python3 - <<'PY'
import json, os, urllib.request, urllib.parse

def read_env(path):
    env={}
    for line in open(path,'r',encoding='utf-8'):
        line=line.strip()
        if not line or line.startswith('#') or '=' not in line:
            continue
        k,v=line.split('=',1)
        env[k.strip()]=v.strip().strip('"').strip("'")
    return env

env=read_env('.env')
base=env['URL'].rstrip('/')
key=env['SUPABASE_ADMIN']

headers={
    'apikey': key,
    'Authorization': f'Bearer {key}',
    'Content-Type': 'application/json',
}

def get(path, params=None):
    url=base+path
    if params:
        url += '?' + urllib.parse.urlencode(params, doseq=True)
    req=urllib.request.Request(url, headers=headers, method='GET')
    with urllib.request.urlopen(req, timeout=30) as r:
        return json.loads(r.read().decode('utf-8'))

rows=get('/rest/v1/worker_heartbeats', params={
    'select':'worker_id,gpu_id,status,current_video_id,current_stage,current_stage_name,last_heartbeat,session_videos_done,session_videos_failed,session_audio_minutes,session_start',
    'machine_id':'eq.vast-29774952',
    'order':'gpu_id.asc'
})
print('worker_heartbeats rows:', len(rows))
for r in rows:
    print(f"gpu{r.get('gpu_id')}: status={r.get('status')} last={r.get('last_heartbeat')} vid={r.get('current_video_id')} stage={r.get('current_stage_name')}")
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
worker_heartbeats rows: 8
gpu0: status=processing last=2026-01-08T15:30:19.119924+00:00 vid=F9edKIR3kgQ stage=Download
gpu1: status=processing last=2026-01-08T15:14:06.37927+00:00 vid=x9AKBCxo4zs stage=Download
gpu2: status=processing last=2026-01-08T15:07:31.430148+00:00 vid=i3e12qjeAnY stage=Download
gpu3: status=processing last=2026-01-08T15:09:58.09049+00:00 vid=28u-6XZMUB8 stage=Download
gpu4: status=processing last=2026-01-08T15:36:57.55056+00:00 vid=XG_RMOEfLOI stage=Download
gpu5: status=processing last=2026-01-08T15:13:55.923976+00:00 vid=MahifKWCi4g stage=Download
gpu6: status=processing last=2026-01-08T15:36:07.292729+00:00 vid=bVB2JLenYs4 stage=Download
gpu7: status=processing last=2026-01-08T15:06:42.174871+00:00 vid=ENACUeJYWRg stage=Download


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.


python3 - <<'PY'
import json, urllib.request, urllib.parse

def read_env(path):
    env={}
    for line in open(path,'r',encoding='utf-8'):
        line=line.strip()
        if not line or line.startswith('#') or '=' not in line:
            continue
        k,v=line.split('=',1)
        env[k.strip()]=v.strip().strip('"').strip("'")
    return env

env=read_env('.env')
base=env['URL'].rstrip('/')
key=env['SUPABASE_ADMIN']
headers={'apikey':key,'Authorization':f'Bearer {key}','Content-Type':'application/json'}

def get(path, params=None):
    url=base+path
    if params:
        url += '?' + urllib.parse.urlencode(params, doseq=True)
    req=urllib.request.Request(url, headers=headers, method='GET')
    with urllib.request.urlopen(req, timeout=30) as r:
        return json.loads(r.read().decode('utf-8'))

vids=['F9edKIR3kgQ','x9AKBCxo4zs','i3e12qjeAnY','28u-6XZMUB8','XG_RMOEfLOI','MahifKWCi4g','bVB2JLenYs4','ENACUeJYWRg']
rows=get('/rest/v1/videos', params={
  'select':'youtube_id,status,claimed_by,claimed_at,lease_expires_at,attempt_count,max_attempts,source_duration_min,title',
  'youtube_id': 'in.(' + ','.join(vids) + ')'
})
by={r['youtube_id']:r for r in rows}
for v in vids:
    r=by.get(v)
    if not r:
        print(v,'MISSING')
        continue
    print(v, 'status='+str(r.get('status')),
          'claimed_by='+str(r.get('claimed_by')),
          'lease_expires_at='+str(r.get('lease_expires_at')),
          'attempt='+str(r.get('attempt_count')),
          'dur_min='+str(r.get('source_duration_min')),
          'title='+(r.get('title') or '')[:40])
PY


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
F9edKIR3kgQ status=CLAIMED claimed_by=vast-29774952_gpu0 lease_expires_at=2026-01-08T15:45:18.998624+00:00 attempt=1 dur_min=10.1 title=Serial Actor Indra Nag About His Propert
x9AKBCxo4zs status=CLAIMED claimed_by=vast-29774952_gpu1 lease_expires_at=2026-01-08T15:29:06.260814+00:00 attempt=1 dur_min=9.3 title=Music Director Koti Felt Very Emotional
i3e12qjeAnY status=CLAIMED claimed_by=vast-29774952_gpu2 lease_expires_at=2026-01-08T15:22:31.305741+00:00 attempt=1 dur_min=6.7 title=Episode 26 : THE UNTOLD STORY | Middle C
28u-6XZMUB8 status=CLAIMED claimed_by=vast-29774952_gpu3 lease_expires_at=2026-01-08T15:24:57.96839+00:00 attempt=1 dur_min=9.8 title=Ep 18 : Playboy | Konchem Clarity and Ko
XG_RMOEfLOI status=COMPLETED claimed_by=vast-29774952_gpu4 lease_expires_at=2026-01-08T15:51:57.420738+00:00 attempt=1 dur_min=13.2 title=We Fought Each Other Very Seriously | Ac
MahifKWCi4g status=CLAIMED claimed_by=vast-29774952_gpu5 lease_expires_at=2026-01-08T15:28:55.793553+00:00 attempt=1 dur_min=11.9 title=I Warned Vishwak Sen Not To Produce Fala
bVB2JLenYs4 status=CLAIMED claimed_by=vast-29774952_gpu6 lease_expires_at=2026-01-08T15:51:07.169511+00:00 attempt=1 dur_min=10.9 title=Revolutionary Singer Gaddar About Realit
ENACUeJYWRg status=CLAIMED claimed_by=vast-29774952_gpu7 lease_expires_at=2026-01-08T15:21:42.060572+00:00 attempt=1 dur_min=9.4 title=செயல்படாத அதிமுக - பாஜக கூட்டணி |  பாஜகவ


Summary: auto


**Analyzing worker lease issues**

It seems the videos are still CLAIMED, while lease_expires_at matches pick time plus 15 minutes. Stale workers have already let their lease expire, but they remain stuck and block those videos unless a recovery job activates. I see two main issues: first, worker processes are hanging on short videos, likely due to a pipeline stall rather than video length. Second, the lease reclaim logic appears broken; it only targets PENDING statuses and won't retry expired CLAIMED videos unless a reclaim function runs periodically. That could explain the problem.


**Identifying hang issues**

I've noticed that the hang seems to occur after the download stage, likely in either `AudioBuffer.from_file` or `run_vad_parallel`. Since the videos are short, VAD should be quick. However, if VAD worker initialization calls `torch.hub.load`, which requires internet access to GitHub, there could be a network issue or rate limit causing the hang. Other GPUs seem fine, possibly because they cached the download. Some workers might be stuck due to file lock contention in the cache directory, leading to a deadlock when multiple VAD workers try to access the same directory simultaneously.


**Investigating VAD process issues**

I think concurrency in Python might cause a lock file issue when using `git clone`. In `src/vad.py`, there's also a `suppress_stderr` context manager that redirects sys.stderr, which might cause complications across multiple processes. It could be interesting to replicate the issue by running `run_vad_parallel` in a local environment with multiple processes. But I’m reasoning that when multiple GPU workers run VAD, they may create a local pool. The `run_vad_parallel` function may use a global pool, possibly indicating a bug, so I’ll search for it in `src/vad.py`.


rg -n "def run_vad_parallel|class GlobalVADPool|process_chunks" -n src/vad.py && sed -n '260,420p' src/vad.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
245:        all_segments = pool.process_chunks(
299:def run_vad_parallel(audio_path: str, config, audio_buffer=None, pool=None) -> List[Dict[str, float]]:
                onnx=False,
                trust_repo=True,
                verbose=False
            )
        except Exception as e:
            logger.warning(f"   Pre-download attempt failed (will retry in workers): {e}")

        with ProcessPoolExecutor(
            max_workers=config.vad_workers,
            initializer=_init_vad_worker
        ) as executor:
            futures = {executor.submit(_vad_worker, args): i for i, args in enumerate(chunk_args)}

            for future in as_completed(futures):
                try:
                    segments = future.result(timeout=120)
                    all_segments.extend(segments)
                    completed += 1

                    if completed % max(1, len(chunk_args) // 10) == 0:
                        logger.info(f"   VAD progress: {completed}/{len(chunk_args)} chunks")
                except Exception as e:
                    logger.error(f"VAD chunk failed: {e}")

    # Sort by start time
    all_segments.sort(key=lambda x: x['start'])

    # Merge adjacent segments (fix boundary splits from chunking)
    merged = _merge_adjacent_vad_segments(all_segments, merge_gap=0.1)

    total_speech = sum(s['duration'] for s in merged)
    elapsed = time.time() - start

    logger.info(f"✅ VAD: {elapsed:.1f}s | {len(merged)} segments | {total_speech:.1f}s speech")
    logger.info(f"   Speedup: {total_duration/elapsed:.1f}x realtime | Mode: {pool_mode}")

    return merged

def run_vad_parallel(audio_path: str, config, audio_buffer=None, pool=None) -> List[Dict[str, float]]:
    """
    Run Silero VAD with TRUE parallelism and persistent workers.

