Output:
Exit code: 0
Wall time: 0 seconds
Output:
    elif '/shorts/' in video_url:
        return video_url.split('/shorts/')[1].split('?')[0]
    # R2/S3 presigned URL format: extract from path before extension
    # e.g., https://xxx.r2.cloudflarestorage.com/bucket/podcasts/---g_VL7ySo.webm?X-Amz-...
    elif 'r2.cloudflarestorage.com' in video_url or 's3.amazonaws.com' in video_url:
        # Extract path from URL (before query string)
        path = video_url.split('?')[0]
        # Get the filename
        filename = path.split('/')[-1]
        # Remove extension
        video_id = filename.rsplit('.', 1)[0] if '.' in filename else filename
        return video_id
    else:
        return hashlib.md5(video_url.encode()).hexdigest()[:11]

# Per-process cached R2 client for source/original videos (bucket: "test").
# We keep this out of module imports to avoid paying boto3 init cost for
# non-R2 workflows.
_R2_SOURCE_CLIENT = None

def _get_r2_source_client(config) -> "R2Client":
    """Lazy-init the R2 client used for reading source videos (bucket_type='source')."""
    global _R2_SOURCE_CLIENT
    if _R2_SOURCE_CLIENT is None:
        from src.r2_client import R2Client, get_r2_config_from_env, R2Config

        base = get_r2_config_from_env()

        # Avoid accidental prefixing from generic R2_PREFIX when accessing the source bucket.
        # Use R2_SOURCE_PREFIX if provided; otherwise default to empty.
        source_prefix = os.environ.get('R2_SOURCE_PREFIX', '')

        cfg = R2Config(
            endpoint_url=base.endpoint_url,
            access_key_id=base.access_key_id,
            secret_access_key=base.secret_access_key,
            bucket=base.bucket,  # overridden by bucket_type='source'
            prefix=source_prefix,
        )
        _R2_SOURCE_CLIENT = R2Client(config=cfg, bucket_type='source')
    return _R2_SOURCE_CLIENT

def _iter_r2_source_prefixes(config) -> List[str]:
    """
    Build a small set of candidate prefixes for source objects without listing the bucket.
    """
    prefixes: List[str] = []

    # Highest priority: explicit env/config.
    env_prefixes = os.environ.get('R2_SOURCE_PREFIXES', '').strip()
    if env_prefixes:
        prefixes.extend([p.strip() for p in env_prefixes.split(',') if p.strip()])

    cfg_prefix = getattr(config, 'r2_source_prefix', '') or ''
    if cfg_prefix:
        prefixes.append(cfg_prefix)

    # Common defaults seen in earlier code/comments.
    prefixes.extend(['', 'podcasts', 'podcasts/', 'videos', 'videos/'])

    # Normalize to "dir/" or "" and dedupe while preserving order.
    normalized: List[str] = []
    seen = set()
    for p in prefixes:
        p = p.strip()
        if not p:
            p_norm = ''
        else:
            p_norm = p if p.endswith('/') else (p + '/')
        if p_norm not in seen:
            seen.add(p_norm)
            normalized.append(p_norm)
    return normalized

def _iter_r2_source_extensions(config) -> List[str]:
    """Return candidate extensions to try for source objects (small list, no listing)."""
    exts: List[str] = []

    env_exts = os.environ.get('R2_SOURCE_EXTENSIONS', '').strip()
    if env_exts:
        exts.extend([e.strip() for e in env_exts.split(',') if e.strip()])

    cfg_exts = getattr(config, 'r2_source_extensions', None)
    if cfg_exts:
        if isinstance(cfg_exts, str):
            exts.extend([e.strip() for e in cfg_exts.split(',') if e.strip()])
        elif isinstance(cfg_exts, (list, tuple)):
            exts.extend([str(e).strip() for e in cfg_exts if str(e).strip()])

    # Reasonable defaults for YouTube-origin files.
    exts.extend(['', '.webm', '.mp4', '.mkv', '.m4a', '.mp3', '.opus'])

    # Normalize: ensure extensions start with '.' (except empty).
    normalized: List[str] = []
    seen = set()
    for e in exts:
        e = e.strip()
        if not e:
            e_norm = ''
        else:
            e_norm = e if e.startswith('.') else ('.' + e)
        if e_norm not in seen:
            seen.add(e_norm)
            normalized.append(e_norm)
    return normalized

def _resolve_r2_source_object(video_id: str, config) -> Tuple[str, Dict[str, Any]]:
    """
    Resolve a video_id to an object key in the R2 source bucket.

    Simplified: All source videos are at podcasts/{video_id}.webm in the 'test' bucket.

    Returns:
        (remote_key, head_object_response)
    """
    client = _get_r2_source_client(config)

    # Simplified: all videos are at podcasts/{video_id}.webm
    key = f"podcasts/{video_id}.webm"

    # Direct HEAD request (no prefix from client)
    try:
        head = client.client.head_object(Bucket=client.bucket, Key=key)
        if head:
            return key, head
    except Exception:
        pass

    raise DownloadError(
        f"R2 source not found: s3://{client.bucket}/{key}"
    )

def is_r2_url(url: str) -> bool:
    """Check if URL is an R2/S3 presigned URL."""
    return 'r2.cloudflarestorage.com' in url or ('s3.' in url and 'amazonaws.com' in url)

def extract_r2_path(presigned_url: str) -> str:
    """
    Extract clean R2 bucket/key path from a presigned URL.

    Example:
        Input: https://xxx.r2.cloudflarestorage.com/test/podcasts/VIDEO.webm?X-Amz-...
        Output: test/podcasts/VIDEO.webm
    """


Summary: auto


**Evaluating resolution errors**

The helper functions are there, but it seems they're simplified to a single key, which might lead to missing errors, especially for certain file types like .mp4. To avoid failures, I think we should restore robust resolution using those helper functions. This approach would involve trying multiple keys with different prefixes and extensions. Also, it looks like the current requests don't properly utilize the prefix, which could be why the resolution isn't working as expected. I'll need to consider that more carefully.


**Adjusting source resolution**

I think missing sources could be an issue if they're under certain prefixes. The list in _iter_r2_source_prefixes includes options like '', 'podcasts', and 'videos'. If R2_SOURCE_PREFIX is set, we definitely need to account for that too, so I’ll adjust _resolve_r2_source_object. I need to compile lists of prefixes and extensions, then construct keys accordingly. Using `client._full_key` for HEAD requests could work, but I might want to explore using R2Client methods directly, like checking for `exists`.


rg -n "def exists\(" -n src/r2_client.py && sed -n '240,340p' src/r2_client.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
240:    def exists(self, remote_key: str) -> bool:
    def exists(self, remote_key: str) -> bool:
        """Check if a key exists in the bucket."""
        try:
            full_key = self._full_key(remote_key)
            self.client.head_object(Bucket=self.bucket, Key=full_key)
            return True
        except:
            return False

    def delete(self, remote_key: str) -> bool:
        """Delete a key from the bucket."""
        try:
            # Safety guard: user explicitly forbids deletions from any bucket other
            # than 1-cleaned-data. Keep this invariant even if callers misuse API.
            if self.bucket != '1-cleaned-data':
                raise PermissionError(
                    f"Refusing to delete from bucket '{self.bucket}'. "
                    f"Only '1-cleaned-data' deletions are permitted."
                )
            full_key = self._full_key(remote_key)
            self.client.delete_object(Bucket=self.bucket, Key=full_key)
            logger.info(f"Deleted s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"Delete failed: {e}")
            return False

def copy_to_baseline(video_ids: List[str], source_type: str = 'production') -> int:
    """
    Copy video outputs from source bucket to baseline bucket.

    Args:
        video_ids: List of video IDs to copy
        source_type: Source bucket type ('production' or 'test')

    Returns:
        Number of successfully copied videos
    """
    source = R2Client(bucket_type=source_type)
    baseline = R2Client(bucket_type='baseline')

    copied = 0
    for video_id in video_ids:
        # Copy metadata.json
        metadata_key = f"{video_id}/metadata.json"
        data = source.download_json(metadata_key)
        if data and baseline.upload_json(data, metadata_key):
            copied += 1
            logger.info(f"Copied {video_id} to baseline")
        else:
            logger.warning(f"Failed to copy {video_id}")

    return copied

if __name__ == "__main__":
    # Quick test
    import sys

    print("R2 Client Test")
    print("-" * 40)

    client = R2Client(bucket_type='test')

    # List some keys
    print(f"Bucket: {client.bucket}")
    print(f"Prefix: {client.prefix}")

    keys = client.list_keys(limit=5)
    print(f"Sample keys: {keys[:5]}")


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                return output_file
        except Exception as e:
            logger.warning(f"Video download attempt {attempt+1} failed: {e}")
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)

    logger.error(f"❌ Video download failed after {max_retries} attempts")
    return None

def download_audio(
    video_url: str,
    config,
    validate: bool = True,
    max_retries: int = 3,
    supabase_metadata: Dict[str, Any] = None,
) -> Tuple[str, Dict[str, Any]]:
    """
    Download audio from YouTube URL or R2 presigned URL and prepare for processing.

