, which corroborates the times I’m seeing. Consequently, they are still processing the originally claimed videos, not new ones. This means the workers have been stuck since the initial download stage. I need to check if they’re blocked in `torchaudio.load` or a compute monitoring thread. It seems I could use `py-spy` or `faulthandler`, but isn’t installed.


**Exploring troubleshooting options**

I'm considering using `kill -SIGUSR1` if Python has `faulthandler` installed, though that’s not typical. I could also try `gdb`, but it might be overkill. Running a small Python command to register `faulthandler` isn’t an option either. I wonder if I could check the stack trace of a running Python process using `pstack`, but that's not available. I think about installing `py-spy` quickly via pip, but the time and network issues could complicate things. Given the stuck workers, it's likely tied to `torchaudio.load` and deadlocks in the `multiprocessing` Manager queue.


**Investigating stuck workers**

I suspect that the workers might be stuck trying to add something to the `result_queue`, potentially blocked by a broken Manager. They've only processed one video fail on GPU7, which was logged, indicating that some results did reach the main process. Since the 3090 instance hasn't logged any results, it seems the workers are stuck before completing anything. I wonder if the pipeline processing is blocked due to the new `src/vad.py` using spawn and creating an executor while employing ProcessPoolExecutor in `run_vad_from_buffer`. However, the "VAD" start log doesn’t happen until after calling `run_vad_parallel`, suggesting they got stuck earlier.


**Diagnosing potential deadlock issues**

I’m thinking the compute monitor might be causing a deadlock due to NVML, as all workers are in `futex_wait_queue_me` with the thread state `SLl`. It seems like the monitor thread could be blocking while holding the GIL, which then affects the main thread as well. The current hypothesis is that the monitor thread holds the GIL and is stuck in `nvmlInit`, causing the main thread to wait. To fix this, we might need to avoid spawning the monitoring thread or ensure GIL is released during NVML calls.

For the stuck instances, I should recycle them, but I wonder if they’ll hang again since we've been using the same compute monitor. I think we should check if NVML is unstable on the new nodes, potentially caused by an older driver. I'll test whether `import pynvml; nvmlInit()` runs quickly in the instances as a next step.


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14080 root@ssh1.vast.ai 'timeout 8s /workspace/maya3_data/.venv/bin/python - <<"PY"
import pynvml
pynvml.nvmlInit()
h=pynvml.nvmlDeviceGetHandleByIndex(0)
util=pynvml.nvmlDeviceGetUtilizationRates(h)
mem=pynvml.nvmlDeviceGetMemoryInfo(h)
print("ok", util.gpu, mem.used)
pynvml.nvmlShutdown()
PY
echo nvml_exit=$?'


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14084 root@ssh5.vast.ai 'timeout 8s /workspace/maya3_data/.venv/bin/python - <<"PY"
import pynvml
pynvml.nvmlInit()
h=pynvml.nvmlDeviceGetHandleByIndex(0)
util=pynvml.nvmlDeviceGetUtilizationRates(h)
mem=pynvml.nvmlDeviceGetMemoryInfo(h)
print("ok", util.gpu, mem.used)
pynvml.nvmlShutdown()
PY
echo nvml_exit=$?'


Output:
Exit code: 0
Wall time: 1.5 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'pynvml'
nvml_exit=1


Output:
Exit code: 0
Wall time: 1.8 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'pynvml'
nvml_exit=1


**Checking file contents**

I’m considering checking which file the user is reading. I can do this by using the command `ls -la /workspace/maya3_data/output/<video>/` for a specific video. I think inspecting one particular directory, like `DFp9GO7sK7E`, would be a good first step. That way, I can list the files and see what’s there! I’m looking forward to discovering what’s included in that directory.


