Input:
*** Begin Patch
*** Update File: scripts/monitor/dashboard.py
@@
 def format_number(n: int) -> str:
     """Format number with commas."""
     return f"{n:,}"

+def format_eta_hours(hours: float) -> str:
+    """Format ETA (hours) as human-readable duration."""
+    if hours <= 0:
+        return "-"
+    return format_duration(hours * 3600)
+
+
 class Dashboard:
     def __init__(self):
         self.sb = create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])
         self.start_time = datetime.utcnow()
@@
     def get_hourly_throughput(self) -> List[Dict]:
         """Get videos completed per hour (last 24h)."""
@@
         return [{'hour': k, **v} for k, v in sorted(hourly.items())]

+    def get_recent_performance(self, window_minutes: int = 60) -> Dict[str, Any]:
+        """Compute recent throughput + RTF using 'done' events."""
+        since = (datetime.utcnow() - timedelta(minutes=window_minutes)).isoformat()
+        result = self.sb.table('processing_events').select(
+            'audio_minutes, duration_seconds'
+        ).eq('event_type', 'done').gte('created_at', since).execute()
+
+        rows = result.data or []
+        done_count = len(rows)
+        audio_min_total = sum((r.get('audio_minutes') or 0) for r in rows)
+        duration_sec_total = sum((r.get('duration_seconds') or 0) for r in rows)
+
+        window_hours = window_minutes / 60.0
+        videos_per_hour = (done_count / window_hours) if window_hours > 0 else 0.0
+        audio_hours_per_hour = ((audio_min_total / 60.0) / window_hours) if window_hours > 0 else 0.0
+        speed_x = ((audio_min_total * 60.0) / duration_sec_total) if duration_sec_total > 0 else 0.0
+        avg_audio_min = (audio_min_total / done_count) if done_count > 0 else 0.0
+
+        return {
+            'window_minutes': window_minutes,
+            'done_count': done_count,
+            'audio_min_total': audio_min_total,
+            'videos_per_hour': videos_per_hour,
+            'audio_hours_per_hour': audio_hours_per_hour,
+            'speed_x': speed_x,
+            'avg_audio_min': avg_audio_min,
+        }
+
     def get_stale_workers(self, threshold_sec: int = 300) -> List[Dict]:
         """Find workers that haven't reported in threshold seconds."""
@@
-    def print_queue_stats(self, stats: Dict[str, int]):
+    def print_queue_stats(self, stats: Dict[str, int], perf: Dict[str, Any] = None):
         """Print queue statistics."""
         total = sum(stats.values())
         completed = stats.get('COMPLETED', 0)
         failed = stats.get('FAILED', 0)
@@
         # Stats grid
         print(f"  {C.GREEN}✓ Completed:{C.RESET} {format_number(completed):>8}  │  {C.YELLOW}⏳ Pending:{C.RESET} {format_number(pending):>8}")
         print(f"  {C.RED}✗ Failed:{C.RESET}    {format_number(failed):>8}  │  {C.BLUE}🔄 Claimed:{C.RESET} {format_number(claimed):>8}")
         print(f"  {C.DIM}Total:{C.RESET}       {format_number(total):>8}  │  {C.DIM}Error Rate:{C.RESET} {error_rate:.2f}%")

+        if perf and perf.get('done_count', 0) > 0:
+            window_min = int(perf.get('window_minutes', 60))
+            vph = float(perf.get('videos_per_hour', 0.0) or 0.0)
+            ahph = float(perf.get('audio_hours_per_hour', 0.0) or 0.0)
+            speed_x = float(perf.get('speed_x', 0.0) or 0.0)
+            avg_audio_min = float(perf.get('avg_audio_min', 0.0) or 0.0)
+
+            eta_hours = (pending / vph) if vph > 0 else 0.0
+            print(
+                f"  {C.CYAN}Last {window_min}m:{C.RESET} "
+                f"{vph:.1f} vids/hr │ {ahph:.2f} audio-hr/hr │ {speed_x:.1f}x realtime │ "
+                f"avg {avg_audio_min:.1f} min/video │ ETA {format_eta_hours(eta_hours)}"
+            )
+
     def print_worker_status(self, workers: List[Dict]):
         """Print worker status table."""
@@
     def run(self, watch: bool = False, interval: int = 30):
         """Run the dashboard."""
         try:
             while True:
@@
                 queue_stats = self.get_queue_stats()
+                perf_60m = self.get_recent_performance(window_minutes=60)
                 workers = self.get_worker_status()
                 errors = self.get_error_summary()
                 recent_errors = self.get_recent_errors(5)
                 hourly = self.get_hourly_throughput()
                 stale = self.get_stale_workers()
@@
                 # Print sections
                 self.print_header()
-                self.print_queue_stats(queue_stats)
+                self.print_queue_stats(queue_stats, perf=perf_60m)
                 self.print_worker_status(workers)
                 self.print_throughput(hourly)
                 self.print_error_summary(errors, recent_errors)
                 self.print_stale_workers(stale)
*** End Patch


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


Summary: auto


**Running dashboard checks**

The dashboard now includes metrics like the last 60 minutes of throughput, speed, average audio minutes, and estimated time of arrival (ETA). I need to make sure that the typing import includes 'Any' since we added the `format_eta_hours` function that uses `format_duration`. Next, I’ll run `python scripts/monitor/dashboard.py` to check for errors, but I need to ensure the Supabase environment is loaded. It might take a few seconds to run all the queries, so I’ll execute that.


python scripts/monitor/dashboard.py | head -n 60


Output:
Exit code: 0
Wall time: 0 seconds
Output:
/bin/bash: line 1: python: command not found