    === v7.0 OPTIMIZATION: Download-Time Resample ===
    Uses ffmpeg directly for dual-output in single pass:
    - 16kHz mono WAV for processing pipeline
    - Original quality mono WAV for high-quality export

    Previous: yt-dlp → temp.wav → torchaudio load → Python resample → save both
    Now:      yt-dlp → single ffmpeg → dual output (NO Python resampling!)

    === v2: R2 URL SUPPORT ===
    When video_url is an R2 presigned URL, we use ffmpeg directly instead of yt-dlp.

    === v3: Supabase Metadata ===
    When supabase_metadata is provided (from distributed queue), use it for:
    - Title, chapters, duration (no yt-dlp metadata fetch needed)
    - Chapter-based intro/outro skip

    Args:
        video_url: YouTube URL or R2 presigned URL
        config: Pipeline configuration
        validate: Whether to validate video before download (default: True)
        max_retries: Number of download retries (default: 3)
        supabase_metadata: Pre-fetched metadata from Supabase (chapters, title, etc.)

    Returns:
        (audio_path, metadata): Path to processed audio and video metadata

    Raises:
        VideoValidationError: If video fails validation
        DownloadError: If download fails after retries
    """
    logger.info(f"📥 Downloading: {video_url[:80]}...")
    start = time.time()

    # Extract video ID
    video_id = extract_video_id(video_url)

    output_dir = Path(config.output_dir) / video_id
    output_dir.mkdir(parents=True, exist_ok=True)

    # === R2 SOURCE MODE (video_id -> R2 object) ===
    # For throughput benchmarking we want to avoid YouTube rate limits entirely.
    # When enabled, we resolve the video_id to an object in the R2 *source* bucket
    # (bucket_type='source' -> bucket name "test") using HEAD requests only.
    input_source = getattr(config, 'input_source', 'youtube')
    if input_source == 'r2' and not is_r2_url(video_url):
        client = _get_r2_source_client(config)
        remote_key, head = _resolve_r2_source_object(video_id, config)

        # Presigned URL lets ffmpeg stream directly (no local video download).
        ttl = int(getattr(config, 'r2_presign_ttl_sec', 3600))
        presigned_url = client.generate_presigned_get_url(remote_key, expires_in_sec=ttl)

        logger.info(
            f"📦 R2 source resolved: s3://{client.bucket}/{remote_key} "
            f"({head.get('ContentLength', 0) / (1024**2):.1f} MB)"
        )

        audio_path, metadata = _download_from_r2(
            r2_url=presigned_url,
            video_id=video_id,
            output_dir=output_dir,
            config=config,
            max_retries=max_retries,
            supabase_metadata=supabase_metadata,
        )

        elapsed = time.time() - start
        metadata['download_wall_time_sec'] = elapsed
        metadata['r2_source_bucket'] = client.bucket
        metadata['r2_source_key'] = remote_key
        metadata['r2_source_size_bytes'] = head.get('ContentLength')

        logger.info(f"✅ Download (R2): {elapsed:.1f}s | Duration: {metadata.get('processed_duration', 0):.0f}s")
        return audio_path, metadata

    # === v2: R2 URL HANDLING ===
    # R2 presigned URLs use ffmpeg directly (not yt-dlp)
    if is_r2_url(video_url):
        logger.info(f"📦 R2 source detected, using ffmpeg direct download")
        audio_path, metadata = _download_from_r2(
            r2_url=video_url,
            video_id=video_id,
            output_dir=output_dir,
            config=config,
            max_retries=max_retries,
            supabase_metadata=supabase_metadata,
        )
        elapsed = time.time() - start
        metadata['download_wall_time_sec'] = elapsed
        logger.info(f"✅ Download (R2 URL): {elapsed:.1f}s | Duration: {metadata.get('processed_duration', 0):.0f}s")
        return audio_path, metadata

    # === Standard YouTube path ===
    info = {}
    if validate:
        is_valid, message, info = validate_video(video_url)
        if not is_valid:
            raise VideoValidationError(f"Video validation failed: {message}")

    # Fetch metadata if not already fetched during validation
    if not info:
        logger.info("📋 Fetching video metadata...")
        ydl_info_opts = {
            'quiet': True,
            'no_warnings': True,
            'extract_flat': False,
        }

        try:
            with yt_dlp.YoutubeDL(ydl_info_opts) as ydl:
                info = ydl.extract_info(video_url, download=False)
        except Exception as e:
            logger.warning(f"Could not fetch metadata: {e}")
            info = {'title': 'Unknown', 'chapters': []}

    # Output file paths
    # v7.2: Original audio saved as FLAC (lossless, ~50% smaller than WAV)
    trimmed_file = output_dir / f"{video_id}_trimmed.wav"
    original_file = output_dir / f"{video_id}_original.flac"

    # Check cache - both files must exist for full cache hit
    preserve_original = getattr(config, 'preserve_original_audio', False)

    # Also check for legacy .wav original files (backward compatibility)
    legacy_original_file = output_dir / f"{video_id}_original.wav"


**Checking configuration settings**

I’m looking into how the `download_audio` function decides on the R2 source mode using `config.input_source`. First, I need to find out where `config.input_source` is set. It’s likely in the Config class, probably with a default value of 'r2'. Okay, so I should check the `src/config.py` file to confirm that and see what’s going on there. It’s important to clarify this before moving forward!


rg -n "input_source" -n src/config.py pipeline.py massive_process.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
massive_process.py:1243:        'input_source': 'r2',
src/config.py:38:    # bare 11-char video IDs and set input_source='r2' to stream originals from
src/config.py:40:    input_source: str = "youtube"  # 'youtube' | 'r2'
src/config.py:43:    # When input_source='r2', download.py will:


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Configuration for Fast Pipeline v6.0 - Adaptive Compute-Aware

Key improvements:
- Auto-detects system resources (nproc, vCPUs, GPU vRAM)
- Adapts worker counts and batch sizes to available compute
- Optimized for micro-level speaker detection (0.4s events)
"""

import os
from dataclasses import dataclass, field
from typing import Optional

@dataclass
class Config:
    """
    Pipeline configuration with adaptive compute settings.

    Flow (per instructions.md):
    1. Download (max parallel workers)
    2. Quick VAD (parallel, get speech outline)
    3. Chunk at silence boundaries (VAD-aware)
    4. OSD (mark overlaps as unusable FIRST)
    5. Frame-level segmentation (17ms resolution for micro-changes)
    6. Embeddings + Conservative clustering
    7. Output metadata JSON only
    """

    # === AUTHENTICATION ===
    # NOTE: Must be provided via environment variable HF_TOKEN
    # Never hard-code tokens in repo (HF gated model access + security).
    hf_token: str = os.environ.get('HF_TOKEN', '')

    # === INPUT SOURCE (YouTube vs R2) ===
    # Default behavior remains YouTube URLs. For benchmark/testing you can pass
    # bare 11-char video IDs and set input_source='r2' to stream originals from
    # the R2 source bucket (bucket name: "test") without YouTube rate limits.
    input_source: str = "youtube"  # 'youtube' | 'r2'

    # === R2 SOURCE (input) ===
    # When input_source='r2', download.py will:
    # - Resolve video_id -> object key via HEAD (NO bucket listing)
    # - Generate a presigned GET URL
    # - ffmpeg stream-extract audio
    #
    # Prefix/extension knobs are optional; env overrides exist too:
    # - R2_SOURCE_PREFIX / R2_SOURCE_PREFIXES
    # - R2_SOURCE_EXTENSIONS
    r2_source_prefix: str = ""          # e.g. "podcasts/" (optional)
    r2_source_extensions: str = ""      # e.g. ".webm,.mp4" (optional)
    r2_presign_ttl_sec: int = 3600      # Presigned URL TTL (seconds)

    # Avoid any YouTube metadata calls during R2 runs (rate-limit safe).
    # When False, R2 downloads fall back to duration-based dynamic intro skip.
    fetch_youtube_metadata_for_r2: bool = True

    # === R2 OUTPUT (upload processed artifacts) ===
    # When enabled, pipeline will package each video's output directory into a .tar
    # and upload it to the R2 production bucket (1-cleaned-data) by default.
    r2_upload_enabled: bool = False
    r2_upload_bucket_type: str = "production"  # maps to 1-cleaned-data in src/r2_client.py
    r2_upload_prefix: str = ""                 # e.g. "benchmarks/rtx4090/2026-01-03T16-00Z"
    r2_upload_skip_if_exists: bool = False     # avoid overwriting if True
    r2_upload_delete_local_tar: bool = True    # free local disk after upload
    r2_upload_delete_local_output_dir: bool = False  # delete per-video output dir after successful upload (prevents disk fill)