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14080 root@ssh1.vast.ai 'for d in DFp9GO7sK7E GKNDyoHj2kw sSw-yz7q3_4; do echo "--- $d"; ls -la "/workspace/maya3_data/output/$d"; done | head -n 200'


Output:
Exit code: 0
Wall time: 1.5 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
--- DFp9GO7sK7E
total 30880
drwxr-xr-x 2 root root       82 Jan  8 14:37 .
drwxr-xr-x 1 root root     4096 Jan  8 14:37 ..
-rw-r--r-- 1 root root 16629721 Jan  8 14:37 DFp9GO7sK7E_original.flac
-rw-r--r-- 1 root root 14983486 Jan  8 14:37 DFp9GO7sK7E_trimmed.wav
--- GKNDyoHj2kw
total 24240
drwxr-xr-x 2 root root       82 Jan  8 14:37 .
drwxr-xr-x 1 root root     4096 Jan  8 14:37 ..
-rw-r--r-- 1 root root 12600520 Jan  8 14:37 GKNDyoHj2kw_original.flac
-rw-r--r-- 1 root root 12213438 Jan  8 14:37 GKNDyoHj2kw_trimmed.wav
--- sSw-yz7q3_4
total 25576
drwxr-xr-x 2 root root       82 Jan  8 14:37 .
drwxr-xr-x 1 root root     4096 Jan  8 14:37 ..
-rw-r--r-- 1 root root 13623476 Jan  8 14:37 sSw-yz7q3_4_original.flac
-rw-r--r-- 1 root root 12556722 Jan  8 14:37 sSw-yz7q3_4_trimmed.wav


**Investigating torchaudio issues**

I’m looking into why torchaudio.load might be hanging. The trimmed wav files are about 12-15MB, so they shouldn’t be too large. I wonder if it's a concurrency issue, given that multiple processes might be calling torchaudio.load at the same time. It’s possible there’s a problem with how torchaudio interacts with Sox if there are many processes running simultaneously. I’ll test torchaudio.load manually inside the container using a timeout to avoid hanging. Let's do that on the 3090 instance.


ssh -o StrictHostKeyChecking=no -i /home/ubuntu/.ssh/id_ed25519 -p 14080 root@ssh1.vast.ai 'timeout 15s /workspace/maya3_data/.venv/bin/python - <<"PY"
import torchaudio
p="/workspace/maya3_data/output/DFp9GO7sK7E/DFp9GO7sK7E_trimmed.wav"
print("loading", p)
w,sr=torchaudio.load(p)
print("ok", w.shape, sr)
PY
echo load_exit=$?'


Output:
Exit code: 0
Wall time: 3.4 seconds
Output:
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
loading /workspace/maya3_data/output/DFp9GO7sK7E/DFp9GO7sK7E_trimmed.wav
ok torch.Size([1, 7491704]) 16000
load_exit=0


Output:
Exit code: 0
Wall time: 0 seconds
Output:
"""
Clean, minimal pipeline logger.

Provides consistent, single-line logging format:
    [GPU0] PICK   dQw4w9WgXcQ
    [GPU0] 1/9   Download      2.1s
    [GPU0] DONE  dQw4w9WgXcQ  18.7s | 12.3min | 5 spk | 72%
"""

from typing import Optional
import sys

class PipelineLogger:
    """Clean, minimal pipeline logger for worker processes."""

    def __init__(self, gpu_id: int):
        self.gpu_id = gpu_id
        self.prefix = f"[GPU{gpu_id}]"
        self.video_id: Optional[str] = None
        self.total_stages: int = 9

    def set_video(self, video_id: str, total_stages: int = 9):
        """Set current video context."""
        self.video_id = video_id
        self.total_stages = total_stages

    def pick(self, video_id: str):
        """Log video picked for processing."""
        self._print(f"PICK   {video_id}")

    def stage(self, num: int, name: str, duration: float, extra: str = ""):
        """Log stage completion."""
        name_padded = name[:12].ljust(12)
        msg = f"{num}/{self.total_stages:<2} {name_padded} {duration:>5.1f}s"
        if extra:
            msg += f"  | {extra}"
        self._print(msg)

    def upload(self, video_id: str, duration: float, size_mb: float):
        """Log upload completion."""
        self._print(f"UPLOAD {video_id} {duration:.1f}s | {size_mb:.1f}MB")

    def done(self, video_id: str, total_time: float, audio_min: float,
             speakers: int, usable_pct: float):
        """Log successful completion."""
        self._print(f"DONE   {video_id} {total_time:.1f}s | {audio_min:.1f}min | {speakers} spk | {usable_pct:.0f}%")

