Output:
Exit code: 0
Wall time: 1.2 seconds
Output:
=== vast-29774951 workers 8
vast-29774951_gpu0 done 1 fail 0 last 2026-01-08T15:07:27.389366+00:00 status processing stage Download
vast-29774951_gpu1 done 0 fail 0 last 2026-01-08T15:06:34.648835+00:00 status processing stage Download
vast-29774951_gpu2 done 1 fail 0 last 2026-01-08T15:07:16.48767+00:00 status processing stage Download
vast-29774951_gpu3 done 0 fail 0 last 2026-01-08T15:06:35.256068+00:00 status processing stage Download
vast-29774951_gpu4 done 1 fail 0 last 2026-01-08T15:07:31.497631+00:00 status processing stage Download
vast-29774951_gpu5 done 1 fail 0 last 2026-01-08T15:07:16.137251+00:00 status processing stage Download
vast-29774951_gpu6 done 1 fail 1 last 2026-01-08T15:07:33.050341+00:00 status processing stage Download
vast-29774951_gpu7 done 0 fail 1 last 2026-01-08T15:06:44.22204+00:00 status processing stage Download
=== vast-29774952 workers 8
vast-29774952_gpu0 done 0 fail 0 last 2026-01-08T15:06:42.660807+00:00 status processing stage Download
vast-29774952_gpu1 done 1 fail 0 last 2026-01-08T15:07:33.275811+00:00 status processing stage Download
vast-29774952_gpu2 done 1 fail 0 last 2026-01-08T15:07:31.430148+00:00 status processing stage Download
vast-29774952_gpu3 done 1 fail 0 last 2026-01-08T15:07:31.790053+00:00 status processing stage Download
vast-29774952_gpu4 done 1 fail 0 last 2026-01-08T15:07:32.720005+00:00 status processing stage Download
vast-29774952_gpu5 done 1 fail 0 last 2026-01-08T15:07:30.714708+00:00 status processing stage Download
vast-29774952_gpu6 done 1 fail 0 last 2026-01-08T15:07:30.869311+00:00 status processing stage Download
vast-29774952_gpu7 done 0 fail 0 last 2026-01-08T15:06:42.174871+00:00 status processing stage Download


Summary: auto

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


Summary: auto


**Verifying uploads and processing**

I'm seeing that heartbeats are updating, but the current stage is still Download, which is just reporting progress. Some GPUs are showing increasing done counts, which is encouraging, but there are a few stale instances likely due to long processing times. I see lines in the logs indicating uploads are complete, but I want to double-check that everything is correct. The user asked me to verify if anything is stuck after our last attempt to recreate the instance.


**Confirming instance updates**

I've created new instances 29774951 and 29774952, and they're currently processing and completing videos with heartbeats indicating done counts. The previous instances have already been destroyed and release claims have been processed. I should mention that the stage displays as Download since we only update on pick/done/fail. If the user prefers stage updates, we can implement that, but it’s not essential. Some videos may fail quickly due to missing R2 sources, which is expected. Overall, things are running smoothly now!


**Checking deployment updates**

I need to confirm that the default tag for `deploy_from_offers` has been changed to fix2, which is good news. I should ensure that the vastctl destroy error due to a missing instance is okay. We’ve modified the code to revert spawn modifications and have built and pushed the fix2 changes to the repo. It’s also important to note that we shouldn’t share secrets from logs, which I haven’t done. I’ll provide the new instance IDs and details on monitoring the logs. Also, the stage remains Download, indicating activity.


Destroyed/recreated (the stuck test instances `29774081`/`29774085` were already gone in Vast, so I released their stuck Supabase CLAIMED rows and recreated fresh ones).