    # === AUDIO SEGMENTATION ===
    # segment_audio: Cut original FLAC into individual speaker segment files
    # Uses parallel ffmpeg (8 workers) - typically 2-3s for 500 segments
    segment_audio: bool = True

    # === BACKGROUND EXPORT ===
    # NOTE: Background export is experimental and not recommended for multiprocessing.
    # In GPU worker subprocesses, background threads die when the process exits.
    # The sync export (5-6s for 60min video) runs AFTER GPU work completes,
    # and overlaps with the NEXT video's download prefetch, so overhead is minimal.
    background_export: bool = False  # Keep False for stability
    background_export_workers: int = 4  # Number of parallel export workers (unused currently)

    # === AUDIO PROCESSING ===
    sample_rate: int = 16000
    intro_skip_seconds: float = 0.0
    outro_skip_seconds: float = 0.0

    # === INTRO SKIP (v6.8 - Dynamic) ===
    # auto_intro_skip: Automatically skip intro based on video duration
    # When True and no chapter-based intro detected:
    #   - Video ≤ 30 min: skip 180s (3 min)
    #   - Video > 30 min: skip 300s (5 min)
    # Set to False to disable automatic intro skipping
    # Manual intro_skip_seconds > 0 always takes priority
    auto_intro_skip: bool = True

    # === HIGH QUALITY AUDIO PRESERVATION (v6.8+) ===
    # preserve_original_audio: Download at highest quality, process at 16kHz
    # When True:
    #   - Downloads original audio format (bestaudio - typically 48kHz)
    #   - Creates 16kHz copy for processing (VAD, diarization, embeddings)
    #   - After processing, deletes 16kHz and keeps only original
    # Final export cuts from original high-quality audio using timestamps
    preserve_original_audio: bool = True
    original_audio_sample_rate: int = 0  # 0 = keep original (highest), or specify (e.g. 48000)

    # === PARALLELISM (Auto-detected, these are fallbacks) ===
    # Actual values computed by COMPUTE.get_optimal_config()
    vad_workers: int = 32          # Will be auto-tuned
    max_workers: int = 32          # Will be auto-tuned
    download_workers: int = 4      # Parallel downloads for batch
    chunk_workers: int = 4         # GPU diarization (VRAM limited)

    # === CHUNKING (for diarization input) ===
    chunk_duration: float = 300.0  # 5 min max chunks
    min_chunk_duration: float = 30.0

    # === VAD (Silero) - Tuned for 0.4s detection ===
    # Per instructions.md: min_duration_on = 0.2s to catch short events
    vad_threshold: float = 0.5
    vad_min_speech_ms: int = 200   # CHANGED: 250 -> 200 for 0.4s events


**Examining video processing requirements**

I see that in the massive_process main config, it sets the input source to 'r2', which means all processing needs to rely on R2 source mode without a YouTube fallback. If R2 is missing any videos, that's a failure point. We need to ensure the _resolve_r2_source_object method can check for varying prefixes and extensions to find the videos. The current check seems too limiting, so enhancing our search capability is crucial to minimizing failures as we aim for successful processing of the entire set.


**Implementing object resolution logic**

I want to ensure we can find objects even when they're stored under different extensions or prefixes. For instance, we could be looking for something like 'podcasts/9bZkp7q19f0.mp4' or similar. So, let's implement the function to resolve the R2 source object.

I'll loop through all prefixes and extensions to create candidate keys. I also need to be careful about the formatting, ensuring we handle trailing slashes properly. It’s crucial to normalize the output to avoid generating duplicates like 'podcasts//' when accessing objects. That's key to avoiding errors when trying to find the correct object!


**Clarifying key handling in downloads**

I've removed duplicates, and the extensions array includes options like '', '.webm', and '.mp4'. The empty string means we might want to try using the key as `podcasts/{video_id}` without an extension. It's important that we return the remote_key relative to the bucket root, without including the client.prefix in it. Then, in the download_audio function, the `client.generate_presigned_get_url(remote_key, ...)` method gets called. I'll check if this uses the _full_key method, which might automatically include the prefix. Let’s ensure that’s functioning as expected!


rg -n "generate_presigned_get_url" -n src/r2_client.py && sed -n '160,240p' src/r2_client.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
195:    def generate_presigned_get_url(self, remote_key: str, expires_in_sec: int = 3600) -> str:
        try:
            full_key = self._full_key(remote_key)
            body = json.dumps(data, indent=2).encode('utf-8')
            self.client.put_object(
                Bucket=self.bucket,
                Key=full_key,
                Body=body,
                ContentType='application/json'
            )
            logger.info(f"Uploaded JSON -> s3://{self.bucket}/{full_key}")
            return True
        except Exception as e:
            logger.error(f"JSON upload failed: {e}")
            return False

    def download_json(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Download JSON data from R2."""
        try:
            full_key = self._full_key(remote_key)
            response = self.client.get_object(Bucket=self.bucket, Key=full_key)
            data = json.loads(response['Body'].read().decode('utf-8'))
            logger.info(f"Downloaded JSON <- s3://{self.bucket}/{full_key}")
            return data
        except Exception as e:
            logger.error(f"JSON download failed: {e}")
            return None

    def head_object(self, remote_key: str) -> Optional[Dict[str, Any]]:
        """Return S3 head_object response for a key, or None if missing/unreadable."""
        try:
            full_key = self._full_key(remote_key)
            return self.client.head_object(Bucket=self.bucket, Key=full_key)
        except Exception:
            return None

    def generate_presigned_get_url(self, remote_key: str, expires_in_sec: int = 3600) -> str:
        """
        Generate a presigned GET URL for an object.

        Note: This is a Class B request pattern (no bucket listing).
        """
        full_key = self._full_key(remote_key)
        return self.client.generate_presigned_url(
            'get_object',
            Params={'Bucket': self.bucket, 'Key': full_key},
            ExpiresIn=expires_in_sec,
        )

    def list_keys(self, prefix: str = "", limit: int = 1000) -> List[str]:
        """
        List keys in bucket with optional prefix.

        Args:
            prefix: Additional prefix to filter by
            limit: Maximum keys to return

        Returns:
            List of keys
        """
        try:
            full_prefix = self._full_key(prefix) if prefix else self.prefix
            response = self.client.list_objects_v2(
                Bucket=self.bucket,
                Prefix=full_prefix,
                MaxKeys=limit
            )

            keys = []
            for obj in response.get('Contents', []):
                key = obj['Key']
                # Remove prefix for cleaner output
                if self.prefix and key.startswith(self.prefix):
                    key = key[len(self.prefix):].lstrip('/')
                keys.append(key)

            return keys
        except Exception as e:
            logger.error(f"List failed: {e}")
            return []

    def exists(self, remote_key: str) -> bool:


I need to think about logging attempted keys for debugging, but I don’t want to log too many. It seems like truncating the error messages might help.

If we change _get_r2_source_client to set the bucket type as 'source', that requires pushing a new Docker image and updating the instance. The user wants everything to start fresh, but I see we already have the cluster running.

This might mean stopping it, deploying the new image, and starting again, but I'm unsure about the evidence for storing sources with different prefixes.


I'm considering whether the actual object might just have a different extension, like .mp4 or .mkv. That could be part of the solution. The code includes helper functions and comments about supporting multiple extensions and prefixes, so it seems they intended to accommodate that.

I think reverting to a more robust approach is necessary, but I'm also aware that making multiple HEAD requests for each video could slow things down. If there are up to five prefixes and seven extensions, that could mean 35 requests per video, which is heavy.

To optimize, we can try the most likely combination first, like podcasts/{id}.webm, and if that fails, then check the other options. This way, if the most common ones work, the overhead will remain minimal.


I’m thinking about allowing customization through environment variables like `R2_SOURCE_EXTENSIONS` and `R2_SOURCE_PREFIXES` to narrow down the list. If we know the sources are always `podcasts/{id}.webm`, we might not need to check others, but if that fails, we could have a problem.

Implementing caching for negative results could help too. If something isn’t found on the first try, we should consider running a list. So, I’ll be working on patching `src/download.py` to ensure that `config.r2_source_prefix` and `r2_source_extensions` are utilized. They default to empty right now, but environment variables might be set.

I’ll move forward with implementing the patch, replacing the simplified `_resolve_r2_source_object` with a more iterative approach and updating the docstring as necessary.


Input:
*** Begin Patch
*** Update File: src/download.py
@@
 def _resolve_r2_source_object(video_id: str, config) -> Tuple[str, Dict[str, Any]]:
     """
     Resolve a video_id to an object key in the R2 source bucket.

-    Simplified: All source videos are at podcasts/{video_id}.webm in the 'test' bucket.
+    We avoid bucket listing for performance/cost reasons. Instead we try a small
+    set of likely prefixes/extensions via HEAD requests only.