    def fail(self, video_id: str, error: str):
        """Log failure with full error message."""
        self._print(f"FAIL   {video_id} | {error}")

    def skip(self, video_id: str, reason: str):
        """Log skipped video."""
        self._print(f"SKIP   {video_id} | {reason}")

    def info(self, message: str):
        """Log general info message."""
        self._print(message)

    def _print(self, message: str):
        """Print with GPU prefix, flush immediately."""
        print(f"{self.prefix} {message}", flush=True)

def silence_verbose_loggers():
    """Silence verbose third-party and internal loggers."""
    import logging
    import warnings
    import os

    # Suppress Python warnings
    warnings.filterwarnings('ignore', category=FutureWarning)
    warnings.filterwarnings('ignore', category=UserWarning)
    warnings.filterwarnings('ignore', message='.*torchaudio.*')
    warnings.filterwarnings('ignore', message='.*deprecated.*')

    # Suppress torch hub "Using cache found" messages
    os.environ['TORCH_HUB_VERBOSE'] = '0'

    # Suppress torch.hub loading messages by setting _HUBCONF_VERBOSE
    try:
        import torch.hub
        torch.hub._VERBOSE = False
    except (ImportError, AttributeError):
        pass

    # Set root logging level to WARNING to catch misc debug logs
    logging.getLogger().setLevel(logging.WARNING)

    # Set verbose loggers to ERROR level (more aggressive than WARNING)
    verbose_loggers = [
        # Internal pipeline loggers
        'FastPipelineV6',
        'FastPipelineV5',
        'FastPipelineV6.Pipeline',
        'FastPipelineV6.Download',
        'FastPipelineV6.VAD',
        'FastPipelineV6.Diarization',
        'FastPipelineV6.Embeddings',
        'FastPipelineV6.UnifiedEmbeddings',
        'FastPipelineV6.Clustering',
        'FastPipelineV6.ChunkReassignment',
        'FastPipelineV6.MusicDetection',
        'FastPipelineV6.HybridMusicDetection',
        'FastPipelineV6.SpectralAnalysis',
        'FastPipelineV6.OSD',
        'FastPipelineV6.BoundaryRefinement',
        'FastPipelineV6.AudioBuffer',
        'FastPipelineV6.Models',
        'FastPipelineV6.Export',
        'FastPipelineV5.Utils',
        'FastPipelineV5.Compute',
        'FastPipelineV5.Clustering',
        'SupabaseClient',
        'R2Client',
        'WorkerPools',
        'ChunkParallel',
        # Third-party - speechbrain (all submodules)
        'speechbrain',
        'speechbrain.utils',
        'speechbrain.utils.checkpoints',
        'speechbrain.utils.fetching',
        'speechbrain.utils.parameter_transfer',
        'speechbrain.dataio',
        'speechbrain.dataio.encoder',
        # Third-party - pyannote
        'pyannote',
        'pyannote.audio',
        'pyannote.audio.pipelines',
        # Third-party - others
        'torch',
        'torchaudio',
        'transformers',
        'urllib3',
        'httpx',
        'httpcore',
        'numba',
        'filelock',
    ]

    for name in verbose_loggers:
        logging.getLogger(name).setLevel(logging.ERROR)


I’m considering that it could only print when at the stage “DONE.” So, I’ll check into how the PipelineLogger is being used to ensure everything is functioning correctly. My next step is to search for where the PipelineLogger.stage is called. By examining that, I should be able to understand what's happening and see if there’s a specific reason for the output limit. Let’s dig into that bit!