    === OPTIMIZATION (v6.2) ===
    - If audio_buffer provided, uses pre-loaded numpy array (no file re-read)
    - Uses initializer to load Silero model ONCE per worker (not per chunk!)

    === OPTIMIZATION (v7.9 - Stage 1) ===
    - Accepts optional GlobalVADPool for cross-video persistence
    - Eliminates ~2-3s pool creation overhead per video

    Args:
        audio_path: Path to audio file (used if audio_buffer is None)
        config: Pipeline configuration
        audio_buffer: Optional AudioBuffer with pre-loaded waveform
        pool: Optional GlobalVADPool for persistent workers

    Returns:
        List of speech segments with start/end/duration
    """
    # Use buffer if provided, otherwise load from file
    if audio_buffer is not None:
        return run_vad_from_buffer(audio_buffer.waveform_np, audio_buffer.sample_rate, config, pool=pool)

    # Legacy path: load from file (still uses persistent workers)
    import torchaudio
    waveform, sr = torchaudio.load(audio_path)
    waveform_np = waveform.squeeze(0).numpy()
    return run_vad_from_buffer(waveform_np, sr, config, pool=pool)

def _merge_adjacent_vad_segments(segments: List[Dict], merge_gap: float = 0.1) -> List[Dict]:
    """
    Merge VAD segments that were split at chunk boundaries.

    Args:
        segments: Sorted list of VAD segments
        merge_gap: Maximum gap (seconds) to merge across
    """
    if not segments:
        return []

    merged = []
    current = segments[0].copy()

    for seg in segments[1:]:
        gap = seg['start'] - current['end']

        # Merge if very close (likely chunk boundary artifact)
        if gap < merge_gap:
            current['end'] = seg['end']
            current['duration'] = current['end'] - current['start']
        else:
            merged.append(current)
            current = seg.copy()

    merged.append(current)
    return merged

def find_silence_boundaries(vad_segments: List[Dict],
                           total_duration: float,
                           min_silence: float = 0.3) -> List[Dict]:
    """
    Find silence regions (gaps between VAD segments).

    Useful for:
    - Finding optimal chunk boundaries
    - Marking non-speech regions

    Args:
        vad_segments: List of speech segments from VAD
        total_duration: Total audio duration
        min_silence: Minimum silence duration to report

    Returns:
        List of silence segments with start/end/duration
    """
    silences = []

    # Check start of audio
    if vad_segments and vad_segments[0]['start'] >= min_silence:
        silences.append({
            'start': 0.0,
            'end': vad_segments[0]['start'],
            'duration': vad_segments[0]['start']
        })

    # Gaps between segments
    for i in range(len(vad_segments) - 1):
        gap_start = vad_segments[i]['end']
        gap_end = vad_segments[i + 1]['start']
        gap_duration = gap_end - gap_start

        if gap_duration >= min_silence:
            silences.append({
                'start': gap_start,
                'end': gap_end,
                'duration': gap_duration
            })

    # Check end of audio
    if vad_segments and total_duration - vad_segments[-1]['end'] >= min_silence:
        silences.append({
            'start': vad_segments[-1]['end'],
            'end': total_duration,
            'duration': total_duration - vad_segments[-1]['end']
        })

    return silences

def find_optimal_cut_point(vad_segments: List[Dict],
                          target_time: float,
                          window: float = 30.0) -> float:
    """
    Find the best silence point near target_time for chunking.

    Strategy: Find the largest gap within ±window of target_time.
    This ensures we don't cut in the middle of speech.


**Addressing video processing hangs**

I’m exploring options to fix the hang issue during video processing. I could add a `timeout` to `as_completed(futures, timeout=...)` and handle any `TimeoutError`, which would involve canceling pending tasks and properly terminating the process pool. Alternatively, using a `GlobalVADPool` with a timeout for each chunk could enhance robustness.

I also consider using `multiprocessing.get_context('spawn')` to potentially avoid torch fork issues, but there are concerns about it hanging. The user mentioned that the stalling isn’t just about missing videos in R2; instead, it relates to the VAD hanging. It's essential to validate the specific videos that are stuck, as they are relatively short, indicating that the hang likely occurs within VAD rather than just needing longer videos. It's worth checking if these videos might be corrupted, which could lead to issues with processing.


**Investigating audio loading hangs**

I’m realizing that if the download stage successfully produced an audio path but the load buffer hangs, it would likely stop before printing the VAD stage completion. This issue seems specific to certain audio files.

To troubleshoot, I plan to check if these files exist locally in R2 by using a HEAD request to verify their status, but that might be tricky. Running a HEAD check for one stuck video and one that works could give insights into file size. If the stuck videos show 0 bytes or are incomplete, it may explain why torchaudio hangs during loading. However, the download stage results are monitored through COMPUTE, meaning that if it returns, the audio path should exist.


**Examining potential audio issues**

I’m considering the possibility that the audio file might be large or corrupted, which could cause the load buffer to hang if it tries to use `AudioBuffer.from_file` with `torchaudio.load`. However, in the `_download_from_r2` function, it produces both a trimmed WAV and an original FLAC, with the audio path being a trimmed WAV, so that should be valid.

It’s crucial to confirm if the stuck videos still exist in R2 since they were downloaded previously. Given that stage one finished in just 4.3 seconds, it seems the stall occurs after this point, likely during the load buffer or VAD stages. I’ll check the implementation in `src/audio_buffer.py` to see how it uses `torchaudio.load`, which might be causing the hang.


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


**Analyzing potential hangs**

I'm looking into why `AudioBuffer.from_file` might hang. It uses `torchaudio.load`, which shouldn't hang on a short WAV file but might if the file's huge or corrupted.

I’m digging deeper into the VAD's forking process: each worker creates a new `multiprocessing.Process` and loads multiple models with `MODELS.load_all`. When processing, `run_vad_parallel` uses a `ProcessPoolExecutor`, which forks again after PyTorch initialization. This could lead to deadlocks, as PyTorch advises using 'spawn' instead of 'fork' due to multithreading issues. So, something might be going awry with the fork.


**Investigating random deadlocks**

I’m observing many random deadlocks. The `_init_vad_worker` function calls `torch.hub.load`, which could involve `subprocess`, git, and file operations that might deadlock when used in parallel across processes due to file locks on `.git`.

It’s puzzling why only certain GPUs hang, and it seems non-deterministic. I can see that stuck workers were identified at certain times, leaving some GPUs active, indicating a random deadlock pattern. It looks like the root issue stems from the use of `ProcessPoolExecutor`, lack of a timeout, and heartbeats not updating, leading to zombie workers. I should verify if those worker processes still exist, but it might not be necessary.


**Proposing fix options**

I’m thinking about potential fixes without making code changes for now. One option is to add a watchdog that runs each `gpu_worker_supabase`. If it doesn't produce a result within a set time, it can kill and restart the worker process. Another simpler approach is to implement stage-level timeouts around VAD by using `ProcessPoolExecutor` with a spawn context and timeout management.

The most immediate fix would be to use a global VAD pool, `GlobalVADPool`, with a per-chunk timeout, which seems to be in `src/worker_pools.py`. This could help reduce process churn. Lastly, I could disable parallel VAD by setting `vad_workers=1` to avoid hangs, sacrificing some speed.


**Considering stability and throughput**

The user is prioritizing high throughput, but stability is also important. One possible fix is to preload the Silero VAD model in the main worker process and run VAD sequentially using `get_speech_timestamps`, which could be efficient enough.

Another option could be running VAD with TorchScript or on a GPU. However, I’m realizing that the stalling isn’t due to missing videos in R2, but rather workers getting stuck in the pipeline without a watchdog. They stay in a CLAIMED state and don’t get reclaimed, making the dashboard appear stale.

I need to recommend an operational step: running `scripts/monitor/lease_recovery.py` periodically to recover expired leases and refresh stale workers. They might already have this in place. The user has requested a root cause analysis before discussing fixes.


**Presenting evidence and proposing fixes**

I want to lay out some evidence regarding the stuck workers. Logs indicate that workers 1, 2, 3, 5, and 7 have their last outputs stuck on "1/10 Download" and "PICK...," with no updates after that.

Data from the Supabase worker heartbeats shows that the last heartbeat for the stuck workers matches their pick time, and they remain stuck at the Download stage. Additionally, the videos in the table that are in the CLAIMED status with expired leases aren't being picked, leading to a throughput decay.

The root cause seems to be a pipeline stall due to VAD ProcessPool deadlock and missing lease recovery. For fixes, I propose we can implement one of the following: (A) add periodic heartbeats to show progress; (B) add watchdogs and timeouts for `process_single_video` to mark hung processes as FAILED; or (C) adjust the claim function to consider expired CLAIMED statuses.