I see that my attempt to use the `python` command isn’t working because it’s not found in this environment. No worries! I’ll switch to using `python3` instead. That should do the trick. I’ll go ahead and run that command now to see if I can get it running properly. It's all about adapting as I go along! Let’s proceed with `python3` and see what happens.


python3 scripts/monitor/dashboard.py | head -n 80


Output:
Exit code: 0
Wall time: 2.1 seconds
Output:

╔══════════════════════════════════════════════════════════════════════╗
║            MAYA3 DISTRIBUTED PIPELINE DASHBOARD                      ║
║                       2026-01-07 19:49:43 UTC                       ║
╚══════════════════════════════════════════════════════════════════════╝

📊 QUEUE STATUS
──────────────────────────────────────────────────
  Progress: [░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░] 0.0%
  ✓ Completed:        3  │  ⏳ Pending:  172,598
  ✗ Failed:          10  │  🔄 Claimed:        0
  Total:        172,611  │  Error Rate: 76.92%
  Last 60m: 2.0 vids/hr │ 1.34 audio-hr/hr │ 4.2x realtime │ avg 40.2 min/video │ ETA 86299.0h

👷 WORKER STATUS (7 workers)
──────────────────────────────────────────────────────────────────────

  1771a3784a80
    ◌ GPU0 │ offline     │ -           │ -               │ ✓0 ✗1 │ 0min │ 19.1h

  35cc9369afdd
    ◌ GPU0 │ offline     │ -           │ -               │ ✓1 ✗0 │ 74min │ 25.5m

  360b1bc34426
    ◌ GPU0 │ offline     │ -           │ -               │ ✓0 ✗1 │ 0min │ 19.1h

  9e4c8cf392f0
    ◌ GPU0 │ offline     │ -           │ -               │ ✓0 ✗1 │ 0min │ ?

  9f2f27bcfc5f
    ◌ GPU0 │ offline     │ -           │ -               │ ✓0 ✗0 │ 0min │ 19.1h

  a45bdf6e1243
    ◌ GPU0 │ offline     │ -           │ -               │ ✓0 ✗0 │ 0min │ 19.1h

  c298e606357b
    ◌ GPU0 │ offline     │ -           │ -               │ ✓0 ✗0 │ 0min │ 19.1h

📈 THROUGHPUT (last 24h)
──────────────────────────────────────────────────
  07T00 │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓                │    1 videos │ 51 min
  07T19 │ ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │    2 videos │ 80 min

  24h Total: 3 videos, 2.2 hours audio

🚨 ERROR ANALYSIS
──────────────────────────────────────────────────
  No errors recorded

Press Ctrl+C to exit


**Updating progress report**

The dashboard prints indicate a huge ETA due to low throughput and many pending tasks—that's alright. However, the error rate is high at 76.92%, likely because only a few were processed and there have been 10 failures, some from tests. We need to clarify that the `python` command isn't found, but we can suggest using `python3`. Also, step 2 can now be marked as completed since we checked the Vast instances and logs and stopped them. Moving on to step 3!


Explanation: Vast test instances are understood and now stopped to avoid accidental queue churn/costs. Next I’m pulling candidate 8‑GPU on‑demand offers that meet your VRAM/reliability/<$4/hr constraints.


Output: Plan updated


vastai search offers --type on-demand 'num_gpus=1 rentable=True verified=True reliability>0.995 gpu_ram>=24 gpu_ram<=48 dph_total<0.5' -o 'dph_total' | head -n 40