rg -n "PipelineLogger\(|pipeline_logger\.|log_stage\(" pipeline.py | head -n 120


Output:
Exit code: 0
Wall time: 0 seconds
Output:
179:    plog = PipelineLogger(gpu_id)
183:    def log_stage(stage_num: int, stage_name: str, status: str = "START", extra: str = ""):
203:    log_stage(1, "Download", "START")
222:    log_stage(1, "Download", "DONE", f"{timings['download']:.1f}s")
240:    log_stage(2, "VAD", "START", f"duration={total_duration:.0f}s")
245:    log_stage(2, "VAD", "DONE", f"{timings['vad']:.1f}s | {len(vad_segments)} segments")
252:    log_stage(3, "Chunking", "START")
258:    log_stage(3, "Chunking", "DONE", f"{timings['chunking']:.1f}s | {len(chunks)} chunks")
267:    log_stage(4, "Diarization+OSD", "START", f"{len(chunks)} chunks")
283:    log_stage(4, "Diarization+OSD", "DONE", f"{timings['diarization']:.1f}s | {len(segments)} segments")
406:    log_stage(5, "Embeddings", "START")
421:    log_stage(5, "Embeddings", "DONE", f"{timings['unified_embeddings']:.1f}s | {num_chunks} chunks")
434:    log_stage(6, "Clustering", "START")
439:    log_stage(6, "Clustering", "DONE", f"{timings['clustering']:.1f}s")
510:        log_stage(6, "MicroContamination", "START", "Purity scrubber for brief interjections")
534:        log_stage(6, "MicroContamination", "DONE", f"{micro_contamination_stats.get('contamination_found', 0)} contaminated")
540:    log_stage(7, "MusicDetection", "START")
641:        log_stage(7, "MusicDetection", "DONE", f"{timings['music_detection']:.1f}s")
643:        log_stage(7, "MusicDetection", "SKIP", "disabled")
658:    log_stage(8, "Finalization", "START")
790:    log_stage(8, "Finalize", "DONE", f"{timings['finalization']:.1f}s")
799:    log_stage(9, "Output", "START")
809:    log_stage(9, "Output", "DONE", f"total={total_time:.1f}s")
983:        log_stage(10, "Export", "START")
1013:                log_stage(10, "Export", "QUEUED", f"background job submitted")
1102:            log_stage(10, "Export", "DONE", f"{timings['export']:.1f}s | {segments_exported} segs | {tar_size_bytes/(1024**2):.1f}MB")


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    # === FIX: Ensure models are loaded before processing ===
    # When called from backend/API, main() is skipped so models may not be loaded
    if not MODELS._loaded:
        logger.info("Loading models (first call to process_single_video)...")
        MODELS.load_all(config)

    total_start = time.time()
    timings = {}
    compute_stats = {}

    total_stages = 10 if getattr(config, 'r2_upload_enabled', False) else 9

    # Extract video_id for clean logging
    video_id = video_url.split("v=")[-1][:11] if "v=" in video_url else video_url[-11:]

    # Get GPU ID from config (set by worker)
    gpu_id = getattr(config, 'gpu_id', 0)

    # Create clean pipeline logger
    plog = PipelineLogger(gpu_id)
    plog.set_video(video_id, total_stages)
    plog.pick(video_id)

    def log_stage(stage_num: int, stage_name: str, status: str = "START", extra: str = ""):
        """Stage logging - only logs DONE status with clean format."""
        if status == "DONE":
            # Extract duration from extra (e.g., "2.1s" or "2.1s | 45 segments")
            duration = 0.0
            detail = ""
            if extra:
                parts = extra.split("|")
                if parts:
                    try:
                        duration = float(parts[0].strip().rstrip("s"))
                    except ValueError:
                        pass
                    if len(parts) > 1:
                        detail = parts[1].strip()
            plog.stage(stage_num, stage_name, duration, detail)

    # ========================================
    # STAGE 1: Download (or use prefetched)
    # ========================================
    log_stage(1, "Download", "START")
    if prefetched_audio is not None and prefetched_metadata is not None:
        # === v7.0: Use prefetched download (batch optimization) ===
        logger.info("⚡ Using prefetched download (batch optimization)")
        audio_path = prefetched_audio
        metadata = prefetched_metadata
        # Prefetched in background thread/process - still record download wall time
        # so end-to-end benchmark metrics include source I/O even when overlapped.
        try:
            timings['download'] = float(metadata.get('download_wall_time_sec', 0.0))
        except Exception:
            timings['download'] = 0.0
        compute_stats['download'] = {'cpu': 0, 'gpu': 0}
    else:
        # Standard download (with optional Supabase metadata for chapters)
        with COMPUTE.monitor_stage("Download") as metrics:
            audio_path, metadata = download_audio(video_url, config, supabase_metadata=supabase_metadata)
        timings['download'] = metrics.duration
        compute_stats['download'] = {'cpu': metrics.cpu_percent_avg, 'gpu': metrics.gpu_percent_avg}
    log_stage(1, "Download", "DONE", f"{timings['download']:.1f}s")