I also think we should treat missing R2 videos as SKIPPED instead of FAILED. We can use `scripts/monitor/lease_recovery.py` to check for stale workers and potentially inspect processing events to investigate further. Let's open that script.


sed -n '1,220p' scripts/monitor/lease_recovery.py


sed -n '220,420p' scripts/monitor/lease_recovery.py


sed -n '120,260p' scripts/supabase_migrations.sql


rg -n "get_cluster_stats|reclaim_expired_leases" -S src/supabase_client.py dashboard-ui -n | head -n 200


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Lease Recovery Service for Maya3 Pipeline.

Runs periodically to reclaim expired video leases from crashed workers.
This is a CRITICAL service for production - should run on a reliable node.

Usage:
    python lease_recovery.py                    # One-time run
    python lease_recovery.py --daemon           # Run as daemon
    python lease_recovery.py --interval 300    # Custom interval (default: 5 min)
"""

import os
import sys
import time
import argparse
import logging
from datetime import datetime, timezone

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))

from dotenv import load_dotenv
load_dotenv()

from src.supabase_client import SupabaseClient, STATUS_PENDING, STATUS_CLAIMED

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s | %(levelname)-8s | %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger("LeaseRecovery")

def reclaim_expired_leases(client: SupabaseClient) -> int:
    """
    Reset videos with expired leases back to PENDING.

    Returns:
        Number of videos reclaimed
    """
    now = datetime.now(timezone.utc).isoformat()

    # Find and update expired claims
    count = client.reclaim_expired_leases()

    if count > 0:
        logger.info(f"Reclaimed {count} expired leases")

    return count

def check_stale_workers(client: SupabaseClient, threshold_sec: int = 600) -> list:
    """
    Find workers that haven't reported heartbeat in threshold seconds.

    Returns:
        List of stale worker info dicts
    """
    from supabase import create_client

    sb = create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
    threshold = datetime.now(timezone.utc) - __import__('datetime').timedelta(seconds=threshold_sec)

    result = sb.table('worker_heartbeats').select(
        'worker_id, machine_id, gpu_id, status, current_video_id, last_heartbeat'
    ).neq('status', 'offline').lt('last_heartbeat', threshold.isoformat()).execute()

    stale = result.data or []

    if stale:
        logger.warning(f"Found {len(stale)} stale workers:")
        for w in stale:
            logger.warning(f"  {w['worker_id']} - last seen: {w['last_heartbeat']}")

    return stale

def run_daemon(interval_sec: int = 300):
    """Run lease recovery as a daemon."""
    logger.info(f"Starting lease recovery daemon (interval: {interval_sec}s)")

    client = SupabaseClient()

    while True:
        try:
            # Reclaim expired leases
            reclaimed = reclaim_expired_leases(client)

            # Check for stale workers (warning only)
            stale = check_stale_workers(client)

            # Log summary
            logger.info(f"Cycle complete - Reclaimed: {reclaimed}, Stale workers: {len(stale)}")

        except Exception as e:
            logger.error(f"Error in recovery cycle: {e}")

        time.sleep(interval_sec)

def main():
    parser = argparse.ArgumentParser(description="Maya3 Lease Recovery Service")
    parser.add_argument('--daemon', '-d', action='store_true', help='Run as daemon')
    parser.add_argument('--interval', '-i', type=int, default=300, help='Check interval (seconds)')
    args = parser.parse_args()

    client = SupabaseClient()

    if args.daemon:
        run_daemon(args.interval)
    else:
        # One-time run
        reclaimed = reclaim_expired_leases(client)
        stale = check_stale_workers(client)
        print(f"Reclaimed: {reclaimed} leases, Stale workers: {len(stale)}")

if __name__ == "__main__":
    main()


Output:
Exit code: 0
Wall time: 0 seconds
Output:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
-- ============================================================================

-- ============================================================================
-- STEP 8: Worker heartbeats (real-time status, 1 row per worker)
-- ============================================================================
CREATE TABLE IF NOT EXISTS worker_heartbeats (
    worker_id TEXT PRIMARY KEY,
    machine_id TEXT NOT NULL,
    gpu_id INT NOT NULL,
    status TEXT DEFAULT 'idle',
    current_video_id TEXT,
    current_stage INT,
    current_stage_name TEXT,
    last_heartbeat TIMESTAMPTZ DEFAULT NOW(),
    session_start TIMESTAMPTZ DEFAULT NOW(),
    session_videos_done INT DEFAULT 0,
    session_videos_failed INT DEFAULT 0,
    session_audio_minutes FLOAT DEFAULT 0,
    session_usable_minutes FLOAT DEFAULT 0
);

-- ============================================================================
-- STEP 9: Worker lifetime metrics (aggregated stats)
-- ============================================================================
CREATE TABLE IF NOT EXISTS worker_metrics (
    worker_id TEXT PRIMARY KEY,
    machine_id TEXT NOT NULL,
    gpu_id INT NOT NULL,
    total_videos_processed INT DEFAULT 0,
    total_videos_failed INT DEFAULT 0,
    total_audio_hours FLOAT DEFAULT 0,
    total_usable_hours FLOAT DEFAULT 0,
    total_processing_hours FLOAT DEFAULT 0,
    avg_rtf FLOAT DEFAULT 0,
    first_seen TIMESTAMPTZ DEFAULT NOW(),
    last_active TIMESTAMPTZ DEFAULT NOW()
);

-- ============================================================================
-- STEP 10: Processing events (done/fail only, for analysis)
-- ============================================================================
CREATE TABLE IF NOT EXISTS processing_events (
    id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
    worker_id TEXT NOT NULL,
    video_id TEXT NOT NULL,
    event_type TEXT NOT NULL,
    stage_name TEXT,
    duration_seconds FLOAT,
    audio_minutes FLOAT,
    speakers INT,
    usable_pct FLOAT,
    error_type TEXT,
    error_message TEXT,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX IF NOT EXISTS idx_events_worker ON processing_events(worker_id, created_at DESC);
CREATE INDEX IF NOT EXISTS idx_events_video ON processing_events(video_id);
CREATE INDEX IF NOT EXISTS idx_events_type ON processing_events(event_type, created_at DESC);

-- ============================================================================
-- STEP 11: Error logs (detailed error tracking)
-- ============================================================================
CREATE TABLE IF NOT EXISTS error_logs (
    id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
    worker_id TEXT NOT NULL,
    video_id TEXT NOT NULL,
    stage TEXT,
    error_type TEXT,
    error_message TEXT,
    stack_trace TEXT,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX IF NOT EXISTS idx_errors_worker ON error_logs(worker_id, created_at DESC);
CREATE INDEX IF NOT EXISTS idx_errors_recent ON error_logs(created_at DESC);

-- ============================================================================
-- STEP 12: Cluster overview view (aggregated dashboard)
-- ============================================================================
CREATE OR REPLACE VIEW cluster_overview AS
SELECT
    COUNT(DISTINCT machine_id) as total_machines,
    COUNT(*) as total_workers,
    COUNT(*) FILTER (WHERE status = 'processing') as active_workers,
    COUNT(*) FILTER (WHERE status = 'idle') as idle_workers,
    COUNT(*) FILTER (WHERE last_heartbeat < NOW() - INTERVAL '2 minutes') as stale_workers,
    COALESCE(SUM(session_videos_done), 0) as session_total_videos,
    COALESCE(SUM(session_audio_minutes), 0) as session_total_audio_min,
    COALESCE(SUM(session_usable_minutes), 0) as session_total_usable_min
FROM worker_heartbeats;

-- ============================================================================
-- STEP 13: Function to get cluster stats with RTF calculation
-- ============================================================================
CREATE OR REPLACE FUNCTION get_cluster_stats()
RETURNS TABLE(
    total_machines BIGINT,
    total_workers BIGINT,
    active_workers BIGINT,
    idle_workers BIGINT,
    stale_workers BIGINT,
    session_videos_done BIGINT,
    session_audio_hours FLOAT,
    session_usable_hours FLOAT,
    hourly_throughput_videos BIGINT,
    hourly_throughput_audio_hours FLOAT
) AS $$
BEGIN
    RETURN QUERY
    SELECT
        COUNT(DISTINCT wh.machine_id)::BIGINT,
        COUNT(*)::BIGINT,
        COUNT(*) FILTER (WHERE wh.status = 'processing')::BIGINT,
        COUNT(*) FILTER (WHERE wh.status = 'idle')::BIGINT,
        COUNT(*) FILTER (WHERE wh.last_heartbeat < NOW() - INTERVAL '2 minutes')::BIGINT,
        COALESCE(SUM(wh.session_videos_done), 0)::BIGINT,
        COALESCE(SUM(wh.session_audio_minutes) / 60.0, 0)::FLOAT,
        COALESCE(SUM(wh.session_usable_minutes) / 60.0, 0)::FLOAT,
        (SELECT COUNT(*) FROM processing_events pe
         WHERE pe.event_type = 'done'
         AND pe.created_at > NOW() - INTERVAL '1 hour')::BIGINT,
        (SELECT COALESCE(SUM(pe.audio_minutes) / 60.0, 0) FROM processing_events pe
         WHERE pe.event_type = 'done'
         AND pe.created_at > NOW() - INTERVAL '1 hour')::FLOAT
    FROM worker_heartbeats wh;
END;
$$ LANGUAGE plpgsql;

-- ============================================================================
-- STEP 14: Function to update worker lifetime metrics (called on done/fail)
-- ============================================================================
CREATE OR REPLACE FUNCTION update_worker_metrics()
RETURNS TRIGGER AS $$
BEGIN
    IF NEW.event_type = 'done' THEN
        INSERT INTO worker_metrics (worker_id, machine_id, gpu_id, total_videos_processed,
                                    total_audio_hours, total_usable_hours, total_processing_hours, last_active)
        VALUES (
            NEW.worker_id,
            split_part(NEW.worker_id, '_gpu', 1),


Output:
Exit code: 0
Wall time: 0 seconds
Output:
src/supabase_client.py:483:    def reclaim_expired_leases(self) -> int:


Summary: auto


**Analyzing GPU Stalling Issues**

I’m looking into the root cause of stalled GPU workers. Each stale GPU relates to a worker that completed downloading a video but got stuck between states, indicated by an unchanged worker heartbeat and a video row marked as CLAIMED.

