Input:
*** Begin Patch
*** Update File: src/supabase_client.py
@@
 import os
 import json
 import logging
 import time
+import random
+import socket
 from datetime import datetime, timezone
 from typing import Dict, Any, Optional, List
 from urllib.request import Request, urlopen
 from urllib.error import HTTPError, URLError
 from urllib.parse import urlencode

 logger = logging.getLogger("SupabaseClient")
@@
 class SupabaseClient:
@@
     def __init__(self, url: str = None, key: str = None):
@@
         self.rest_url = f"{self.url}/rest/v1"
         self.headers = {
             'apikey': self.key,
             'Authorization': f'Bearer {self.key}',
             'Content-Type': 'application/json',
             'Prefer': 'return=representation'
         }

+        self.timeout_sec = float(os.environ.get('SUPABASE_HTTP_TIMEOUT_SEC', '30') or 30)
+        self.max_retries = int(os.environ.get('SUPABASE_HTTP_MAX_RETRIES', '5') or 5)
+        self.backoff_base_sec = float(os.environ.get('SUPABASE_HTTP_BACKOFF_BASE_SEC', '0.5') or 0.5)
+        self.backoff_max_sec = float(os.environ.get('SUPABASE_HTTP_BACKOFF_MAX_SEC', '15') or 15)
+
         self._initialized = True
         logger.info(f"SupabaseClient initialized: {self.url}")

+    def _sleep_backoff(self, attempt: int) -> None:
+        # Full-jitter exponential backoff.
+        cap = max(0.01, self.backoff_max_sec)
+        base = max(0.01, self.backoff_base_sec)
+        delay = min(cap, base * (2 ** attempt))
+        time.sleep(random.random() * delay)
+
+    @staticmethod
+    def _is_retryable_http_status(status_code: int) -> bool:
+        return status_code in {408, 425, 429, 500, 502, 503, 504}
+
     def _request(
         self,
         method: str,
         endpoint: str,
         data: dict = None,
         params: dict = None,
-        headers_override: dict = None
+        headers_override: dict = None,
+        timeout: Optional[float] = None,
+        max_retries: Optional[int] = None,
     ) -> Any:
         """Make HTTP request to Supabase REST API."""
         url = f"{self.rest_url}/{endpoint}"
         if params:
             url = f"{url}?{urlencode(params)}"

         headers = {**self.headers, **(headers_override or {})}

         body = json.dumps(data).encode('utf-8') if data else None

         req = Request(url, data=body, headers=headers, method=method)

-        try:
-            with urlopen(req, timeout=30) as resp:
-                content = resp.read().decode('utf-8')
-                return json.loads(content) if content else None
-        except HTTPError as e:
-            error_body = e.read().decode('utf-8')
-            logger.error(f"Supabase error {e.code}: {error_body}")
-            raise
-        except URLError as e:
-            logger.error(f"Network error: {e.reason}")
-            raise
+        request_timeout = self.timeout_sec if timeout is None else timeout
+        if max_retries is None:
+            # Avoid automatic retries on non-idempotent calls (e.g., POST).
+            max_retries = self.max_retries if method.upper() in {'GET', 'PATCH', 'PUT', 'DELETE'} else 0
+
+        for attempt in range(max_retries + 1):
+            try:
+                with urlopen(req, timeout=request_timeout) as resp:
+                    content = resp.read().decode('utf-8')
+                    return json.loads(content) if content else None
+            except HTTPError as e:
+                error_body = ""
+                try:
+                    error_body = e.read().decode('utf-8')
+                except Exception:
+                    pass
+
+                if attempt < max_retries and self._is_retryable_http_status(e.code):
+                    logger.warning(f"Supabase transient error {e.code} on {method} {endpoint}: {error_body[:200]}")
+                    self._sleep_backoff(attempt)
+                    continue
+
+                logger.error(f"Supabase error {e.code} on {method} {endpoint}: {error_body[:500]}")
+                raise
+            except (TimeoutError, socket.timeout) as e:
+                if attempt < max_retries:
+                    logger.warning(f"Supabase timeout on {method} {endpoint}: {e} (retry {attempt + 1}/{max_retries})")
+                    self._sleep_backoff(attempt)
+                    continue
+                logger.error(f"Supabase timeout on {method} {endpoint}: {e}")
+                raise
+            except URLError as e:
+                # URLError.reason often wraps socket.timeout, DNS failures, connection resets, etc.
+                if attempt < max_retries:
+                    logger.warning(
+                        f"Supabase network error on {method} {endpoint}: {getattr(e, 'reason', e)} "
+                        f"(retry {attempt + 1}/{max_retries})"
+                    )
+                    self._sleep_backoff(attempt)
+                    continue
+                logger.error(f"Supabase network error on {method} {endpoint}: {getattr(e, 'reason', e)}")
+                raise

-    def _rpc(self, function_name: str, params: dict = None) -> Any:
+    def _rpc(
+        self,
+        function_name: str,
+        params: dict = None,
+        timeout: Optional[float] = None,
+        max_retries: int = 0,
+    ) -> Any:
         """Call Supabase RPC function."""
         url = f"{self.url}/rest/v1/rpc/{function_name}"

         body = json.dumps(params or {}).encode('utf-8')
         req = Request(url, data=body, headers=self.headers, method='POST')

-        try:
-            with urlopen(req, timeout=30) as resp:
-                content = resp.read().decode('utf-8')
-                return json.loads(content) if content else None
-        except HTTPError as e:
-            error_body = e.read().decode('utf-8')
-            logger.error(f"RPC error {e.code}: {error_body}")
-            raise
+        request_timeout = self.timeout_sec if timeout is None else timeout
+
+        for attempt in range(max_retries + 1):
+            try:
+                with urlopen(req, timeout=request_timeout) as resp:
+                    content = resp.read().decode('utf-8')
+                    return json.loads(content) if content else None
+            except HTTPError as e:
+                error_body = ""
+                try:
+                    error_body = e.read().decode('utf-8')
+                except Exception:
+                    pass
+
+                if attempt < max_retries and self._is_retryable_http_status(e.code):
+                    logger.warning(f"RPC transient error {e.code} on {function_name}: {error_body[:200]}")
+                    self._sleep_backoff(attempt)
+                    continue
+
+                logger.error(f"RPC error {e.code} on {function_name}: {error_body[:500]}")
+                raise
+            except (TimeoutError, socket.timeout) as e:
+                if attempt < max_retries:
+                    logger.warning(f"RPC timeout on {function_name}: {e} (retry {attempt + 1}/{max_retries})")
+                    self._sleep_backoff(attempt)
+                    continue
+                logger.error(f"RPC timeout on {function_name}: {e}")
+                raise
+            except URLError as e:
+                if attempt < max_retries:
+                    logger.warning(
+                        f"RPC network error on {function_name}: {getattr(e, 'reason', e)} "
+                        f"(retry {attempt + 1}/{max_retries})"
+                    )
+                    self._sleep_backoff(attempt)
+                    continue
+                logger.error(f"RPC network error on {function_name}: {getattr(e, 'reason', e)}")
+                raise
+
+    def get_active_claim(self, worker_id: str) -> Optional[Dict[str, Any]]:
+        """Return an existing (non-expired) claim for this worker, if any."""
+        now = datetime.now(timezone.utc).isoformat()
+        params = {
+            'status': f'eq.{STATUS_CLAIMED}',
+            'claimed_by': f'eq.{worker_id}',
+            'lease_expires_at': f'gt.{now}',
+            'order': 'claimed_at.desc',
+            'limit': '1',
+        }
+
+        result = self._request('GET', 'videos', params=params)
+        return result[0] if result else None

     def claim_next_video(
         self,
         worker_id: str,
         lease_duration_sec: int = 900,
         language: str = None
     ) -> Optional[Dict[str, Any]]:
@@
         Returns:
             Video dict with youtube_id, youtube_url, title, etc. or None if queue empty
         """
-        try:
-            # Try RPC function first (atomic)
-            result = self._rpc('claim_next_video', {
-                'p_worker_id': worker_id,
-                'p_lease_duration_sec': lease_duration_sec
-            })
-
-            if result and len(result) > 0:
-                video = result[0]
-                logger.info(f"Claimed video: {video.get('youtube_id')} by {worker_id}")
-                return video
-            return None
-
-        except HTTPError as e:
-            if e.code == 404:
-                # RPC function not found, fall back to REST API
-                logger.warning("claim_next_video RPC not found, using REST fallback")
-                return self._claim_video_rest(worker_id, lease_duration_sec, language)
-            raise
+        # If a previous claim succeeded but the worker crashed / lost the response,
+        # resume it instead of claiming yet another video.
+        try:
+            existing = self.get_active_claim(worker_id)
+            if existing:
+                logger.info(f"Resuming existing claim: {existing.get('youtube_id')} by {worker_id}")
+                return existing
+        except Exception:
+            pass
+
+        for attempt in range(self.max_retries + 1):
+            try:
+                # Try RPC function first (atomic). Avoid automatic retries here:
+                # claim_next_video is not strictly idempotent.
+                result = self._rpc('claim_next_video', {
+                    'p_worker_id': worker_id,
+                    'p_lease_duration_sec': lease_duration_sec
+                }, max_retries=0)
+
+                if result and len(result) > 0:
+                    video = result[0]
+                    logger.info(f"Claimed video: {video.get('youtube_id')} by {worker_id}")
+                    return video
+                return None
+
+            except HTTPError as e:
+                if e.code == 404:
+                    # RPC function not found, fall back to REST API
+                    logger.warning("claim_next_video RPC not found, using REST fallback")
+                    return self._claim_video_rest(worker_id, lease_duration_sec, language)
+
+                if attempt < self.max_retries and self._is_retryable_http_status(e.code):
+                    logger.warning(f"Transient RPC HTTP {e.code} for claim_next_video (attempt {attempt + 1}/{self.max_retries})")
+                    self._sleep_backoff(attempt)
+                    continue
+                raise
+            except (URLError, TimeoutError, socket.timeout) as e:
+                # If the request timed out after the claim was committed, we can
+                # recover by looking for an active claim for this worker.
+                try:
+                    existing = self.get_active_claim(worker_id)
+                    if existing:
+                        logger.warning(f"Recovered active claim after RPC error: {existing.get('youtube_id')} ({e})")
+                        return existing
+                except Exception:
+                    pass
+
+                if attempt < self.max_retries:
+                    logger.warning(f"Transient RPC error for claim_next_video: {e} (attempt {attempt + 1}/{self.max_retries})")
+                    self._sleep_backoff(attempt)
+                    continue
+                raise

     def _claim_video_rest(
         self,
         worker_id: str,
         lease_duration_sec: int,
*** End Patch


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


Summary: auto


**Patching code structure**

I'm looking at the code to ensure we didn't break any typings while adding random and socket. We've already imported `Optional`, so that's good. It's time to patch the `massive_process.py` file according to the plan. I need to incorporate the vast instance ID while modifying the pod ID and machine ID. I think I should apply the patch around line 470. Let's go ahead and apply that patch now!