Output:
Exit code: 0
Wall time: 1 seconds
Output:
ID        CUDA   N  Model        PCIE  cpu_ghz  vCPUs    RAM  Disk  $/hr    DLP   DLP/$   score  NV Driver   Net_up   Net_down  R      Max_Days  mach_id  status    host_id  ports  country
25155492  12.8  1x  RTX_3090     8.1   3.7      20.0    48.2  127   0.1041  44.6  429.05  235.2  570.169     96.3     224.1     99.5   172.8     41953    verified  155386   19     ,_CN
25086039  12.8  1x  RTX_3090     7.9   3.7      20.0    48.2  375   0.1041  44.4  426.38  257.0  570.144     97.9     469.2     99.6   172.8     39008    verified  155386   19     ,_CN
22589929  13.0  1x  Tesla_P40    6.9   1.3      12.0    64.1  702   0.1061  5.9   55.43   46.7   580.95.05   804.3    587.5     99.9   33.2      31186    verified  166741   99     Thailand,_TH
26117575  13.0  1x  RTX_3090     10.5  3.0      18.0    32.2  338   0.1214  44.4  365.48  231.1  580.76.05   65.4     784.9     99.7   73.4      43219    verified  155386   19     ,_CN
29741681  12.6  1x  RTX_3090     12.8  4.2      6.0     16.0  135   0.1361  44.6  328.10  293.2  560.35.03   954.4    897.9     99.8   30.0      31745    verified  51345    49     Poland,_PL
29600248  12.6  1x  RTX_3090     12.7  4.2      6.0     16.0  584   0.1374  44.9  326.69  287.9  560.35.03   913.8    920.3     99.6   31.0      14422    verified  51345    49     Poland,_PL
29737330  13.0  1x  RTX_3090     11.2  3.3      14.0    32.1  775   0.1481  42.7  288.23  262.0  580.95.05   914.9    848.0     99.7   4.0       31034    verified  142157   109    Chile,_CL
12364696  12.5  1x  RTX_3090     12.7  3.9      6.0     30.1  6     0.1492  44.9  300.74  153.1  555.58.02   51.9     222.4     99.6   82.9      27610    verified  50627    19     Quebec,_CA
11997119  12.2  1x  RTX_3090     12.7  3.9      6.0     32.1  294   0.1492  44.6  298.89  156.6  535.113.01  53.6     119.4     99.7   82.9      14338    verified  50627    19     Quebec,_CA
25839254  12.2  1x  RTX_3090     1.5   3.9      6.0     32.1  280   0.1492  27.6  185.00  158.8  535.161.07  912.2    922.1     99.8   63.7      18103    verified  50627    19     Quebec,_CA
19069485  12.8  1x  RTX_3090     25.0  2.4      10.7    42.9  223   0.1556  45.7  293.61  232.8  570.86.15   887.6    886.8     99.7   357.9     23999    verified  135723   99     Czechia,_CZ
28870982  13.0  1x  RTX_3090     11.8  3.4      4.0     32.0  1398  0.1609  40.8  253.66  223.9  580.105.08  874.4    894.9     99.9   88.5      16053    verified  96250    998    Spain,_ES
28695890  12.9  1x  RTX_3090     12.8  3.3      16.0    64.4  525   0.1611  44.4  275.36  275.0  575.64.05   2103.5   2146.1    99.9   153.8     42565    verified  195919   499    Alberta,_CA
24985165  12.7  1x  RTX_3090     25.0  2.8      16.0    64.3  692   0.1614  44.6  276.52  243.6  565.57.01   840.0    893.8     99.9   401.2     34292    verified  148689   249    Belgium,_BE
24707617  12.2  1x  RTX_3090     11.9  4.4      6.0     32.1  771   0.1614  42.7  264.39  233.1  535.113.01  933.1    934.3     99.7   60.5      11722    verified  74603    249    New_Zealand,_NZ
29732677  12.8  1x  RTX_3090     11.7  4.8      9.0     32.1  311   0.1619  40.3  248.96  197.7  570.133.20  919.3    929.1     99.9   59.2      3646     verified  1276     24     Norway,_NO
16320069  12.7  1x  RTX_3090     12.7  3.5      21.3    43.0  109   0.1620  44.8  276.75  289.4  565.77      5288.8   7352.5    99.6   70.0      16571    verified  87485    83     Spain,_ES
29302404  13.0  1x  RTX_3090     12.7  2.6      24.0    64.5  274   0.1623  44.3  273.20  291.6  580.119.02  4682.5   7254.8    99.8   72.0      25498    verified  130210   124    Spain,_ES
28889471  13.1  1x  RTX_3090     10.4  2.6      24.0    64.5  189   0.1623  44.0  271.26  304.7  590.44.01   5123.3   7513.9    99.9   71.0      23850    verified  130210   124    Spain,_ES
28684305  13.0  1x  RTX_3090     12.8  2.9      6.0     64.5  672   0.1623  44.4  273.36  259.7  580.95.05   7033.3   6071.2    100.0  63.8      30391    verified  34031    12     Sweden,_SE
11539213  13.0  1x  RTX_3090     12.2  4.4      12.0    15.7  765   0.1669  44.4  265.68  130.9  580.82.09   106.1    277.2     99.7   388.9     14402    verified  85492    98     British_Columbia,_CA
27581881  13.0  1x  RTX_3090_Ti  5.8   2.7      8.0     32.1  1671  0.1736  44.2  254.47  132.2  580.95.05   119.9    67.2      99.5   358.1     11847    verified  47374    98     South_Africa,_ZA
5708969   12.2  1x  RTX_3090     11.7  4.3      7.0     32.1  192   0.1743  44.4  254.56  300.9  535.230.02  5649.8   7400.4    99.9   37.9      7339     verified  34742    24     Utah,_US
17847309  12.4  1x  RTX_3090     5.7   4.1      8.0     32.1  318   0.1747  43.4  248.47  227.5  550.90.07   873.6    922.7     99.9   52.9      27710    verified  143243   49     Wisconsin,_US
21161540  12.7  1x  Tesla_V100   11.7  3.7      20.0   128.8  738   0.1770  25.7  145.24  134.4  565.57.01   914.8    913.1     99.6   1147.8    30821    verified  170701   499    Bulgaria,_BG
29427895  13.0  1x  RTX_4080S    21.5  3.5      18.0    64.4  173   0.1785  60.0  336.14  221.2  580.95.05   89.9     369.3     99.5   149.7     46392    verified  124072   124    ,_CN
28985942  13.0  1x  RTX_3090     12.7  7.0      6.4     51.5  263   0.1809  42.7  235.86  206.0  580.119.02  929.0    936.0     99.6   174.1     42715    verified  3497     199    Czechia,_CZ
18811410  12.8  1x  RTX_3090     12.4  3.9      5.3     42.9  123   0.1809  44.4  245.21  210.1  570.144     842.5    842.6     99.8   174.1     8966     verified  3497     166    Czechia,_CZ
16796664  13.0  1x  RTX_3090     10.5  2.9      12.0    32.2  216   0.1809  44.4  245.21  219.3  580.95.05   874.9    841.6     99.9   174.1     26839    verified  3497     249    Czechia,_CZ
28621950  13.0  1x  RTX_A5000    24.4  2.6      12.0    32.2  362   0.1809  34.9  192.98  209.1  580.95.05   3297.0   7077.6    99.7   174.1     45777    verified  3497     62     Czechia,_CZ
25913996  13.0  1x  Tesla_V100   12.4  2.3      32.0    42.9  978   0.1809  25.7  142.12  84.3   580.65.06   25.9     557.6     99.7   24.4      27165    verified  149988   32     Virginia,_US
29404436  12.8  1x  RTX_4080S    21.7  2.6      24.0    48.3  314   0.1826  72.0  394.08  253.7  570.144     115.9    241.5     99.9   172.5     36413    verified  124072   124    ,_CN
26894960  12.7  1x  RTX_3090     25.0  2.8      16.0   128.8  569   0.1881  44.7  237.44  199.8  565.57.01   804.4    826.7     99.9   464.5     19381    verified  98743    249    Belgium,_BE
26404839  13.0  1x  RTX_A5000    24.3  2.2      16.0    64.5  694   0.1903  34.9  183.48  180.0  580.82.09   2265.2   2141.8    99.8   417.0     44113    verified  214845   249    Czechia,_CZ
28082766  13.0  1x  RTX_3090     6.1   3.8      5.3     42.9  531   0.1947  43.8  224.69  236.9  580.76.05   2348.0   2186.3    99.7   82.5      21282    verified  122017   332    Quebec,_CA
27541422  13.0  1x  RTX_A5000    24.4  2.5      16.0    64.4  654   0.2009  34.4  171.06  198.6  580.95.05   8326.5   8499.5    99.7   417.1     46599    verified  214845   1999   Czechia,_CZ
29457805  13.0  1x  RTX_3090     6.7   2.4      12.0    30.0  1599  0.2009  44.7  222.67  181.6  580.95.05   516.8    759.3     99.9   176.5     17662    verified  102063   98     Taiwan,_TW
27416672  12.4  1x  RTX_3090     24.1  4.7      12.0    32.0  682   0.2009  44.3  220.58  182.8  550.120     929.2    921.4     99.9   34.6      27185    verified  78246    18     Poland,_PL
27768360  12.7  1x  RTX_3090     22.3  2.0      85.3    85.9  413   0.2014  44.3  220.18  128.6  565.57.01   22.3     573.2     99.7   23.8      28719    verified  149988   333    Virginia,_US


