Input:
*** Begin Patch
*** Update File: scripts/docker/smoke_test.sh
@@
 if [ "${E2E}" = "1" ]; then
   mkdir -p "${OUTPUT_DIR}"
   ts="$(date +%Y%m%d_%H%M%S)"
   upload_prefix="smoke_test/${ts}"
+  expected_video_id=""

   if [ -n "${VIDEO_ID}" ]; then
@@
     echo "==> E2E test: process VIDEO_ID ${VIDEO_ID} on GPU ${GPU_DEVICE} (no Supabase claims)"
     extra_args=(
       --urls "${video_url}"
       --gpus "${GPU_DEVICE}"
       --workers-per-gpu 1
       --background-export
       --export-workers 2
       --output ./output
     )
+    expected_video_id="${VIDEO_ID}"
+    if [[ "${expected_video_id}" == *"://"* ]]; then
+      if [[ "${expected_video_id}" == *"v="* ]]; then
+        expected_video_id="$(printf '%s' "${expected_video_id}" | sed -E 's/.*[?&]v=([^&]+).*/\\1/')"
+      else
+        expected_video_id="$(basename "${expected_video_id}")"
+      fi
+    fi
   else
     echo "==> E2E test: process 1 Supabase-queued video on GPU ${GPU_DEVICE}"
     extra_args=(
       --supabase-queue
@@
     extra_args+=(
       --r2-upload
       --r2-upload-prefix "${upload_prefix}"
       --r2-upload-skip-if-exists
     )
   fi

   docker run --rm \
     --gpus "device=${GPU_DEVICE}" \
     --shm-size 16g \
     --env-file .env \
     -v "${OUTPUT_DIR}:/workspace/maya3_data/output" \
     "${IMAGE_TAG}" "${extra_args[@]}"

-  latest_tar="$(ls -t "${OUTPUT_DIR}"/*.tar 2>/dev/null | head -n 1 || true)"
-  if [ -z "${latest_tar}" ]; then
-    echo "Warning: no .tar found in ${OUTPUT_DIR} (maybe R2 upload deleted it, or export disabled)."
-    exit 0
-  fi
-
-  echo "==> Verifying TAR does not contain _original.flac: ${latest_tar}"
-  if tar -tf "${latest_tar}" | grep -q '_original\.flac$'; then
-    echo "FAIL: _original.flac found inside ${latest_tar}"
-    exit 2
-  fi
-  echo "OK: _original.flac not present in TAR"
+  if [ -n "${expected_video_id}" ]; then
+    expected_tar="${OUTPUT_DIR}/${expected_video_id}.tar"
+    if [ -f "${expected_tar}" ]; then
+      echo "==> Verifying TAR does not contain _original.flac: ${expected_tar}"
+      if tar -tf "${expected_tar}" | grep -q '_original\.flac$'; then
+        echo "FAIL: _original.flac found inside ${expected_tar}"
+        exit 2
+      fi
+      echo "OK: _original.flac not present in TAR"
+    else
+      if [ "${R2_UPLOAD}" = "1" ]; then
+        remote_key="${upload_prefix}/${expected_video_id}.tar"
+        echo "==> Local TAR deleted (expected with upload). Verifying remote exists: ${remote_key}"
+        docker run --rm --env-file .env \
+          --entrypoint /workspace/maya3_data/.venv/bin/python \
+          "${IMAGE_TAG}" -c "from src.r2_client import R2Client; r2=R2Client(bucket_type='production'); print(r2.exists('${remote_key}'))"
+      else
+        echo "Warning: expected TAR not found: ${expected_tar}"
+      fi
+    fi
+  else
+    latest_tar="$(ls -t "${OUTPUT_DIR}"/*.tar 2>/dev/null | head -n 1 || true)"
+    if [ -z "${latest_tar}" ]; then
+      echo "Warning: no .tar found in ${OUTPUT_DIR} (maybe R2 upload deleted it, or export disabled)."
+      exit 0
+    fi
+
+    echo "==> Verifying TAR does not contain _original.flac: ${latest_tar}"
+    if tar -tf "${latest_tar}" | grep -q '_original\.flac$'; then
+      echo "FAIL: _original.flac found inside ${latest_tar}"
+      exit 2
+    fi
+    echo "OK: _original.flac not present in TAR"
+  fi
 fi
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/docker/smoke_test.sh\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Summary: auto


**Improving script checks**

I'm noticing that the improved smoke_test.sh outputs True/False when checking for remote existence, which is good. However, if the remote doesn't exist, we should make the script fail rather than just printing and continuing. Failing ensures that the R2 upload is validated properly. I think it’s best to exit with an error if the remote is not found. So, let’s adjust it to capture the output and check accordingly to enhance safety.


**Patching the script**

Since it's in bash, I can check for the existence of the remote key by running a docker command. I'll capture the output and ensure it’s cleaned up with `tr -d '\r'`. Then I'll implement an if statement to verify that the output is either "True" or "true." If it’s not, I’ll print a failure message and exit with status code 2. This will help make sure the script behaves correctly. Now, I just need to patch this into the script accordingly.


Input:
*** Begin Patch
*** Update File: scripts/docker/smoke_test.sh
@@
       if [ "${R2_UPLOAD}" = "1" ]; then
         remote_key="${upload_prefix}/${expected_video_id}.tar"
         echo "==> Local TAR deleted (expected with upload). Verifying remote exists: ${remote_key}"
-        docker run --rm --env-file .env \
+        exists="$(
+          docker run --rm --env-file .env \
           --entrypoint /workspace/maya3_data/.venv/bin/python \
-          "${IMAGE_TAG}" -c "from src.r2_client import R2Client; r2=R2Client(bucket_type='production'); print(r2.exists('${remote_key}'))"
+          "${IMAGE_TAG}" -c "from src.r2_client import R2Client; r2=R2Client(bucket_type='production'); print(r2.exists('${remote_key}'))" \
+          | tail -n 1 | tr -d '\r'
+        )"
+        if [ "${exists}" != "True" ] && [ "${exists}" != "true" ]; then
+          echo "FAIL: remote TAR not found in R2: ${remote_key}"
+          exit 2
+        fi
+        echo "OK: remote TAR exists in R2"
       else
         echo "Warning: expected TAR not found: ${expected_tar}"
       fi
     fi
   else
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/docker/smoke_test.sh\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Evaluating smoke test options**

It seems like the remote existence check fails if it's missing, so I’m thinking about rerunning the smoke test quickly with R2_UPLOAD=1, but maybe not the full pipeline since we just ran it. I want to confirm the new code verifies remote functionality, but it might take some time. Validating the logic by checking if the remote key exists from the last run could work. However, I just realized the smoke script will run heavy processing again, which isn't ideal. Maybe we can check the remote key manually in the log file?


**Checking upload confirmation**

I realize we didn't confirm the last run because we didn't see an "Upload failed" message. The issue might be that our smoke script verified the local tar incorrectly. So, we updated it. Now I'm considering running the e2e again but opting for a shorter pipeline and disabling music detection to save time. Alternatively, we could check the export manager behavior with a minimal fake job, but maybe I should just look into the e2e log file for "Uploaded to R2" or "Export failed" messages.


rg -n "Uploaded to R2|Export failed|Upload failed" /tmp/maya3_smoke_e2e.log | tail -n 50


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


**Checking log details**

I should have seen a log entry saying "BackgroundExportManager started with 2 workers," but it wasn't included in the last 120 lines we displayed. Maybe it appeared earlier, but I can use grep to check for "BackgroundExportManager." I’m curious if it'll show up that way. Let’s give it a try! This will help clarify what's happening and ensure I'm not missing any important details in the logs.


rg -n "BackgroundExportManager" /tmp/maya3_smoke_e2e.log | head -n 20


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


rg -n "background export|export manager|ExportManager" /tmp/maya3_smoke_e2e.log | head -n 50


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


**Checking log file size**

No matches, which is surprising. Maybe the `/tmp/maya3_smoke_e2e.log` file is huge, so `rg` might be failing? It returned exit code 0, meaning the file exists but the pattern wasn’t found. I should check the file size to confirm it’s the log from the second run. I'll use the command `ls -lh /tmp/maya3_smoke_e2e.log` to look into this further. Let's see what that reveals!


ls -lh /tmp/maya3_smoke_e2e.log && head -n 40 /tmp/maya3_smoke_e2e.log | sed -n '1,40p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
-rw-r--r-- 1 root root 840K Jan  7 19:39 /tmp/maya3_smoke_e2e.log
==> Building image: maya3-pipeline:local
#0 building with "default" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 2.44kB done
#1 DONE 0.0s

#2 [internal] load metadata for docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#2 DONE 0.0s

#3 [internal] load .dockerignore
#3 transferring context: 255B done
#3 DONE 0.0s

#4 [1/8] FROM docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 52.30kB done
#5 DONE 0.0s