Input:
*** Begin Patch
*** Update File: massive_process.py
@@
-    # Get pod ID for unique worker identification
-    pod_id = os.environ.get('RUNPOD_POD_ID', os.environ.get('HOSTNAME', 'local'))
-    supabase_worker_id = f"gpu-{gpu_id}-{pod_id}"
+    # Get unique worker identification.
+    # Vast.ai sets CONTAINER_ID to the instance (contract) id.
+    pod_id = os.environ.get('RUNPOD_POD_ID', os.environ.get('HOSTNAME', 'local'))
+    vast_instance_id = os.environ.get('CONTAINER_ID')
+    machine_id = vast_instance_id or pod_id
+    supabase_worker_id = f"gpu-{gpu_id}-{machine_id}"

     worker_logger.info(f"Starting worker {supabase_worker_id} on GPU {gpu_id}")
@@
         analytics = create_reporter(
             gpu_id=gpu_id,
             supabase_url=os.environ.get('URL') or os.environ.get('SUPABASE_URL'),
             supabase_key=os.environ.get('SUPABASE_ADMIN'),
-            machine_id=pod_id,
+            machine_id=machine_id,
             enabled=True
         )
         analytics.session_start()
         worker_logger.info(f"Analytics initialized: {analytics.worker_id}")
@@
-            # Claim next video from Supabase
-            video = supabase.claim_next_video(
-                worker_id=supabase_worker_id,
-                lease_duration_sec=lease_duration_sec
-            )
+            # Claim next video from Supabase (transient network failures should
+            # never kill the worker process).
+            try:
+                video = supabase.claim_next_video(
+                    worker_id=supabase_worker_id,
+                    lease_duration_sec=lease_duration_sec
+                )
+            except Exception as e:
+                worker_logger.warning(f"Supabase claim_next_video failed: {e}")
+                time.sleep(poll_interval_sec)
+                continue
@@
-            # Fetch full video record to get chapters
-            full_video = supabase.get_video(video_id)
-            if not full_video:
-                full_video = video  # Fallback to claim result
+            # Fetch full video record to get chapters (best-effort; fall back to
+            # the claim payload if Supabase is slow/unreachable).
+            full_video = None
+            try:
+                full_video = supabase.get_video(video_id)
+            except Exception as e:
+                worker_logger.warning(f"Supabase get_video failed for {video_id}: {e}")
+            if not full_video:
+                full_video = video  # Fallback to claim result
@@
                     'processing_meta': {
                         'worker_id': supabase_worker_id,
                         'gpu_id': gpu_id,
+                        'vast_instance_id': vast_instance_id,
                         'timing_total': result.get('timing_total', 0),
                         'completed_at': datetime.now().isoformat(),
                         'pipeline_version': result.get('pipeline_version', ''),
                     },
@@
                 supabase.update_status(video_id, STATUS_FAILED, {
                     'last_error': error_msg[:1000],  # Truncate long errors
                     'last_error_type': type(e).__name__,
                     'processing_meta': {
                         'worker_id': supabase_worker_id,
                         'gpu_id': gpu_id,
+                        'vast_instance_id': vast_instance_id,
                         'failed_at': datetime.now().isoformat(),
                     },
                 })
@@
-            # Clear GPU cache between videos
-            MODELS.clear_cache(aggressive=True)
+            # Clear GPU cache between videos
+            try:
+                MODELS.clear_cache(aggressive=True)
+            except Exception as e:
+                worker_logger.warning(f"GPU cache clear failed: {e}")

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


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


Input:
*** Begin Patch
*** Update File: src/analytics.py
@@
 import threading
 import queue
 import time
 import socket
+import os
 import hashlib
 import traceback
 from typing import Optional, Dict, Any
 from dataclasses import dataclass, field
 from datetime import datetime
@@
 class AnalyticsReporter:
@@
     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()
+        # Vast.ai exposes the current instance/contract id as CONTAINER_ID.
+        self.vast_instance_id = os.environ.get('CONTAINER_ID') or os.environ.get('VAST_INSTANCE_ID')
         self.worker_id = f"{self.machine_id}_gpu{gpu_id}"
@@
     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,
+            'vast_instance_id': self.vast_instance_id,
             'gpu_id': self.gpu_id,
             'status': 'idle',
             'current_video_id': None,
             'current_stage': None,
             'current_stage_name': None,
@@
     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,
+            'vast_instance_id': self.vast_instance_id,
             'gpu_id': self.gpu_id,
             'status': 'processing',
             'current_video_id': video_id,
             'current_stage': 1,
@@
     def stage_update(self, video_id: str, stage_num: int, stage_name: str):
@@
         self._queue.put(AnalyticsEvent('heartbeat', {
             'worker_id': self.worker_id,
             'machine_id': self.machine_id,
+            'vast_instance_id': self.vast_instance_id,
             'gpu_id': self.gpu_id,
             'status': 'processing',
             'current_video_id': video_id,
             'current_stage': stage_num,
@@
     def done(
         self,
         video_id: str,
         total_time: float,
         audio_min: float,
         speakers: int,
         usable_pct: float
     ):
@@
         # Queue done event (for processing_events table)
         self._queue.put(AnalyticsEvent('done', {
             'worker_id': self.worker_id,
+            'vast_instance_id': self.vast_instance_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,
+            'vast_instance_id': self.vast_instance_id,
             'gpu_id': self.gpu_id,
             'status': 'idle',
             'current_video_id': None,
             'current_stage': None,
@@
     def fail(
         self,
         video_id: str,
         stage: str,
         error_type: str,
         message: str,
         stack_trace: Optional[str] = None
     ):
@@
         # Queue fail event (for processing_events + error_logs tables)
         self._queue.put(AnalyticsEvent('fail', {
             'worker_id': self.worker_id,
+            'vast_instance_id': self.vast_instance_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,
+            'vast_instance_id': self.vast_instance_id,
             'gpu_id': self.gpu_id,
             'status': 'idle',
             'current_video_id': None,
             'current_stage': None,
@@
     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,
+            'vast_instance_id': self.vast_instance_id,
             'gpu_id': self.gpu_id,
             'status': 'offline',
             'current_video_id': None,
             'current_stage': None,
*** End Patch


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


**Patching database updates**

I need to ensure the schema changes when we push a new image. I’m considering patching the _flush_events error_rows to include the vast_instance_id. Also, for the worker_heartbeats upsert, it already has the vast_instance_id field, so I should make sure the column is correct. I wonder if I’ve missed anything else that needs updating. Let’s make sure everything aligns well!


Input:
*** Begin Patch
*** Update File: src/analytics.py
@@
                 # Also log to error_logs table with full details
                 error_rows = [{
                     'worker_id': e.data['worker_id'],
+                    'vast_instance_id': e.data.get('vast_instance_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()
*** End Patch


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


set -a; source .env; set +a; python3 - <<'PY'
import os
import psycopg2

url = os.environ['DATABASE_URL']
conn = psycopg2.connect(url)
conn.autocommit = True
cur = conn.cursor()
cur.execute('ALTER TABLE IF EXISTS public.worker_heartbeats ADD COLUMN IF NOT EXISTS vast_instance_id text;')
cur.execute('ALTER TABLE IF EXISTS public.processing_events ADD COLUMN IF NOT EXISTS vast_instance_id text;')
cur.execute('ALTER TABLE IF EXISTS public.error_logs ADD COLUMN IF NOT EXISTS vast_instance_id text;')
cur.close()
conn.close()
print('ok: added vast_instance_id columns (if missing)')
PY


Output:
Exit code: 0
Wall time: 0.8 seconds
Output:
ok: added vast_instance_id columns (if missing)


**Addressing video processing issues**

I need to fix a situation where workers fail and don’t recover or report back after a failed video. I'm planning to allow for their recovery, but if the `supabase.get_video` function fails, it shouldn't mark the video as failed — we should just keep going. However, there's a chance that a failure could lead to minimal video data when processing. I also want to make sure that the `claim_next_video` function doesn’t crash if `get_active_claim` raises an error. Now, I need to confirm this by simulating a timeout in local tests.


**Implementing error handling fixes**

I can create a snippet to patch `supabase_client.urlopen` to raise exceptions, but since our code already catches exceptions in `massive_process`, we should be okay. I'll run a quick test to check that our code still works with `python3` commands, even though it's not necessary. They’re asking for a definitive fix, so I’ll add safeguards like wrapping the `get_video` call in a try block. However, I'm concerned that a failure in `supabase.get_video` could still cause issues. I'll need to ensure that any supabase calls, like `analytics.pick(video_id)`, don't lead to worker crashes either.


**Planning fixes and updates**

The user wants a definitive fix, maybe by changing `SupabaseClient._request` to set a longer timeout. I can definitely get that sorted. They also want to push a new image and restart containers. I’ll provide instructions to build the image and push it using Docker.