**Considering GPU cluster strategy**

I’m noticing that 1x 3090 offers are very cheap, which would make building 8 of them under $1.5/hr totally feasible. However, I have to think about managing them since they could have different host IDs. The user wants a cluster increment of 8 with high reliability. I could propose using one 8-GPU instance, but maybe eight 1-GPU instances are more accessible. Perhaps we can create Vast templates and use a Docker image to streamline environment setup, addressing their concerns about environmental variables and secrets.


**Designing launch script for clusters**

I’m thinking of creating a script, either `launch_cluster.py` or `launch_cluster.sh`, that can do a few essential tasks. It would read the `.env` file, construct the environment string, and choose the best offers based on constraints. Then it could create N instances—either one for an 8-GPU setup or eight for single GPU instances. I’ll label them as `maya3-cluster-{cluster_name}-{i}` and handle private image logins if needed.

We could also monitor the instances until they're running and allow scaling. While the user hasn’t specifically requested this script yet, we can build it alongside existing SSH-based scripts, as Vast prefers using Docker containers without SSH but could still keep SSH available for debugging.


**Assessing worker setup options**

For running a worker, using args mode is definitely okay. To view logs, we could use the command `vastai logs <id>`, which might suffice for monitoring. However, for debugging inside the container, it might be necessary to use SSH. I wonder if we could create a separate debug instance with the same image in SSH mode. It seems more efficient to run in args mode for production and rely on `vastai logs` for monitoring.

Regarding environment variable injection, in args mode, using `--env '-e KEY=VALUE ...'` is effective for setting them in the container. However, in SSH mode, those variables might not be accessible during interactive sessions, but we can find a workaround. It’s also important to avoid using `deploy_all.py`, since that’s meant for traditional SSH clusters and isn't suitable for Vast.


**Explaining instance status**

The user mentioned that two instances were successfully created and started, and they asked about the status of those instances and the third one with the 4090 GPU. I’m planning to explain that the first two were test instances, which are now stopped, while the third is a worker test. Earlier, the user also asked for instructions on adding an SSH key to Vast AI's console, which automatically applies to any new machines created. I can confirm that their public key was added through the CLI.

Now, I need to finish our plan by searching for offers and selecting potential options. I could choose the top three offers by filtering for criteria like verified status, reliability, GPU count, and RAM, potentially specifying country as the US, but that’s up to the user.


**Proposing GPU cluster options**

I’m considering three options for clusters:

- Option A: a single node with 8x 3090 GPUs at around $1.23/hr, reliability of 99.8%.
- Option B: a single node with 8x 4090 GPUs at about $2.24/hr, reliability of 99.9%.
- Option C: a single node with 8x A5000 GPUs for $0.96/hr and reliability of 99.3%.

I should mention that cheaper offers may come from regions with limited network access. The user might want on-demand instances, so we’ll focus on that. It seems they prefer each cluster to cost under $4/hr, which clarifies our approach. We want to propose high reliability with GPUs having 24-48 GB of VRAM.

As part of our strategy, we may need to destroy existing test instances to avoid potential storage costs. Although it feels destructive, given their purpose, I think it's safe to proceed. I'll check the status of instances now to confirm they’re exited before destroying them.


Output:
Exit code: 0
Wall time: 0.6 seconds
Output:
ID        Machine  Status  Num  Model     Util. %  vCPUs    RAM  Storage  SSH Addr      SSH Port  $/hr    Image                                          Net up  Net down  R     Label               age(hours)  uptime(mins)
29733687  27004    exited   1x  RTX_3090  0.0      3.0    257.6  25       ssh1.vast.ai  13686     0.1163  nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04  911.7   838.6     99.2  maya3-ssh-smoke     955.78      -245.71
29734739  7805     exited   1x  RTX_3090  0.0      12.0    31.8  10       ssh8.vast.ai  14738     0.1265  nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04  509.3   516.3     99.0  maya3-envvar-test   26141.82    -
29735679  8771     exited   1x  RTX_4090  0.0      36.0    64.1  80       -             -         0.2967  bharathkumar192/maya3-pipeline:6cfd8aa         475.9   497.0     99.8  maya3-worker-e2e-1  26144.05    -


**Evaluating Supabase Queue Handling**

It seems the Supabase queue is including items without R2 audio, which might be the issue. I'm thinking we should adjust the code to treat missing R2 sources as SKIPPED instead of FAILED or just pick videos that have R2 audio for production. We need to confirm if the queue is R2-available and consider patching the logic. On a positive note, we've improved background export in multiprocessing and updated the smoke test, plus pushed a new Docker tag.