#6 [2/8] RUN apt-get update && apt-get install -y --no-install-recommends     python3     python3-venv     python3-dev     python3-pip     python-is-python3     build-essential     ffmpeg     libsndfile1     libsox-fmt-all     sox     git     curl     wget     && rm -rf /var/lib/apt/lists/*
#6 CACHED

#7 [4/8] RUN mkdir -p /root/.cache/torch/hub     && mkdir -p /root/.cache/huggingface     && mkdir -p /ephemeral/models
#7 CACHED

#8 [5/8] COPY requirements.txt .
#8 CACHED

#9 [3/8] WORKDIR /workspace/maya3_data
#9 CACHED

#10 [6/8] RUN python3 -m venv .venv &&     .venv/bin/pip install --upgrade pip &&     .venv/bin/pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cu121 &&     .venv/bin/pip install --no-cache-dir -r requirements.txt &&     .venv/bin/pip install --no-cache-dir supabase &&     .venv/bin/pip install --no-cache-dir "nvidia-nccl-cu12==2.28.9"
#10 CACHED

#11 [7/8] COPY . .
#11 DONE 0.0s

#12 [8/8] RUN mkdir -p ./output


**Analyzing log file size**

The log file size is 840K, which isn’t huge. It contains the entire output, but I think we truncated it. We redirected the output, so it includes the `wget` progress, which could be substantial. The first run output had 7253 lines, likely more than 840K. I want to check for export logs; perhaps they aren't matching due to case sensitivity. I’ll search for "Export" using a simple command to see if I can find them.


rg -n "Export" /tmp/maya3_smoke_e2e.log | head -n 40


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


**Analyzing stage log functions**

I’m thinking about how the stage log function might be used differently in the pipeline. I see stages like Download, VAD, Chunking, and Diarization, leading to output and export. But the `[GPU0]` lines only show up to Output 9/10, then DONE, not showing 10/10 for Export. Maybe Export isn’t part of those logs, since it mentions "📦 Segmented..." and includes emojis. I should check for terms like "Uploaded" or "R2 upload" instead.


rg -n "Uploaded|R2 upload|R2Client|upload" /tmp/maya3_smoke_e2e.log | head -n 80


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


rg -n "def log_stage|log_stage\(" pipeline.py | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
183:    def log_stage(stage_num: int, stage_name: str, status: str = "START", extra: str = ""):
203:    log_stage(1, "Download", "START")
222:    log_stage(1, "Download", "DONE", f"{timings['download']:.1f}s")
240:    log_stage(2, "VAD", "START", f"duration={total_duration:.0f}s")
245:    log_stage(2, "VAD", "DONE", f"{timings['vad']:.1f}s | {len(vad_segments)} segments")
252:    log_stage(3, "Chunking", "START")
258:    log_stage(3, "Chunking", "DONE", f"{timings['chunking']:.1f}s | {len(chunks)} chunks")
267:    log_stage(4, "Diarization+OSD", "START", f"{len(chunks)} chunks")
283:    log_stage(4, "Diarization+OSD", "DONE", f"{timings['diarization']:.1f}s | {len(segments)} segments")
405:    log_stage(5, "Embeddings", "START")
420:    log_stage(5, "Embeddings", "DONE", f"{timings['unified_embeddings']:.1f}s | {num_chunks} chunks")
433:    log_stage(6, "Clustering", "START")
438:    log_stage(6, "Clustering", "DONE", f"{timings['clustering']:.1f}s")
509:        log_stage(6, "MicroContamination", "START", "Purity scrubber for brief interjections")
533:        log_stage(6, "MicroContamination", "DONE", f"{micro_contamination_stats.get('contamination_found', 0)} contaminated")
539:    log_stage(7, "MusicDetection", "START")
625:        log_stage(7, "MusicDetection", "DONE", f"{timings['music_detection']:.1f}s")
627:        log_stage(7, "MusicDetection", "SKIP", "disabled")
642:    log_stage(8, "Finalization", "START")
774:    log_stage(8, "Finalize", "DONE", f"{timings['finalization']:.1f}s")
783:    log_stage(9, "Output", "START")
793:    log_stage(9, "Output", "DONE", f"total={total_time:.1f}s")
967:        log_stage(10, "Export", "START")
993:                log_stage(10, "Export", "QUEUED", f"background job submitted")
1060:            log_stage(10, "Export", "DONE", f"{timings['export']:.1f}s | {segments_exported} segs | {tar_size_bytes/(1024**2):.1f}MB")


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

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

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

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

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

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

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

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


**Examining shutdown processes**

I need to consider the shutdown process and how it interacts with export threads. I’m thinking those threads should be running and logging, but maybe the logs aren’t showing up because of the root logger? It’s a bit confusing, and I’m not entirely sure. However, I should check the logs for "FastPipelineV6.Export" to get a clearer picture of what's happening. Let's search for that to find out more!


rg -n "FastPipelineV6\.Export" /tmp/maya3_smoke_e2e.log | head -n 40


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


Summary: auto


**Investigating export logs**

I’m trying to figure out why the export manager logs are absent. It seems that logs might not be captured if they happen after the container exits, but since the entire output was logged during the container run, maybe that’s not the issue. Background export threads run while the main process is active, and we call shutdown_export_manager at worker shutdown, which should log exports. The absence suggests that the pipeline may not have submitted a job correctly.


**Analyzing pipeline logging**

I’m puzzled about the PipelineLogger stage output. It might truncate the stage name, but I expected to see `[GPU0] 10/10 Export ...`. Since we didn’t see stage 10, it indicates it wasn’t in sync mode. It could still be in background mode, but if the export manager isn’t initialized, it would set background_export to false and revert to sync mode. In that case, a warning should log that the background export manager wasn’t initialized. I should search for "falling back" to confirm.


rg -n "falling back" /tmp/maya3_smoke_e2e.log | head -n 40


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


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

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

from typing import Optional
import sys

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


**Analyzing logging behavior**

I've figured out that `silence_verbose_loggers` sets the root logger to WARNING, silencing many logs, including the export logs which are set to ERROR. That explains why we hadn't seen export information. The GPU worker calls this function, so INFO logs from the export manager are suppressed, leaving only errors visible. This might be why we found no success logs, but our updated smoke test should validate remote key existence. However, our second run still had issues because it used an older script without the necessary remote key checks.


**Evaluating remote checks and logging**

I think we can check the remote key by looking at the smoke test log for the upload prefix and expected video ID, even though it might not be printed explicitly. We can try to compute it from the timestamp, but the script sets the date at runtime. Instead, I can check the R2 production bucket for objects with the prefix `smoke_test/`. It seems our script will automatically perform the remote existence check now. Additionally, I need to update the logger to suppress success logs. We should rerun the smoke test quickly to ensure everything is working.


**Preparing to rerun smoke test**

After making modifications, it’s important to rerun the basic smoke test to check for any errors. This should be a quick process, so I'll execute `sudo bash scripts/docker/smoke_test.sh` again, focusing on the non-E2E aspects. This will allow us to rebuild and perform the essential checks efficiently. I want to make sure everything is functioning smoothly without any hiccups! Let’s keep this quick and straightforward.


Output:
Exit code: 0
Wall time: 4.5 seconds
Output:
==> Building image: maya3-pipeline:local
#0 building with "default" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 2.44kB done
#1 DONE 0.0s

#2 [internal] load metadata for docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#2 DONE 0.0s

#3 [internal] load .dockerignore
#3 transferring context: 255B done
#3 DONE 0.0s

#4 [1/8] FROM docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 7.78kB done
#5 DONE 0.0s

#6 [4/8] RUN mkdir -p /root/.cache/torch/hub     && mkdir -p /root/.cache/huggingface     && mkdir -p /ephemeral/models
#6 CACHED

#7 [5/8] COPY requirements.txt .
#7 CACHED

#8 [2/8] RUN apt-get update && apt-get install -y --no-install-recommends     python3     python3-venv     python3-dev     python3-pip     python-is-python3     build-essential     ffmpeg     libsndfile1     libsox-fmt-all     sox     git     curl     wget     && rm -rf /var/lib/apt/lists/*
#8 CACHED

#9 [3/8] WORKDIR /workspace/maya3_data
#9 CACHED

#10 [6/8] RUN python3 -m venv .venv &&     .venv/bin/pip install --upgrade pip &&     .venv/bin/pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cu121 &&     .venv/bin/pip install --no-cache-dir -r requirements.txt &&     .venv/bin/pip install --no-cache-dir supabase &&     .venv/bin/pip install --no-cache-dir "nvidia-nccl-cu12==2.28.9"
#10 CACHED

#11 [7/8] COPY . .
#11 DONE 0.0s

#12 [8/8] RUN mkdir -p ./output
#12 DONE 0.1s

#13 exporting to image
#13 exporting layers 0.0s done
#13 writing image sha256:8cfaa4bceab7935741c22da85834f06b45050fa7ed5bd7e27c5baf42ec73685c done
#13 naming to docker.io/library/maya3-pipeline:local done
#13 DONE 0.0s
==> Basic entrypoint check (help output)
==> GPU + imports smoke test
torch 2.5.1+cu121
cuda_available True
gpu_count 1
==> Supabase connectivity smoke test
{'PENDING': 172598, 'CLAIMED': 0, 'COMPLETED': 3, 'FAILED': 10, 'SKIPPED': 0}
==> R2 connectivity smoke test
source_bucket test
production_bucket 1-cleaned-data
==> Done


**Evaluating E2E testing options**

So, the smoke test passed! I'm considering running E2E again, but maybe with R2_UPLOAD set to 0 to avoid heavy processing. I want to test the new remote existence check using a previously uploaded remote key, but that’s uncertain. Running E2E with a shorter video using the `--no-music-detection` flag could reduce time, but it’s not included in the script right now. It might be useful to add that as an optional environment variable. For local tests, I need to make sure our new fix prevents R2 upload failure at worker exit, even though success logs are suppressed. It's time-consuming, but I think we should run E2E again for confirmation.