I suspect the hang occurs at the VAD stage since the last printed stage for stuck workers is Download. The issue likely stems from the `run_vad_from_buffer`, which can block indefinitely if any subprocess fails. Additionally, failing to run a lease recovery script means expired CLAIMED rows will remain unprocessed.


**Proposing Fix Options**

I’m thinking about some options to fix the issues with GPU workers. For immediate mitigation without code, we could run a lease recovery daemon every 5 minutes to reclaim expired leases and unblock stuck tasks. We could also reboot the instance, but that would just reset everything.

For code changes, I suggest implementing a watchdog around `process_single_video` to mark tasks as FAILED or SKIPPED if they don’t progress within 10 minutes. Improving the VAD by adding a timeout when using `as_completed` could prevent stalls, and updating analytics during processing can help keep heartbeats active, too.


**Addressing Missing R2 Issues**

I need to tackle the problem of missing R2 items. When they’re missing, tasks fail quickly, but we want to mark them as SKIPPED instead of stalling the process. Since the user wants to discuss the fix before implementation, I'll ask for their preferred approach.

Regarding the "3/8 active," we have 5 workers stuck on claimed video tasks. They haven’t released those tasks, preventing further processing. I can compute the exact stuck times, noting that these videos are only 6.7-11.9 minutes long, so they should complete quickly. I'll also highlight that they haven't been retried, as their attempt_count is just 1.


**Discussing Lease and Stalling Issues**

I should highlight that the lease_expires_at is expired, yet the row remains CLAIMED, and the claim_next_video function doesn’t reclaim. This means we need periodic reclamation. I'll also note that the dashboard marks tasks as "stale" if the last_heartbeat is older than 5 minutes since it only updates on pick, done, or fail. This can show videos as stale even if they’re processing, indicating a hang.

The user has asked for debug logs regarding worker failures and stalling, which we’ve analyzed. For an example, GPU7 picked ENACUeJYWRg at 15:06:42 and still hasn’t updated its status. The immediate step I propose is to run lease recovery to clear the queue, but if the worker is still hung, it may refuse the GPU while others can pick up tasks.


**Proposing GPU Compute Solutions**

To regain GPU compute, we should consider rebooting the instance to terminate hung worker processes. However, the user seems to prefer a code fix instead. We can suggest several improvements: implementing a heartbeat ticker, adding a processing watchdog, and fixing the VAD hang.

Notably, there’s an unused global pool optimization in `src/vad.py`. The `GlobalVADPool` in `src/worker_pools.py` might resolve these issues. Also, since the parent process doesn’t restart dead workers, we should confirm if the stuck workers are still alive on the instance. For further investigation, we could check running processes via SSH with the user’s approval.

To enhance certainty, we could query Supabase for processing events related to those workers, identifying their current_video_id, last_heartbeat, and lease_expires_at. If we discover they’ve gone “zombied,” it indicates they lack per-video timeouts and lease recovery, meaning they won’t self-heal. I’ll present this with evidence in a concise table and outline these options.


Those “stale” GPUs are genuinely stuck, not just “missing R2 and moving on”.

**What I’m seeing (evidence)**
- In the Vast instance log, the last output for the stale GPUs is: `PICK <video_id>` then `1/10 Download ...` and then nothing further (no `DONE`, no `FAIL`).
- In Supabase `worker_heartbeats` for `machine_id=vast-29774952`, the stale GPUs are all still `status=processing`, `current_stage_name=Download`, with `last_heartbeat` equal to when they *picked* that video (no further heartbeat updates).
- In Supabase `videos` table, those same `youtube_id`s are stuck in `status=CLAIMED` by those workers, and **their `lease_expires_at` is already in the past** (e.g. GPU7’s claim expired) but the row remains `CLAIMED`.

Example stuck set right now:
- GPU7 `ENACUeJYWRg`: last heartbeat `15:06:42`, `videos.status=CLAIMED`, lease expired `15:21:42`
- GPU2 `i3e12qjeAnY`: last heartbeat `15:07:31`, `CLAIMED`, lease expired `15:22:31`
- GPU3 `28u-6XZMUB8`: last heartbeat `15:09:58`, `CLAIMED`, lease expired `15:24:57`
- GPU5 `MahifKWCi4g`: last heartbeat `15:13:55`, `CLAIMED`, lease expired `15:28:55`
- GPU1 `x9AKBCxo4zs`: last heartbeat `15:14:06`, `CLAIMED`, lease expired `15:29:06`

**Root cause**
1) **Workers can hang mid-pipeline with no watchdog.** Heartbeats only happen on `pick()` and then on `done()/fail()`. If a worker hangs inside `process_single_video()` (most likely during/around Stage 2 VAD), it stops reporting and the GPU looks “stale”.
2) **Expired CLAIMED leases are not automatically reclaimed by the queue.** Your `claim_next_video()` SQL only selects rows where `status='PENDING'`, so an expired `CLAIMED` row is *never* eligible again unless `reclaim_expired_leases()` is run (there’s a script for this: `scripts/monitor/lease_recovery.py`). So hung workers permanently strand videos in `CLAIMED`, and throughput decays over time.

**Why this isn’t “R2 missing”**
- “R2 source not found” errors fail fast and the worker immediately moves on (we saw those in logs). The stall cases are different: they successfully pick + download, then never reach fail/done.

**Fix options to discuss (no changes yet)**
- **Operational (immediate):** run `python3 scripts/monitor/lease_recovery.py --daemon --interval 300` somewhere reliable to continuously reclaim expired `CLAIMED` rows.
- **Stability:** add a per-video (or per-stage) watchdog so a hung `process_single_video()` gets aborted and the worker continues (or the worker process is restarted).
- **VAD hang hardening:** `src/vad.py` uses `as_completed(futures)` without a global timeout; a single stuck chunk can hang the whole VAD stage indefinitely. Add an overall timeout + forced pool teardown/restart, or switch to the “global pool” path that already supports per-chunk timeouts.
- **Observability:** emit `analytics.stage_update(...)` periodically so “Last seen” reflects real progress (and shows the actual stage, not always “Download”).

Which direction do you want first: **(A) quick operational recovery + watchdog**, or **(B) fix VAD parallelism to prevent the hang in the first place**?


<environment_context>
  <approval_policy>never</approval_policy>
  <sandbox_mode>danger-full-access</sandbox_mode>
  <network_access>enabled</network_access>
  <shell>bash</shell>
</environment_context>


all the workers in the vast ai are either errored out or something has happened. connect to vast ai instances and then check why they're stopped and debug why they're stopped.


**Refreshing instance list**

I’m initiating a refresh of the instance list to see what’s currently available. After that, I need to identify which instances aren’t running. I’ll fetch the logs, including any daemon logs, to gather information about their status. If I find any instances that have stopped, I’ll attempt to SSH into one of them to inspect the processes and see if the Docker logs provide any clues about what’s going wrong. Using an update plan seems like a solid approach for this multi-step task!


Explanation: Investigate Vast.ai instance states, pull logs/daemon logs for stopped/errored instances, and SSH in (if keys available) to inspect why workers stopped.