Once we confirm the fix, they’d like to stop the current instances and pull the updated image. We can instruct them to use a command like `vastai update instance <id> --image <newtag>`. I’ll check the CLI help documentation for more on that.


vastai update instance --help | sed -n '1,160p'


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: vastai update instance ID [OPTIONS]

positional arguments:
 id                                   id of instance to update

options:
 -h, --help                           show this help message and exit
 --template_id TEMPLATE_ID            new template ID to associate with the instance
 --template_hash_id TEMPLATE_HASH_ID  new template hash ID to associate with the instance
 --image IMAGE                        new image UUID for the instance
 --args ARGS                          new arguments for the instance
 --env ENV                            new environment variables for the instance
 --onstart ONSTART                    new onstart script for the instance
 --url URL                            server REST api url
 --retry RETRY                        retry limit
 --raw                                output machine-readable json
 --explain                            output verbose explanation of mapping of CLI calls to HTTPS API endpoints
 --curl                               show a curl equivalency to the call
 --api-key API_KEY                    api key. defaults to using the one stored in /home/ubuntu/.config/vastai/vast_api_key
 --version                            show version

Example: vastai update instance 1234 --template_hash_id 661d064bbda1f2a133816b6d55da07c3


vastai recycle instance --help | sed -n '1,120p'


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: vastai recycle instance ID [OPTIONS]

positional arguments:
 id                 id of instance to recycle

options:
 -h, --help         show this help message and exit
 --url URL          server REST api url
 --retry RETRY      retry limit
 --raw              output machine-readable json
 --explain          output verbose explanation of mapping of CLI calls to HTTPS API endpoints
 --curl             show a curl equivalency to the call
 --api-key API_KEY  api key. defaults to using the one stored in /home/ubuntu/.config/vastai/vast_api_key
 --version          show version

Destroys and recreates container in place (from newly pulled image) without any risk of losing GPU priority.


**Preparing for updates**

I believe that the image change preserves the environment, so I can use a new template hash for reliability. Creating a new template with an updated image tag and recycling is a safer plan. The user mentioned their intent to debug and fix before pushing, which means we should implement code changes and run tests. Although there are uncommitted modifications in the current repository, we can still build a new image. I'll need to run `python3 -m py_compile` quickly to check for any syntax errors.


**Running import tests**

I need to run a minimal import test to ensure everything is functioning properly. I'll use the command `python3 -c "import src.supabase_client; import src.analytics"` among others. This will help verify that the necessary modules are loading correctly without any issues. It's a straightforward way to catch any potential problems early on, and I want to stay proactive about this! Let's go ahead and run that test now.


python3 -m py_compile massive_process.py src/supabase_client.py src/analytics.py


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:


**Evaluating the user situation**

I’m thinking about the user’s immediate needs—they have 10 instances running an old image, and some workers are failing without recovering. The code fix isn't deployed yet, but they want a confirmation on their issues. We've already modified the schema and code. Maybe I should simulate a run of one container locally to test the fix? Even though our local environment differs from the larger setup, running `python3 -c` could help. Since they want a 100% fix, I could update `gpu_worker_supabase` to handle the TimeoutError better.


**Addressing worker restart issues**

I've noted that the worker isn't restarting when claiming a new or failed video because the process dies. Our fix should prevent network timeouts from causing this issue, so the worker can continue. However, I'm concerned about timeouts that may occur in `supabase.update_status`. While it returns False and doesn't raise an error, it might keep the status as CLAIMED without releasing it. So it won't kill the worker, which is good. Now the user wants to incorporate additional machine details like machine-id or instance ID into Supabase, which we've already set up for migration and code changes.


**Considering worker stability**

The user pointed out that we might need to add information to the `supabase_worker_id`, which currently has a machine_id tied to the vast instance ID. The claimed_by column holds the instance ID and might be sufficient, but since they requested a new column, I’ll deliver that. They're also mentioning workers failing with a specific error without recovering. It seems that the issue could be with `supabase.update_status` failing, leaving the workers stuck—it likely relates to timeouts in `supabase.get_video(video_id)`, which we believe we've addressed.


**Addressing worker issues**

I'm considering that the worker might get stuck at the "Chunking" stage. The error appears to come from Worker-4 while reading Supabase metadata at `get_video`, which is followed by an error log and stack trace. It seems one GPU process, possibly GPU5, could be involved since the worker stops sending heartbeats. With our fix, it shouldn't die. Now, even if there's a Supabase timeout within `get_video`, we still want to hold onto the claimed video. They plan to stop all current instances, so I'll propose building a new image to update, confirming the fix for them.


**Planning the fix and updates**

I’m considering proposing a local reproduction by monkey-patching `urlopen` to raise a `TimeoutError` and see if the worker stays active instead of exiting. I could also run a simulation of `supabase.get_video` with a forced TimeoutError by adjusting the timeout settings and using an invalid endpoint, but that’s not straightforward. I’ll want to wrap `get_video` calls in a try-catch block to ensure stability. We aim to catch network timeouts so they don’t kill the workers. The user wants to debug and fix this while including machine details, so I’ll outline the necessary steps for updating instances and explain that the worker died due to the `get_video` call being outside the try block. I’ll provide a roadmap for building and deploying the new docker image and updating all instances as requested. I’ll also check for any possible bugs.


git diff --stat massive_process.py src/supabase_client.py src/analytics.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
 massive_process.py     | 112 +++++++++++++++++---------
 src/analytics.py       |  20 ++++-
 src/supabase_client.py | 214 ++++++++++++++++++++++++++++++++++++++++---------
 3 files changed, 264 insertions(+), 82 deletions(-)


Summary: auto


**Inspecting code changes**

I’m thinking about all the lines we added. It could be okay, but I really want to make sure to check for any indentation issues in the massive_process.py file. It’s crucial to inspect the differences around my modifications, just to confirm everything looks good. Let's view the snippet to ensure everything is in order. This careful check could help catch any sneaky errors before they become a problem!


git diff -U5 massive_process.py | sed -n '1,220p'


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 220
Output:
diff --git a/massive_process.py b/massive_process.py
index 3fe280a..5d7c63f 100755
--- a/massive_process.py
+++ b/massive_process.py
@@ -268,10 +268,21 @@ def gpu_worker(
             output_dir=output_dir,
             **config_overrides
         )
         config.gpu_id = gpu_id  # For PipelineLogger

+        # Background export must be initialized inside the worker process.
+        # Initializing thread pools in the parent process before fork leads to
+        # unreliable behavior and can drop uploads on worker exit.
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import init_export_manager
+                init_export_manager(max_workers=int(getattr(config, 'background_export_workers', 4) or 4))
+            except Exception as e:
+                worker_logger.warning(f"Failed to init background export manager, falling back to sync export: {e}")
+                config.background_export = False
+
         # Load models ONCE for this worker
         MODELS.load_all(config)

         # PREFETCH OPTIMIZATION: Download next video while processing current
         # Uses a background thread to hide download latency (15-20s per video)
@@ -411,10 +422,18 @@ def gpu_worker(
                 continue

         # Stop prefetch thread
         prefetch_stop[0] = True
         prefetch_thread.join(timeout=5.0)
+
+        # Flush background exports on shutdown (best-effort).
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import shutdown_export_manager
+                shutdown_export_manager(wait=True, timeout=600)
+            except Exception as e:
+                worker_logger.warning(f"Background export shutdown error: {e}")
         worker_logger.info(f"Shutting down. Processed {videos_processed} videos.")

     except Exception as e:
         worker_logger.error(f"Worker failed: {e}")
         traceback.print_exc()
@@ -445,13 +464,16 @@ def gpu_worker_supabase(

     # Worker-specific logging
     worker_logger = logging.getLogger(f"Worker-{worker_id}")
     worker_logger.setLevel(logging.INFO)

-    # Get pod ID for unique worker identification
+    # Get unique worker identification.
+    # Vast.ai sets CONTAINER_ID to the instance (contract) id.
     pod_id = os.environ.get('RUNPOD_POD_ID', os.environ.get('HOSTNAME', 'local'))
-    supabase_worker_id = f"gpu-{gpu_id}-{pod_id}"
+    vast_instance_id = os.environ.get('CONTAINER_ID')
+    machine_id = vast_instance_id or pod_id
+    supabase_worker_id = f"gpu-{gpu_id}-{machine_id}"

     worker_logger.info(f"Starting worker {supabase_worker_id} on GPU {gpu_id}")

     # Initialize analytics to None (will be set in try block)
     analytics = None
@@ -481,11 +503,11 @@ def gpu_worker_supabase(
         # Background thread handles async reporting - zero overhead to main processing
         analytics = create_reporter(
             gpu_id=gpu_id,
             supabase_url=os.environ.get('URL') or os.environ.get('SUPABASE_URL'),
             supabase_key=os.environ.get('SUPABASE_ADMIN'),
-            machine_id=pod_id,
+            machine_id=machine_id,
             enabled=True
         )
         analytics.session_start()
         worker_logger.info(f"Analytics initialized: {analytics.worker_id}")

@@ -494,10 +516,19 @@ def gpu_worker_supabase(
         for key, value in config_overrides.items():
             if hasattr(config, key):
                 setattr(config, key, value)
         config.gpu_id = gpu_id  # For PipelineLogger

+        # Background export must be initialized inside the worker process.
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import init_export_manager
+                init_export_manager(max_workers=int(getattr(config, 'background_export_workers', 4) or 4))
+            except Exception as e:
+                worker_logger.warning(f"Failed to init background export manager, falling back to sync export: {e}")
+                config.background_export = False
+
         # Load models once
         MODELS.load_all(config)

         # Process loop
         videos_processed = 0
@@ -508,15 +539,21 @@ def gpu_worker_supabase(
             # Check max videos limit
             if max_videos > 0 and videos_processed >= max_videos:
                 worker_logger.info(f"Reached max videos limit ({max_videos})")
                 break

-            # Claim next video from Supabase
-            video = supabase.claim_next_video(
-                worker_id=supabase_worker_id,
-                lease_duration_sec=lease_duration_sec
-            )
+            # Claim next video from Supabase (transient network failures should
+            # never kill the worker process).
+            try:
+                video = supabase.claim_next_video(
+                    worker_id=supabase_worker_id,
+                    lease_duration_sec=lease_duration_sec
+    …398 chars truncated…os, stopping worker")
@@ -530,16 +567,21 @@ def gpu_worker_supabase(
             video_url = video['youtube_url']