     Returns:
         (remote_key, head_object_response)
     """
     client = _get_r2_source_client(config)

-    # Simplified: all videos are at podcasts/{video_id}.webm
-    key = f"podcasts/{video_id}.webm"
+    prefixes = _iter_r2_source_prefixes(config)
+    exts = _iter_r2_source_extensions(config)

-    # Direct HEAD request (no prefix from client)
-    try:
-        head = client.client.head_object(Bucket=client.bucket, Key=key)
-        if head:
-            return key, head
-    except Exception:
-        pass
+    last_key = None
+    for prefix in prefixes:
+        for ext in exts:
+            key = f"{prefix}{video_id}{ext}"
+            last_key = key
+            head = client.head_object(key)
+            if head:
+                return key, head

     raise DownloadError(
-        f"R2 source not found: s3://{client.bucket}/{key}"
+        f"R2 source not found: s3://{client.bucket}/{last_key or video_id}"
     )
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/download.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Considering Docker image updates**

I need to rebuild the Docker image and push a new tag, like 6cfd8aa-nonfail3. After that, I’ll update the template to reflect this new image and handle the running instance. But I wonder if the user is fine with the current setup. For fewer failures, we should make the changes, yet I also think failing due to truly missing sources could complicate things and increase overhead. I'll need to decide based on what evidence I can gather about the source storage.


**Analyzing video source prefixes**

The existence of _iter_r2_source_prefixes and extensions indicates that there might be more complexity here. A comment says all videos are located at podcasts/{video_id}.webm, but this could just be an assumption made for benchmarking. For the production dataset, there should be multiple extensions or prefixes, which is why those helper functions exist. I’ll check the .env or config files for r2_source_prefix and extensions. It’s possible that some video IDs are in Supabase but missing from R2, so I’ll test a failing video ID using our local Python and R2Client to see what’s there.


**Checking video file existence**

I’m planning to write a Python snippet to verify if `podcasts/9bZkp7q19f0.webm` exists, since that failed. I’ll also check for `podcasts/9bZkp7q19f0.mp4` and maybe other formats too. To do this, I’ll use the R2Client with `bucket_type='source'` and specify the bucket as 'test'. It should be a straightforward process, and this way, I can quickly find out what’s going on with those video files.


python3 - <<'PY'
import os
from pathlib import Path

def parse_env(path: str) -> dict:
    env = {}
    for raw in Path(path).read_text(encoding='utf-8').splitlines():
        line = raw.strip()
        if not line or line.startswith('#'):
            continue
        if line.startswith('export '):
            line = line[len('export '):]
        if '=' not in line:
            continue
        k, v = line.split('=', 1)
        k = k.strip(); v=v.strip()
        if (v.startswith('"') and v.endswith('"')) or (v.startswith("'") and v.endswith("'")):
            v=v[1:-1]
        env[k]=v
    return env

os.environ.update(parse_env('.env'))
from src.r2_client import R2Client
r2 = R2Client(bucket_type='source')
vid='9bZkp7q19f0'
keys=[
    f'podcasts/{vid}.webm',
    f'podcasts/{vid}.mp4',
    f'videos/{vid}.webm',
    f'videos/{vid}.mp4',
    f'{vid}.webm',
    f'{vid}.mp4',
]
for k in keys:
    print(k, r2.exists(k))
PY


Output:
Exit code: 0
Wall time: 2 seconds
Output:
podcasts/9bZkp7q19f0.webm False
podcasts/9bZkp7q19f0.mp4 False
videos/9bZkp7q19f0.webm False
videos/9bZkp7q19f0.mp4 False
9bZkp7q19f0.webm False
9bZkp7q19f0.mp4 False


Summary: auto


**Exploring bucket keys**

I’m considering that there could be prefixes outside the provided list. My search would likely involve more prefixes if I had set the R2_SOURCE_PREFIXES environment variable, but I haven't. I should probably list some keys in the bucket to identify patterns. To do this, I might use R2Client.list_keys, even though it utilizes list_objects_v2, which could list with prefixes. For the 'test' bucket, listing just the first 20 keys with the prefix 'podcasts/' might give me insights into the extensions and so on.


python3 - <<'PY'
import os
from pathlib import Path

def parse_env(path: str) -> dict:
    env = {}
    for raw in Path(path).read_text(encoding='utf-8').splitlines():
        line = raw.strip()
        if not line or line.startswith('#'):
            continue
        if line.startswith('export '):
            line = line[len('export '):]
        if '=' not in line:
            continue
        k, v = line.split('=', 1)
        k = k.strip(); v=v.strip()
        if (v.startswith('"') and v.endswith('"')) or (v.startswith("'") and v.endswith("'")):
            v=v[1:-1]
        env[k]=v
    return env

os.environ.update(parse_env('.env'))
from src.r2_client import R2Client
r2=R2Client(bucket_type='source')
keys=r2.list_keys(prefix='podcasts/', limit=20)
print('sample keys:', keys)
PY


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
sample keys: []


**Troubleshooting source bucket**

I noticed that the previous environment variable listings didn't include the necessary prefix for sources. Since the current source prefix is empty, I should be able to list objects with the prefix 'podcasts/' if they exist. But, it seems I got nothing in return, which makes me think the sources might not be in the 'test' bucket after all. I’ll check again for keys with no prefix and see if they look like '<id>.webm'. Let's list the first 20 keys with that approach!


python3 - <<'PY'
import os
from pathlib import Path

def parse_env(path: str) -> dict:
    env = {}
    for raw in Path(path).read_text(encoding='utf-8').splitlines():
        line = raw.strip()
        if not line or line.startswith('#'):
            continue
        if line.startswith('export '):
            line = line[len('export '):]
        if '=' not in line:
            continue
        k, v = line.split('=', 1)
        k = k.strip(); v=v.strip()
        if (v.startswith('"') and v.endswith('"')) or (v.startswith("'") and v.endswith("'")):
            v=v[1:-1]
        env[k]=v
    return env

os.environ.update(parse_env('.env'))
from src.r2_client import R2Client
r2=R2Client(bucket_type='source')
keys=r2.list_keys(limit=20)
print('count', len(keys))
print('\n'.join(keys))
PY


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
count 0


**Evaluating list_key functionality**

I'm contemplating the functionality of list_keys and whether it might fail. I wonder if bucket listing is disabled or if Cloudflare R2 doesn't permit list_objects. It could be due to credentials being limited to head/get, or maybe the bucket has a policy that disables listing. If AWS S3 policies deny ListBucket, list_keys would return an empty list due to exceptions. We don't see error outputs, which might be due to logging issues or suppression.


**Analyzing list_keys and access issues**

The list_keys function is returning an empty array because of an AccessDenied error, which is fine. This means I can’t infer whether the absence of results is due to objects not being present or just inaccessible. Therefore, I still need the robust _resolve_r2_source_object function. For the video ID 9bZkp7q19f0, the object may truly be missing from R2.

When processing the entire set, we might find some items are present, while others are not. Our patch for _resolve_r2_source_object could create some overhead, but it might not be significant, especially if most videos return results on the first key check.


**Optimizing order of prefixes and extensions**

I need to ensure that the order in which prefixes are returned by _iter_r2_source_prefixes is correct. The expected order should include environment prefixes, configuration prefixes, and then standardized ones like '', 'podcasts/', and 'videos/'. The first prefix will likely be an empty string. Similarly, for extensions, I’ll focus on the order including environment and configuration extensions, starting with an empty string and then formats like '.webm' and '.mp4'.

For each video, I'll first attempt the video ID without a prefix or extension, which probably won’t exist. The process could lead to multiple head requests before finding the correct key, so I definitely want to optimize this ordering. My goal is to try the most likely keys first, like podcasts/{video_id}.webm, followed by others to reduce overhead.


**Customizing key order**

I can simplify the process by customizing the order of candidate keys instead of using nested loops. I’ll explicitly build a priority list. For prefixes, I want to place 'podcasts/' at the front, ideally right after the empty string. Since the key format will likely be podcasts/{id}.webm, moving 'podcasts/' first is crucial.

The same goes for extensions; I’ll want '.webm' at the top. I can create a helper function called _prioritize to reorder these lists easily. For instance, I might prioritize prefixes like ['podcasts/', 'videos/', ''] and extensions like ['.webm', '.mp4', ...], with the empty string last.


**Implementing key ordering and error handling**

By using nested loops, we can prioritize trying podcasts/.webm first, which will significantly reduce overhead. I need to implement this along with our existing patch, as performance could degrade without it. Although we’ve patched _resolve_r2_source_object, it currently checks the empty prefix and extension first, which might unnecessarily increase head requests for each video.

Implementing a priority reorder is crucial. Additionally, I think we should enhance the error message to include not only the last key checked but also the known default `podcasts/{id}.webm`. Let’s update our function _prioritize for readability and patch the necessary file, src/download.py, accordingly.