    # ========================================
    # === OPTIMIZATION (v6.2): SHARED AUDIO BUFFER ===
    # Load audio ONCE here, pass buffer to all stages
    # Previously: Each stage called torchaudio.load() separately (4-5 times!)
    # Now: Single load, reuse across VAD, chunking, quality, embeddings
    # ========================================
    with COMPUTE.monitor_stage("LoadBuffer") as metrics:


**Investigating VAD hanging issue**

I'm considering the potential for a hang occurring before futures are created, especially in `executor.submit`, which uses multiprocessing and can lead to deadlocks. It seems that VAD might be the culprit since our manual loading with torchaudio works fine. I think it’d be helpful to test the VAD function on an instance using the same configuration. I’ll try a small test script with trimmed audio and a timeout, running with a minimal number of workers to prevent resource conflicts.


**Analyzing VAD deadlock scenario**

I'm looking into why the running workers are stuck, likely due to VAD issues. Our persistent spawn pool modifications may cause problems since using spawn context in a container environment might require the `if __name__` guard. The `_vad_worker` function is defined at the top level, which should work, but deadlocks could also be occurring because `_get_local_executor` uses the spawn context. It involves multiprocess and multithreading interactions that need careful management. I need to confirm whether the background thread from AnalyticsReporter is causing issues when VAD calls the ProcessPoolExecutor.


**Exploring spawn and threading concerns**

I'm realizing that using spawn starts a new Python interpreter, which imports the main module but doesn't fork an existing process, so it shouldn't be impacted by threads. However, while creating `ProcessPoolExecutor` with spawn, it utilizes `multiprocessing` machinery, which could still lead to deadlocks when starting from a threaded process. It's usually forking that's problematic, but it sounds like the persistent executor change with atexit shutdown is also a factor. Plus, we need to ensure `_vad_worker` is modified to be picklable since it handles numpy arrays, which could complicate things.


**Analyzing VAD execution context**

Previously, VAD employed `ProcessPoolExecutor` within a context manager for each video, creating and shutting down processes, which, while having overhead, worked fine. Now, with the persistent pool that might reuse processes across videos, I wonder why it would hang when it didn't before. The use of spawn context in this environment seems crucial; the previous method (fork) functioned correctly. This shift to spawn may be problematic, particularly given how child processes access the main module’s file path. Even though it should work, the relative path in the container setup may introduce issues.


**Investigating spawn and VAD issues**

When spawn attempts to import the main file, it might fail if it's not in the current directory. It uses `runpy`, recording the main file path, and if the working directory changes, that could be problematic. Although we run from `/workspace/maya3_data`, it seems that inside the pipeline, the worker processes may behave differently. Looking at child processes, we see only 21 total, indicating VAD did not spawn processes. It suggests hanging at `ProcessPoolExecutor` creation.

To fix this, we might want to revert to using the default fork context for VAD. We could patch the code to avoid spawn, defaulting to fork while ensuring stability and performance. An immediate recommendation might be to recycle the stuck instances and check the `--vad-workers` CLI option to assist in resolving the issue.


rg -n "add_argument\(.*vad|add_argument\(.*max-utilization|add_argument\(.*monitor|add_argument\(.*workers" massive_process.py | head -n 120


Output:
Exit code: 0
Wall time: 0 seconds
Output:
1187:    parser.add_argument('--workers-per-gpu', type=int, default=1,
1217:    parser.add_argument('--export-workers', type=int, default=4,