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

-            # Fetch full video record to get chapters
-            full_video = supabase.get_video(video_id)
+            # Fetch full video record to get chapters (best-effort; fall back to
+            # the claim payload if Supabase is slow/unreachable).
+            full_video = None
+            try:
+                full_video = supabase.get_video(video_id)
+            except Exception as e:
+                worker_logger.warning(f"Supabase get_video failed for {video_id}: {e}")
             if not full_video:
                 full_video = video  # Fallback to claim result

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

@@ -568,10 +610,11 @@ def gpu_worker_supabase(
                     'needs_demucs_count': quality_stats.get('music_detection', {}).get('segments_needs_demucs', 0),
                     'heavy_music_count': quality_stats.get('music_detection', {}).get('segments_heavy_contamination', 0),
                     'processing_meta': {
                         'worker_id': supabase_worker_id,
                         'gpu_id': gpu_id,
+                        'vast_instance_id': vast_instance_id,
                         'timing_total': result.get('timing_total', 0),
                         'completed_at': datetime.now().isoformat(),
                         'pipeline_version': result.get('pipeline_version', ''),
                     },
                     'quality_stats': quality_stats,
@@ -637,10 +680,11 @@ def gpu_worker_supabase(
                     'last_error': error_msg[:1000],  # Truncate long errors
                     'last_error_type': type(e).__name__,
                     'processing_meta': {
                         'worker_id': supabase_worker_id,
                         'gpu_id': gpu_id,
+                        'vast_instance_id': vast_instance_id,
                         'failed_at': datetime.now().isoformat(),
                     },
                 })

                 video_result = VideoResult(
@@ -655,11 +699,22 @@ def gpu_worker_supabase(

             result_queue.put(video_result)
             videos_processed += 1

             # Clear GPU cache between videos
-            MODELS.clear_cache(aggressive=True)
+            try:
+                MODELS.clear_cache(aggressive=True)
+            except Exception as e:
+                worker_logger.warning(f"GPU cache clear failed: {e}")
+
+        # Flush background exports on shutdown (best-effort).
+        if getattr(config, 'background_export', False):
+            try:
+                from src.export import shutdown_export_manager
+                shutdown_export_manager(wait=True, timeout=600)
+            except Exception as e:
+                worker_logger.warning(f"Background export shutdown error: {e}")

         # Graceful shutdown - flush remaining analytics events
         analytics.shutdown()
         worker_logger.info(f"Shutting down. Processed {videos_processed} videos.")

@@ -1106,10 +1161,12 @@ Examples:
                        help='Upload per-video .tar into R2 bucket 1-cleaned-data (no deletes)')
     parser.add_argument('--r2-upload-prefix', type=str, default='',
                        help="Optional prefix within 1-cleaned-data for uploads (e.g. 'benchmarks/rtx4090/2026-01-03')")
     parser.add_argument('--r2-upload-skip-if-exists', action='store_true',
                        help='Skip upload if remote key already exists')
+    parser.add_argument('--delete-local-output-dir', action='store_true',
+                       help='Delete per-video output dir after successful R2 upload (prevents disk fill)')

     # Export options
     parser.add_argument('--segment-audio', action='store_true', default=True,
                        help='Segment original audio into individual speaker clips (default: True)')
     parser.add_argument('--no-segment-audio', action='store_false', dest='segment_audio',
@@ -1209,22 +1266,20 @@ Examples:
         # R2 output upload
         'r2_upload_enabled': bool(args.r2_upload),
         'r2_upload_bucket_type': 'production',
         'r2_upload_prefix': args.r2_upload_prefix,
         'r2_upload_skip_if_exists': bool(args.r2_upload_skip_if_exists),
+        'r2_upload_delete_local_output_dir': bool(args.delete_local_output_dir),
         # Export options
         'segment_audio': args.segment_audio,
         'background_export': args.background_export,


**Debugging process overview**

The user's request involves debugging and implementing a confident fix before pushing a new image and restarting containers. They want to include additional machine details in Supabase, like the machine-id or vast-ai's instance ID, in a new column. My response should cover changes made, the root cause related to getting video outside the try block, and specific fixes, including robust retries and adding the vast_instance_id. I'll also need to confirm the system isn’t broken and run `git status` to verify modifications.


**Proposing instance update process**

The user wants to confirm the fix before stopping all current instances. They’re considering updating the instances to pull the new image and start running again with the fix. I can propose building a new image tag, pushing it, updating the template, and recycling the instances—though I'm unclear if they want to move forward with this now. To confirm the fix, I might suggest building a local Docker image to test for Supabase timeouts to ensure it doesn’t crash.


**Proposing canary deployment strategy**

I can suggest updating one instance to the new image to observe its performance as a real test. Since we have ten instances running, a canary deployment could be effective—updating one instance and monitoring for timeout recovery. However, since they mentioned wanting to stop all instances first, I’m not sure if they want to proceed now. Additionally, we need to consider changes to the Supabase schema and possibly adjust the `scripts/monitor/dashboard.py` for error analysis, as there may be no errors recorded due to unpopulated error logs.


**Implementing fixes and preparing deployment**

We've included the vast_instance_id in the error_logs, which required adding a new column. However, we haven't yet deployed the fix that removes the stack_trace from failed event insertions, although the current analytics file reflects previous fixes. Once we deploy the new image, fail events will be captured correctly, addressing both the worker crashes from get_video timeouts and analytics log errors. The user may want to stop and restart all workers, but we need to ensure the current cluster runs smoothly, especially since we’ve changed the schema. They'll expect a "100% confident fix," so we should be ready to explain any changes related to the supabase_client, including introducing the random and socket updates.


**Finalizing the fix and preparation for deployment**

We've made several important updates, including adding the vast_instance_id for error logs and making necessary changes to handle timeout errors in the supabase_client. We need to ensure the changes in the analytics events and supabase tables are in sync. We're ready to propose a fix that ensures the worker continues processing even when get_video fails.

The dashboard is now primarily based on machine_id, which will default to the vast instance id. However, we still added the new column. While we need to verify all changes before pushing the new image, we should also handle potential read timeouts for retrieving video data. We might want to continue testing locally before deploying.


**Planning deployment and verification**

I'll deliver the fix and ask if the user wants to proceed, as their mention of "repushing with new changes" suggests they might be ready to push now. Their instructions include debugging and confirming a 100% fix, adding machine info to Supabase, and providing a method to update instances after the push.

We’ll also ensure to mention that schema migration was performed and verify the new columns’ existence by querying the information_schema using a Python script, just to be safe.


set -a; source .env; set +a; python3 - <<'PY'
import os
import psycopg2

url = os.environ['DATABASE_URL']
conn = psycopg2.connect(url)
cur = conn.cursor()
for table in ['worker_heartbeats', 'processing_events', 'error_logs']:
    cur.execute("""
        select column_name
        from information_schema.columns
        where table_schema='public' and table_name=%s and column_name='vast_instance_id'
    """, (table,))
    row = cur.fetchone()
    print(table, 'vast_instance_id' if row else 'MISSING')
cur.close(); conn.close()
PY


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
worker_heartbeats vast_instance_id
processing_events vast_instance_id
error_logs vast_instance_id


**Considering docker image updates**

I think we need to confirm with the user before pushing the new docker image since code changes require that. Even though we can build and push the new image to the registry now, it might be better to wait for their confirmation. They mentioned, "then we will think about pushing." In the meantime, I could suggest recycling instances to restart the container, which can temporarily help with the crashing workers. They can run `vastai recycle instance <id>` for that.


**Reviewing worker status and system settings**

I want to check the current dashboard for stale workers since some may have died, but it's not strictly necessary. With command access, I could use `python3 scripts/monitor/dashboard.py | head` to check if the worker count is below 80. The user already has some context. There’s a safety concern regarding the `supabase_client` max_retries for GET/PATCH and its impact on the `claim_next_video` function. Setting `SUPABASE_HTTP_MAX_RETRIES` too high might cause long stalls, potentially leading to issues. Balancing between retries and timeouts is key!


**Analyzing worker loops and timeouts**

The worker loops do catch exceptions from `claim_next_video` and then sleep for `poll_interval`, but I realize that `claim_next_video` might take a while due to timeouts. It uses `_rpc` without retries, but it loops for max_retries, which can lead to blocking that worker for up to 180 seconds. While that’s better than crashing, it could mark the worker stale. I wonder if reducing the timeout to 10 seconds for `claim_next_video` might help, but it was initially set to 30 seconds for a reason. However, the timeout could still waste time.


**Improving timeout settings**

To avoid stalling, I think we should set a shorter timeout for `get_video`, maybe around 10 seconds, with 2 retries, so worst case would be a 20-second wait. The error is infrequent, but being proactive could help. Reducing the Supabase GET timeout to 10 seconds seems smart since we only need metadata. When the Supabase is slow, we can proceed with a fallback without waiting for 30 seconds. I’m considering adjusting the `SUPABASE_HTTP_TIMEOUT_SEC` environment variable, but I also want to make sure our `get_video` call uses a shorter timeout effectively.


**Adjusting timeout settings for video calls**

I need to consider that there might be other callers for the Supabase client, but it seems mainly used within `massive_process`. Since the user specifically asked for a fix, I want to keep the default behavior consistent while making necessary adjustments. I think it's best to implement a new method, `get_video(youtube_id, timeout=None, max_retries=None)` to give us flexibility. Assuming the pipeline is high-throughput, we should make sure `get_video` doesn’t block workers for too long. I’ll set a shorter timeout, maybe 10 seconds, and handle the implementation accordingly.