Input:
*** Begin Patch
*** Update File: src/download.py
@@
 def _resolve_r2_source_object(video_id: str, config) -> Tuple[str, Dict[str, Any]]:
@@
     """
     client = _get_r2_source_client(config)

-    prefixes = _iter_r2_source_prefixes(config)
-    exts = _iter_r2_source_extensions(config)
+    def _prioritize(items: List[str], preferred: List[str]) -> List[str]:
+        """Return items with preferred values moved to the front (stable)."""
+        out: List[str] = []
+        seen = set()
+        for p in preferred:
+            if p in items and p not in seen:
+                out.append(p)
+                seen.add(p)
+        for it in items:
+            if it not in seen:
+                out.append(it)
+                seen.add(it)
+        return out
+
+    prefixes = _prioritize(
+        _iter_r2_source_prefixes(config),
+        # Most common layout: podcasts/{id}.webm
+        ["podcasts/", "videos/", ""],
+    )
+    exts = _prioritize(
+        _iter_r2_source_extensions(config),
+        # Prefer common YouTube container formats first.
+        [".webm", ".mp4", ".mkv", ".m4a", ".opus", ".mp3", ""],
+    )
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/download.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Checking imports and linting**

I need to confirm that List is imported for type, and it looks like download.py already does that at the top. I'll verify by checking the top of the file since it's lengthy but already utilizes List. Now that our patch uses List in the _prioritize signature, it’s confirmed to be in scope. Next, I should run a Python lint check, so I'll just go ahead and import it now.


python3 -c "from src.download import _resolve_r2_source_object; print('ok')"


Output:
Exit code: 1
Wall time: 1.9 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/home/ubuntu/maya3/src/download.py", line 22, in <module>
    import yt_dlp
ModuleNotFoundError: No module named 'yt_dlp'


**Considering failure causes**

I'm thinking about our current queue situation. If we kill the cluster now, we risk leaving 8 claims unprocessed until the lease expires, which could take a while. But we might be able to reclaim expired leases or update statuses. However, I wonder if we should delay killing the cluster since the user didn't request a fix for the download resolution. Still, an error rate of 43.9% seems too high! We really need to investigate possible causes, like missing resources. Should we consider limiting the queue to only those with sources?


**Investigating video processing**

I’m considering the possibility that processing might be rate-limited. The user has mentioned a connection with 171,945 pending videos in the queue and that some are accessible in R2. This suggests that there are likely videos available, but maybe not all of them are uploaded yet. The user wants to process the entire set, assuming all videos are indeed present, which could mean the failures are mainly from test entries.

There were 18 failures early on, so I think it’s important to inspect these errors to pinpoint what's causing the issues.


**Examining error logs and analytics**

I’m realizing that the dashboard’s error analysis is empty because the error_logs table isn't being utilized. Instead, I wonder if we can find failure events in the processing_events table, specifically looking for the 'fail' event_type. The dashboard currently focuses only on 'done' events, while the error summary relies on error_logs. There might be a chance that our analytics reporter only inserts data into processing_events. I think it makes sense to check the src/analytics.py file to see which tables are being written to.


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 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)
    """
    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()

        # Drain the queue fully on shutdown so final "offline"/"done" events
        # aren't lost if they were enqueued right before stop.
        while not self._stop_event.is_set() or not self._queue.empty():
            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,
            '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,


**Evaluating error handling**

I’m considering how the analytics reporter logs failures to both processing events and error logs, which seems good. However, in the dashboard output, even though the failed count shows 18, it states no errors were recorded. This implies the error logs might be empty. Maybe failures are being handled before the analytics reporter is created? Although, they create the analytics before processing, so I need to check for the `.fail(` call in massive_process.py.


rg -n "analytics\.fail\(" -n massive_process.py | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
655:                analytics.fail(


Summary: auto


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                        'gpu_id': gpu_id,
                        '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)

        # Flush background exports on shutdown (best-effort).


**Investigating analytics issue**

I see that analytics.fail was called and it should be inserting into error_logs, but the dashboard isn’t showing anything. Maybe it’s because we truncated error_logs too quickly after errors occurred? The dashboard might be using a summary of error_logs that doesn't reflect the latest data, particularly with event types. I really think inspecting the get_error_summary function in dashboard.py will help clarify what’s going wrong.


rg -n "def get_error_summary" -n scripts/monitor/dashboard.py && sed -n '200,320p' scripts/monitor/dashboard.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
95:    def get_error_summary(self) -> Dict[str, int]:
            print(
                f"  {C.CYAN}Last {window_min}m:{C.RESET} "
                f"{vph:.1f} vids/hr │ {ahph:.2f} audio-hr/hr │ {speed_x:.1f}x realtime │ "
                f"avg {avg_audio_min:.1f} min/video │ ETA {format_eta_hours(eta_hours)}"
            )

    def print_worker_status(self, workers: List[Dict]):
        """Print worker status table."""
        print(f"\n{C.BOLD}👷 WORKER STATUS ({len(workers)} workers){C.RESET}")
        print(f"{'─'*70}")

        if not workers:
            print(f"  {C.DIM}No workers registered{C.RESET}")
            return

        # Group by machine
        machines = {}
        for w in workers:
            machine = w.get('machine_id', 'unknown')
            if machine not in machines:
                machines[machine] = []
            machines[machine].append(w)

        for machine, machine_workers in sorted(machines.items()):
            print(f"\n  {C.BOLD}{machine}{C.RESET}")

            for w in sorted(machine_workers, key=lambda x: x.get('gpu_id', 0)):
                status = w.get('status', 'unknown')
                gpu = w.get('gpu_id', '?')
                video = w.get('current_video_id', '')
                stage = w.get('current_stage_name', '')
                done = w.get('session_videos_done', 0)
                failed = w.get('session_videos_failed', 0)
                audio_min = w.get('session_audio_minutes', 0)

                # Status indicator
                if status == 'processing':
                    status_icon = f"{C.GREEN}●{C.RESET}"
                    status_text = f"{C.GREEN}processing{C.RESET}"
                elif status == 'idle':
                    status_icon = f"{C.YELLOW}○{C.RESET}"
                    status_text = f"{C.YELLOW}idle{C.RESET}"
                else:
                    status_icon = f"{C.RED}◌{C.RESET}"
                    status_text = f"{C.RED}{status}{C.RESET}"

                # Last heartbeat age
                last_hb = w.get('last_heartbeat', '')
                if last_hb:
                    try:
                        hb_time = datetime.fromisoformat(last_hb.replace('Z', '+00:00'))
                        age = (datetime.utcnow().replace(tzinfo=hb_time.tzinfo) - hb_time).total_seconds()
                        age_str = format_duration(age)
                        if age > 300:
                            age_str = f"{C.RED}{age_str}{C.RESET}"
                    except:
                        age_str = "?"
                else:
                    age_str = "?"

                video_display = video[:11] if video else '-'
                stage_display = stage[:15] if stage else '-'

                print(f"    {status_icon} GPU{gpu} │ {status_text:20} │ {video_display:11} │ {stage_display:15} │ ✓{done} ✗{failed} │ {audio_min:.0f}min │ {age_str}")

    def print_error_summary(self, errors: Dict[str, int], recent: List[Dict]):
        """Print error summary and recent errors."""
        print(f"\n{C.BOLD}🚨 ERROR ANALYSIS{C.RESET}")
        print(f"{'─'*50}")

        if not errors:
            print(f"  {C.GREEN}No errors recorded{C.RESET}")
            return

        # Error type breakdown
        print(f"  {C.BOLD}By Type:{C.RESET}")
        for err_type, count in sorted(errors.items(), key=lambda x: -x[1])[:5]:
            print(f"    {C.RED}•{C.RESET} {err_type}: {count}")

        # Recent errors
        if recent:
            print(f"\n  {C.BOLD}Recent Errors:{C.RESET}")
            for err in recent[:5]:
                video = err.get('video_id', '?')
                stage = err.get('stage', '?')
                msg = (err.get('error_message', '') or '')[:60]
                print(f"    {C.RED}•{C.RESET} {video} @ {stage}: {msg}")

    def print_throughput(self, hourly: List[Dict]):
        """Print throughput chart."""
        print(f"\n{C.BOLD}📈 THROUGHPUT (last 24h){C.RESET}")
        print(f"{'─'*50}")

        if not hourly:
            print(f"  {C.DIM}No data yet{C.RESET}")
            return

        # Recent hours
        recent = hourly[-8:]
        max_count = max(h['count'] for h in recent) if recent else 1

        for h in recent:
            hour = h['hour'][-5:]  # HH:MM
            count = h['count']
            audio = h['audio_min']

            bar_len = int(30 * count / max_count) if max_count > 0 else 0
            bar = '▓' * bar_len

            print(f"  {hour} │ {C.CYAN}{bar:30}{C.RESET} │ {count:4} videos │ {audio:.0f} min")