Output: Plan updated


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py instances


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
Instances: 15
- 29760494 | running | 8x RTX 3090 | ssh4.vast.ai | maya3-prod-3090x8-us
- 29760732 | rebooting | 8x RTX 3090 | ssh3.vast.ai | maya3-prod-3090x8-ca
- 29761464 | running | 8x RTX 4090 | ssh5.vast.ai | maya3-prod-auto-4090x8-28881569
- 29761465 | running | 8x RTX 4090 | ssh1.vast.ai | maya3-prod-auto-4090x8-19523356
- 29761466 | running | 8x RTX 4090 | ssh7.vast.ai | maya3-prod-auto-4090x8-26537139
- 29761469 | running | 8x RTX 4090 | ssh5.vast.ai | maya3-prod-auto-4090x8-29551039
- 29761470 | running | 8x RTX 4090 | ssh4.vast.ai | maya3-prod-auto-4090x8-29351745
- 29761613 | running | 8x RTX 3090 | ssh9.vast.ai | maya3-prod-3090x8-auto-29244333
- 29774951 | running | 8x RTX 3090 | ssh8.vast.ai | maya3-stabilitytest2-3090x8-Finland_FI-29381071-1
- 29774952 | running | 8x RTX 4090 | ssh5.vast.ai | maya3-stabilitytest2-4090x8-Norway_NO-26025652-1
- 29777138 | running | 8x RTX 3080 | ssh2.vast.ai | maya3-prod-California_US-29765899-1
- 29777151 | running | 8x RTX 4090 | ssh3.vast.ai | maya3-prod-Taiwan_TW-29345810-1
- 29777152 | running | 8x RTX 4090 | ssh6.vast.ai | maya3-prod-Hong_Kong_HK-25522278-2
- 29777521 | running | 8x RTX 3090 | ssh8.vast.ai | maya3-prod-California_US-28909590-1
- 29777715 | running | 8x RTX 4090 | ssh1.vast.ai | maya3-prod-Taiwan_TW-29345609-2


python3 - <<'PY'
import json, urllib.request, urllib.parse
from datetime import datetime, timezone

def read_env(path):
    env={}
    for line in open(path,'r',encoding='utf-8'):
        line=line.strip()
        if not line or line.startswith('#') or '=' not in line:
            continue
        k,v=line.split('=',1)
        env[k.strip()]=v.strip().strip('"').strip("'")
    return env

env=read_env('.env')
base=env['URL'].rstrip('/')
key=env['SUPABASE_ADMIN']
headers={'apikey':key,'Authorization':f'Bearer {key}','Content-Type':'application/json'}

def get(path, params=None):
    url=base+path
    if params:
        url += '?' + urllib.parse.urlencode(params, doseq=True)
    req=urllib.request.Request(url, headers=headers, method='GET')
    with urllib.request.urlopen(req, timeout=30) as r:
        return json.loads(r.read().decode('utf-8'))

# Fetch up to 2000 heartbeats (should be enough)
rows=get('/rest/v1/worker_heartbeats', params={
    'select':'worker_id,machine_id,gpu_id,status,current_video_id,current_stage_name,last_heartbeat,session_videos_done,session_videos_failed,session_audio_minutes',
    'limit':'2000'
})
now=datetime.now(timezone.utc)

def parse_ts(s):
    if not s:
        return None
    try:
        # Accept Z or +00:00
        if s.endswith('Z'):
            s=s[:-1]+'+00:00'
        return datetime.fromisoformat(s)
    except Exception:
        return None

machines={}
for r in rows:
    mid=r.get('machine_id') or 'unknown'
    hb=parse_ts(r.get('last_heartbeat'))
    age_min=None
    if hb:
        age_min=(now-hb).total_seconds()/60.0
    r['_age_min']=age_min
    machines.setdefault(mid,[]).append(r)

# Summarize machines with stale workers (>10min)
items=[]
for mid, ws in machines.items():
    total=len(ws)
    active=sum(1 for w in ws if w.get('_age_min') is not None and w['_age_min']<=5)
    stale10=sum(1 for w in ws if w.get('_age_min') is None or w['_age_min']>10)
    max_age=max((w['_age_min'] or 10**9) for w in ws)
    items.append((stale10, max_age, total, active, mid, ws))

items.sort(reverse=True)
print(f"machines: {len(items)} workers: {len(rows)}")
print("Top machines by stale(>10m):")
for stale10, max_age, total, active, mid, ws in items[:10]:
    if stale10==0:
        continue
    # show worst 2 workers
    ws_sorted=sorted(ws, key=lambda w: (w.get('_age_min') is None, w.get('_age_min') or 10**9), reverse=True)
    worst=[]
    for w in ws_sorted[:2]:
        age=w.get('_age_min')
        age_s='?' if age is None else f"{age:.0f}m"
        worst.append(f"gpu{w.get('gpu_id')}:{age_s}:{w.get('status')}:{w.get('current_stage_name')}")
    print(f"- {mid}: stale={stale10}/{total} active5m={active} worst={', '.join(worst)}")
PY


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
machines: 15 workers: 120
Top machines by stale(>10m):
- vast-29777152: stale=8/8 active5m=0 worst=gpu1:?:offline:None, gpu3:19m:offline:None
- vast-29777151: stale=8/8 active5m=0 worst=gpu5:?:offline:None, gpu3:?:offline:None
- vast-29777138: stale=8/8 active5m=0 worst=gpu0:?:offline:None, gpu6:19m:offline:None
- vast-29761613: stale=8/8 active5m=0 worst=gpu5:?:offline:None, gpu3:?:offline:None
- vast-29761466: stale=8/8 active5m=0 worst=gpu5:?:processing:Download, gpu0:162m:processing:Download
- vast-29774951: stale=8/8 active5m=0 worst=gpu1:66m:processing:Download, gpu5:19m:offline:None
- vast-29761470: stale=8/8 active5m=0 worst=gpu0:38m:processing:Download, gpu7:19m:offline:None
- vast-29777715: stale=8/8 active5m=0 worst=gpu7:22m:processing:Download, gpu6:19m:offline:None
- vast-29761465: stale=8/8 active5m=0 worst=gpu0:22m:processing:Download, gpu1:19m:offline:None
- vast-29760494: stale=8/8 active5m=0 worst=gpu7:19m:offline:None, gpu4:19m:offline:None


Output: Plan updated


cat > /tmp/vast_inspect.sh <<'SH'
#!/usr/bin/env bash
set -euo pipefail
export VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)"
ids=(29777152 29777151 29777138 29761613 29760494 29761466 29761470 29777715 29761465 29774951 29774952 29760732)
pat='(Worker failed|Shutting down|No GPUs available|Killed|OOM|out of memory|CUDA error|CUBLAS|cuDNN|No space left|ENOSPC|disk|Traceback|Exception|Segmentation fault|SIGKILL|exit code|docker: Error|nvidia-container-cli)'
for id in "${ids[@]}"; do
  echo "==== $id ===="
  f="/tmp/vast-${id}.log"
  df="/tmp/vast-${id}.daemon.log"
  python3 scripts/vast/vastctl.py logs "$id" --tail 3000 >"$f" || true
  python3 scripts/vast/vastctl.py logs "$id" --tail 3000 --daemon-logs >"$df" || true
  echo "-- matches (container logs)"
  rg -n "$pat" "$f" | tail -n 40 || true
  echo "-- matches (daemon logs)"
  rg -n "$pat" "$df" | tail -n 40 || true
  echo
done
SH
bash /tmp/vast_inspect.sh


Output:
Exit code: 124
Wall time: 10 seconds
Output:
command timed out after 10005 milliseconds
==== 29777152 ====
-- matches (container logs)
340:Traceback (most recent call last):
418:Traceback (most recent call last):
709:Traceback (most recent call last):
1190:Traceback (most recent call last):
1220:Traceback (most recent call last):
1552:Traceback (most recent call last):
1736:Traceback (most recent call last):
2052:Traceback (most recent call last):
2152:Traceback (most recent call last):
2189:Traceback (most recent call last):
2854:06:31:55 | Worker-3     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2855:Traceback (most recent call last):
2864:06:31:58 | Worker-1     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2865:Traceback (most recent call last):
2876:06:32:05 | Worker-6     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2877:Traceback (most recent call last):
2908:06:32:38 | Worker-5     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2909:Traceback (most recent call last):
2936:06:32:58 | Worker-4     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2937:Traceback (most recent call last):
2942:06:32:58 | Worker-7     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2943:Traceback (most recent call last):
2955:06:33:10 | Worker-2     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2956:Traceback (most recent call last):
2968:06:33:35 | Worker-0     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
2969:Traceback (most recent call last):
-- matches (daemon logs)
205:#7 2.648 After this operation, 87.1 MB of additional disk space will be used.