Input:
*** Begin Patch
*** Update File: src/supabase_client.py
@@
-    def get_video(self, youtube_id: str) -> Optional[Dict[str, Any]]:
-        """Get a single video by YouTube ID."""
+    def get_video(
+        self,
+        youtube_id: str,
+        timeout: Optional[float] = None,
+        max_retries: Optional[int] = None,
+    ) -> Optional[Dict[str, Any]]:
+        """Get a single video by YouTube ID."""
         params = {
             'youtube_id': f'eq.{youtube_id}',
             'limit': '1'
         }

-        result = self._request('GET', 'videos', params=params)
+        result = self._request('GET', 'videos', params=params, timeout=timeout, max_retries=max_retries)
         return result[0] if result else None
*** End Patch


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


**Updating process parameters**

I need to update massive_process.py to include a shorter timeout, probably around 10 seconds, and set max_retries to maybe 2. This should help with performance and ensure that the process doesn't hang for too long. I just want to make sure everything runs smoothly. It feels like finding that right balance between efficiency and reliability is really important here! Let's patch it and see how it performs.


Input:
*** Begin Patch
*** Update File: massive_process.py
@@
             full_video = None
             try:
-                full_video = supabase.get_video(video_id)
+                # Chapters/title are nice-to-have; keep this fast so a slow
+                # Supabase read never stalls a GPU worker.
+                full_video = supabase.get_video(video_id, timeout=10, max_retries=2)
             except Exception as e:
                 worker_logger.warning(f"Supabase get_video failed for {video_id}: {e}")
             if not full_video:
                 full_video = video  # Fallback to claim result
*** End Patch


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


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


**Evaluating API calls and error handling**

I need to check that `supabase_client.get_active_claim` uses `_request('GET', 'videos', params=params)` with default max_retries and timeout. Since we updated the API by adding optional arguments to `get_video`, this should be fine. I might consider reducing timeouts for other optional Supabase calls, but the important part is creating an update plan.

The user mentioned an error where processing fails without any recovery or restart, which raises concerns about videos getting stuck in claimed status if an update fails. This needs some attention.


**Improving worker functionality and logging**

The user asked about reading Supabase records to get chapters, and we can manage that. I think it’s crucial to ensure that worker logs display messages like "Supabase get_video failed... using claim payload" instead of crashing. We should aim for a definitive fix and consider providing metrics to recover stuck workers.

I might also want to implement a script that detects stale workers based on their heartbeat, but that wasn't requested. Updating `scripts/monitor/dashboard.py` to reflect the expected worker count is another idea. Additionally, I need to check for error logs, especially `TimeoutError: The read operation timed out`, which our Supabase client now catches and handles properly.


**Confirming worker stability and error handling**

I need to confirm that the fix will keep worker processes from terminating. Before, the `get_video` call raised exceptions outside of the try block, which would end the function. Now, exceptions from `get_video` are caught, allowing warnings to be logged while the worker continues processing.

But there's still a concern with `claim_next_video`, which could raise due to repeated timeouts. Fortunately, we catch that too. Also, I want to ensure that if `supabase.update_status` or `analytics.shutdown` raises an error, it won’t crash the process. Luckily, `update_status` returns false without raising, and while `analytics.shutdown` might raise, generally, it’s designed to be safe. Lastly, I changed `machine_id` to `instance_id`, which feels secure.


**Updating instance details and proposing actions**

I think it’s important to mention that the environment variable `CONTAINER_ID` is set automatically by Vast, so there’s no need to pass it per instance. We've also done a schema alteration in Supabase that I should note.

While the user is focused on debugging and fixing issues, I might propose pushing a new image and updating instances. Gathering the current instance IDs from `vastai show instances` could be helpful, even if the user hasn't specifically requested this.

Finally, I need to assure that we're addressing their concern about some workers failing and not recovering, as our fix should help with that.


**Preparing update details and actions**

I should mention the specific file changes: in `massive_process.py`, the `get_video` call is now inside a try block; in `supabase_client.py`, I've added retries; and `analytics.py` now includes the `vast_instance_id` column. The database migration is complete.

Next, I’d propose building a new Docker image, updating the template, and recycling all instances, including the commands in my response since the user might want to proceed.

I also need to highlight that the recent fix is definitive given how exceptions are now managed. I'll gather line numbers for all modifications using `nl -ba` to clarify changes.

For file references, I’ll ensure to include the relevant starting line numbers. It’s crucial for clarity!


nl -ba massive_process.py | sed -n '440,640p'


Output:
Exit code: 0
Wall time: 0 seconds
Total output lines: 201
Output:
   440
   441
   442	def gpu_worker_supabase(
   443	    worker_id: int,
   444	    gpu_id: int,
   445	    result_queue,
   446	    stop_signal,
   447	    output_dir: str,
   448	    config_overrides: dict,
   449	    lease_duration_sec: int = 900,
   450	    poll_interval_sec: int = 10,
   451	    max_videos: int = 0  # 0 = unlimited
   452	):
   453	    """
   454	    GPU worker that claims videos from Supabase queue.
   455
   456	    Instead of reading from a file queue, this worker:
   457	    1. Claims the next PENDING video from Supabase
   458	    2. Processes it
   459	    3. Updates Supabase with results (COMPLETED/FAILED)
   460	    4. Repeats until stop signal or no more videos
   461	    """
   462	    # Set GPU for this process
   463	    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
   464
   465	    # Worker-specific logging
   466	    worker_logger = logging.getLogger(f"Worker-{worker_id}")
   467	    worker_logger.setLevel(logging.INFO)
   468
   469	    # Get unique worker identification.
   470	    # Vast.ai sets CONTAINER_ID to the instance (contract) id.
   471	    pod_id = os.environ.get('RUNPOD_POD_ID', os.environ.get('HOSTNAME', 'local'))
   472	    vast_instance_id = os.environ.get('CONTAINER_ID')
   473	    machine_id = vast_instance_id or pod_id
   474	    supabase_worker_id = f"gpu-{gpu_id}-{machine_id}"
   475
   476	    worker_logger.info(f"Starting worker {supabase_worker_id} on GPU {gpu_id}")
   477
   478	    # Initialize analytics to None (will be set in try block)
   479	    analytics = None
   480
   481	    try:
   482	        # Aggressively silence speechbrain before any imports
   483	        for name in ['speechbrain', 'speechbrain.utils', 'speechbrain.utils.checkpoints',
   484	                     'speechbrain.utils.fetching', 'speechbrain.utils.parameter_transfer']:
   485	            sb_logger = logging.getLogger(name)
   486	            sb_logger.setLevel(logging.CRITICAL)
   487	            sb_logger.handlers = []  # Remove all handlers
   488	            sb_logger.propagate = False
   489
   490	        from src.logger import silence_verbose_loggers
   491	        silence_verbose_loggers()
   492
   493	        # Import pipeline (loads models)
   494	        from pipeline import process_single_video
   495	        from src.config import Config
   496	        from src.models import MODELS
   497	        from src.download import download_audio
   498
   499	        # Initialize Supabase client
   500	        supabase = SupabaseClient()
   501
   502	        # Initialize analytics reporter (uses same Supabase credentials)
   503	        # Background thread handles async reporting - zero overhead to main processing
   504	        analytics = create_reporter(
   505	            gpu_id=gpu_id,
   506	            supabase_url=os.environ.get('URL') or os.environ.get('SUPABASE_URL'),
   507	            supabase_key=os.environ.get('SUPABASE_ADMIN'),
   508	            machine_id=machine_id,
   509	            enabled=True
   510	        )
   511	        analytics.session_start()
   512	        worker_logger.info(f"Analytics initialized: {analytics.worker_id}")
   513
   514	        # Build config with gpu_id for pipeline logging
   515	        config = Config(output_dir=output_dir)
   516	        for key, value in config_overrides.items():
   517	            if hasattr(config, key):
   518	                setattr(config, key, value)
   519	        config.gpu_id = gpu_id  # For PipelineLogger
   520
   521	        # Background export must be initialized inside the worker process.
   522	        if getattr(config, 'background_export', False):
   523	            try:
   524	                from src.export import init_export_manager
   525	                init_export_manager(max_workers=int(getattr(config, 'background_export_workers', 4) or 4))
   526	            except Exception as e:
   527	                worker_logger.warning(f"Failed to init background export manager, falling back to sync export: {e}")
   528	                config.background_export = False
   529
   530	        # Load models once
   531	        MODELS.load_all(config)
   532
   533	        # Process loop
   534	        videos_processed = 0
   535	        consecutive_empty = 0
   536	        max_consecutive_empty = 6  # Stop after 1 minute of no videos
   537
   538	        while not stop_signal.value:
   539	            # Check max videos limit
   540	            if max_videos > 0 and videos_processed >= max_videos:
   541	                worker_logger.info(f"Reached max videos limit ({max_videos})")
   542	                break
   543
   544	            # Claim next video from Supabase (transient network failures should
   545	            # never kill the worker process).
   546	            try:
   547	                video = supabase.claim_next_video(
   548	                    worker_id=supabase_worker_id,
   549	                    lease_duration_sec=lease_duration_sec
   55…296 chars truncated…  consecutive_empty += 1
   558	                if consecutive_empty >= max_consecutive_empty:
   559	                    worker_logger.info("No more pending videos, stopping worker")
   560	                    break
   561	                worker_logger.debug(f"No pending videos, sleeping {poll_interval_sec}s...")
   562	                time.sleep(poll_interval_sec)
   563	                continue
   564
   565	            consecutive_empty = 0
   566	            video_id = video['youtube_id']
   567	            video_url = video['youtube_url']
   568
   569	            # Report video pick to analytics (non-blocking)
   570	            analytics.pick(video_id)
   571
   572	            # Fetch full video record to get chapters (best-effort; fall back to
   573	            # the claim payload if Supabase is slow/unreachable).
   574	            full_video = None
   575	            try:
   576	                # Chapters/title are nice-to-have; keep this fast so a slow
   577	                # Supabase read never stalls a GPU worker.
   578	                full_video = supabase.get_video(video_id, timeout=10, max_retries=2)
   579	            except Exception as e:
   580	                worker_logger.warning(f"Supabase get_video failed for {video_id}: {e}")
   581	            if not full_video:
   582	                full_video = video  # Fallback to claim result
   583
   584	            worker_logger.info(f"Processing: {video_id} - {(full_video.get('title') or '')[:50]}")
   585	            has_chapters = full_video.get('has_chapters', False)
   586	            if has_chapters:
   587	                worker_logger.info(f"   Chapters: {full_video.get('chapter_count', 0)} from Supabase")
   588	            start_time = time.time()
   589
   590	            try:
   591	                # Process video with Supabase metadata (chapters, title, etc.)
   592	                result = process_single_video(video_url, config, supabase_metadata=full_video)
   593	                process_time = time.time() - start_time
   594
   595	                # Extract results for DB update
   596	                quality_stats = result.get('quality_stats', {}) or {}
   597	                segment_summary = result.get('segment_summary', {}) or {}
   598	                download_meta = result.get('download_meta', {}) or {}
   599
   600	                # Update Supabase with success
   601	                update_data = {
   602	                    'num_speakers': result.get('num_speakers', 0),
   603	                    'total_segments': result.get('total_segments', 0),
   604	                    'usable_percentage': quality_stats.get('usable_percentage', 0),
   605	                    'usable_segments': quality_stats.get('usable_segments', 0),
   606	                    'usable_duration_sec': quality_stats.get('usable_duration', 0),
   607	                    'r2_tar_key': result.get('r2_upload_key', ''),
   608	                    'pipeline_version': result.get('pipeline_version', ''),
   609	                    'audio_native_sample_rate': download_meta.get('native_sample_rate'),
   610	                    'audio_channels': download_meta.get('channels'),
   611	                    'audio_duration_sec': result.get('processed_duration', 0),
   612	                    'needs_demucs_count': quality_stats.get('music_detection', {}).get('segments_needs_demucs', 0),
   613	                    'heavy_music_count': quality_stats.get('music_detection', {}).get('segments_heavy_contamination', 0),
   614	                    'processing_meta': {
   615	                        'worker_id': supabase_worker_id,
   616	                        'gpu_id': gpu_id,
   617	                        'vast_instance_id': vast_instance_id,
   618	                        'timing_total': result.get('timing_total', 0),
   619	                        'completed_at': datetime.now().isoformat(),
   620	                        'pipeline_version': result.get('pipeline_version', ''),
   621	                    },
   622	                    'quality_stats': quality_stats,
   623	                    'segment_summary': segment_summary,
   624	                    'download_meta': download_meta,
   625	                }
   626
   627	                supabase.update_status(video_id, STATUS_COMPLETED, update_data)
   628
   629	                worker_logger.info(
   630	                    f"Completed: {video_id} | "
   631	                    f"{process_time:.1f}s | "
   632	                    f"{result.get('num_speakers', 0)} speakers | "
   633	                    f"{quality_stats.get('usable_percentage', 0):.1f}% usable"
   634	                )
   635
   636	                # Report success to analytics (non-blocking)
   637	                analytics.done(
   638	                    video_id=video_id,
   639	                    total_time=result.get('timing_total', process_time),
   640	                    audio_min=result.get('processed_duration', 0) / 60.0,