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    logger.info(f"Wall time: {stats.wall_time_sec:.1f}s ({stats.wall_time_sec/60:.1f} min)")
    logger.info(f"Total audio: {stats.total_audio_duration_sec/3600:.2f} hours")
    logger.info("-" * 70)
    logger.info(f"THROUGHPUT:")
    logger.info(f"   Videos/min: {stats.throughput_videos_per_min:.2f}")
    logger.info(f"   Audio hours/wall hour: {stats.throughput_hours_audio_per_hour:.2f}x realtime")
    logger.info(f"   Parallel efficiency: {stats.parallel_efficiency*100:.1f}%")
    logger.info("-" * 70)
    logger.info(f"Avg process time/video: {stats.avg_process_time_sec:.1f}s")
    if stage_avgs:
        logger.info("-" * 70)
        logger.info("AVG PER-STAGE TIME (successful videos):")
        for stage, avg_t in sorted(stage_avgs.items(), key=lambda x: -x[1]):
            pct = stage_pcts.get(stage, 0.0)
            logger.info(f"   {stage:22s} {avg_t:7.2f}s  ({pct:5.1f}%)")
    logger.info("=" * 70)

    # Save results
    results_file = Path(output_dir) / "benchmark_results.json"
    with open(results_file, 'w') as f:
        json.dump({
            'stats': asdict(stats),
            'stage_avgs_sec': {k: round(v, 4) for k, v in stage_avgs.items()},
            'stage_pcts': {k: round(v, 2) for k, v in stage_pcts.items()},
            'results': [asdict(r) for r in results]
        }, f, indent=2)
    logger.info(f"Results saved to: {results_file}")

    if failed:
        logger.warning(f"\nFailed videos:")
        for r in failed:
            logger.warning(f"   {r.video_url}: {r.error}")

    return stats

def main():
    parser = argparse.ArgumentParser(
        description="MASSIVE COMPUTE - 8x H100 Parallel Processing",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Process videos from file
  python massive_process.py --input videos.txt

  # Benchmark with 16 test videos
  python massive_process.py --benchmark --num-videos 16

  # Use specific GPUs
  python massive_process.py --input videos.txt --gpus 0,1,2,3

  # Custom output directory
  python massive_process.py --input videos.txt --output /data/results
        """
    )

    # Input options
    input_group = parser.add_mutually_exclusive_group(required=True)
    input_group.add_argument('--input', '-i', type=str, help='File with video URLs (one per line)')
    input_group.add_argument('--benchmark', action='store_true', help='Run benchmark with test videos')
    input_group.add_argument('--urls', nargs='+', help='Direct URL list')
    input_group.add_argument('--supabase-queue', action='store_true',
                            help='Use Supabase as distributed video queue (claims PENDING videos from DB)')

    # Processing options
    parser.add_argument('--gpus', type=str, default=None,
                       help='Comma-separated GPU IDs (default: all available)')
    parser.add_argument('--workers-per-gpu', type=int, default=1,
                       help='Number of workers per GPU (default: 1, try 2-4 for H100)')
    parser.add_argument('--num-videos', type=int, default=8,
                       help='Number of videos for benchmark mode (default: 8)')
    parser.add_argument('--output', '-o', type=str, default='data/massive_output',
                       help='Output directory (default: data/massive_output)')

    # Pipeline config overrides
    parser.add_argument('--merge-threshold', type=float, default=0.80,
                       help='Speaker merge threshold (default: 0.80)')
    parser.add_argument('--min-segment', type=float, default=0.2,
                       help='Minimum segment duration (default: 0.2s)')
    parser.add_argument('--no-music-detection', action='store_true',
                       help='Disable music detection')

    # R2 upload (processed .tar -> 1-cleaned-data)
    parser.add_argument('--r2-upload', action='store_true',
                       help='Upload per-video .tar into R2 bucket 1-cleaned-data (no deletes)')
    parser.add_argument('--r2-upload-prefix', type=str, default='',
                       help="Optional prefix within 1-cleaned-data for uploads (e.g. 'benchmarks/rtx4090/2026-01-03')")
    parser.add_argument('--r2-upload-skip-if-exists', action='store_true',
                       help='Skip upload if remote key already exists')