**New instances (image `bharathkumar192/maya3-pipeline:vast-20260108-stabilityfix2`)**
- 8×3090: `29774951` (`ssh8.vast.ai`, label `maya3-stabilitytest2-3090x8-Finland_FI-29381071-1`)
- 8×4090: `29774952` (`ssh5.vast.ai`, label `maya3-stabilitytest2-4090x8-Norway_NO-26025652-1`)

**Not stuck anymore**
- Vast logs show progress past `1/10 Download` into `2/10 VAD`, `3/10 Chunking`, `4/10 Diarization+`, and multiple `DONE` lines on both instances.
- Supabase `worker_heartbeats` shows `session_videos_done` incrementing on multiple GPUs for both `vast-29774951` and `vast-29774952`.

Note: heartbeats will still display `current_stage_name=Download` a lot because we only heartbeat on `pick/done/fail` (not per-stage), so “stage” isn’t a reliable stuck-signal by itself.


# Context from my IDE setup:

## Open tabs:
- .env: .env

## My request for Codex:
also remove vast-29774081 and vast-29774085 from analytics table as well.


**Planning the deletion process**

I'm thinking the easiest way is to ask the user which tables they want to remove from the analytics table. There's an "analytics" schema, so I want to check if the table exists using Supabase’s REST API. I can't list the tables directly, but I can rely on the migration files.

I’ll need to build a plan that outlines the tables to identify, delete rows from, and verify that they’ve been removed. I’ll proceed by using the Supabase library in Python to handle potential errors during this process.


**Outlining the deletion steps**

I can delete entries by using the `worker_id` prefix with the `.like` method. After performing the deletion, it’s important to verify the counts by selecting from the database. I want to provide those result counts to ensure everything has gone smoothly. Considering that our environment has specific network and permission constraints, I think it's best to approach this process step-by-step using the appropriate tools. This way, I can make sure everything runs as intended!


Explanation: Remove stale analytics rows for the two deleted Vast instances by deleting matching worker rows/events from Supabase analytics tables, then verify they’re gone.


Output: Plan updated


rg -n "worker_heartbeats|worker_metrics|processing_events|error_logs" scripts/supabase_migrations.sql | head -n 80


set -a; source .env; set +a; python3 - <<'PY'
import os
from supabase import create_client
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
# Quick existence checks
for t in ['worker_heartbeats','worker_metrics','processing_events','error_logs']:
    try:
        sb.table(t).select('worker_id').limit(1).execute()
        print(t,'OK')
    except Exception as e:
        print(t,'ERR',e)
PY


set -a; source .env; set +a; python3 - <<'PY'
import os
from supabase import create_client
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
ids=['vast-29774081','vast-29774085']
for mid in ids:
    hb=sb.table('worker_heartbeats').select('worker_id').eq('machine_id', mid).execute().data or []
    wm=sb.table('worker_metrics').select('worker_id').eq('machine_id', mid).execute().data or []
    pe=sb.table('processing_events').select('worker_id').like('worker_id', f"{mid}_gpu%").limit(1).execute().data or []
    el=sb.table('error_logs').select('worker_id').like('worker_id', f"{mid}_gpu%").limit(1).execute().data or []
    print(mid,'heartbeats',len(hb),'metrics',len(wm),'events_any',bool(pe),'errors_any',bool(el))
PY