        # Total stats
        total_videos = sum(h['count'] for h in hourly)
        total_audio = sum(h['audio_min'] for h in hourly)
        print(f"\n  {C.BOLD}24h Total:{C.RESET} {total_videos} videos, {total_audio/60:.1f} hours audio")

    def print_stale_workers(self, stale: List[Dict]):
        """Print stale worker warnings."""
        if not stale:
            return


sed -n '70,120p' scripts/monitor/dashboard.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
class Dashboard:
    def __init__(self):
        self.sb = create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
        self.start_time = datetime.utcnow()

    def get_queue_stats(self) -> Dict[str, int]:
        """Get video counts by status."""
        stats = {}
        for status in ['PENDING', 'CLAIMED', 'COMPLETED', 'FAILED', 'SKIPPED']:
            result = self.sb.table('videos').select('id', count='exact').eq('status', status).execute()
            stats[status] = result.count or 0
        return stats

    def get_worker_status(self) -> List[Dict]:
        """Get all worker heartbeats."""
        result = self.sb.table('worker_heartbeats').select('*').order('worker_id').execute()
        return result.data or []

    def get_recent_errors(self, limit: int = 10) -> List[Dict]:
        """Get recent error logs."""
        result = self.sb.table('error_logs').select(
            'video_id, stage, error_type, error_message, created_at'
        ).order('created_at', desc=True).limit(limit).execute()
        return result.data or []

    def get_error_summary(self) -> Dict[str, int]:
        """Get error counts by type."""
        result = self.sb.table('error_logs').select('error_type').execute()
        counts = {}
        for row in (result.data or []):
            err_type = row.get('error_type', 'Unknown')
            counts[err_type] = counts.get(err_type, 0) + 1
        return counts

    def get_hourly_throughput(self) -> List[Dict]:
        """Get videos completed per hour (last 24h)."""
        # Get recent processing events
        since = (datetime.utcnow() - timedelta(hours=24)).isoformat()
        result = self.sb.table('processing_events').select(
            'created_at, audio_minutes'
        ).eq('event_type', 'done').gte('created_at', since).execute()

        # Bucket by hour
        hourly = {}
        for row in (result.data or []):
            ts = row.get('created_at', '')[:13]  # YYYY-MM-DDTHH
            if ts not in hourly:
                hourly[ts] = {'count': 0, 'audio_min': 0}
            hourly[ts]['count'] += 1
            hourly[ts]['audio_min'] += row.get('audio_minutes', 0) or 0


python3 - <<'PY'
import os
from pathlib import Path

def parse_env(path: str) -> dict:
    env={}
    for raw in Path(path).read_text(encoding='utf-8').splitlines():
        line=raw.strip()
        if not line or line.startswith('#'):
            continue
        if line.startswith('export '):
            line=line[len('export '):]
        if '=' not in line:
            continue
        k,v=line.split('=',1)
        k=k.strip(); v=v.strip()
        if (v.startswith('"') and v.endswith('"')) or (v.startswith("'") and v.endswith("'")):
            v=v[1:-1]
        env[k]=v
    return env

os.environ.update(parse_env('.env'))
from supabase import create_client
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])

err_count=sb.table('error_logs').select('id', count='exact').execute().count
fail_events=sb.table('processing_events').select('id', count='exact').eq('event_type','fail').execute().count
done_events=sb.table('processing_events').select('id', count='exact').eq('event_type','done').execute().count
print('error_logs count', err_count)
print('processing_events fail count', fail_events)
print('processing_events done count', done_events)
PY


Output:
Exit code: 0
Wall time: 1.4 seconds
Output:
error_logs count 0
processing_events fail count 0
processing_events done count 76


rg -n "def create_reporter" -n src/analytics.py && sed -n '260,420p' src/analytics.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
417:def create_reporter(
        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,


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


**Fixing analytics insertion**

I’m considering how to fix the analytics insertion problem by removing the stack_trace field from processing_events, keeping it only in error_logs. In the AnalyticsReporter._flush_events method, fail_events currently include the stack_trace when constructing event_rows. I can strip that key before inserting. The simplest solution seems to be creating event_rows without the stack_trace key. This change will help keep track of fail events while ensuring that error logs and dashboard analysis are effective for monitoring. Reducing confusion here is key!


**Implementing Docker fix**

I'm planning to implement the fix and rebuild the Docker image, possibly calling it nonfail3. This will require updating both the analytics.py and download.py files. I'll push a new image tag like `6cfd8aa-nonfail3` while retaining the same commit hash.

Next, I need to decide whether to update the template and existing instance to this new tag or simply recycle the instance’s image. Restarting early could disrupt processing, but since some videos are completed and others failed, I might need to weigh the risks of leaving processes incomplete.


**Applying fixes for monitoring**

I’m deciding to apply the necessary fixes now to ensure monitoring works and reduce overhead. It seems better for long-term performance since we haven’t scaled up yet. The user wants to monitor failures in real-time, and currently, error analysis shows none, which isn’t accurate.

To fix this, I’ll patch analytics.py to strip the stack_trace during processing_events insertion. I’ll also ensure error_logs continue to be updated. Then, I’ll rebuild the Docker image with a new tag like `6cfd8aa-nonfail3` and update the production template with attention to include environment variables to preserve secrets.


**Creating a new production template**

I think the best approach is to create a new production template from scratch, using the same environment string but with a new image. This seems like the simplest option and minimizes risks. I’m considering naming the new template `maya3-worker-prod-nonfail3`.

We can choose to delete the old template, though it isn't necessary. To update the running instance from the previous template hash (1f8cd...), I’ll use the command with the new image tag. After updating the instance's image, I can recycle it to apply the changes. Plus, the extra environment variables should persist, which is great!


**Updating instance and pushing changes**

I’ve realized that shm-size is part of the template environment string, and while the instance likely stores the template_hash_id, it may use that configuration again upon recycling. If we only update the image, the template_hash_id and environment options remain unchanged, which means the environment persists—great!

To process with the new code, I’ll run the command to update the instance with the new image, then recycle it. I need to ensure that our local repository modifications are consistent, especially with the analytics fix before I build and push the new image. I'll patch analytics.py next!


Input:
*** Begin Patch
*** Update File: src/analytics.py
@@
             # Batch insert fail events + error_logs
             if fail_events:
-                event_rows = [e.data for e in fail_events]
+                # processing_events table intentionally stores only summary fields
+                # (no stack traces). Keep full traces in error_logs instead.
+                event_rows = [{k: v for k, v in e.data.items() if k != 'stack_trace'} 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'],
*** End Patch


Output: {"output":"Success. Updated the following files:\nM src/analytics.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


# AGENTS.md instructions for /home/ubuntu/maya3

<INSTRUCTIONS>
## Skills
These skills are discovered at startup from multiple local sources. Each entry includes a name, description, and file path so you can open the source for full instructions.
- 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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
- Discovery: Available skills are listed in project docs and may also appear in a runtime "## Skills" section (name + description + file path). These are the sources of truth; skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description, 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.
- Description as trigger: The YAML `description` in `SKILL.md` is the primary trigger signal; rely on it to decide applicability. If unsure, ask a brief clarification before proceeding.
- 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 deeply nested references; prefer one-hop files explicitly linked from `SKILL.md`.
  - 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.
</INSTRUCTIONS>

<environment_context>
  <cwd>/home/ubuntu/maya3</cwd>
  <approval_policy>on-request</approval_policy>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <shell>bash</shell>
</environment_context>

<environment_context>
  <sandbox_mode>workspace-write</sandbox_mode>
  <network_access>restricted</network_access>
  <writable_roots>
    <root>/home/ubuntu/maya3</root>
  </writable_roots>
  <shell>bash</shell>
</environment_context>

context from previous agent.

✅ TEST SUMMARY

Docker Build

Docker not available in this environment
All prerequisites validated (Python, dependencies, GPU access)
Pipeline Test Results

Test	Status	Details
Python imports	✅ Pass	torch, supabase, r2, analytics, export all work
GPU detection	✅ Pass	8× GPUs detected (torch 2.8.0+cu128)
Supabase connection	✅ Pass	171,945 PENDING videos in queue
R2 connection	✅ Pass	Source and Production buckets accessible
Environment validation	✅ Pass	All required env vars present
Single video processing	✅ Pass	OBMrIJI_dn0 processed in 34.8s
Multi-GPU workers	✅ Pass	8 workers running in parallel
Analytics	✅ Pass	Workers reporting to Supabase
TAR creation	✅ Pass	Segments + metadata only (no _original.flac)
Fix Applied

Fixed _original.flac removal in synchronous export path (pipeline.py:1021-1026)

TAR Contents (After Fix)

{video_id}/
├── metadata.json # Full processing metadata
└── segments/ # Individual speaker clips (FLAC)
├── SPEAKER_00_0000_xxx.flac
├── SPEAKER_00_0001_xxx.flac
└── ...