==== 29777151 ====


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 176
Output:
  2820	06:31:31 | MassiveCompute | INFO     | [6113] OK: tcCFHVGavtc (137.2s on GPU 2)
  2821	[GPU3] 7/10 MusicDetecti   5.9s
  2822	[GPU3] 8/10 Finalize       0.1s
  2823	[GPU3] 9/10 Output         0.0s
  2824	06:31:31 | Worker-2     | INFO     | Processing: ePVP4hoCT7M - A painful love story of 3000 km from PunjabA Hindu
  2825	[GPU2] PICK   ePVP4hoCT7M
  2826	[GPU6] 4/10 Diarization+  28.7s  | 205 segments
  2827	[GPU1] 4/10 Diarization+  10.4s  | 178 segments
  2828	[GPU5] 1/10 Download      12.6s
  2829	[GPU6] 5/10 Embeddings     1.6s  | 1867 chunks
  2830	[GPU6] 6/10 Clustering     0.2s
  2831	[GPU6] 6/10 MicroContami   0.0s
  2832	[GPU1] 5/10 Embeddings     0.5s  | 665 chunks
  2833	[GPU1] 6/10 Clustering     0.1s
  2834	[GPU1] 6/10 MicroContami   0.0s
  2835	[GPU1] 7/10 MusicDetecti   1.6s
  2836	[GPU1] 8/10 Finalize       0.0s
  2837	[GPU1] 9/10 Output         0.0s
  2838	[GPU6] 7/10 MusicDetecti   4.2s
  2839	[GPU6] 8/10 Finalize       0.0s
  2840	[GPU6] 9/10 Output         0.0s
  2841	[GPU7] 10/10 Export        19.3s  | 308 segs
  2842	[GPU7] DONE   -j6DVn-bOyE 170.9s | 72.8min | 3 spk | 71%
  2843	06:31:46 | Worker-7     | INFO     | Completed: -j6DVn-bOyE | 171.3s | 3 speakers | 70.7% usable
  2844	06:31:46 | MassiveCompute | INFO     | [6114] OK: -j6DVn-bOyE (171.3s on GPU 7)
  2845	06:31:47 | Worker-7     | INFO     | Processing: Of0MBgI9IRQ - நான் நினைச்சிருந்தா 1985ல MLA ஆகியிருப்பேன்..என்னோ
  2846	[GPU7] PICK   Of0MBgI9IRQ
  2847	[GPU2] 1/10 Download      16.3s
  2848	[GPU4] 2/10 VAD           58.6s  | 780 segments
  2849	[GPU4] 3/10 Chunking       0.0s  | 13 chunks
  2850	[GPU3] 10/10 Export        22.4s  | 520 segs
  2851	[GPU3] DONE   qxZDrK4-4kk 172.0s | 72.7min | 6 spk | 72%
  2852	06:31:54 | Worker-3     | INFO     | Completed: qxZDrK4-4kk | 172.5s | 6 speakers | 72.0% usable
  2853	06:31:54 | MassiveCompute | INFO     | [6115] OK: qxZDrK4-4kk (172.5s on GPU 3)
  2854	06:31:55 | Worker-3     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
  2855	Traceback (most recent call last):
  2856	  File "/workspace/maya3_data/massive_process.py", line 603, in gpu_worker_supabase
  2857	    worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
  2858	                                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
  2859	TypeError: 'NoneType' object is not subscriptable
  2860	[GPU1] 10/10 Export        13.4s  | 110 segs
  2861	[GPU1] DONE   4Iv0x0qSMsY 56.9s | 20.1min | 3 spk | 65%
  2862	06:31:57 | Worker-1     | INFO     | Completed: 4Iv0x0qSMsY | 57.3s | 3 speakers | 65.4% usable
  2863	06:31:57 | MassiveCompute | INFO     | [6116] OK: 4Iv0x0qSMsY (57.3s on GPU 1)
  2864	06:31:58 | Worker-1     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
  2865	Traceback (most recent call last):
  2866	  File "/workspace/maya3_data/massive_process.py", line 603, in gpu_worker_supabase
  2867	    worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
  2868	                                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
  2869	TypeError: 'NoneType' object is not subscriptable
  2870	[GPU7] 1/10 Download      11.1s
  2871	[GPU6] 10/10 Export        18.2s  | 119 segs
  2872	[GPU6] DONE   s0dS40pL2yA 137.9s | 58.2min | 3 spk | 81%
  2873	06:32:04 | Worker-6     | INFO     | Completed: s0dS40pL2yA | 138.3s | 3 speakers | 81.3% usable
  2874	06:32:04 | MassiveCompute | INFO     | [6117] OK: s0dS40pL2yA (138.3s on GPU 6)
  2875	[GPU5] 2/10 VAD           24.5s  | 250 segments
  2876	06:32:05 | Worker-6     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
  2877	Traceback (most recent call last):
  2878	  File "/workspace/maya3_data/massive_process.py", line 603, in gpu_worker_supabase
  2879	    worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
  2880	                                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
  2881	TypeError: 'NoneType' object is not subscriptable
  2882	[GPU5] 3/10 Chunking       0.0s  | 6 chunks
  2883	[GPU5] 4/10 Diarization+  13.4s  | 309 segments
  2884	[GPU0] 2/10 VAD           73.4s  | 1083 segments
  2885	[GPU0] 3/10 Chunking       0.0s  | 16 chunks
  2886	[GPU4] 4/10 Diarization+  30.7s  | 1403 segments
  2887	[GPU5] 5/10 Embeddings     1.1s  | 843 chunks
  2888	[GPU5] 6/10 Clustering     0.1s
  2889	[GPU5] 6/10 MicroContami   0.0s
  2890	[GPU2] 2/10 VAD           35.6s  | 148 segments
  2891	[GPU2] 3/10 Chunking       0.0s  | 8 chunks
  2892	[GPU7] 2/10 VAD           25.2s  | 459 segments
  2893	[GPU7] 3/10 Chunking       0.0s  | 6 chunks
  2894	[GPU5] 7/10 MusicDetecti   2.1s
  2895	[GPU5] 8/10 Finalize       0.0s
  2896	[GPU5] 9/10 Output         0.0s
  2897	[GPU4] 5/10 Embeddings     1.6s  | 1847 chunks
  2898	[GPU4] 6/10 Clustering     0.6s
  2899	[GPU4] 6/10 MicroContami   0.0s
  2900	[GPU4] 7/10 Mus…1816 chars truncated…ted: zCrix-3IDy8 | 155.9s | 2 speakers | 70.6% usable
  2933	06:32:57 | MassiveCompute | INFO     | [6119] OK: zCrix-3IDy8 (155.9s on GPU 4)
  2934	06:32:58 | Worker-7     | INFO     | Completed: Of0MBgI9IRQ | 70.3s | 6 speakers | 64.8% usable
  2935	06:32:58 | MassiveCompute | INFO     | [6120] OK: Of0MBgI9IRQ (70.3s on GPU 7)
  2936	06:32:58 | Worker-4     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
  2937	Traceback (most recent call last):
  2938	  File "/workspace/maya3_data/massive_process.py", line 603, in gpu_worker_supabase
  2939	    worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
  2940	                                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
  2941	TypeError: 'NoneType' object is not subscriptable
  2942	06:32:58 | Worker-7     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
  2943	Traceback (most recent call last):
  2944	  File "/workspace/maya3_data/massive_process.py", line 603, in gpu_worker_supabase
  2945	    worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
  2946	                                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
  2947	TypeError: 'NoneType' object is not subscriptable
  2948	[GPU0] 5/10 Embeddings     3.2s  | 2029 chunks
  2949	[GPU0] 6/10 Clustering     0.6s
  2950	[GPU0] 6/10 MicroContami   0.0s
  2951	[GPU2] 10/10 Export        18.6s  | 203 segs
  2952	[GPU2] DONE   ePVP4hoCT7M 96.5s | 37.1min | 4 spk | 64%
  2953	06:33:09 | Worker-2     | INFO     | Completed: ePVP4hoCT7M | 97.1s | 4 speakers | 64.3% usable
  2954	06:33:09 | MassiveCompute | INFO     | [6121] OK: ePVP4hoCT7M (97.1s on GPU 2)
  2955	06:33:10 | Worker-2     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
  2956	Traceback (most recent call last):
  2957	  File "/workspace/maya3_data/massive_process.py", line 603, in gpu_worker_supabase
  2958	    worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
  2959	                                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
  2960	TypeError: 'NoneType' object is not subscriptable
  2961	[GPU0] 7/10 MusicDetecti   5.7s
  2962	[GPU0] 8/10 Finalize       0.1s
  2963	[GPU0] 9/10 Output         0.0s
  2964	[GPU0] 10/10 Export        22.9s  | 389 segs
  2965	[GPU0] DONE   EVcNu7tvgOY 179.7s | 76.0min | 6 spk | 24%
  2966	06:33:35 | Worker-0     | INFO     | Completed: EVcNu7tvgOY | 180.1s | 6 speakers | 24.5% usable
  2967	06:33:35 | MassiveCompute | INFO     | [6122] OK: EVcNu7tvgOY (180.1s on GPU 0)
  2968	06:33:35 | Worker-0     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
  2969	Traceback (most recent call last):
  2970	  File "/workspace/maya3_data/massive_process.py", line 603, in gpu_worker_supabase
  2971	    worker_logger.info(f"Processing: {video_id} - {full_video.get('title', '')[:50]}")
  2972	                                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^
  2973	TypeError: 'NoneType' object is not subscriptable
  2974	06:34:07 | MassiveCompute | INFO     | ======================================================================
  2975	06:34:07 | MassiveCompute | INFO     | SUPABASE QUEUE COMPLETE
  2976	06:34:07 | MassiveCompute | INFO     | ======================================================================
  2977	06:34:07 | MassiveCompute | INFO     | Processed: 6122 (5835 OK, 287 failed)
  2978	06:34:07 | MassiveCompute | INFO     | Wall time: 839.3 min
  2979	06:34:07 | MassiveCompute | INFO     | Audio hours: 2590.7h
  2980	06:34:07 | MassiveCompute | INFO     | ----------------------------------------------------------------------
  2981	06:34:07 | MassiveCompute | INFO     | Queue Status:
  2982	06:34:07 | MassiveCompute | INFO     |    CLAIMED: 584
  2983	06:34:07 | MassiveCompute | INFO     |    COMPLETED: 147,098
  2984	06:34:07 | MassiveCompute | INFO     |    FAILED: 25,006
  2985	06:34:07 | MassiveCompute | INFO     |    PENDING: 62,859
  2986	06:34:07 | MassiveCompute | INFO     |    SKIPPED: 0
  2987	06:34:07 | MassiveCompute | INFO     | ======================================================================
  2988	06:34:07 | MassiveCompute | WARNING  |
  2989	Failed videos:
  2990	06:34:07 | MassiveCompute | WARNING  |    hPKD3KgODpY: R2 source not found: s3://test/podcasts/hPKD3KgODpY.webm
  2991	06:34:07 | MassiveCompute | WARNING  |    zpqLzbYhIso: R2 source not found: s3://test/podcasts/zpqLzbYhIso.webm
  2992	06:34:07 | MassiveCompute | WARNING  |    Kc8xvxQFa94: R2 source not found: s3://test/podcasts/Kc8xvxQFa94.webm
  2993	06:34:07 | MassiveCompute | WARNING  |    PDEXK4N0vb4: R2 source not found: s3://test/podcasts/PDEXK4N0vb4.webm
  2994	06:34:07 | MassiveCompute | WARNING  |    EpuJI6dG4eY: R2 source not found: s3://test/podcasts/EpuJI6dG4eY.webm
  2995	06:34:07 | MassiveCompute | WARNING  |    LEYXUiPu8k8: R2 source not found: s3://test/podcasts/LEYXUiPu8k8.webm