Output:
Exit code: 0
Wall time: 0 seconds
Output:
125:CREATE TABLE IF NOT EXISTS worker_heartbeats (
144:CREATE TABLE IF NOT EXISTS worker_metrics (
161:CREATE TABLE IF NOT EXISTS processing_events (
176:CREATE INDEX IF NOT EXISTS idx_events_worker ON processing_events(worker_id, created_at DESC);
177:CREATE INDEX IF NOT EXISTS idx_events_video ON processing_events(video_id);
178:CREATE INDEX IF NOT EXISTS idx_events_type ON processing_events(event_type, created_at DESC);
183:CREATE TABLE IF NOT EXISTS error_logs (
194:CREATE INDEX IF NOT EXISTS idx_errors_worker ON error_logs(worker_id, created_at DESC);
195:CREATE INDEX IF NOT EXISTS idx_errors_recent ON error_logs(created_at DESC);
210:FROM worker_heartbeats;
239:        (SELECT COUNT(*) FROM processing_events pe
242:        (SELECT COALESCE(SUM(pe.audio_minutes) / 60.0, 0) FROM processing_events pe
245:    FROM worker_heartbeats wh;
252:CREATE OR REPLACE FUNCTION update_worker_metrics()
256:        INSERT INTO worker_metrics (worker_id, machine_id, gpu_id, total_videos_processed,
269:            total_videos_processed = worker_metrics.total_videos_processed + 1,
270:            total_audio_hours = worker_metrics.total_audio_hours + COALESCE(NEW.audio_minutes, 0) / 60.0,
271:            total_usable_hours = worker_metrics.total_usable_hours + COALESCE(NEW.audio_minutes * NEW.usable_pct / 100.0, 0) / 60.0,
272:            total_processing_hours = worker_metrics.total_processing_hours + COALESCE(NEW.duration_seconds, 0) / 3600.0,
274:                WHEN worker_metrics.total_processing_hours + COALESCE(NEW.duration_seconds, 0) / 3600.0 > 0
275:                THEN (worker_metrics.total_audio_hours + COALESCE(NEW.audio_minutes, 0) / 60.0) /
276:                     (worker_metrics.total_processing_hours + COALESCE(NEW.duration_seconds, 0) / 3600.0)
281:        INSERT INTO worker_metrics (worker_id, machine_id, gpu_id, total_videos_failed, last_active)
290:            total_videos_failed = worker_metrics.total_videos_failed + 1,
299:DROP TRIGGER IF EXISTS trigger_update_worker_metrics ON processing_events;
300:CREATE TRIGGER trigger_update_worker_metrics
301:    AFTER INSERT ON processing_events
303:    EXECUTE FUNCTION update_worker_metrics();
308:GRANT ALL ON worker_heartbeats TO anon, authenticated, service_role;
309:GRANT ALL ON worker_metrics TO anon, authenticated, service_role;
310:GRANT ALL ON processing_events TO anon, authenticated, service_role;
311:GRANT ALL ON error_logs TO anon, authenticated, service_role;


Output:
Exit code: 0
Wall time: 1.4 seconds
Output:
worker_heartbeats OK
worker_metrics OK
processing_events OK
error_logs OK


Output:
Exit code: 0
Wall time: 1.7 seconds
Output:
vast-29774081 heartbeats 8 metrics 0 events_any False errors_any False
vast-29774085 heartbeats 8 metrics 0 events_any False errors_any False


Output: Plan updated


set -a; source .env; set +a; python3 - <<'PY'
import os
from supabase import create_client
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
machines=['vast-29774081','vast-29774085']

# worker_heartbeats: delete by machine_id
res=sb.table('worker_heartbeats').delete().in_('machine_id', machines).execute()
del_rows=res.data or []
print('worker_heartbeats deleted', len(del_rows))

# worker_metrics: delete by machine_id (may be none)
res=sb.table('worker_metrics').delete().in_('machine_id', machines).execute()
del_rows=res.data or []
print('worker_metrics deleted', len(del_rows))

# processing_events + error_logs: delete by worker_id prefix
for t in ['processing_events','error_logs']:
    total=0
    for mid in machines:
        res=sb.table(t).delete().like('worker_id', f"{mid}_gpu%").execute()
        total += len(res.data or [])
    print(t,'deleted',total)
PY


Output:
Exit code: 0
Wall time: 1.5 seconds
Output:
worker_heartbeats deleted 16
worker_metrics deleted 0
processing_events deleted 0
error_logs deleted 0


Output: Plan updated


Output:
Exit code: 0
Wall time: 1.6 seconds
Output:
vast-29774081 heartbeats 0 metrics 0 events_any False errors_any False
vast-29774085 heartbeats 0 metrics 0 events_any False errors_any False