Summary: auto


nl -ba src/supabase_client.py | sed -n '1,220p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	#!/usr/bin/env python3
     2	"""
     3	Supabase Client for Distributed Video Processing Queue
     4
     5	Provides:
     6	- Atomic video claiming with lease-based locking
     7	- Status updates after processing
     8	- Bulk upsert for CSV imports
     9	- Queue statistics
    10
    11	Usage:
    12	    from src.supabase_client import SupabaseClient
    13
    14	    client = SupabaseClient()
    15	    video = client.claim_next_video("worker-1")
    16	    if video:
    17	        # Process video...
    18	        client.update_status(video['youtube_id'], 'COMPLETED', results)
    19	"""
    20
    21	import os
    22	import json
    23	import logging
    24	import time
    25	import random
    26	import socket
    27	from datetime import datetime, timezone
    28	from typing import Dict, Any, Optional, List
    29	from urllib.request import Request, urlopen
    30	from urllib.error import HTTPError, URLError
    31	from urllib.parse import urlencode
    32
    33	logger = logging.getLogger("SupabaseClient")
    34
    35	# Status constants
    36	STATUS_PENDING = 'PENDING'
    37	STATUS_CLAIMED = 'CLAIMED'
    38	STATUS_COMPLETED = 'COMPLETED'
    39	STATUS_FAILED = 'FAILED'
    40	STATUS_SKIPPED = 'SKIPPED'
    41
    42
    43	class SupabaseClient:
    44	    """
    45	    Supabase REST API client for video processing queue.
    46
    47	    Uses atomic claims via RPC function for distributed workers.
    48	    """
    49
    50	    _instance = None
    51
    52	    def __new__(cls, *args, **kwargs):
    53	        """Singleton pattern - reuse connection."""
    54	        if cls._instance is None:
    55	            cls._instance = super().__new__(cls)
    56	            cls._instance._initialized = False
    57	        return cls._instance
    58
    59	    def __init__(self, url: str = None, key: str = None):
    60	        """
    61	        Initialize Supabase client.
    62
    63	        Args:
    64	            url: Supabase project URL (loads from URL env var if None)
    65	            key: Service role key (loads from SUPABASE_ADMIN env var if None)
    66	        """
    67	        if self._initialized:
    68	            return
    69
    70	        self.url = url or os.environ.get('URL', '')
    71	        self.key = key or os.environ.get('SUPABASE_ADMIN', '')
    72
    73	        if not self.url or not self.key:
    74	            logger.warning("Supabase credentials not configured. Set URL and SUPABASE_ADMIN env vars.")
    75
    76	        self.rest_url = f"{self.url}/rest/v1"
    77	        self.headers = {
    78	            'apikey': self.key,
    79	            'Authorization': f'Bearer {self.key}',
    80	            'Content-Type': 'application/json',
    81	            'Prefer': 'return=representation'
    82	        }
    83
    84	        self.timeout_sec = float(os.environ.get('SUPABASE_HTTP_TIMEOUT_SEC', '30') or 30)
    85	        self.max_retries = int(os.environ.get('SUPABASE_HTTP_MAX_RETRIES', '5') or 5)
    86	        self.backoff_base_sec = float(os.environ.get('SUPABASE_HTTP_BACKOFF_BASE_SEC', '0.5') or 0.5)
    87	        self.backoff_max_sec = float(os.environ.get('SUPABASE_HTTP_BACKOFF_MAX_SEC', '15') or 15)
    88
    89	        self._initialized = True
    90	        logger.info(f"SupabaseClient initialized: {self.url}")
    91
    92	    def _sleep_backoff(self, attempt: int) -> None:
    93	        # Full-jitter exponential backoff.
    94	        cap = max(0.01, self.backoff_max_sec)
    95	        base = max(0.01, self.backoff_base_sec)
    96	        delay = min(cap, base * (2 ** attempt))
    97	        time.sleep(random.random() * delay)
    98
    99	    @staticmethod
   100	    def _is_retryable_http_status(status_code: int) -> bool:
   101	        return status_code in {408, 425, 429, 500, 502, 503, 504}
   102
   103	    def _request(
   104	        self,
   105	        method: str,
   106	        endpoint: str,
   107	        data: dict = None,
   108	        params: dict = None,
   109	        headers_override: dict = None,
   110	        timeout: Optional[float] = None,
   111	        max_retries: Optional[int] = None,
   112	    ) -> Any:
   113	        """Make HTTP request to Supabase REST API."""
   114	        url = f"{self.rest_url}/{endpoint}"
   115	        if params:
   116	            url = f"{url}?{urlencode(params)}"
   117
   118	        headers = {**self.headers, **(headers_override or {})}
   119
   120	        body = json.dumps(data).encode('utf-8') if data else None
   121
   122	        req = Request(url, data=body, headers=headers, method=method)
   123
   124	        request_timeout = self.timeout_sec if timeout is None else timeout
   125	        if max_retries is None:
   126	            # Avoid automatic retries on non-idempotent calls (e.g., POST).
   127	            max_retries = self.max_retries if method.upper() in {'GET', 'PATCH', 'PUT', 'DELETE'} else 0
   128
   129	        for attempt in range(max_retries + 1):
   130	            try:
   131	                with urlopen(req, timeout=request_timeout) as resp:
   132	                    content = resp.read().decode('utf-8')
   133	                    return json.loads(content) if content else None
   134	            except HTTPError as e:
   135	                error_body = ""
   136	                try:
   137	                    error_body = e.read().decode('utf-8')
   138	                except Exception:
   139	                    pass
   140
   141	                if attempt < max_retries and self._is_retryable_http_status(e.code):
   142	                    logger.warning(f"Supabase transient error {e.code} on {method} {endpoint}: {error_body[:200]}")
   143	                    self._sleep_backoff(attempt)
   144	                    continue
   145
   146	                logger.error(f"Supabase error {e.code} on {method} {endpoint}: {error_body[:500]}")
   147	                raise
   148	            except (TimeoutError, socket.timeout) as e:
   149	                if attempt < max_retries:
   150	                    logger.warning(f"Supabase timeout on {method} {endpoint}: {e} (retry {attempt + 1}/{max_retries})")
   151	                    self._sleep_backoff(attempt)
   152	                    continue
   153	                logger.error(f"Supabase timeout on {method} {endpoint}: {e}")
   154	                raise
   155	            except URLError as e:
   156	                # URLError.reason often wraps socket.timeout, DNS failures, connection resets, etc.
   157	                if attempt < max_retries:
   158	                    logger.warning(
   159	                        f"Supabase network error on {method} {endpoint}: {getattr(e, 'reason', e)} "
   160	                        f"(retry {attempt + 1}/{max_retries})"
   161	                    )
   162	                    self._sleep_backoff(attempt)
   163	                    continue
   164	                logger.error(f"Supabase network error on {method} {endpoint}: {getattr(e, 'reason', e)}")
   165	                raise
   166
   167	    def _rpc(
   168	        self,
   169	        function_name: str,
   170	        params: dict = None,
   171	        timeout: Optional[float] = None,
   172	        max_retries: int = 0,
   173	    ) -> Any:
   174	        """Call Supabase RPC function."""
   175	        url = f"{self.url}/rest/v1/rpc/{function_name}"
   176
   177	        body = json.dumps(params or {}).encode('utf-8')
   178	        req = Request(url, data=body, headers=self.headers, method='POST')
   179
   180	        request_timeout = self.timeout_sec if timeout is None else timeout
   181
   182	        for attempt in range(max_retries + 1):
   183	            try:
   184	                with urlopen(req, timeout=request_timeout) as resp:
   185	                    content = resp.read().decode('utf-8')
   186	                    return json.loads(content) if content else None
   187	            except HTTPError as e:
   188	                error_body = ""
   189	                try:
   190	                    error_body = e.read().decode('utf-8')
   191	                except Exception:
   192	                    pass
   193
   194	                if attempt < max_retries and self._is_retryable_http_status(e.code):
   195	                    logger.warning(f"RPC transient error {e.code} on {function_name}: {error_body[:200]}")
   196	                    self._sleep_backoff(attempt)
   197	                    continue
   198
   199	                logger.error(f"RPC error {e.code} on {function_name}: {error_body[:500]}")
   200	                raise
   201	            except (TimeoutError, socket.timeout) as e:
   202	                if attempt < max_retries:
   203	                    logger.warning(f"RPC timeout on {function_name}: {e} (retry {attempt + 1}/{max_retries})")
   204	                    self._sleep_backoff(attempt)
   205	                    continue
   206	                logger.error(f"RPC timeout on {function_name}: {e}")
   207	                raise
   208	            except URLError as e:
   209	                if attempt < max_retries:
   210	                    logger.warning(
   211	                        f"RPC network error on {function_name}: {getattr(e, 'reason', e)} "
   212	                        f"(retry {attempt + 1}/{max_retries})"
   213	                    )
   214	                    self._sleep_backoff(attempt)
   215	                    continue
   216	                logger.error(f"RPC network error on {function_name}: {getattr(e, 'reason', e)}")
   217	                raise
   218
   219	    def get_active_claim(self, worker_id: str) -> Optional[Dict[str, Any]]:
   220	        """Return an existing (non-expired) claim for this worker, if any."""