**Patching for music detection**

I’m considering how to bypass long music detection using the `--no-music-detection` flag. I want to quickly modify smoke_test.sh to allow for the `NO_MUSIC_DETECTION=1` environment variable to add this flag to extra_args for E2E, which would speed things up. While we can set `BACKGROUND_EXPORT=1`, we'll need background export for testing our fix. Disabling music detection will help keep the run shorter and avoid unnecessary downloads. I’ll make these updates across the VIDEO_ID and Supabase queue branches too.


Input:
*** Begin Patch
*** Update File: scripts/docker/smoke_test.sh
@@
 R2_UPLOAD="${R2_UPLOAD:-0}"
 VIDEO_ID="${VIDEO_ID:-}"
+NO_MUSIC_DETECTION="${NO_MUSIC_DETECTION:-0}"
@@
   if [ -n "${VIDEO_ID}" ]; then
@@
     extra_args=(
       --urls "${video_url}"
       --gpus "${GPU_DEVICE}"
       --workers-per-gpu 1
       --background-export
       --export-workers 2
       --output ./output
     )
@@
   else
@@
     extra_args=(
       --supabase-queue
       --gpus "${GPU_DEVICE}"
       --workers-per-gpu 1
       --max-videos 1
       --background-export
       --export-workers 2
       --output ./output
     )
   fi
+
+  if [ "${NO_MUSIC_DETECTION}" = "1" ]; then
+    extra_args+=(--no-music-detection)
+  fi
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/docker/smoke_test.sh\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


sudo bash -lc 'cd /home/ubuntu/maya3 && E2E=1 VIDEO_ID=--E8hBLmJvw R2_UPLOAD=1 NO_MUSIC_DETECTION=1 GPU_DEVICE=0 bash scripts/docker/smoke_test.sh > /tmp/maya3_smoke_e2e2.log 2>&1; echo "exit_code=$?"; tail -n 120 /tmp/maya3_smoke_e2e2.log'


Output:
Exit code: 0
Wall time: 39.4 seconds
Output:
exit_code=0
#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 2.44kB done
#1 DONE 0.0s

#2 [internal] load metadata for docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#2 DONE 0.0s

#3 [internal] load .dockerignore
#3 transferring context: 255B done
#3 DONE 0.0s

#4 [1/8] FROM docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 7.92kB done
#5 DONE 0.0s

#6 [4/8] RUN mkdir -p /root/.cache/torch/hub     && mkdir -p /root/.cache/huggingface     && mkdir -p /ephemeral/models
#6 CACHED

#7 [5/8] COPY requirements.txt .
#7 CACHED

#8 [2/8] RUN apt-get update && apt-get install -y --no-install-recommends     python3     python3-venv     python3-dev     python3-pip     python-is-python3     build-essential     ffmpeg     libsndfile1     libsox-fmt-all     sox     git     curl     wget     && rm -rf /var/lib/apt/lists/*
#8 CACHED

#9 [3/8] WORKDIR /workspace/maya3_data
#9 CACHED

#10 [6/8] RUN python3 -m venv .venv &&     .venv/bin/pip install --upgrade pip &&     .venv/bin/pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cu121 &&     .venv/bin/pip install --no-cache-dir -r requirements.txt &&     .venv/bin/pip install --no-cache-dir supabase &&     .venv/bin/pip install --no-cache-dir "nvidia-nccl-cu12==2.28.9"
#10 CACHED

#11 [7/8] COPY . .
#11 DONE 0.0s

#12 [8/8] RUN mkdir -p ./output
#12 DONE 0.1s

#13 exporting to image
#13 exporting layers 0.0s done
#13 writing image sha256:7a2545b66c1385d05d1f5ab6d3039859dffd195ec12f24c38711c503508d064c done
#13 naming to docker.io/library/maya3-pipeline:local done
#13 DONE 0.0s
==> Basic entrypoint check (help output)
==> GPU + imports smoke test
torch 2.5.1+cu121
cuda_available True
gpu_count 1
==> Supabase connectivity smoke test
{'PENDING': 172598, 'CLAIMED': 0, 'COMPLETED': 3, 'FAILED': 10, 'SKIPPED': 0}
==> R2 connectivity smoke test
source_bucket test
production_bucket 1-cleaned-data
==> E2E test: process VIDEO_ID --E8hBLmJvw on GPU 0 (no Supabase claims)
19:44:28 | MassiveCompute | INFO     | Detected 1 GPUs: [0]
19:44:28 | MassiveCompute | INFO     | HF_TOKEN captured (length=37)
19:44:28 | MassiveCompute | INFO     | ======================================================================
19:44:28 | MassiveCompute | INFO     | MASSIVE COMPUTE - Multi-GPU Parallel Processing
19:44:28 | MassiveCompute | INFO     | ======================================================================
19:44:28 | MassiveCompute | INFO     | Videos: 1
19:44:28 | MassiveCompute | INFO     | GPUs: 1 ([0])
19:44:28 | MassiveCompute | INFO     | Workers/GPU: 1 (total: 1)
19:44:28 | MassiveCompute | INFO     | Output: ./output
19:44:28 | MassiveCompute | INFO     | ======================================================================
19:44:28 | MassiveCompute | INFO     | Started worker 0 on GPU 0
19:44:28 | Worker-0     | INFO     | Starting on GPU 0
19:44:29 | Worker-0     | INFO     | GPU: NVIDIA A100-SXM4-80GB (85.1GB)
Downloading: "https://github.com/snakers4/silero-vad/zipball/master" to /root/.cache/torch/hub/master.zip
19:44:39 | Worker-0     | INFO     | Prefetch thread started
19:44:42 | Worker-0     | INFO     | Processing (prefetched): https://www.youtube.com/watch?v=--E8hBLmJvw
[GPU0] PICK   --E8hBLmJvw
[GPU0] 1/10 Download       3.1s
[GPU0] 2/10 VAD            2.5s  | 1 segments
[GPU0] 3/10 Chunking       0.0s  | 1 chunks
[GPU0] 4/10 Diarization+   6.3s  | 1 segments
[GPU0] 5/10 Embeddings     6.5s  | 188 chunks
[GPU0] 6/10 Clustering     0.0s
[GPU0] 6/10 MicroContami   0.0s
[GPU0] 8/10 Finalize       0.5s
[GPU0] 9/10 Output         0.0s
[GPU0] DONE   --E8hBLmJvw 18.8s | 4.7min | 1 spk | 30%
19:45:01 | Worker-0     | INFO     | Done: --E8hBLmJvw | 19.1s | 1 speakers | 29.7% usable
19:45:01 | MassiveCompute | INFO     | [1/1] OK: --E8hBLmJvw (19.1s on GPU 0)
19:45:01 | Worker-0     | INFO     | Received stop signal
19:45:02 | Worker-0     | INFO     | Shutting down. Processed 1 videos.
19:45:02 | MassiveCompute | INFO     |
======================================================================
19:45:02 | MassiveCompute | INFO     | BENCHMARK RESULTS
19:45:02 | MassiveCompute | INFO     | ======================================================================
19:45:02 | MassiveCompute | INFO     | Videos: 1/1 successful
19:45:02 | MassiveCompute | INFO     | Failed: 0
19:45:02 | MassiveCompute | INFO     | Wall time: 33.8s (0.6 min)
19:45:02 | MassiveCompute | INFO     | Total audio: 0.08 hours
19:45:02 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:45:02 | MassiveCompute | INFO     | THROUGHPUT:
19:45:02 | MassiveCompute | INFO     |    Videos/min: 1.78
19:45:02 | MassiveCompute | INFO     |    Audio hours/wall hour: 8.41x realtime
19:45:02 | MassiveCompute | INFO     |    Parallel efficiency: 56.7%
19:45:02 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:45:02 | MassiveCompute | INFO     | Avg process time/video: 19.1s
19:45:02 | MassiveCompute | INFO     | ----------------------------------------------------------------------
19:45:02 | MassiveCompute | INFO     | AVG PER-STAGE TIME (successful videos):
19:45:02 | MassiveCompute | INFO     |    unified_embeddings        6.52s  ( 29.7%)
19:45:02 | MassiveCompute | INFO     |    diarization               6.29s  ( 28.6%)
19:45:02 | MassiveCompute | INFO     |    download                  3.13s  ( 14.2%)
19:45:02 | MassiveCompute | INFO     |    vad                       2.51s  ( 11.4%)
19:45:02 | MassiveCompute | INFO     |    micro_contamination       1.01s  (  4.6%)
19:45:02 | MassiveCompute | INFO     |    load_buffer               0.51s  (  2.3%)
19:45:02 | MassiveCompute | INFO     |    chunk_reassignment        0.51s  (  2.3%)
19:45:02 | MassiveCompute | INFO     |    quality_filter            0.50s  (  2.3%)
19:45:02 | MassiveCompute | INFO     |    segment_embeddings        0.50s  (  2.3%)
19:45:02 | MassiveCompute | INFO     |    finalization              0.50s  (  2.3%)
19:45:02 | MassiveCompute | INFO     |    chunking                  0.00s  (  0.0%)
19:45:02 | MassiveCompute | INFO     |    clustering                0.00s  (  0.0%)
19:45:02 | MassiveCompute | INFO     | ======================================================================
19:45:02 | MassiveCompute | INFO     | Results saved to: output/benchmark_results.json
==> Local TAR deleted (expected with upload). Verifying remote exists: smoke_test/20260107_194428/--E8hBLmJvw.tar
OK: remote TAR exists in R2
==> Done