**Identifying a crash issue**

I've found the error in the code! It seems to be at line 603: the slicing `[:50]` is trying to operate on a string, but `full_video.get('title', '')` can return None. If the title is null, it crashes the worker process entirely. This explains why many workers are offline, as they stop when hitting a row with a null title. It could also affect the analytics if the crash occurs before shutting down properly. So, I’ll need to address this NullType issue.


**Investigating worker issues**

It seems like the worker process might not be running a proper shutdown. It could still be stuck at the "processing Download" stage, causing stale statuses. The root cause of the "zombied/stalled" state appears to be a TypeError from a null title, causing workers to exit abruptly. This leaves claimed videos stuck until the reclaim service runs. We saw the "SUPABASE QUEUE COMPLETE" log indicating all workers died, leading to an active worker drop to zero. I need to debug why some workers stopped while others may still be processing!


**Checking for fatal errors**

I suspect there could be other fatal errors in the instances. We need to connect to the vast AI instances to investigate why they’ve stopped. Using SSH to inspect the container state can help—I'll check if the maya process is running. We also need to review the logs to see if the main process has exited. Even if the instance appears running, the container might have exited. I’ll fetch the daemon logs, adjust my timeout settings, and examine other instances for consistency in errors. It's crucial to confirm if the issues are replicated elsewhere!


**Fetching error logs**

I need to fetch the logs for the other instance, specifically looking for entries around "Worker failed." I'll use the command `python3 scripts/vast/vastctl.py logs 29777151 --tail 3000 --filter "Worker failed"` to get relevant details. To speed things up, I’ll run this in parallel for multiple instances. This way, I can efficiently gather all the necessary information to identify any recurring issues that might be causing the worker failures.


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py logs 29777151 --tail 4000 --filter "Worker failed" | tail -n 50


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py logs 29777138 --tail 4000 --filter "Worker failed" | tail -n 50


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py logs 29761613 --tail 4000 --filter "Worker failed" | tail -n 50


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py logs 29760494 --tail 4000 --filter "Worker failed" | tail -n 50


Output:
Exit code: 0
Wall time: 3.7 seconds
Output:
06:32:03 | Worker-2     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:14 | Worker-6     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:30 | Worker-7     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:35 | Worker-1     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:46 | Worker-0     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:04 | Worker-5     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:10 | Worker-3     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:34 | Worker-4     | ERROR    | Worker failed: 'NoneType' object is not subscriptable


Output:
Exit code: 0
Wall time: 2.2 seconds
Output:
06:32:12 | Worker-6     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:12 | Worker-5     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:21 | Worker-3     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:22 | Worker-1     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:46 | Worker-4     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:03 | Worker-0     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:14 | Worker-7     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:28 | Worker-2     | ERROR    | Worker failed: 'NoneType' object is not subscriptable


Output:
Exit code: 0
Wall time: 3.8 seconds
Output:
06:31:57 | Worker-5     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:28 | Worker-2     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:42 | Worker-4     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:14 | Worker-6     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:20 | Worker-3     | ERROR    | Worker failed: 'NoneType' object is not subscriptable


Output:
Exit code: 0
Wall time: 3.7 seconds
Output:
06:31:56 | Worker-7     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:31:57 | Worker-4     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:04 | Worker-5     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:06 | Worker-2     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:14 | Worker-1     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:18 | Worker-0     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:28 | Worker-3     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:35 | Worker-6     | ERROR    | Worker failed: 'NoneType' object is not subscriptable


Summary: auto


**Debugging Instance Crashes**

I've noticed many instances had the same fatal bug causing worker processes to crash. The user has asked me to connect to these instances to understand why they stopped. It seems the issue stems from an unhandled TypeError when the title is None. There's also a second issue with the reclaim service. Although the user suggested SSH for connection, I think I can clarify things using the logs first. I might still attempt to SSH into an instance to confirm the processes aren't running.


**Preparing for SSH Connection**

It looks like I need to connect via SSH on port 35714 to the instance at ssh5.vast.ai. To establish this connection, I would use the command `ssh root@ssh5.vast.ai -p 35714`. However, I need to confirm the port first. The information might be in the /instances/ output where I can possibly find "ssh_port." Since `vastctl` only prints the ssh_host, I'll call the API to fetch the instances and specifically request both ssh_host and ssh_port. Time to run a Python script for that!


python3 - <<'PY'
import json, os
from urllib.request import Request, urlopen

api_key=os.environ.get('VAST_AI') or open('.env','r',encoding='utf-8').read().split('VAST_AI=')[1].splitlines()[0].strip()

req=Request('https://console.vast.ai/api/v0/instances/', headers={'Authorization': f'Bearer {api_key}'}, method='GET')
with urlopen(req, timeout=60) as r:
    data=json.loads(r.read().decode('utf-8'))
rows=data.get('instances',[])
need={29777152,29777151,29777138,29761613,29760494,29774952}
for r in rows:
    iid=r.get('id')
    if iid in need:
        print(iid, 'status=', r.get('actual_status') or r.get('status'), 'host=', r.get('ssh_host'), 'port=', r.get('ssh_port'), 'direct=', r.get('direct_port_start') or r.get('direct_port'))
PY


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
29760494 status= running host= ssh4.vast.ai port= 10494 direct= 50038
29761613 status= running host= ssh9.vast.ai port= 11612 direct= 40232
29774952 status= running host= ssh5.vast.ai port= 14952 direct= 35737
29777138 status= running host= ssh2.vast.ai port= 17138 direct= 36170
29777151 status= running host= ssh3.vast.ai port= 17150 direct= 41749
29777152 status= running host= ssh6.vast.ai port= 17152 direct= 18533


**Searching for SSH key**

I want to start by checking the ports. Next, I'll look for the SSH key in the `/home/ubuntu/.ssh` directory. I’ll use the command `ls -la` to list the files and see if I can find `id_ed25519` or anything similar. It’s a straightforward process, and I just want to make sure I’m thorough in checking all relevant files for the key. Let's see what I can find!


ls -la ~/.ssh || true; ls -la .ssh || true; find . -maxdepth 3 -type f \( -name "*.pem" -o -name "id_*" -o -name "*.key" \) -print


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 44
drwx------  2 ubuntu ubuntu 4096 Jan  8 23:44 .
drwxr-x--- 40 ubuntu ubuntu 4096 Jan  9 06:49 ..
-rw-rw-r--  1 ubuntu ubuntu  725 Dec 10 05:58 authorized_keys
-rw-------  1 ubuntu ubuntu  419 Dec 31 08:39 id_ed25519
-rw-r--r--  1 ubuntu ubuntu  105 Dec 31 08:39 id_ed25519.pub
-rw-------  1 ubuntu ubuntu 9730 Jan  8 23:44 known_hosts
-rw-------  1 ubuntu ubuntu 8894 Jan  8 23:44 known_hosts.old
ls: cannot access '.ssh': No such file or directory


Output: Plan updated


ssh -i ~/.ssh/id_ed25519 -o StrictHostKeyChecking=no -o ConnectTimeout=10 -p 17152 root@ssh6.vast.ai 'hostname; date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python|uvicorn|gunicorn" | head -n 50; echo "---"; ps -eo pid,ppid,cmd --sort=pid | head -n 20; echo "---"; ls -la /workspace/maya3_data 2>/dev/null | head; echo "---"; test -f /tmp/maya3.log && tail -n 40 /tmp/maya3.log || echo "no /tmp/maya3.log"'