nl -ba src/analytics.py | sed -n '1,220p'


Output:
Exit code: 0
Wall time: 0 seconds
Output:
     1	"""
     2	Analytics Reporter for Maya3 Pipeline
     3
     4	Lightweight, non-blocking analytics that reports worker status to Supabase.
     5
     6	Design principles:
     7	- Never blocks main processing thread (background queue + thread)
     8	- Batches writes to reduce Supabase calls (~2-3 per video, not 10+)
     9	- Uses upserts for heartbeats (single row per worker)
    10	- Gracefully handles failures (analytics never crashes worker)
    11	- Local counters remain accurate even if Supabase is down
    12	"""
    13
    14	import threading
    15	import queue
    16	import time
    17	import socket
    18	import os
    19	import hashlib
    20	import traceback
    21	from typing import Optional, Dict, Any
    22	from dataclasses import dataclass, field
    23	from datetime import datetime
    24
    25
    26	@dataclass
    27	class AnalyticsEvent:
    28	    """Event to be sent to Supabase."""
    29	    event_type: str  # 'heartbeat', 'done', 'fail'
    30	    data: Dict[str, Any]
    31	    timestamp: datetime = field(default_factory=datetime.utcnow)
    32
    33
    34	def get_machine_id() -> str:
    35	    """
    36	    Generate a unique, readable machine ID.
    37	    Format: hostname-xxxx (where xxxx is a short hash)
    38	    """
    39	    hostname = socket.gethostname()[:20]  # Truncate long hostnames
    40	    # Create short hash from hostname + MAC-like identifier
    41	    unique_str = f"{hostname}-{socket.getfqdn()}"
    42	    short_hash = hashlib.md5(unique_str.encode()).hexdigest()[:4]
    43	    return f"{hostname}-{short_hash}"
    44
    45
    46	class AnalyticsReporter:
    47	    """
    48	    Non-blocking analytics reporter with background flushing.
    49
    50	    Usage:
    51	        reporter = AnalyticsReporter(gpu_id=0, supabase_url=..., supabase_key=...)
    52	        reporter.session_start()
    53
    54	        # For each video:
    55	        reporter.pick(video_id)
    56	        reporter.stage_update(video_id, 3, "Diarization")  # Optional stage updates
    57	        reporter.done(video_id, total_time, audio_min, speakers, usable_pct)
    58	        # or
    59	        reporter.fail(video_id, "Download", "NetworkError", "Connection timeout", traceback_str)
    60
    61	        # On shutdown:
    62	        reporter.shutdown()
    63	    """
    64
    65	    def __init__(
    66	        self,
    67	        gpu_id: int,
    68	        supabase_url: str,
    69	        supabase_key: str,
    70	        machine_id: Optional[str] = None,
    71	        flush_interval: float = 5.0,
    72	        batch_size: int = 10
    73	    ):
    74	        self.gpu_id = gpu_id
    75	        self.machine_id = machine_id or get_machine_id()
    76	        # Vast.ai exposes the current instance/contract id as CONTAINER_ID.
    77	        self.vast_instance_id = os.environ.get('CONTAINER_ID') or os.environ.get('VAST_INSTANCE_ID')
    78	        self.worker_id = f"{self.machine_id}_gpu{gpu_id}"
    79
    80	        # Background queue for non-blocking writes
    81	        self._queue: queue.Queue = queue.Queue()
    82	        self._stop_event = threading.Event()
    83
    84	        # Configuration
    85	        self._flush_interval = flush_interval
    86	        self._batch_size = batch_size
    87
    88	        # Local counters (always accurate, even if Supabase fails)
    89	        self.session_videos_done = 0
    90	        self.session_videos_failed = 0
    91	        self.session_audio_minutes = 0.0
    92	        self.session_usable_minutes = 0.0
    93	        self.session_start_time = datetime.utcnow()
    94
    95	        # Supabase client (lazy init in background thread)
    96	        self._supabase_url = supabase_url
    97	        self._supabase_key = supabase_key
    98	        self._client = None
    99	        self._client_lock = threading.Lock()
   100
   101	        # Start background flusher
   102	        self._flush_thread = threading.Thread(target=self._flush_loop, daemon=True)
   103	        self._flush_thread.start()
   104
   105	    def _get_client(self):
   106	        """Lazy init Supabase client in background thread (thread-safe)."""
   107	        if self._client is None:
   108	            with self._client_lock:
   109	                if self._client is None:  # Double-check after acquiring lock
   110	                    try:
   111	                        from supabase import create_client
   112	                        self._client = create_client(self._supabase_url, self._supabase_key)
   113	                    except Exception:
   114	                        pass  # Silently fail - analytics should never crash worker
   115	        return self._client
   116
   117	    def _flush_loop(self):
   118	        """Background thread that flushes events to Supabase."""
   119	        events_buffer = []
   120	        last_flush = time.time()
   121
   122	        # Drain the queue fully on shutdown so final "offline"/"done" events
   123	        # aren't lost if they were enqueued right before stop.
   124	        while not self._stop_event.is_set() or not self._queue.empty():
   125	            try:
   126	                # Collect events (non-blocking with timeout)
   127	                try:
   128	                    event = self._queue.get(timeout=1.0)
   129	                    events_buffer.append(event)
   130	                except queue.Empty:
   131	                    pass
   132
   133	                # Flush conditions: batch_size+ events OR flush_interval passed
   134	                should_flush = (
   135	                    len(events_buffer) >= self._batch_size or
   136	                    (events_buffer and time.time() - last_flush >= self._flush_interval)
   137	                )
   138
   139	                if should_flush and events_buffer:
   140	                    self._flush_events(events_buffer)
   141	                    events_buffer = []
   142	                    last_flush = time.time()
   143
   144	            except Exception:
   145	                # Never crash the flush thread
   146	                pass
   147
   148	        # Final flush on shutdown
   149	        if events_buffer:
   150	            self._flush_events(events_buffer)
   151
   152	    def _flush_events(self, events: list):
   153	        """Batch write events to Supabase."""
   154	        client = self._get_client()
   155	        if not client:
   156	            return
   157
   158	        try:
   159	            # Separate by type
   160	            heartbeats = [e for e in events if e.event_type == 'heartbeat']
   161	            done_events = [e for e in events if e.event_type == 'done']
   162	            fail_events = [e for e in events if e.event_type == 'fail']
   163
   164	            # Upsert latest heartbeat (only 1 per worker, use most recent)
   165	            if heartbeats:
   166	                latest = heartbeats[-1]
   167	                client.table('worker_heartbeats').upsert(
   168	                    latest.data,
   169	                    on_conflict='worker_id'
   170	                ).execute()
   171
   172	            # Batch insert done events
   173	            if done_events:
   174	                rows = [e.data for e in done_events]
   175	                client.table('processing_events').insert(rows).execute()
   176
   177	            # Batch insert fail events + error_logs
   178	            if fail_events:
   179	                # processing_events table intentionally stores only summary fields
   180	                # (no stack traces). Keep full traces in error_logs instead.
   181	                event_rows = [{k: v for k, v in e.data.items() if k != 'stack_trace'} for e in fail_events]
   182	                client.table('processing_events').insert(event_rows).execute()
   183
   184	                # Also log to error_logs table with full details
   185	                error_rows = [{
   186	                    'worker_id': e.data['worker_id'],
   187	                    'vast_instance_id': e.data.get('vast_instance_id'),
   188	                    'video_id': e.data['video_id'],
   189	                    'stage': e.data.get('stage_name'),
   190	                    'error_type': e.data.get('error_type'),
   191	                    'error_message': e.data.get('error_message'),
   192	                    'stack_trace': e.data.get('stack_trace')
   193	                } for e in fail_events]
   194	                client.table('error_logs').insert(error_rows).execute()
   195
   196	        except Exception:
   197	            # Silently fail - analytics should never crash worker
   198	            pass
   199
   200	    # =========================================================================
   201	    # Public API (all non-blocking)
   202	    # =========================================================================
   203
   204	    def session_start(self):
   205	        """Register worker on startup."""
   206	        self.session_start_time = datetime.utcnow()
   207	        self._queue.put(AnalyticsEvent('heartbeat', {
   208	            'worker_id': self.worker_id,
   209	            'machine_id': self.machine_id,
   210	            'vast_instance_id': self.vast_instance_id,
   211	            'gpu_id': self.gpu_id,
   212	            'status': 'idle',
   213	            'current_video_id': None,
   214	            'current_stage': None,
   215	            'current_stage_name': None,
   216	            'last_heartbeat': datetime.utcnow().isoformat(),
   217	            'session_start': self.session_start_time.isoformat(),
   218	            'session_videos_done': 0,
   219	            'session_videos_failed': 0,
   220	            'session_audio_minutes': 0,