**Updating progress on testing**

Great news! The E2E test passed, and the remote tar existence check succeeded. That indicates our background export fix is working for this case, and the R2 upload succeeded while the tar was deleted locally, just as expected. Alright, we've finished step 1 of the plan (local smoke + E2E). Now we should note that step 1 is complete and that step 2 is in progress, which involves checking Vast instances and logs. We've already checked and stopped them, so I'll update the plan.


Explanation: Local Docker smoke + E2E (with R2 upload verification) now pass. Next I’m confirming the current Vast test instances are in a safe state and summarizing what they were for.


Output: Plan updated


**Searching for GPU clusters**

I'm currently summarizing the situation for the user. They've asked about the three instances created, including one with a 4090 that failed due to a missing R2 object. I'll mention the current status where they have stopped or exited. Next, I'll begin step 3, searching for machines that fit the budget constraints. Using the Vast API key provided, I want to find offers for 8-GPU clusters under $4/hour, with 24-48GB GPU RAM and high reliability.


**Finding budget-friendly GPU clusters**

The user is looking for instances with 24-48GB of GPU RAM, ideally grouped in sets of 8, and for a total cost of less than $4/hour. I need to consider using 3090s and 4090s, creating either one instance with 8 GPUs or eight individual instances with 1 GPU.

I'll use the search parameters: `num_gpus=8, gpu_ram>=24, gpu_ram<=48, reliability>0.98, verified=true, rentable=true, dph_total<4`. Alternatively, I may also look for 1-GPU offers with some adjustments. We need to ensure they're high reliability and possibly prefer on-demand options as well.


**Preparing for GPU offers search**

The user requested high reliability and clusters under $4/hour, and on-demand options might work since I've previously found 8-GPU offers meeting these criteria. I’ll run searches for both on-demand and potentially reserved instances, prioritizing on-demand. I need to manage the output volume, perhaps using `--limit` or piping to head.

I'll look for offers with either 8x 3090s or 8x 4090s on a single host, considering GPU RAM from 24-48GB. No restriction on the GPU name, just ensuring the cost stays below $4. Let’s move ahead with the search!


**Running the GPU search command**

I’m preparing to execute the command to search for on-demand offers. The parameters include looking for 8 GPUs with conditions like being rentable, verified, and having high reliability, along with GPU RAM between 24 and 48 GB, while keeping the total cost under $4.

I’ll sort the results by `dph_total` and limit the output to the top 30 offers. Now, let’s run this command and see what we find!


vastai search offers --type on-demand 'num_gpus=8 rentable=True verified=True reliability>0.98 gpu_ram>=24 gpu_ram<=48 dph_total<4' -o 'dph_total' | head -n 40