Ready for Deployment

✅ Codebase tested and working
✅ Supabase queue functional (171K+ videos pending)
✅ R2 upload ready
✅ Deployment scripts created
✅ Monitoring tools ready
When you return with SSH keys, run:

1. Edit cluster config
vi scripts/deploy/cluster_config.json

2. Deploy to all clusters
python scripts/deploy/deploy_all.py --deploy

3. Start workers
python scripts/deploy/deploy_all.py --start

4. Monitor
python scripts/monitor/dashboard.py --watch

And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of how we're going to deploy the application into multiple clusters at vast ai. If you need me to add SSH key into vast ai's console, give instructions for that and then by default it shall be added to any machines we create. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

and do you think it's better if we cerate a docker image and deploy the workflow or should we directly depend on teh scripts at deploy_all.py scripts and is it reliable. i'm worried hte new clusters might have different versions and environments. so it's better if we create a docker image and bake all the models that we're gonna use into it. and we just deploy the docker image and it works seamlessly.

Vast.ai Multi-Cluster Deployment (High Level)

Vast.ai runs each “machine” as a Docker container; we deploy by launching multiple identical instances and running massive_process.py workers on each (they coordinate via Supabase queue + R2, so no inter-node networking needed).
Provision via CLI (vastai search offers → vastai create instance) or Python SDK (VastAI.search_offers() / launch_instance() in vast_ai_sdk.md (line 105)), then label/group instances and monitor with logs/analytics.
SSH Key (So New Instances Get It Automatically)

Generate a key: ssh-keygen -t ed25519 -C "you@example.com" -f ~/.ssh/maya3_vast (vast_ai.md (line 286)).
Add it to your Vast account: maya3_vast.pub)" and verify with vastai show ssh-keys (vast_ai.md (line 294)).
Important: keys only auto-apply to instances created after you add them (vast_ai.md (line 331)); for existing instances use vastai attach ssh <instance_id> (vast_ai.md (line 367)).
Plan: 24–48GB GPUs, “clusters” in 8s, < $4/hr total

Pick cluster shape: either 1×8-GPU instance (simpler ops) or 8×1-GPU instances (often easier to source); budget is sum(dph_total) across the instances.
Search offers (examples; tune thresholds):
8-GPU node: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 reliability>0.99 verified=true rentable=true dph_total<4 gpu_name=RTX_4090' -o dph_total (vast_ai.md (line 45))
1-GPU nodes: same but num_gpus=1 dph_total<0.5, then create 8 of them.
Standardize with a template so every instance uses the same image/env/onstart (vast_ai.md (line 578)), then create N instances and label them maya3-cluster-01, maya3-cluster-02, etc.
Expectation: 48GB-per-GPU options are much less likely to fit <$4/hr for 8 GPUs; 24GB (3090/4090-class) is the realistic target.
Docker Image vs deploy_all.py

For reliability, prefer a pinned Docker image + template: the repo already has a production-ish container (Dockerfile (line 13)) that installs deps into .venv and runs massive_process.py as the container entrypoint (Dockerfile (line 72)), which avoids “different machine, different env” drift.
Current SSH deploy scripts (deploy_all.py (line 36), deploy_cluster.sh (line 21)) assume ssh user@host with no port; Vast.ai SSH is typically host+port (vastai ssh-url <id>), so you’ll either need a small patch to support ports or lean into container-native ops (start/stop/recycle + vastai logs <id>).
Do you want a “cluster” to be one 8-GPU instance or eight 1-GPU instances, and are bid/interruptible instances acceptable or on-demand only?

Can you build docker image and serve it locally and test first in this machine, before serving & scaling it to other systems.

Docker can’t be built/tested in this environment: docker (and podman/buildah/nerdctl) isn’t installed, so there’s no local container runtime to run docker build/docker run.

What I did to make local Docker testing reliable/safe on your GPU machine:

Added .dockerignore to prevent baking secrets like .env into the image (and to shrink build context): .dockerignore (line 1)
Added a one-command local build + smoke-test script: smoke_test.sh (line 1)
Documented usage in README.md (line 47)
On a machine with Docker + NVIDIA Container Toolkit:

Build + basic smoke tests (GPU + imports + Supabase + R2): smoke_test.sh
End-to-end single-video test (claims 1 Supabase PENDING video on GPU 0, verifies TAR has no _original.flac): smoke_test.sh
“Serve” locally (run workers continuously): docker compose up -d (logs: docker compose logs -f)

Can you continue with docker testing from this machine, so we can safely proceed with next steps of vhoosing machines. For now, build docker, run the docker application, monitor analytics, then reset everything and be ready so that we can surely deploy and monitor in vast ai

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

continue, now you have full access

is the docker image done ? can we test it locally now, before we can deploy it to other machines ?

so, if i have to scale this to multiple machines, is it just the same, deploy the docker image, then just keep monitoring ? that's it ?

# Context from my IDE setup:

## Open tabs:
- .env: .env
- dashboard.py: scripts/monitor/dashboard.py

## My request for Codex:
Can you check for machines as per our configuration from vast ai ? use the below api key. now dont warn me about the security key being sent in chat here. i will refresh once we are done with testing. so you are free to use this key for now.

VAST_AI=f3ccede42742fd7f0072b93b4d9edfe180b5252286d93093c48c00ea93883c35

And can you check the documenation of vast ai either using SDK or cli from vast_ai.md and vast_ai_sdk.md and give me a highlevel overview of a strategic-non-failure way,  we're going to deploy the application into multiple clusters at vast ai. and then create plan for creating instances of 24-48GB with high reliability, and is multiple of 8s as a cluster and is cheaper than 4$/hr as a cluster in total. And we can choose anything like 3090s, 4090s, or anything similar from 24GB to 48GB and is the entire cluster is less than 4$/hr in total.

Now that we have the docker service built and running here. did we publish it btw, if not use my docker PAT to push it so, we can use it while deploying pods. docker_pat=dckr_pat_bi0bQyWCMs0EQFKckt0__nuwKkM

And then give a strategic way of keep adding new pods while we can scale this application horizontally, and then also observe any failures in real-time and see ETA and RTF rates and everything together once i'm satisfied with the amount of machines that are already working for me on this. You can also do a trail run on pulling machines, and see if you can connect as well, if you couldnt, that means we need to add an SSH key from console, apart from that, finish other things and plan strategically about our deployment strategy.

# Context from my IDE setup:

## Open tabs:
- Dockerfile: Dockerfile
- .env: .env
- dashboard.py: scripts/monitor/dashboard.py

## My request for Codex:
i was wondering why did we not bake the env variables along with the docker image and inject them during the startup command in the docker file while deploying and hence we dont have to worry about the env variables passing to each instance. Also, i can now see you have successfully created and started 2 instances, what happened to them, and you created a 3rd one with 4090. can you explain what's going on here. here's more info on the isntances currently starting/running already.

All (3)

maya3-ssh-smoke (1)

maya3-envvar-test (1)

maya3-worker-e2e-1 (1)
1x RTX 3090
Verified
199.167.160.162

19m
1 mon 9d
$0.117/hr
Instance ID: 29733687
Host: 150913
Machine ID: 27004
Vol:
No Volumes
Max CUDA: 12.4
35.3 TFLOPS
VRAM 0.3/24.0 GB
805.0 GB/s
DLPerf
-
805.0 DLP/$/hr
Network
62 ports
911.7 Mbps
838.6 Mbps
CPU
AMD Ryzen Threadripper PRO 5945WX 12-Cores
3.0/24 CPU
0 / 32.2 GB
Disk
NX-1TB 2280
990.0 MB/s
0.0 / 25.0 GB
Motherboard
MC62-G40-00
PCIE 4.0/4x
6.4 GB/s
maya3-ssh-smoke
GPU: 0% 5°C, CPU: 0%, Status: success, running nvidia/cuda_12.1.0-cudnn8-runtime-ubuntu22.04/ssh

1x RTX 3090
Verified
209.227.186.229

11m
36 mon
$0.127/hr
Instance ID: 29734739
Host: 3483
Machine ID: 7805
Vol:
No Volumes
Max CUDA: 13.0
35.3 TFLOPS
VRAM 0.7/24.0 GB
802.2 GB/s
DLPerf
27.2 DLPerf
802.2 DLP/$/hr
Network
100 ports
509.3 Mbps
516.3 Mbps
CPU
11th Gen Core™ i5-11400F
12.0/12 CPU
0 / 31.8 GB
Disk
ST3000VN007
165.7 MB/s
0.0 / 10.0 GB
Motherboard
07W25T
PCIE 4.0/8x
12.1 GB/s
maya3-envvar-test
GPU: 0% 24°C, CPU: 0%, Status: success, running nvidia/cuda_12.1.0-cudnn8-runtime-ubuntu22.04/ssh

1x RTX 4090
Verified
209.227.186.229

4m 52s
36 mon
$0.051/hr
Instance ID: 29735679
Host: 3483
Machine ID: 8771
Vol:
No Volumes
Max CUDA: 13.0
81.8 TFLOPS
VRAM 0/24.0 GB
878.6 GB/s
DLPerf
35.4 DLPerf
878.6 DLP/$/hr
Network
100 ports
475.9 Mbps
497.0 Mbps
CPU
Xeon® E5-2686 v4
36.0/36 CPU
0 / 64.1 GB
Disk
ST4000NM0115
1847.1 MB/s
0.0 / 80.0 GB
Motherboard
X99-QD4
PCIE 2.0/16x
5.8 GB/s
maya3-worker-e2e-1
Status: 652492f8fcb0: Verifying Checksum 652492f8fcb0: Download complete

i was wondering why did we not bake the env variables along with the docker image and inject them during the startup command in the docker file while deploying and hence we dont have to worry about the env variables passing to each instance. Also, i can now see you have successfully created and started 2 instances, what happened to them, and you created a 3rd one with 4090. can you explain what's going on here. here's more info on the isntances currently starting/running already.