**Preparing Cluster Plan for GPUs**

I need to deliver a cluster plan for 8 GPUs under $4. We can suggest using on-demand offers with `num_gpus=8` and a reliability greater than 0.995. I’ve found options like 8x 3090 at around $1.23/hr, 8x A5000 at about $0.96/hr, and 8x 4090 at roughly $2.24/hr. For budgeting, I think 3090 is best, 4090 for throughput, and A5000 for cost efficiency.

Now, I should outline some key deployment strategies, starting with prerequisites like having the Docker image ready, adding an SSH key, and deciding on cluster shape. Plus, I'll create a Vast template with specific configurations and environmental variables.


**Clarifying R2 Configuration**

I’m considering passing `R2_PREFIX=` as blank. The `config_overrides` set `r2_source_prefix='podcasts/'` and uses the R2 client's bucket. There seems to be some connection with the `R2_PREFIX` environment variable too, as it was observed in the R2Client and appeared in the logs. The error indicated a missing video file, which makes me think the bucket setup may be right, but the file isn't there.

In terms of command args, I'm thinking of including options like `--supabase-queue`, `--r2-upload`, and others. I might not need `--output`, since the default is fine, but setting `--r2-upload-prefix` to organize uploads might be a good idea. Plus, including `--r2-upload-skip-if-exists` can prevent overwriting files.


**Launching and Monitoring Instances**

I’m planning to launch instances from a template by searching for offers that match our constraints. The command I’ll use is `vastai create instance <offer_id> --template_hash <hash> --label maya3-cluster-01`. For multiple clusters, I’ll repeat this process.

In terms of monitoring, I can run `python3 scripts/monitor/dashboard.py --watch` to check the queue and worker status, along with error rates. For scaling, I’ll start with one cluster for about 30 minutes to test stability. If I can’t find 8-GPU offers, I could consider 8 single-GPU instances instead, while keeping the R2 upload prefix consistent per cluster.


**Mitigating Failures and Environment Setup**

For failure mitigation, I’m thinking of using on-demand instances for reliability, setting a reliability threshold, and selecting verified hosts. Using the `--direct` option seems sensible too. I might need to establish health checks for our environment. Our Dockerfile already verifies CUDA, but I can consider adding Supabase checks.

Now for cleaning up, we’ll stop and destroy instances when necessary, emphasizing a strategic approach to avoid failures, such as creating a template for immutability and pinning image tags.

Regarding environment variables, we can propose that using a template could help avoid manual input, allowing us to bake non-sensitive defaults but keep sensitive information at runtime. Also, I can mention running `sudo bash scripts/docker/smoke_test.sh` for local Docker testing. When users inquire about the status of instances, I’ll clarify that I stopped them to save on costs, though they were running earlier. Finally, I’ll check `git diff` to review code changes.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
 Dockerfile                   |  19 ++++----
 README.md                    |   6 +++
 massive_process.py           |  68 +++++++++++++++------------
 scripts/docker/smoke_test.sh | 108 ++++++++++++++++++++++++++++++++++---------
 scripts/monitor/dashboard.py |  54 +++++++++++++++++++++-
 src/analytics.py             |   4 +-
 src/models.py                |  19 ++++++--
 7 files changed, 211 insertions(+), 67 deletions(-)


Summary: auto


**Planning Execution Steps**

I've been going through the files and it seems like we're making good progress! We should probably mark step 3 as completed after summarizing and proposing offers. We might still need to store results or create a script for automation. Step 4 involves drafting a plan for creating instances as the user requested. The user asked about checking for machines and trying to connect, and since we've already done a trial run, we might want to build a scaling script to help out.


**Exploring Cluster Deployment Options**

Since we have a new Docker image and the user has asked for a deployment plan, I think we could just provide some commands. However, as a coding agent, I see the benefit of creating a script to automate cluster deployment with Vast CLI. This approach would speed things up and reduce potential errors. The user mentioned wanting to create instances and check for machines with an API key. I wonder if they might later want to create and start clusters, which we could actually automate right now.


**Assessing Cluster Creation Considerations**

The user didn't specifically ask to create a large cluster right now; they were exploring earlier suggestions like checking for machines and trial runs for pulling machines. We’ve completed a test run, but we haven’t fully run an 8-GPU cluster yet. Although creating an 8-GPU instance could cost around $1-3 per hour, which is significant, we could consider a more budget-friendly option like an 8x 3090 instance to run a worker. However, we need to remember that the user hasn't explicitly requested to start a big cluster.


**Clarifying Deployment Status and Plans**

We’ve already created three test instances, so that might be enough for now. I think the next step is to propose creating a template and launching a cluster once the user confirms. Since they asked about the instances and costs, we should hold off on creating more until we hear back. Also, they mentioned baking environment variables into the image, which I’d advise against, but we can use a template to simplify things. They’re curious about the three instances we've started, so we'll definitely need to explain that.


**Updating Deployment Plans**

We need to mention that we've updated the Docker image and now have a new tag for the Vast deployment. I should highlight that we’ll be using this new tag going forward. Also, I think it's time to update our plan: we can mark step 3 as completed and move step 4 to in progress. This way, the user is aware of our current status and next steps!


Explanation: I’ve pulled multiple on‑demand, verified, high‑reliability 8‑GPU offers well under $4/hr (plus 1‑GPU options for 8×1). Next I’ll summarize a non‑failure deployment strategy (template + pinned image + monitoring + scaling playbook).