Output:
Exit code: 0
Wall time: 0.6 seconds
Output:
ID        CUDA   N  Model       PCIE  cpu_ghz  vCPUs     RAM  Disk   $/hr    DLP    DLP/$   score  NV Driver   Net_up  Net_down  R      Max_Days  mach_id  status    host_id  ports  country
29516770  13.0  8x  RTX_A5000   25.0  2.3      96.0    386.9  1843   0.9614  227.0  236.07  211.2  580.95.05   638.6   1353.1    99.3   9.1       37643    verified  253867   798    Austria,_AT
29319005  13.0  8x  RTX_3090    12.7  2.8      53.9    217.2  2440   1.1747  226.0  192.36  157.4  580.95.05   835.6   891.7     98.8   181.8     50637    verified  203531   420    Bulgaria,_BG
26286151  13.0  8x  RTX_3090    21.5  3.1      112.0   515.6  3159   1.2279  272.0  221.49  144.8  580.82.09   85.2    420.9     99.8   79.8      44052    verified  124072   998    ,_CN
28570797  12.6  8x  RTX_3090    23.2  2.9      128.0   386.8  3093   1.2279  281.5  229.24  139.2  560.35.03   100.5   239.7     99.8   82.2      34731    verified  124072   998    ,_CN
27038514  12.4  8x  RTX_3090    23.3  2.9      128.0   773.8  5803   1.2281  281.3  229.02  138.5  550.120     137.3   183.8     99.4   102.6     25671    verified  135676   998    Hong_Kong,_HK
26286142  13.0  8x  RTX_3090    7.7   3.1      96.0    483.3  1566   1.2281  175.2  142.65  86.9   580.82.09   93.0    374.4     99.8   79.8      44051    verified  124072   998    ,_CN
29413020  13.0  8x  RTX_3090    12.5  3.9      32.0    257.6  2412   1.2313  217.2  176.41  150.2  580.82.09   731.1   933.2     99.8   24.5      5914     verified  142      998    Germany,_DE
27353962  12.9  8x  RTX_3090    16.0  3.3      48.0    257.6  1490   1.2812  166.5  129.99  89.3   575.64.03   129.7   360.9     99.8   110.6     42369    verified  124072   998    ,_CN
29427899  13.0  8x  RTX_4080S   18.3  3.5      144.0   515.6  1381   1.4199  203.8  143.51  102.8  580.95.05   89.9    369.3     99.5   149.7     46392    verified  124072   998    ,_CN
29354796  13.0  8x  Tesla_V100  11.8  3.9      80.0    772.7  12859  1.6014  155.0  96.77   88.8   580.95.05   549.7   787.2     99.9   51.8      2255     verified  4535     399    Minnesota,_US
28951025  13.0  8x  Tesla_V100  11.8  3.9      80.0    772.7  12875  1.6863  205.7  121.99  113.5  580.95.05   583.6   752.8     100.0  50.8      28601    verified  4535     399    ,_US
25562595  13.0  8x  Tesla_V100  11.7  3.9      64.0   1031.7  6025   1.7078  205.7  120.45  111.2  580.76.05   422.0   815.4     99.9   50.8      32334    verified  4535     798    ,_US
28498167  13.0  8x  RTX_3090    12.8  2.6      96.0    515.6  4640   1.7090  327.2  191.45  172.1  580.95.05   928.8   938.4     99.9   141.8     6811     verified  47994    98     Idaho,_US
28470822  12.0  8x  RTX_A5000   6.3   3.0      40.0    128.6  75     1.8147  118.5  65.28   55.4   525.60.11   614.3   687.5     99.8   146.9     7566     verified  11885    98     Washington,_US
13185448  12.4  8x  RTX_A5000   23.2  3.5      56.9    458.5  4118   1.8467  236.0  127.77  110.6  550.120     538.0   929.7     99.7   23.7      29126    verified  1647     887    Romania,_RO
29340260  12.8  8x  RTX_4090    10.3  3.5      144.0   644.6  2771   2.0814  254.7  122.36  109.6  570.195.03  652.1   823.6     99.7   356.5     43764    verified  109790   998    Taiwan,_TW
26760235  12.6  8x  RTX_4090    21.0  2.9      128.0   773.8  6021   2.2411  387.3  172.82  110.5  560.35.03   103.6   389.1     99.9   143.7     25321    verified  135676   998    ,_CN
23506072  12.6  8x  RTX_4090    23.6  2.9      128.0   515.8  2569   2.2411  442.5  197.43  119.3  560.35.03   84.7    194.3     99.9   143.4     26191    verified  124072   998    ,_CN
27358264  12.6  8x  RTX_4090    18.8  2.9      128.0   773.8  5886   2.2411  364.2  162.53  83.6   560.35.05   46.6    73.6      99.9   45.4      25430    verified  124072   998    ,_CN
29345609  12.8  8x  RTX_4090    23.9  2.0      224.0   515.7  6705   2.3494  420.9  179.14  164.9  570.195.03  841.2   1580.8    99.8   82.2      26415    verified  150178   98     Taiwan,_TW
25522278  12.9  8x  RTX_4090    12.8  2.8      64.0    257.6  2486   2.5614  277.2  108.22  85.4   575.64.03   905.5   804.6     98.7   143.8     42593    verified  134693   16383  Hong_Kong,_HK
27958980  13.0  8x  RTX_4090    23.8  2.2      256.0  1031.8  2582   2.6671  445.5  167.05  194.8  580.82.09   3332.0  4633.1    99.6   173.8     47429    verified  161561   29999  ,_US
29109351  13.0  8x  RTX_4090    23.8  2.2      256.0   515.7  7458   2.6681  445.6  167.00  150.9  580.95.05   1659.7  1424.5    99.1   205.6     51751    verified  213498   9998   The_Netherlands,_NL
24566585  12.8  8x  RTX_4090    23.2  2.4      128.0   515.8  3208   2.6681  443.3  166.15  186.7  570.158.01  944.1   6472.6    99.8   24.2      40894    verified  150781   64534  ,_US
29412581  13.0  8x  RTX_5090    48.1  4.0      144.0   515.6  6373   2.6736  901.4  337.15  336.7  580.95.05   916.8   889.1     99.5   1.4       48595    verified  355297   18999  Maine,_US
27444543  12.2  8x  RTX_4090    24.8  2.4      128.0  1032.0  3136   2.7534  454.6  165.11  140.2  535.230.02  516.0   938.3     99.4   23.7      18803    verified  70847    798    Thailand,_TH
28563358  13.0  8x  RTX_4090    11.7  3.0      80.0    385.6  2420   2.8814  247.7  85.96   97.4   580.105.08  4676.6  7606.2    99.2   149.9     49786    verified  44771    999    United_Kingdom,_GB
29667710  13.0  8x  RTX_4090    25.4  3.7      128.0  1019.4  6145   2.8814  461.3  160.10  150.5  580.95.05   867.5   902.0     99.9   158.1     28143    verified  78459    897    Norway,_NO
28623764  13.0  8x  RTX_5090    54.9  3.7      384.0  1031.7  12734  2.9874  826.3  276.61  209.3  580.95.05   140.0   629.6     98.9   52.4      37412    verified  94688    998    British_Columbia,_CA
28745100  13.0  8x  RTX_4090    7.5   3.0      80.0    418.8  1781   2.9876  205.4  68.74   73.9   580.105.08  5645.4  8698.2    98.0   138.1     48113    verified  44771    999    United_Kingdom,_GB
28611810  12.8  8x  RTX_5090    26.9  2.4      384.0   515.5  6310   2.9880  572.9  191.74  136.9  570.195.03  154.8   526.7     98.8   151.6     49894    verified  124072   998    ,_CN
26536132  12.6  8x  RTX_4090    21.4  3.7      113.8   458.5  5258   2.9881  421.6  141.11  158.0  560.35.05   3830.1  4563.1    99.8   52.1      30508    verified  178654   798    Bulgaria,_BG
29641498  12.8  8x  RTX_5090    46.2  4.0      128.0   515.6  5819   2.9881  873.4  292.29  212.2  570.181     117.5   293.5     99.6   180.5     42652    verified  135676   998    ,_CN
29526265  13.0  8x  RTX_5090    54.2  2.1      384.0   580.0  15045  2.9881  822.4  275.24  269.4  580.105.08  807.9   865.6     99.5   33.9      12704    verified  47786    998    Sweden,_SE
29425612  12.8  8x  RTX_5090    27.1  3.5      144.0   515.6  5719   2.9893  610.9  204.36  193.2  570.124.04  629.9   898.5     99.9   51.5      23978    verified  120872   98     Taiwan,_TW
28317627  13.0  8x  RTX_4090    23.7  3.5      128.0   515.8  5781   3.2014  444.7  138.92  144.3  580.82.09   2120.1  3668.9    99.9   142.9     9140     verified  18       398    Alberta,_CA
29505164  13.0  8x  RTX_5090    54.2  2.7      128.0   451.1  2913   3.2014  798.8  249.51  247.4  580.82.09   914.4   920.5     99.3   266.1     40113    verified  280921   999    Alberta,_CA
25550252  12.4  8x  RTX_4090    12.7  2.0      204.8   412.7  3664   3.2019  291.2  90.94   96.7   550.76      3199.7  4102.6    98.7   9.1       25976    verified  110667   958    Norway,_NO
26025652  12.4  8x  RTX_4090    12.8  2.5      170.7   343.9  3268   3.2019  265.5  82.91   90.3   550.76      5244.5  4476.0    99.6   13.1      31653    verified  207289   132    Norway,_NO
Exception ignored in: <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>
BrokenPipeError: [Errno 32] Broken pipe


Summary: auto


**Evaluating GPU offers**

The output has a lot of lines, and it ended with a BrokenPipeError, but that's alright. I've found many offers that meet the budget constraints. For clusters under $4/hr total, I discovered 8-GPU offers as low as $0.96/hr for 8x RTX A5000, which seems incredibly cheap! I need to check if these are on-demand and verify their reliability. The user is looking for GPUs with 24-48GB, and the A5000 fits that with 24GB, along with other models like the 3090 and 4090. I'll tighten the reliability search to >0.995 and proceed.


vastai search offers --type on-demand 'num_gpus=8 rentable=True verified=True reliability>0.995 gpu_ram>=24 gpu_ram<=48 dph_total<4' -o 'dph_total' | head -n 40