All (3)

maya3-ssh-smoke (1)

maya3-envvar-test (1)

maya3-worker-e2e-1 (1)
1x RTX 3090
Verified
199.167.160.162

19m
1 mon 9d
$0.117/hr
Instance ID: 29733687
Host: 150913
Machine ID: 27004
Vol:
No Volumes
Max CUDA: 12.4
35.3 TFLOPS
VRAM 0.3/24.0 GB
805.0 GB/s
DLPerf
-
805.0 DLP/$/hr
Network
62 ports
911.7 Mbps
838.6 Mbps
CPU
AMD Ryzen Threadripper PRO 5945WX 12-Cores
3.0/24 CPU
0 / 32.2 GB
Disk
NX-1TB 2280
990.0 MB/s
0.0 / 25.0 GB
Motherboard
MC62-G40-00
PCIE 4.0/4x
6.4 GB/s
maya3-ssh-smoke
GPU: 0% 5°C, CPU: 0%, Status: success, running nvidia/cuda_12.1.0-cudnn8-runtime-ubuntu22.04/ssh

1x RTX 3090
Verified
209.227.186.229

11m
36 mon
$0.127/hr
Instance ID: 29734739
Host: 3483
Machine ID: 7805
Vol:
No Volumes
Max CUDA: 13.0
35.3 TFLOPS
VRAM 0.7/24.0 GB
802.2 GB/s
DLPerf
27.2 DLPerf
802.2 DLP/$/hr
Network
100 ports
509.3 Mbps
516.3 Mbps
CPU
11th Gen Core™ i5-11400F
12.0/12 CPU
0 / 31.8 GB
Disk
ST3000VN007
165.7 MB/s
0.0 / 10.0 GB
Motherboard
07W25T
PCIE 4.0/8x
12.1 GB/s
maya3-envvar-test
GPU: 0% 24°C, CPU: 0%, Status: success, running nvidia/cuda_12.1.0-cudnn8-runtime-ubuntu22.04/ssh

1x RTX 4090
Verified
209.227.186.229

4m 52s
36 mon
$0.051/hr
Instance ID: 29735679
Host: 3483
Machine ID: 8771
Vol:
No Volumes
Max CUDA: 13.0
81.8 TFLOPS
VRAM 0/24.0 GB
878.6 GB/s
DLPerf
35.4 DLPerf
878.6 DLP/$/hr
Network
100 ports
475.9 Mbps
497.0 Mbps
CPU
Xeon® E5-2686 v4
36.0/36 CPU
0 / 64.1 GB
Disk
ST4000NM0115
1847.1 MB/s
0.0 / 80.0 GB
Motherboard
X99-QD4
PCIE 2.0/16x
5.8 GB/s
maya3-worker-e2e-1
Status: 652492f8fcb0: Verifying Checksum 652492f8fcb0: Download complete

A

# Context from my IDE setup:

## Open tabs:
- massive_process.py: massive_process.py
- smoke_test.sh: scripts/docker/smoke_test.sh

## My request for Codex:
Vast “Non-Failure” Multi-Cluster Plan (8-GPU clusters, <$4/hr each)

Pick on-demand + verified + high reliability offers: vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 verified=True rentable=True reliability>0.995 dph_total<4' -o dph_total.
Create one Vast template using bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix + --env '-e URL=… -e SUPABASE_ADMIN=… -e HF_TOKEN=… -e R2_* … --shm-size 16g', then launch N identical 8‑GPU instances labeled maya3-cluster-01, maya3-cluster-02, …
Run exactly one lease reclaimer somewhere: lease_recovery.py --daemon. -> this we can always do at the end of finishing of all videos right.
Monitoring/scaling is basically “launch more instances + watch”: dashboard.py --watch (now includes ETA + realtime speed).

And also, can we start with one GPU cluster ? answering your question on weather 1×8-GPU instances or 8×1-GPU instances, we have to choose the max throughput option. which is, 1×8-GPU instances. correct me if i'm wrong here.

ANd then let's go ahead and create that template first, followed by creating multiple similar identical instances or closest possible. I'm looking at 8*3090s or 4090s or similar priced instances cause they are cheaper at that scale and all 8GPUs combined are way less than 4$/hr. few such instanaces are what i will be pasting here, just check their configs and see if they are a good fit for us. Also, i'm now thinking, if we create template and launch identical resources, we will be limited. we need to be flexible with machines. and somehow need to pull the env, or inject during run-time. any other better option or does it not matter even if we choose other machine config like 3090 vs 4090, etc.

TypeIDs i want you to check and suggest if they are suitable for us at cheaper rate and good throughput rates as well.

Type #29454430
Type #29548135
Type #28470822
Type #27347138
Type #27958980

And similar ones which are 8GPUs and is cheaper and has higher Tflops and better performance as well and has good overall throughput.

# Context from my IDE setup:

## Open tabs:
- models.py: src/models.py
- config.py: src/config.py
- export.py: src/export.py
- massive_process.py: massive_process.py
- smoke_test.sh: scripts/docker/smoke_test.sh

## My request for Codex:

cleared old instances.

ubuntu@0321-dsm2-nvdgxa100-prxmx70052:~/maya3$ vastai destroy instances 29733687 29734739 29735679
destroying instance 29733687.
destroying instance 29734739.
destroying instance 29735679.
ubuntu@0321-dsm2-nvdgxa100-prxmx70052:~/maya3$

Now, what do i do. Now that you have tested the docker images working, and env variables, set, monitoring done, instances checked, and everything is set. Can you do a search with vast locally, find some beautiful instances like let's start with cheaper options like under 2$/hr for entire cluster and if they seem to be either un-available or less throughput than the costlier ones like around 2-3$/hr and then let's explore 3-4 if we're out of all options. Then, let's choose best clusters in these price ranges, and can you deploy our template in it. also does the program start working immediately after deploying instances ? or is htere a triggering cmd. also, before doing the run, can you reset the suapabase's videoID statusses to pending for all videos, so it means we need to process entire set of videos. Also, the previous testing instance that you just deployed seems to be running still. can you check, stop it, and reset everything and then if you're clean on a single instance, we can keep adding more and more and calculate estimates. Also, i've cleared the old instances. start everything new. ALso, ensure to reset the analytics table in supabase as well.


**Searching for affordable clusters**

I'm planning to search locally for clusters, starting with options under $2 per hour. If those aren’t available or have lower throughput than the more expensive ones around $2 to $3, then I'll look at options in the $3 to $4 range. I want to identify the best clusters within these price points and deploy our template on them. Before running, I also need to reset Supabase's video ID statuses to pending for all videos to ensure they’re ready for processing.


**Resetting testing instances**

I need to check the previous testing instance that’s still running and stop it. Once I've reset everything, if that single instance is clean, we can start adding more and calculate estimates. I've already cleared the old instances, so I want to start fresh. I also need to reset the analytics table in Supabase. We’ve patched the code for the analytics bug, so I’ll rebuild the image, update the template, and redeploy or update the instance as needed.


**Building and managing Docker images**

First, I need to build the Docker image locally and push it to Docker Hub with the provided PAT. Then, I’ll either update the existing Vast template or create a new one that references the new image tag and manage the instances accordingly. The user is concerned about handling environment variables; it's important not to embed secrets in the image for security reasons. Instead, we can use the Vast template's environment variable injection or create a template with non-sensitive config. This way, we can rotate secrets safely.


**Clarifying instance creation**

We need to respond to the user about the instances they asked about. They mentioned two instances were successfully created and started, which were previously used for testing SSH and environment variable injection. The third instance with the 4090 cluster was supposed to start, but we’ve destroyed them now. The user has a new request to choose clusters by price ranges and deploy the template immediately. I also need to confirm the entrypoint in the Dockerfile, making sure that it triggers the process correctly when the container starts.