Output: Plan updated


**Crafting a cleanup strategy**

I'm in the process of developing a high-level strategy and cleaning up as needed. I need to address why we shouldn’t bake environment variables into the image and clarify instances where it applies. I’ll also mention that I pushed the updated image tags `6cfd8aa-bgexportfix` and `latest`. For the cleanup, we have some stopped test resources and a local output directory with .tar files. We could clean it up, but it’s not strictly necessary. I’ll ensure to handle everything accordingly.


**Finalizing the cleanup process**

We've changed the repository and built Docker images several times, leaving many `<none>` images that we could prune to free up disk space, even though it wasn't specifically requested. It's good that we didn't store the user-provided API key or Docker PAT in the repo. Before crafting the final message, I need to ensure we didn't commit any secrets or modify the `.env` file— and we didn’t, despite printing secrets earlier. I’ll update the `.dockerignore` accordingly and check the Docker image digest. I'll suggest using Docker Compose with an `env_file` for variable convenience.


**Proposing SSH key instructions**

I need to provide clear instructions for adding an SSH key in the Vast console. Users can do this through the Vast UI by navigating to Account -> SSH keys -> Add key, or they can use the CLI command: `vastai create ssh-key "$(cat /.ssh/id_ed25519.pub)"`. It's important they complete this step before creating any new instances, as the key will be associated with them.

To verify the key addition, they can execute `vastai show ssh-keys`. For existing instances, the command is `vastai attach ssh <instance_id>`.

Next, we’ll plan for cluster creation by determining we need one 8-GPU instance. I’ll suggest using a search query for offers, specifically:
`vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 verified=True rentable=True reliability>0.995 dph_total<4' -o dph_total`. After selecting the appropriate offer ID, we can create the instance with:
`vastai create instance <offer_id> --image bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix --disk 100 --direct --label maya3-cluster-01 --env '<env>' --args '<args>'`.


**Clarifying template creation**

I need to clarify that the `--env` string must include `-e` for environment variables, and `--args` should be separated. According to the documentation, `--args` pass parameters to the entrypoint, which is our Python script, specifically `python massive_process.py`. I can consider using `--args '--supabase-queue ...'`, but that could get quite large.

Alternatively, creating a template using `--template_hash` seems more efficient. Here’s what I propose:

1. Create the template with:
`vastai create template "maya3-worker" bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix --env '-e URL=... -e SUPABASE_ADMIN=... -e HF_TOKEN=... -e R2_ENDPOINT_URL=... -e R2_ACCESS_KEY_ID=... -e R2_SECRET_ACCESS_KEY=... -e R2_PREFIX=' --onstart ''`.

I need to ensure that the template creation command also accommodates `--args`, since the documentation indicates that it includes environment, onstart, image, runtype, and recommended disk, but args might need separate handling. I should verify if the CLI for creating templates accepts the same options as for instance creation.


vastai create template -h | sed -n '1,120p'


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: vastai create template

options:
 -h, --help                     show this help message and exit
 --name NAME                    name of the template
 --image IMAGE                  docker container image to launch
 --image_tag IMAGE_TAG          docker image tag (can also be appended to end of image_path)
 --href HREF                    link you want to provide
 --repo REPO                    link to repository
 --login LOGIN                  docker login arguments for private repo authentication, surround with ''
 --env ENV                      Contents of the 'Docker options' field
 --ssh                          Launch as an ssh instance type
 --jupyter                      Launch as a jupyter instance instead of an ssh instance
 --direct                       Use (faster) direct connections for jupyter & ssh
 --jupyter-dir JUPYTER_DIR      For runtype 'jupyter', directory in instance to use to launch jupyter. Defaults to image's working directory
 --jupyter-lab                  For runtype 'jupyter', Launch instance with jupyter lab
 --onstart-cmd ONSTART_CMD      contents of onstart script as single argument
 --search_params SEARCH_PARAMS  search offers filters
 -n, --no-default               Disable default search param query args
 --disk_space DISK_SPACE        disk storage space, in GB
 --readme README                readme string
 --hide-readme                  hide the readme from users
 --desc DESC                    description string
 --public                       make template available to public
 --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

Create a template that can be used to create instances with

Example:
    vastai create template --name "tgi-llama2-7B-quantized" --image "ghcr.io/huggingface/text-generation-inference:1.0.3"
                            --env "-p 3000:3000 -e MODEL_ARGS='--model-id TheBloke/Llama-2-7B-chat-GPTQ --quantize gptq'"
                            --onstart-cmd 'wget -O - https://raw.githubusercontent.com/vast-ai/vast-pyworker/main/scripts/launch_tgi.sh | bash'
                            --search_params "gpu_ram>=23 num_gpus=1 gpu_name=RTX_3090 inet_down>128 direct_port_count>3 disk_space>=192 driver_version>=535086005 rented=False"
                            --disk_space 8.0 --ssh --direct


**Reviewing background export**

I've noted that the only missing piece is `--background-export`, which is in CMD. We found out that it's experimental, but we’ve fixed it. The good news is that we can use the template without needing to specify arguments, which addresses the user’s concerns. We just have to ensure we pass environment variables every time, although they can be included in the template. Storing secrets in the template could work, but updating them for rotation might be a hassle. We might also consider account-level environment variables.


**Creating a worker template**

I'm planning to create a Vast template for our worker, using the image `bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix`. I’ll include environment variables like URLs and tokens, and I'm considering adding `--shm-size 16g` to avoid out-of-memory issues, especially for PyTorch. It seems like it might be supported in the Docker options field, which I think passes to the `docker run` command. I could also set up GPU configurations since Vast automatically handles that. Additionally, I'm looking at mounting the host's `/workspace` for cache, though I need to confirm that caches persist as expected.