Output:
Exit code: 0
Wall time: 0.6 seconds
Output:
ID        CUDA   N  Model       PCIE  cpu_ghz  vCPUs     RAM  Disk   $/hr    DLP    DLP/$   score  NV Driver   Net_up  Net_down  R      Max_Days  mach_id  status    host_id  ports  country
28570797  12.6  8x  RTX_3090    23.2  2.9      128.0   386.8  3093   1.2279  281.5  229.24  145.5  560.35.03   100.5   239.7     99.8   82.2      34731    verified  124072   998    ,_CN
26286142  13.0  8x  RTX_3090    7.7   3.1      96.0    483.3  1566   1.2281  175.2  142.65  92.3   580.82.09   93.0    374.4     99.8   79.8      44051    verified  124072   998    ,_CN
29413020  13.0  8x  RTX_3090    12.5  3.9      32.0    257.6  2412   1.2313  217.2  176.41  152.3  580.82.09   731.1   933.2     99.8   24.5      5914     verified  142      998    Germany,_DE
27353962  12.9  8x  RTX_3090    16.0  3.3      48.0    257.6  1490   1.2812  166.5  129.99  85.1   575.64.03   129.7   360.9     99.8   110.6     42369    verified  124072   998    ,_CN
27856967  12.8  8x  RTX_3090    17.6  3.1      112.0   515.6  3098   1.2814  254.0  198.26  123.7  570.144     110.3   285.5     99.8   126.5     37162    verified  124072   998    ,_CN
29427899  13.0  8x  RTX_4080S   18.3  3.5      144.0   515.6  1381   1.4199  203.8  143.51  99.3   580.95.05   89.9    369.3     99.5   149.7     46392    verified  124072   998    ,_CN
29354796  13.0  8x  Tesla_V100  11.8  3.9      80.0    772.7  12859  1.6014  155.0  96.77   94.4   580.95.05   549.7   787.2     99.9   51.8      2255     verified  4535     399    Minnesota,_US
29006760  13.0  8x  Tesla_V100  11.8  3.9      80.0    772.7  12825  1.6867  205.7  121.96  110.4  580.95.05   543.4   739.0     100.0  22.8      27801    verified  4535     399    ,_US
25562595  13.0  8x  Tesla_V100  11.7  3.9      64.0   1031.7  6025   1.7078  205.7  120.45  113.4  580.76.05   422.0   815.4     99.9   50.8      32334    verified  4535     798    ,_US
28498167  13.0  8x  RTX_3090    12.8  2.6      96.0    515.6  4640   1.7090  327.2  191.45  163.1  580.95.05   928.8   938.4     99.9   141.8     6811     verified  47994    98     Idaho,_US
28470822  12.0  8x  RTX_A5000   6.3   3.0      40.0    128.6  75     1.8147  118.5  65.28   56.1   525.60.11   614.3   687.5     99.8   146.9     7566     verified  11885    98     Washington,_US
13185448  12.4  8x  RTX_A5000   23.2  3.5      56.9    458.5  4118   1.8467  236.0  127.77  104.4  550.120     538.0   929.7     99.7   23.7      29126    verified  1647     887    Romania,_RO
26760235  12.6  8x  RTX_4090    21.0  2.9      128.0   773.8  6021   2.2411  387.3  172.82  109.5  560.35.03   103.6   389.1     99.9   143.7     25321    verified  135676   998    ,_CN
23506072  12.6  8x  RTX_4090    23.6  2.9      128.0   515.8  2569   2.2411  442.5  197.43  122.5  560.35.03   84.7    194.3     99.9   143.4     26191    verified  124072   998    ,_CN
27358264  12.6  8x  RTX_4090    18.8  2.9      128.0   773.8  5886   2.2411  364.2  162.53  83.6   560.35.05   46.6    73.6      99.9   45.4      25430    verified  124072   998    ,_CN
29427430  12.8  8x  RTX_4090    24.0  2.0      224.0   515.7  6395   2.2428  421.8  188.09  167.0  570.195.03  876.1   1576.9    99.8   82.2      26344    verified  150178   98     Taiwan,_TW
29347820  12.8  8x  RTX_4090    12.4  3.7      152.0   773.4  2629   2.3481  268.4  114.32  95.0   570.195.03  594.2   808.5     99.8   401.9     38900    verified  109790   998    Taiwan,_TW
27958980  13.0  8x  RTX_4090    23.8  2.2      256.0  1031.8  2582   2.6671  445.5  167.05  198.3  580.82.09   3332.0  4633.1    99.6   173.8     47429    verified  161561   29999  ,_US
24566585  12.8  8x  RTX_4090    23.2  2.4      128.0   515.8  3208   2.6681  443.3  166.15  188.0  570.158.01  944.1   6472.6    99.8   24.2      40894    verified  150781   64534  ,_US
29667710  13.0  8x  RTX_4090    25.4  3.7      128.0  1019.4  6145   2.8814  461.3  160.10  138.1  580.95.05   867.5   902.0     99.9   158.1     28143    verified  78459    897    Norway,_NO
29370422  13.0  8x  RTX_5090    49.1  4.0      128.0   515.6  6342   2.9880  902.8  302.15  206.9  580.95.05   108.6   274.6     99.6   173.5     45964    verified  124072   998    ,_CN
26536132  12.6  8x  RTX_4090    21.4  3.7      113.8   458.5  5258   2.9881  421.6  141.11  160.4  560.35.05   3830.1  4563.1    99.8   52.1      30508    verified  178654   798    Bulgaria,_BG
29425612  12.8  8x  RTX_5090    27.1  3.5      144.0   515.6  5719   2.9893  610.9  204.36  186.6  570.124.04  629.9   898.5     99.9   51.5      23978    verified  120872   98     Taiwan,_TW
28745142  13.0  8x  RTX_4090    7.5   2.5      80.0    385.6  1407   3.2009  200.9  62.75   72.8   580.105.08  5485.6  8121.0    99.6   134.2     48136    verified  44771    999    United_Kingdom,_GB
28317627  13.0  8x  RTX_4090    23.7  3.5      128.0   515.8  5781   3.2014  444.7  138.92  153.1  580.82.09   2120.1  3668.9    99.9   142.9     9140     verified  18       398    Alberta,_CA
26442074  12.4  8x  RTX_4090    12.7  2.5      170.7   343.9  3391   3.2019  291.0  90.89   95.1   550.76      4592.0  4107.0    99.5   13.1      29809    verified  207289   132    Norway,_NO
26025652  12.4  8x  RTX_4090    12.8  2.5      170.7   343.9  3268   3.2019  265.5  82.91   97.1   550.76      5244.5  4476.0    99.6   13.1      31653    verified  207289   132    Norway,_NO
28017356  12.4  8x  RTX_4090    10.6  2.5      170.7   343.9  3039   3.2019  268.7  83.93   75.5   550.76      913.8   889.6     99.8   13.1      36643    verified  207289   132    Norway,_NO
29365333  13.0  8x  RTX_5090    53.8  3.8      192.0  1031.7  6390   3.2028  780.2  243.60  261.9  580.105.08  1041.5  2016.2    99.6   82.2      44852    verified  295624   98     Taiwan,_TW
29508715  13.0  8x  RTX_5090    54.9  2.4      384.0  1031.6  6437   3.2028  785.7  245.31  262.6  580.95.05   925.9   1778.1    99.8   82.2      40986    verified  150178   98     Taiwan,_TW
29317618  12.9  8x  RTX_5090    54.2  3.1      512.0  1031.6  5325   3.3734  937.4  277.88  256.5  575.64.05   167.8   1729.4    99.8   31.0      44288    verified  299311   999    India,_IN
28853575  13.0  8x  RTX_5090    54.1  2.4      192.0   515.4  6565   3.5752  912.7  255.29  227.1  580.105.08  657.2   787.4     99.6   51.2      34046    verified  125276   296    North_Macedonia,_MK
29451912  12.9  8x  RTX_5090    54.2  3.0      64.0    289.9  5338   3.7347  695.4  186.19  247.0  575.64.03   5552.6  4183.8    99.8   181.9     42793    verified  60400    49999  Texas,_US
29128375  12.8  8x  RTX_5090    54.1  3.1      128.0   386.6  6385   3.7347  936.6  250.79  342.5  570.169     7527.0  4086.6    99.8   128.9     40290    verified  60400    49999  Texas,_US
29249484  13.0  8x  RTX_5090    54.2  2.6      192.0   354.3  262    3.7347  798.0  213.67  245.4  580.95.05   3388.2  3483.6    99.7   203.9     44354    verified  18       248    Alberta,_CA
29167287  12.9  8x  RTX_5090    54.2  2.4      384.0   741.3  6163   3.9476  938.6  237.77  236.3  575.57.08   894.3   743.5     99.8   205.2     43180    verified  24953    898    Oregon,_US
28934719  12.9  8x  RTX_5090    54.2  2.6      192.0   773.6  4526   3.9485  937.8  237.50  280.9  575.57.08   3136.5  3928.3    99.6   265.9     39532    verified  18       248    Alberta,_CA


**Considering deployment factors**

I need to think about factors like CPU, disk, network, and more. For our pipeline, having a moderate disk for model caching and output seems necessary. I might consider at least 50-100GB disk for the docker image, especially since we're downloading large models that can be cached. We should also be careful about the GPU choice, considering reliability for long-term use and proposing a non-failure deployment strategy with pinned docker images and monitoring.


**Evaluating deployment methods**

The user is asking whether to create a Docker image for the workflow or depend on scripts. There's concern about new clusters having different versions. Using a Docker image is better for a reproducible environment since we’ve created and published one. But baking models into the image could make it huge and slow to pull, plus some models need a token for baking. I might suggest mounting a persistent volume for caching instead, or using onstart for prefetching models if we keep the instance running.


**Managing caches and deployment**

I’m considering whether caches persist if I restart a container or stop an instance. In this case, stopping keeps the disk, but destroying it means losing the caches. This is manageable, meaning we might not need to bake the models. I could consider a two-stage build to download models at build time using the HF_TOKEN, but that’s not recommended. For environment variables, I can't bake secrets, so I could pass those at runtime. The user also wants me to check machine configurations using an API key and to confirm that their Docker image is published—it's already done, but I need to respond.


**Enhancing pod management**

The user is asking for a strategic way to add new pods while scaling horizontally and observing failures in real-time, including details like ETA and RTF rates. I could suggest enhancing the `scripts/monitor/dashboard.py` to compute ETA, as mentioned in our handoff summary, but it hasn’t been done yet. They’re also curious why we didn't bake environment variables into the Docker image. I’ll explain the importance of secrets and suggest using Vast templates to reduce overhead. Also, they want to confirm that the Docker service is published—it's already taken care of.


**Updating Docker image**

I need to push an updated Docker image since we've made significant changes to scripts, massive_process, and the Dockerfile. The remote tag `6cfd8aa` is likely outdated and doesn't include important fixes, like the background export in `massive_process.py`. It’s critical for deployment, so I should rebuild the image with these changes and push it. While we have the option to tag it with a commit SHA, it might confuse since we haven’t committed yet. Pinned tags like `20260107-bgexportfix` seem better for reliability, so I’ll push that and update any relevant documentation accordingly.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
6cfd8aa


**Considering Docker image tagging**

I’m deciding on a new tag for the Docker image, considering options like `6cfd8aa-bgexportfix` or `6cfd8aa-20260107`. I’m leaning towards the latter since it includes the date. It's crucial not to leak the Docker Personal Access Token (PAT) while pushing. I think we might already be logged in or could try pushing without logging in. If that fails, I’ll log in. I’ll build the image and tag it before pushing it, considering layer changes to optimize the process.


**Analyzing Docker build layers**

I'm considering the steps in the Dockerfile, like using `apt-get`, copying requirements, and installing with pip. The last `COPY . .` step will likely create a new layer with the code, which is probably small. However, the layer with installed dependencies tends to be large and could already be cached on the registry. If I rebuild with the same base and dependencies, the layers should remain the same. Since only changed layers push during the Docker push, the process should be quick. I'll go ahead and build and push, calling the shell command with a long timeout.