    # Export options
    parser.add_argument('--segment-audio', action='store_true', default=True,
                       help='Segment original audio into individual speaker clips (default: True)')
    parser.add_argument('--no-segment-audio', action='store_false', dest='segment_audio',
                       help='Disable audio segmentation')
    parser.add_argument('--background-export', action='store_true',
                       help='Run export (segmentation+tar+upload) in background threads')
    parser.add_argument('--export-workers', type=int, default=4,
                       help='Number of background export workers (default: 4)')

    # Supabase queue options
    parser.add_argument('--max-videos', type=int, default=0,
                       help='Max videos per worker in supabase-queue mode (0=unlimited)')
    parser.add_argument('--lease-duration', type=int, default=900,
                       help='Lease duration in seconds for claimed videos (default: 900)')
    parser.add_argument('--poll-interval', type=int, default=10,
                       help='Seconds to wait when queue empty (default: 10)')

    args = parser.parse_args()

    # Load .env once in the parent process so workers inherit env vars when possible.
    if load_dotenv is not None:
        try:
            load_dotenv(dotenv_path=Path("/workspace/maya3_data/.env"), override=False)
        except Exception:
            pass

    # Validate environment before starting workers
    if not validate_environment():
        logger.error("Environment validation failed. Please set required variables.")
        sys.exit(1)

    # Get GPU list
    if args.gpus:
        gpu_ids = [int(x.strip()) for x in args.gpus.split(',')]
    else:
        gpu_ids = get_available_gpus()

    if not gpu_ids:
        logger.error("No GPUs available!")
        sys.exit(1)

    logger.info(f"Detected {len(gpu_ids)} GPUs: {gpu_ids}")

    # Get video URLs (not needed for supabase-queue mode)
    video_urls = None
    supabase_mode = getattr(args, 'supabase_queue', False)

    if supabase_mode:
        if not SUPABASE_AVAILABLE:
            logger.error("Supabase client not available! Install dependencies or check src/supabase_client.py")
            sys.exit(1)
        logger.info("Supabase queue mode: videos will be claimed from database")
    elif args.benchmark:
        video_urls = get_test_videos(args.num_videos)
        logger.info(f"Benchmark mode: {len(video_urls)} test videos")
    elif args.input:
        video_urls = load_video_urls(args.input)
        logger.info(f"Loaded {len(video_urls)} URLs from {args.input}")
    else:
        video_urls = args.urls

    if not supabase_mode and not video_urls:
        logger.error("No videos to process!")
        sys.exit(1)

    # CRITICAL: Capture HF_TOKEN before spawning workers
    # Spawned processes may not inherit environment variables
    hf_token = os.environ.get('HF_TOKEN', '')
    if not hf_token:
        # Try loading from .env file


**Debugging processing issues**

I'm trying to address some stuck processes in the system. It looks like the workers are currently in the “Download” stage, but they're not updating. Checking logs confirms they're idle. I wonder if the recent changes we made to the VAD pool are causing a deadlock. Maybe I should revert those modifications, possibly going back to the previous setup with a per-video pool. Alternatively, I could keep the persistent setup but switch to a fork context instead of spawn.


**Considering thread-safety in forking**

I'm wrestling with the idea of thread-safety when it comes to forking processes that have threads. The old version seemed to work despite forking within a multithreaded process, which is risky. But it does cause occasional hangs and stale workers. Maybe instead of spawning a new process pool for each GPU worker, I could disable multiprocessing for VAD entirely by setting `vad_workers=1`. This is because VAD runs on the CPU and can be handled more efficiently in-process, though it may slow down throughput. I wonder if using just one core could still be acceptable given the context.


**Reassessing VAD processing approach**

I'm considering whether to use threads instead of processes for VAD since the torch hub model is relatively small and CPU-only. It might also help avoid the issues with forking, although there's some uncertainty about thread-safety. One option could be creating a global VAD pool in the main process and passing results, but that feels complex. A straightforward solution might be to set `vad_workers=1` in the config, reducing CPU usage and avoiding spawning. This could increase the time for VAD but might not significantly impact overall throughput compared to the GPU processes, which dominate the workload.