Output:
Exit code: 0
Wall time: 3.2 seconds
Output:
Warning: Permanently added '[ssh6.vast.ai]:17152' (ED25519) to the list of known hosts.
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
aedbb8f7c9a9
Fri Jan  9 06:52:43 UTC 2026
2735167 2735156 bash -c hostname; date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python|uvicorn|gunicorn" | head -n 50; echo "---"; ps -eo pid,ppid,cmd --sort=pid | head -n 20; echo "---"; ls -la /workspace/maya3_data 2>/dev/null | head; echo "---"; test -f /tmp/maya3.log && tail -n 40 /tmp/maya3.log || echo "no /tmp/maya3.log"
2735171 2735167 grep -E massive_process|python|uvicorn|gunicorn
---
    PID    PPID CMD
      1       0 /bin/sh -c while [ ! -e /.launch ]; do sleep 1; done; bash /.launch
    112       1 bash /.launch
    501       1 sshd: /usr/sbin/sshd -E /proc/1/fd/1 [listener] 0 of 10-100 startups
    507     112 ssh -i /etc/forward_key -R *:17152:localhost:22 -R *:17153:localhost:8080 -N -o TCPKeepAlive=yes -o ServerAliveInterval=10 -o ServerAliveCountMax=2 ubuntu@ssh6.vast.ai -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no -o ConnectTimeout=10 -o RequestTTY=no -o ExitOnForwardFailure=yes
2735156     501 sshd: root@notty
2735167 2735156 bash -c hostname; date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python|uvicorn|gunicorn" | head -n 50; echo "---"; ps -eo pid,ppid,cmd --sort=pid | head -n 20; echo "---"; ls -la /workspace/maya3_data 2>/dev/null | head; echo "---"; test -f /tmp/maya3.log && tail -n 40 /tmp/maya3.log || echo "no /tmp/maya3.log"
2735173 2735167 ps -eo pid,ppid,cmd --sort=pid
2735174 2735167 head -n 20
---
total 600
drwxr-xr-x 1 root root     88 Jan  8 16:34 .
drwxr-xr-x 1 root root     24 Jan  8 02:16 ..
drwxrwxr-x 3 root root     32 Jan  8 00:45 .cache
-rw-rw-r-- 1 root root    213 Jan  6 22:23 .dockerignore
-rw-rw-r-- 1 root root    470 Jan  6 22:23 .env.example
-rw-rw-r-- 1 root root    568 Jan  8 04:16 .gitignore
drwxr-xr-x 1 root root     17 Jan  8 02:18 .venv
drwxrwxr-x 2 root root     56 Jan  8 04:16 .vercel
drwxrwxr-x 2 root root      6 Jan  6 22:31 .xdg_run
---
no /tmp/maya3.log


**Investigating process issues**

I'm looking at the current instance where there's no Python worker process running, just sshd and /.launch. It seems like the container is idle with the workers stopped, which fits with the bug report. To dig deeper, I need to check the logs to see why the process stopped. It looks like the workers crashed due to a NoneType title before the main run exited. I'll check the output directory and see if there's any log file left behind.


**Checking machine statuses**

It looks like many machines are marked as stale with outputs showing 8 out of 8, all offline because their processes ended. Some are stuck in a processing state for downloads, possibly due to hanging or crashing caused by a null title or VAD issues. I'm planning to check one currently running instance, specifically instance 29774952, to see if it has an active process. I can SSH into it and take a quick look.


ssh -i ~/.ssh/id_ed25519 -o StrictHostKeyChecking=no -o ConnectTimeout=10 -p 14952 root@ssh5.vast.ai 'date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python" | head -n 20; echo "---"; tail -n 30 /proc/1/fd/1 | sed -n "1,30p"'


Output:
Exit code: 124
Wall time: 30 seconds
Output:
command timed out after 30005 milliseconds
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@    WARNING: REMOTE HOST IDENTIFICATION HAS CHANGED!     @
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
IT IS POSSIBLE THAT SOMEONE IS DOING SOMETHING NASTY!
Someone could be eavesdropping on you right now (man-in-the-middle attack)!
It is also possible that a host key has just been changed.
The fingerprint for the ED25519 key sent by the remote host is
SHA256:hLjFeVhHhVMBYXK9gz0k+ibv6gFw6WDW/zaHz+HRgLM.
Please contact your system administrator.
Add correct host key in /home/ubuntu/.ssh/known_hosts to get rid of this message.
Offending ECDSA key in /home/ubuntu/.ssh/known_hosts:14
  remove with:
  ssh-keygen -f "/home/ubuntu/.ssh/known_hosts" -R "[ssh5.vast.ai]:14952"
Password authentication is disabled to avoid man-in-the-middle attacks.
Keyboard-interactive authentication is disabled to avoid man-in-the-middle attacks.
UpdateHostkeys is disabled because the host key is not trusted.
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
Fri Jan  9 06:52:56 UTC 2026
1930175 1930164 bash -c date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python" | head -n 20; echo "---"; tail -n 30 /proc/1/fd/1 | sed -n "1,30p"
1930178 1930175 grep -E massive_process|python
---


**Analyzing SSH connection issues**

I see the SSH output is indicating that the host identification has changed, but I'm still connected. The command is timing out possibly due to tailing /proc/1/fd/1, which might be problematic. Instead, I'll check for existing Python processes with `ps`, but it didn't show any (only grep results). It seems like the command is getting stuck. I'll also consider fixing the known_hosts warning, but it might not be necessary if I'm using StrictHostKeyChecking=no. Let's focus on rerunning the SSH command with adjustments.


ssh -i ~/.ssh/id_ed25519 -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no -o ConnectTimeout=10 -p 14952 root@ssh5.vast.ai 'date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python" | head -n 50; echo "---"; ls -la /workspace/maya3_data/output 2>/dev/null | head -n 20 || true'


Output:
Exit code: 0
Wall time: 1.8 seconds
Output:
Warning: Permanently added '[ssh5.vast.ai]:14952' (ED25519) to the list of known hosts.
Welcome to vast.ai. If authentication fails, try again after a few seconds, and double check your ssh key.
Have fun!
Fri Jan  9 06:53:35 UTC 2026
1930175       1 bash -c date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python" | head -n 20; echo "---"; tail -n 30 /proc/1/fd/1 | sed -n "1,30p"
1930193 1930182 bash -c date -u; ps -eo pid,ppid,cmd --sort=pid | egrep "massive_process|python" | head -n 50; echo "---"; ls -la /workspace/maya3_data/output 2>/dev/null | head -n 20 || true
1930196 1930193 grep -E massive_process|python
---
total 264
drwxr-xr-x 3 root root     55 Jan  9 06:04 -04FZ8g74sY
drwxr-xr-x 3 root root     55 Jan  9 01:47 -11PSzwUlGs
drwxr-xr-x 3 root root     55 Jan  8 20:53 -1C-tT_4qo0
drwxr-xr-x 3 root root     55 Jan  8 20:02 -1RrB0PJC7A
drwxr-xr-x 3 root root     55 Jan  8 21:27 -1d48-LuZ-A
drwxr-xr-x 3 root root     55 Jan  9 02:18 -3S7C06bTnI
drwxr-xr-x 3 root root     43 Jan  8 19:41 -497SUhprRI
drwxr-xr-x 3 root root     55 Jan  8 21:09 -52jGoZkbeY
drwxr-xr-x 3 root root     55 Jan  9 03:57 -5jj-ZBMBqE
drwxr-xr-x 3 root root     55 Jan  8 19:30 -5r0C_rRtKA
drwxr-xr-x 3 root root     55 Jan  8 18:38 -61RoK2zHuM
drwxr-xr-x 3 root root     55 Jan  9 02:57 -6LZbrCI8t0
drwxr-xr-x 3 root root     55 Jan  9 04:50 -6Ntp9EqyT4
drwxr-xr-x 3 root root     55 Jan  8 22:18 -6aRMOmQwOo
drwxr-xr-x 3 root root     55 Jan  9 03:28 -7BjFbRDiuk
drwxr-xr-x 3 root root     55 Jan  8 17:08 -7oON7DL6-8
drwxr-xr-x 3 root root     55 Jan  9 05:11 -9IsdDoPpT8
drwxr-xr-x 3 root root     55 Jan  8 20:40 -A4EmYQzTkI
drwxr-xr-x 3 root root     55 Jan  9 04:12 -A9X_uM0nl0


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py logs 29774952 --tail 5000 --filter "Worker failed" | tail -n 50


Output:
Exit code: 0
Wall time: 2.1 seconds
Output:
06:31:56 | Worker-2     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:31:57 | Worker-7     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:11 | Worker-0     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:16 | Worker-3     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:31 | Worker-1     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:32:34 | Worker-5     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:33:12 | Worker-6     | ERROR    | Worker failed: 'NoneType' object is not subscriptable
06:35:38 | Worker-4     | ERROR    | Worker failed: 'NoneType' object is not subscriptable


**Investigating instance issues**

I see that 29774952 has a bug, with many instances ending around 06:34. However, Vast still shows them running while the pipeline processes have ended. That might explain the user's concern. Additionally, I notice that some instances are rebooting, so I should check 29760732 to see if it’s rebooting due to health reasons. I’ll fetch its logs and filter for OOM errors and other related issues.


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py logs 29760732 --tail 4000 --filter "Worker failed" | tail -n 50


VAST_AI="$(sed -n 's/^VAST_AI=//p' .env | tail -n 1)" python3 scripts/vast/vastctl.py logs 29760732 --tail 4000 --daemon-logs | rg -n "(reboot|restart|error|OOM|Killed|nvidia|exit|No space)" | tail -n 80


Output:
Wall time: 1.4 seconds
aborted by user


Output:
Wall time: 0.9 seconds
aborted by user