TAG=6cfd8aa-bgexportfix
sudo docker build -t bharathkumar192/maya3-pipeline:${TAG} -t bharathkumar192/maya3-pipeline:latest .


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
#0 building with "default" instance using docker driver

#1 [internal] load build definition from Dockerfile
#1 transferring dockerfile: 2.44kB done
#1 DONE 0.0s

#2 [internal] load metadata for docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#2 DONE 0.0s

#3 [internal] load .dockerignore
#3 transferring context: 255B done
#3 DONE 0.0s

#4 [1/8] FROM docker.io/nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
#4 DONE 0.0s

#5 [internal] load build context
#5 transferring context: 2.64kB done
#5 DONE 0.0s

#6 [2/8] RUN apt-get update && apt-get install -y --no-install-recommends     python3     python3-venv     python3-dev     python3-pip     python-is-python3     build-essential     ffmpeg     libsndfile1     libsox-fmt-all     sox     git     curl     wget     && rm -rf /var/lib/apt/lists/*
#6 CACHED

#7 [3/8] WORKDIR /workspace/maya3_data
#7 CACHED

#8 [6/8] RUN python3 -m venv .venv &&     .venv/bin/pip install --upgrade pip &&     .venv/bin/pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cu121 &&     .venv/bin/pip install --no-cache-dir -r requirements.txt &&     .venv/bin/pip install --no-cache-dir supabase &&     .venv/bin/pip install --no-cache-dir "nvidia-nccl-cu12==2.28.9"
#8 CACHED

#9 [5/8] COPY requirements.txt .
#9 CACHED

#10 [4/8] RUN mkdir -p /root/.cache/torch/hub     && mkdir -p /root/.cache/huggingface     && mkdir -p /ephemeral/models
#10 CACHED

#11 [7/8] COPY . .
#11 CACHED

#12 [8/8] RUN mkdir -p ./output
#12 CACHED

#13 exporting to image
#13 exporting layers done
#13 writing image sha256:7a2545b66c1385d05d1f5ab6d3039859dffd195ec12f24c38711c503508d064c done
#13 naming to docker.io/bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix done
#13 naming to docker.io/bharathkumar192/maya3-pipeline:latest done
#13 DONE 0.0s


**Pushing tags**

Okay, I've completed the build quickly. Now I need to push both the new tag and the latest one. I should push the new tag first, then follow it up with the latest. It feels pretty straightforward, but I want to make sure I don’t mess it up. It’s always best to double-check that everything is in the right order. Let’s do this!


sudo docker push bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix


Output:
Exit code: 0
Wall time: 5.1 seconds
Output:
The push refers to repository [docker.io/bharathkumar192/maya3-pipeline]
179e1d06b6a9: Preparing
93ac991ad34b: Preparing
a22221bb7527: Preparing
5d1c6eaf0870: Preparing
6f56a5b1463d: Preparing
82996703b6fb: Preparing
f98cd07ef297: Preparing
30cb8a64ad61: Preparing
600c676771a0: Preparing
6ac15100dff6: Preparing
40f0eb1871b9: Preparing
8d113b7b997c: Preparing
cd77f58b80cd: Preparing
e4b1bddcbe63: Preparing
765423415d69: Preparing
82996703b6fb: Waiting
7b9433fba79b: Preparing
256d88da4185: Preparing
30cb8a64ad61: Waiting
6ac15100dff6: Waiting
40f0eb1871b9: Waiting
600c676771a0: Waiting
f98cd07ef297: Waiting
256d88da4185: Waiting
8d113b7b997c: Waiting
765423415d69: Waiting
cd77f58b80cd: Waiting
e4b1bddcbe63: Waiting
7b9433fba79b: Waiting
5d1c6eaf0870: Layer already exists
6f56a5b1463d: Layer already exists
a22221bb7527: Layer already exists
82996703b6fb: Layer already exists
f98cd07ef297: Layer already exists
30cb8a64ad61: Layer already exists
600c676771a0: Layer already exists
6ac15100dff6: Layer already exists
40f0eb1871b9: Layer already exists
8d113b7b997c: Layer already exists
cd77f58b80cd: Layer already exists
e4b1bddcbe63: Layer already exists
765423415d69: Layer already exists
7b9433fba79b: Layer already exists
256d88da4185: Layer already exists
179e1d06b6a9: Pushed
93ac991ad34b: Pushed
6cfd8aa-bgexportfix: digest: sha256:eb6cb18b262ca3c0f657371545516d6606f957721204e639e3d7224879ce2c51 size: 3885


sudo docker push bharathkumar192/maya3-pipeline:latest


Output:
Exit code: 0
Wall time: 2.6 seconds
Output:
The push refers to repository [docker.io/bharathkumar192/maya3-pipeline]
179e1d06b6a9: Preparing
93ac991ad34b: Preparing
a22221bb7527: Preparing
5d1c6eaf0870: Preparing
6f56a5b1463d: Preparing
82996703b6fb: Preparing
f98cd07ef297: Preparing
30cb8a64ad61: Preparing
600c676771a0: Preparing
6ac15100dff6: Preparing
40f0eb1871b9: Preparing
8d113b7b997c: Preparing
cd77f58b80cd: Preparing
e4b1bddcbe63: Preparing
765423415d69: Preparing
7b9433fba79b: Preparing
256d88da4185: Preparing
600c676771a0: Waiting
82996703b6fb: Waiting
6ac15100dff6: Waiting
f98cd07ef297: Waiting
40f0eb1871b9: Waiting
cd77f58b80cd: Waiting
765423415d69: Waiting
e4b1bddcbe63: Waiting
8d113b7b997c: Waiting
7b9433fba79b: Waiting
256d88da4185: Waiting
30cb8a64ad61: Waiting
a22221bb7527: Layer already exists
179e1d06b6a9: Layer already exists
5d1c6eaf0870: Layer already exists
93ac991ad34b: Layer already exists
6f56a5b1463d: Layer already exists
600c676771a0: Layer already exists
f98cd07ef297: Layer already exists
82996703b6fb: Layer already exists
6ac15100dff6: Layer already exists
30cb8a64ad61: Layer already exists
40f0eb1871b9: Layer already exists
8d113b7b997c: Layer already exists
cd77f58b80cd: Layer already exists
e4b1bddcbe63: Layer already exists
765423415d69: Layer already exists
7b9433fba79b: Layer already exists
256d88da4185: Layer already exists
latest: digest: sha256:eb6cb18b262ca3c0f657371545516d6606f957721204e639e3d7224879ce2c51 size: 3885


**Considering cluster options**

I'm looking at offers that may have hosts in China, which could have network restrictions or be slower—it's likely better to choose US or Europe instead. It's also useful to keep the cluster in one region for a stable network connection to supabase and R2.

Additionally, we should think about disk space; 100GB is a good starting point. The user wants multiple clusters with configurations of 8 GPUs, so I might suggest creating clusters like `maya3-cluster-01` and `maya3-cluster-02`.


**Planning cluster deployment**

I can launch one 8-GPU instance at a time, or if availability is low, use eight 1-GPU hosts to still total 8 GPUs. We need to run a lease-recovery script, which interacts with supabase leases, and it can be global—no need to run it on each cluster; just one global instance will do to avoid conflicts.

Now they've asked for a high-level overview of deploying across multiple clusters at Vast AI. I should mention that the user needs to add an SSH public key to their account, either through the UI or CLI, and that the key applies to new instances.


**Providing deployment instructions**

I can guide the user to their Vast account, directing them to the SSH keys section to paste their key or use a CLI command for that. But since we've already added it, they might still ask.

They’re also wondering whether to create a Docker image for deployment or depend on scripts in deploy_all.py. I believe Docker is the better choice. Now that we have the service built, I should confirm if it's published and pushed using the Docker PAT.

To monitor growth and failures, we can suggest keeping a small monitoring process, like dashboard.py, running locally or on a small instance to query Supabase analytics tables.


**Scaling and monitoring strategies**

To scale effectively, each worker will report analytics so we can keep track of RTF, throughput, and failure rates.

I should run `lease_recovery.py --daemon` once to reclaim any stuck claims. Labeling each instance with its cluster and GPU index is also important.

For running the configuration, I’ll pass in the necessary environment variables and worker arguments for the container. With an 8-GPU instance, the script can detect GPUs and assign one worker per GPU, although I think keeping the default at `--workers-per-gpu 1` is wise.

For reliability, I plan to implement "rolling" scaling: starting with one instance for 15 minutes of monitoring, then gradually adding more if things remain stable.


**Implementing scaling and error handling**

I’m considering using auto-scaling for efficiency. For error handling, I think it's essential to categorize issues like missing R2 sources, model download errors, or out-of-memory (OOM) errors. If there's a missing R2 source, marking it as SKIPPED instead of FAILED might lead to improvements.