**Exploring image cache**

Our image uses `/root/.cache` for HF, which should be within the container and possibly persisted on the instance disk. I'm wondering if it’s ephemeral. The root filesystem in the Docker container comes from image layers and has a persistent overlay on the instance disk. This means it generally persists across container restarts as long as the instance is running. If the instance stops and restarts, the container starts from scratch, though the underlying disk should still persist. However, for large model caches, it might be better to use `/workspace` as a volume. When launching instances, I can choose an offer ID and create the instance using `--template_hash <hash>` or the template ID, but I might not need a template at all. I could just create instances with the image and environment string, and for multiple instances, I can script that.


**Addressing SSH and environment variables**

The user asked how to add an SSH key to Vast AI’s console so that it gets automatically included with any machines we create. However, our worker template might not use SSH. For debugging, I could consider a separate SSH template with a base CUDA SSH image.

Now, regarding scaling and monitoring, I can run `python3 scripts/monitor/dashboard.py --watch` locally to query Supabase, along with checking Vast instance statuses using `vastai show instances` and `vastai logs`. However, the user raises a great point: we should consider why we did not bake environment variables directly into the Docker image for easier instance deployment.


**Finalizing instance management**

I can mention that we can avoid setting environment variables on a per-instance basis by using Vast templates, which apply them to all instances launched from the template, though they still won't be baked into the image.

Now, regarding the instances, I see that two test instances were created and stopped, while the third one with the 4090 ran our worker but failed because of a missing R2 configuration. We resolved that by either processing R2-existing videos or using a direct URL. Also, we changed the Docker image tag, as the 4090 used an older tag. We should re-run with the new one. Now, I need to wrap up step 4, create the strategic plan, and outline some recommended commands. Additionally, for step 5, we should clean up the local output directory and any leftover Docker test containers, including those tar files. Let’s check the output directory.


Output:
Exit code: 0
Wall time: 0 seconds
Output:
total 129392
drwxr-xr-x 4 root   root        4096 Jan  7 19:45 .
drwxrwxr-x 7 ubuntu ubuntu      4096 Jan  7 19:14 ..
drwxr-xr-x 3 root   root        4096 Jan  7 19:24 --1yBpF537M
-rw-r--r-- 1 root   root   132474880 Jan  7 19:24 --1yBpF537M.tar
-rw-r--r-- 1 root   root        2263 Jan  7 19:45 benchmark_results.json
drwxr-xr-x 3 root   root        4096 Jan  7 19:45 --E8hBLmJvw


Summary: auto


**Considering output cleanup**

The output directory contains a tar file, and while I could clean it, it's not strictly necessary. For a reset, I might think about removing output and benchmark results, though I'm unsure if the user cares. The output directory is owned by root, which is fine since Docker created it. I could change ownership, but it's not essential. Now, for step 5, I could stop vast instances, which I've done, and maybe clean up local Docker containers if needed. The user's request to "reset everything" could refer to several things.


**Reviewing plan step 5**

Step 5 is still pending, so I need to decide whether to complete or adjust it. I've stopped all vast instances, but I’m considering whether to remove environment variables from disk, like the vast API key. Even though the user mentioned that it's fine to keep it, for safety, I think we should remove it once we're done. The same goes for Docker login credentials since the user will rotate them. I might bring up the importance of rotating keys as a possible next step, even though the user didn’t want warnings about security.


**Evaluating step 5 update**

I need to consider whether to run `vastai destroy instance` for a full cleanup, but I’d prefer not to. Instead, I think we can interpret cleanup as just stopping the test instances, which I've already done. So, I can mark step 5 as completed. However, the instructions emphasize not to jump directly from pending to completed; I need to set it to in-progress first. I guess I can mark it in-progress and then complete it in the same update.


**Planning updates and response**

I can mark step 4 as completed and set step 5 as in-progress in one `update_plan` call. Once I finish step 4, I'll update accordingly. The user likely expects an interactive response, especially since they've asked multiple questions and might want to proceed with deployment. I’ll provide clear bullet points and commands, and I should mention using `python3` instead of `python` since it might not be found. Additionally, we need to discuss baking models into Docker images, as the user suggested.


**Finalizing the update plan**

We can explain to the user that we can't fully bake gated models due to token requirements, but we can warm caches. For now, keeping caches on the instance disk is better. Regarding their question about baking environment variables in the Docker image, it's safer not to include them in the image itself. I've stopped the instances to avoid billing, and they can be destroyed if needed.

I’ll update step 4 to completed and set step 5 to in-progress, then mark it completed after cleanup actions. Finally, I’ll ensure my response includes all necessary details and recommend re-running offers to get the best options.


**Finalizing the response and plan**

I want to clarify to the user that baking environment variables (especially secrets) into the Docker image is insecure, and we recommend using runtime injection instead. This allows for different environment variables per instance. For models, I can explain that while we can warm caches, baking all models into the image would increase its size and create complexity with gated models requiring tokens.

Regarding the instances we created, I should mention their purposes and status updates. I’ll outline my high-level deployment strategy for a multi-cluster setup, noting important details like monitoring and lease recovery processes. I'll also provide examples of machine offers to help the user make informed decisions for deployment. Finally, I’ll ensure to call `update_plan` to finalize the statuses after I prepare the full response.


**Searching for GPU offers**

I want to check for 48GB GPU options that might be under $4. It seems like options like the A6000 could be more expensive, but maybe we can find something that fits. I'll run a search for eight GPUs with exactly 48GB of RAM and meet the criteria for rentals and reliability.

I need to make sure to use the right parameters for my query, which I’m going to execute now to see if there are any suitable offers available.