Output:
Exit code: 0
Wall time: 0 seconds
Output:
 M Dockerfile
 M README.md
 M massive_process.py
 M pipeline.py
 M scripts/docker/smoke_test.sh
 M scripts/monitor/dashboard.py
 M src/analytics.py
 M src/config.py
 M src/download.py
 M src/export.py
 M src/models.py
 M src/supabase_client.py
?? dashboard-ui/


**Evaluating modifications carefully**

There are many modifications in the new directory for dashboard-ui, which might not have been intended. The user specifically asked to fix worker timeouts and machine IDs, so we should focus on that rather than unrelated changes. However, it seems the previous agent made modifications for docker testing, and we must decide if they're part of previous work. Since the user hasn't complained, I want to avoid shipping unnecessary changes and be careful about what I include in the final patch.


**Clarifying the user's requests**

The user is asking again about the issue, and we need to make sure our final patch includes only what's necessary. There are modifications present that haven't been committed, so we can revert some if they're not relevant to the new fix. However, these changes could be significant for earlier tasks like dockerization. The user is running a docker image that likely includes those modifications, and while those changes are now in the repo, we don’t need to revert unless specifically requested.


**Deciding on patch modifications**

To keep the patch minimal for the user's request, I could include modifications to `massive_process.py`, `src/supabase_client.py`, and `src/analytics.py`, along with a migration script if desired. Even though our previous apply_patch has already modified these files, we need to consider whether to revert other changes. Given the user's earlier tasks, like building a docker image and creating a dashboard, those modifications could be essential to operations. I want to avoid presenting a large patch with unrelated changes or breaking previous improvements.


**Assessing necessary modifications**

The user is actively using features like the dashboard ETA and delete local output, so we shouldn't revert those changes. It seems fine that many files are modified since our current request is focused on worker recovery. Plus, the user didn't ask to restrict the patch, and they have open tabs for files like analytics.py and download.py, indicating awareness of those changes. However, the new untracked directory, `dashboard-ui/`, raises some concerns. It might have been created inadvertently, possibly for a new UI. I should inspect it carefully before deciding whether to include it in the final patch.


**Summarizing the fix process**

I need to ensure our patch remains focused. The user expects a definitive fix, so adding a safe wrapper around `get_video` in `gpu_worker_supabase` seems important. Also, we should propose next steps like building and pushing a new image tag and recycling instances. The root cause of the issue was a Supabase network timeout, which we addressed by implementing error handling and fallback mechanisms. Additionally, we updated the Supabase schema and introduced telemetry to track various events. Finally, I'll include relevant file references and commands for updating instances while being concise.


**Crafting the final message**

I want to create a clear, concise response with 5 to 8 bullet points. I can include relevant inline code and line numbers, like `massive_process.py:442`, to refer to the specific changes. I could mention the environment variable, `SUPABASE_HTTP_TIMEOUT_SEC`, as an optional point. The user is looking for a "definitive fix" and details about the column addition, so I'll ensure to provide enough explanation while keeping the message focused and organized. Let's get this final message crafted!


- Root cause: the Supabase `get_video()` call was **outside** the per-video `try/except`, so a transient read timeout killed the whole GPU worker process (no restart), which is why it stopped heartbeating/claiming. Fixed in `massive_process.py:442` (claim/get_video are now guarded; `get_video` is best-effort and falls back to the claim payload).
- Hardening: added retry + exponential backoff for transient Supabase HTTP/network/timeouts (including the raw `TimeoutError` you saw) in `src/supabase_client.py:103`, plus “resume existing claim” logic to avoid orphaning a CLAIMED video if the claim response is lost (`src/supabase_client.py:219`).
- Made chapter/title fetch fast: `get_video(..., timeout=10, max_retries=2)` so a slow Supabase read never stalls a GPU (`massive_process.py:572`).
- Added Vast instance identity to analytics: `vast_instance_id = $CONTAINER_ID` (Vast sets this automatically) and included it in `worker_heartbeats`, `processing_events`, and `error_logs` payloads (`src/analytics.py:65`).
- Supabase schema updated: ran `ALTER TABLE ... ADD COLUMN IF NOT EXISTS vast_instance_id text` on `worker_heartbeats`, `processing_events`, `error_logs` (so the new analytics fields won’t break inserts).

**Updating running instances after you push a new image tag**
- For each instance: `vastai update instance <id> --image bharathkumar192/maya3-pipeline:<new_tag>` then `vastai recycle instance <id>` (recycle forces a fresh pull + restart without losing GPU priority).


# Context from my IDE setup:

## Open tabs:
- supabase_client.py: src/supabase_client.py
- analytics.py: src/analytics.py
- massive_process.py: massive_process.py
- dashboard.py: scripts/monitor/dashboard.py
- download.py: src/download.py

## My request for Codex:
Can you update all instances with new code fix, so they repull new image, restart container and start working all again with all workers being active.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
53:docker_pat=dckr_pat_bi0bQyWCMs0EQFKckt0__nuwKkM


**Planning deployment strategy**

The user wants to update all instances with a new code fix, which involves pulling a new image, restarting containers, and getting all workers active again. They previously expressed a need for a definitive and confident fix, which we've provided. Now, as we prepare to deploy, we need to choose a new image tag. We could label it `6cfd8aa-nonfail3`, as it better reflects the update while avoiding confusion. Let's proceed with this tagging!


**Evaluating modifications for deployment**

I noticed that many files have been changed, and we want to push an image that includes both the current fix and previously modified files. However, I need to ensure that the dependencies are correct, even if this might take some time. We should avoid including untested modifications that aren’t essential. The nonfail2 tag was built earlier, but our workspace has additional changes. I should check the modified files using `git status` to see what’s been affected.


**Ensuring code consistency**

I’m not sure if the nonfail2 image contains all the necessary modifications, but since it’s currently running, I assume it includes changes made before, like the analytics fix. Now that the code in the repository has been updated, we should ensure that the new image reflects the current repository state. I can check the modifications with `git diff`, but I also need to consider any untested changes which could introduce issues if included. It's a bit of a balancing act!


**Planning smoke tests**

Ideally, I’d like to run local smoke tests using scripts or Docker, but there are requirements like needing the .env file and GPU access. I could build the image and run a small Python snippet to import the supabase client as a simplified test. Since we’re on an A100 GPU machine, I can use sudo to build and run the container. Although the smoke test takes time, achieving high confidence is important, so I want to simulate a supabase timeout and check if the worker continues. Creating a small test script for retrying would be beneficial!


**Updating instances and pushing changes**

I can patch the local environment to simulate a timeout, but that doesn't reflect the production setup. My fix catches exceptions effectively, ensuring the loop continues. I could test the `supabase.get_video` function with a very low timeout to force a TimeoutError, confirming that our code catches it properly.

The user wants us to update instances and add a new column for machine IDs to Supabase. I’ll create a new image tag `6cfd8aa-nonfail3`, build the image, and push the changes. After that, I’ll update the Vast instances accordingly.


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
29749950
29750635
29750636
29750637
29751176
29751177
29751178
29751700
29751701
29751702


**Considering instance updates**

I need to update 10 IDs by running `vastai update instance $id --image bharathkumar192/maya3-pipeline:6cfd8aa-nonfail3`, followed by `vastai recycle instance $id`. Updating the template could also be an option, but for now, I’ll focus on updating images and recycling. However, running commands sequentially might take longer, so I could use `multi_tool_use.parallel` to initiate multiple updates simultaneously. I should be cautious about API rate limits and aim to run 3-5 updates at a time while ensuring no downtime.


**Monitoring container updates**

When I update the image and recycle, the container gets destroyed and recreated with the new image. It's important that the container automatically starts the pipeline afterward, as this is handled by the Docker entrypoint. After recycling, I need to monitor the system to ensure everything is running and all workers are active. I can check this using `vastai show instances` and a Python dashboard script.

I should also verify that previously stale workers recover, and I'm aware some may still show as stale due to modifications. It's crucial to keep an eye out for any disk issues or image pulls that might prevent instances from restarting, so I’ll be ready to make adjustments if needed. Additionally, I’ve updated Supabase’s new columns.


**Verifying container updates**

The containers will send new fields in heartbeats and processing events, but if they don’t, that’s okay; I can verify by querying the database. The user requested a definitive fix before pushing, and while I've applied a fix, it’s not confirmed in the running environment yet. Testing on one instance first seems prudent, even though they want an update for all.

If the fix creates an issue during the mass update, it could stop the pipeline. To boost confidence, I should run smoke tests locally. I’ll check if I can execute `scripts/docker/smoke_test.sh` to build the image without needing sudo.


**Checking Docker commands and updates**

I need to be cautious about potential permission issues, but if that happens, I can adapt by using `sudo` or setting up a Docker group. To validate the image, I could run commands like `sudo docker build -t ... .` followed by `sudo docker run --rm --gpus all --env-file .env ...`. This would help without needing extra permissions.

Our Dockerfile installs torch from cu121, and since my GPU has a CUDA 12.8 driver, it should be compatible with driver 570. I’ll also look into updating the existing Vast template, which uses the nonfail2 image, through `vastai update template`, checking its usage with `--help`.


vastai update template --help | sed -n '1,160p'