I’d also like to compute ETA as pending tasks divided by throughput, and we can implement this in the dashboard. I should look into the `scripts/monitor/dashboard.py` to see if it already displays some relevant stats so we can enhance it further with throughput metrics and ETA.


sed -n '1,240p' scripts/monitor/dashboard.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Real-time Monitoring Dashboard for Maya3 Distributed Pipeline.

Displays:
- Cluster-wide progress
- Per-worker status
- Error rates and common failures
- Queue depth and throughput

Usage:
    python dashboard.py                    # One-time status
    python dashboard.py --watch            # Live updates (every 30s)
    python dashboard.py --watch --interval 10  # Custom interval
"""

import os
import sys
import time
import argparse
from datetime import datetime, timedelta
from typing import Dict, List, Any

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

from dotenv import load_dotenv
load_dotenv()

from supabase import create_client

# ANSI colors
class C:
    RESET = '\033[0m'
    BOLD = '\033[1m'
    RED = '\033[91m'
    GREEN = '\033[92m'
    YELLOW = '\033[93m'
    BLUE = '\033[94m'
    CYAN = '\033[96m'
    DIM = '\033[2m'

def clear_screen():
    """Clear terminal screen."""
    print('\033[2J\033[H', end='')

def format_duration(seconds: float) -> str:
    """Format seconds as human-readable duration."""
    if seconds < 60:
        return f"{seconds:.0f}s"
    elif seconds < 3600:
        return f"{seconds/60:.1f}m"
    else:
        return f"{seconds/3600:.1f}h"

def format_number(n: int) -> str:
    """Format number with commas."""
    return f"{n:,}"

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

        return [{'hour': k, **v} for k, v in sorted(hourly.items())]

    def get_stale_workers(self, threshold_sec: int = 300) -> List[Dict]:
        """Find workers that haven't reported in threshold seconds."""
        threshold = (datetime.utcnow() - timedelta(seconds=threshold_sec)).isoformat()
        result = self.sb.table('worker_heartbeats').select('*').neq(
            'status', 'offline'
        ).lt('last_heartbeat', threshold).execute()
        return result.data or []

    def print_header(self):
        """Print dashboard header."""
        now = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S UTC')
        print(f"\n{C.BOLD}{C.CYAN}╔══════════════════════════════════════════════════════════════════════╗{C.RESET}")
        print(f"{C.BOLD}{C.CYAN}║{C.RESET}            {C.BOLD}MAYA3 DISTRIBUTED PIPELINE DASHBOARD{C.RESET}                      {C.CYAN}║{C.RESET}")
        print(f"{C.BOLD}{C.CYAN}║{C.RESET}                       {now}                       {C.CYAN}║{C.RESET}")
        print(f"{C.BOLD}{C.CYAN}╚══════════════════════════════════════════════════════════════════════╝{C.RESET}")

    def print_queue_stats(self, stats: Dict[str, int]):
        """Print queue statistics."""
        total = sum(stats.values())
        completed = stats.get('COMPLETED', 0)
        failed = stats.get('FAILED', 0)
        pending = stats.get('PENDING', 0)
        claimed = stats.get('CLAIMED', 0)

        progress = (completed / total * 100) if total > 0 else 0
        error_rate = (failed / (completed + failed) * 100) if (completed + failed) > 0 else 0

        print(f"\n{C.BOLD}📊 QUEUE STATUS{C.RESET}")
        print(f"{'─'*50}")

        # Progress bar
        bar_width = 40
        filled = int(bar_width * progress / 100)
        bar = '█' * filled + '░' * (bar_width - filled)
        print(f"  Progress: [{C.GREEN}{bar}{C.RESET}] {progress:.1f}%")

        # Stats grid
        print(f"  {C.GREEN}✓ Completed:{C.RESET} {format_number(completed):>8}  │  {C.YELLOW}⏳ Pending:{C.RESET} {format_number(pending):>8}")
        print(f"  {C.RED}✗ Failed:{C.RESET}    {format_number(failed):>8}  │  {C.BLUE}🔄 Claimed:{C.RESET} {format_number(claimed):>8}")
        print(f"  {C.DIM}Total:{C.RESET}       {format_number(total):>8}  │  {C.DIM}Error Rate:{C.RESET} {error_rate:.2f}%")

    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."""


Summary: auto


sed -n '240,520p' scripts/monitor/dashboard.py


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

        print(f"\n{C.BOLD}{C.YELLOW}⚠  STALE WORKERS (no heartbeat >5min){C.RESET}")
        print(f"{'─'*50}")

        for w in stale:
            worker_id = w.get('worker_id', '?')
            last_hb = w.get('last_heartbeat', '')
            video = w.get('current_video_id', '')

            print(f"  {C.YELLOW}•{C.RESET} {worker_id} - last seen: {last_hb[:19]} - video: {video}")

    def run(self, watch: bool = False, interval: int = 30):
        """Run the dashboard."""
        try:
            while True:
                if watch:
                    clear_screen()

                # Gather data
                queue_stats = self.get_queue_stats()
                workers = self.get_worker_status()
                errors = self.get_error_summary()
                recent_errors = self.get_recent_errors(5)
                hourly = self.get_hourly_throughput()
                stale = self.get_stale_workers()

                # Print sections
                self.print_header()
                self.print_queue_stats(queue_stats)
                self.print_worker_status(workers)
                self.print_throughput(hourly)
                self.print_error_summary(errors, recent_errors)
                self.print_stale_workers(stale)

                print(f"\n{C.DIM}Press Ctrl+C to exit{C.RESET}")

                if not watch:
                    break

                time.sleep(interval)

        except KeyboardInterrupt:
            print(f"\n{C.DIM}Dashboard stopped{C.RESET}")

def main():
    parser = argparse.ArgumentParser(description="Maya3 Pipeline Dashboard")
    parser.add_argument('--watch', '-w', action='store_true', help='Live updates')
    parser.add_argument('--interval', '-i', type=int, default=30, help='Update interval (seconds)')
    args = parser.parse_args()

    dashboard = Dashboard()
    dashboard.run(watch=args.watch, interval=args.interval)

if __name__ == "__main__":
    main()


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', {


Output:
Exit code: 0
Wall time: 0 seconds
Output:
        self._queue.put(AnalyticsEvent('heartbeat', {
            'worker_id': self.worker_id,
            'machine_id': self.machine_id,
            'gpu_id': self.gpu_id,
            'status': 'processing',
            'current_video_id': video_id,
            'current_stage': stage_num,
            'current_stage_name': stage_name,
            'last_heartbeat': datetime.utcnow().isoformat(),
            'session_start': self.session_start_time.isoformat(),
            'session_videos_done': self.session_videos_done,
            'session_videos_failed': self.session_videos_failed,
            'session_audio_minutes': self.session_audio_minutes,
            'session_usable_minutes': self.session_usable_minutes
        }))

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    def shutdown(self): pass

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

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

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


**Evaluating processing metrics**

I’m looking at the processing_events table and considering how to compute metrics like the real-time factor (RTF) using duration and audio durations. So if audio_minutes is 4.7 and duration_seconds is 19.1, I’ll calculate RTF based on the audio seconds over the processing time. I can also enhance the dashboard to show metrics like recent throughput and average RTF for completed events. Plus, estimating the pending audio hours might help with overall efficiency.


**Calculating processing metrics**

I'm calculating the estimated time of arrival (ETA) using pending audio hours over throughput audio hours per hour. I can also compute the failure rate based on recent events. I need to make sure I manage Supabase queries efficiently, since some functions can be heavy, especially when counting errors. While the user wants real-time stats like failure rates and RTF, optimizing the queries to limit data pulled might help improve performance. It'd be beneficial to implement grouped queries to streamline this.


**Designing metrics methods**

I’m brainstorming some new methods to compute real-time factors (RTF) and ETA using the last 24 hours of processing_event data. One idea is to create a method called `get_recent_throughput` that queries done events within a given time frame, selecting relevant data like audio minutes and duration seconds. I can compute total audio minutes and real-time audio hours to help track efficiency. I also want to handle failure counts effectively by querying both done and failed events together to minimize API calls, which could streamline the process.


**Estimating metrics and performance**

I'm considering how to implement the dashboard metrics using Supabase. While it supports the `.in_()` method, I could also use separate queries for done and failed events, which is okay. To compute the ETA, I want to gather pending counts from the queue. By estimating average audio minutes per video and throughput based on recently done events, I can effectively calculate ETA in hours or days. I also plan to display the RTF factor and overall cluster runtime with minimal modifications, adding a method for recent performance to the dashboard.


**Implementing performance metrics**

The user likely wants comprehensive real-time stats, like live throughput and ETA, displayed prominently. I can incorporate a new method to calculate recent performance over a specified window. This involves querying done events to get necessary metrics while ensuring I can count fail events without pulling all rows, which helps minimize data load. I'll also need to format and present the information clearly, showing both video and audio throughput, as well as any other useful metrics. This will enhance the dashboard without disrupting existing setup.