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


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


**Assessing GPU Options**

It looks like the 48GB GPUs I was hoping for under $4 aren't available right now. Maybe I should consider 24GB options instead, like the 3090 or 4090. I could also check if there's a single 48GB GPU available for under $0.5. However, it seems that the 48GB GPUs might still exceed the user’s budget for a cluster. I’m thinking of proposing a pre-check at the claim time to ensure any missing R2 source issues don’t cause failures. That seems like an important step!


**Avoiding GPU Waste**

I think we should mention a recommendation to avoid wasted GPU time, even if it wasn't specifically requested. It’s possible that the dataset guarantees all pending videos have audio, but maybe the 4090 instance just happened to select a pending video without audio. It might be a good idea to adjust the queue to focus on the relevant videos.

Also, regarding environment variables in the Vast "ssh" runtype, env var injection might not show, but we’ll be using args runtype for injection. I need to update steps 4 and 5 of our plan. Let's wrap up the final response first!


**Finalizing the Plan**

Since our plan is for the harness, we can always update it later if needed. For now, the final answer should cover a few important points.

First, I want to explain that environment variables aren't baked, and I'll recommend using the Vast template or the `--env` option with an environment string. I should mention that we've already set safe defaults in the Dockerfile.

Next, I'll explain the instances we worked with, noting their current state as stopped. Finally, I’ll confirm that the Docker image has been completed and tested locally, including smoke and E2E tests with R2 upload.


**Providing Commands**

I need to start by confirming that the image is published and mention any relevant tags. Then, I'll outline a high-level deployment strategy for multi-cluster setups, focusing on using 8-GPU on-demand offers under $4.

I'll include steps for adding the SSH key and give the search command for offers, noting typical costs. Monitoring is crucial, so I’ll highlight running the dashboard, which shows ETA and real-time data.

Finally, I’ll suggest the next step of creating a cluster with an 8-GPU 3090 instance using the image tag and environment injection, asking if they’d like me to proceed. I'll also mention the new image version tagged as `6cfd8aa-bgexportfix`.


**Updating Documentation**

I want to mention that we didn't bake environment variables for security purposes and to simplify rotation. Instead, we can store them in the Vast template to avoid passing them every time. Before sending the final message, we should check if the README or Docker files need updates due to the new smoke_test variables, especially regarding python3.

I recall the README was recently updated, but since we changed the script to add NO_MUSIC_DETECTION, improved tar verification, and video IDs starting with '-', we should definitely review and update that section accordingly. Let’s check the README about the smoke test usage!


Output:
Exit code: 0
Wall time: 0 seconds
Output:
# Maya3 Audio Diarization Pipeline

High-throughput, GPU-accelerated audio diarization and segmentation pipeline. Processes podcast audio from R2 storage, performs speaker diarization, music detection, and outputs clean speaker-segmented data.

## Performance

- **361x real-time** throughput (8x RTX 4090)
- **90+ hours** of audio processed in ~15 minutes
- Distributed processing via Supabase queue

## Architecture

```
R2 (Source Audio) → Supabase Queue → GPU Workers → R2 (Output .tar)
                         ↓
                    Status Updates
```

## Pipeline Stages

| Stage | Description |
|-------|-------------|
| 1. Download | Fetch audio from R2 bucket |
| 2. VAD | Voice activity detection (Silero) |
| 3. Chunking | Split into processable chunks |
| 4. Diarization | Speaker identification (PyAnnote) |
| 5. Embeddings | Speaker embedding extraction |
| 6. Clustering | Speaker clustering & refinement |
| 7. Music Detection | INA + PANNs hybrid detection |
| 8. Finalize | Quality filtering & validation |
| 9. Output | Generate segmented JSON |
| 10. Export | Upload .tar to R2 |

## Quick Start

### Prerequisites

- NVIDIA GPU with CUDA 12.1+
- Docker with NVIDIA Container Toolkit
- Hugging Face token (for PyAnnote models)
- Supabase project (for queue)
- Cloudflare R2 bucket (for storage)

### Setup

1. Clone and configure:
```bash
cp .env.example .env
# Edit .env with your credentials
```

2. Build container:
```bash
docker-compose build
```

3. Run workers:
```bash
docker-compose up -d
```

### Local Docker Smoke Test

On a machine with Docker + NVIDIA Container Toolkit:

```bash
chmod +x scripts/docker/smoke_test.sh
./scripts/docker/smoke_test.sh
```

Optional end-to-end test (process 1 Supabase-queued video on GPU 0 and validate TAR contents):

```bash
E2E=1 GPU_DEVICE=0 ./scripts/docker/smoke_test.sh
```

Safer end-to-end test (process a specific `VIDEO_ID` from R2 without claiming from Supabase):

```bash
E2E=1 VIDEO_ID=---g_VL7ySo GPU_DEVICE=0 ./scripts/docker/smoke_test.sh
```

### Environment Variables

| Variable | Description |
|----------|-------------|
| `HF_TOKEN` | Hugging Face API token |
| `URL` | Supabase project URL |
| `SUPABASE_ADMIN` | Supabase service role key |
| `ACCOUNT_ID` | Cloudflare account ID |
| `API_TOKEN` | Cloudflare R2 API token |

## Local Development

```bash
# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run with Supabase queue
python massive_process.py --supabase-queue --output ./output
```

## Output Format

Each processed video produces a `.tar` containing:
- `{video_id}_segments.json` - Speaker-segmented data
- Metadata and quality statistics

## Scaling

Workers can be deployed across multiple machines. Each worker:
1. Claims videos from Supabase queue
2. Processes independently
3. Reports completion/failure back to Supabase

## Files

```
├── massive_process.py    # Main entry point
├── pipeline.py           # Core pipeline logic
├── src/
│   ├── config.py         # Configuration
│   ├── download.py       # R2 download
│   ├── vad.py            # Voice activity detection
│   ├── diarization.py    # Speaker diarization
│   ├── embeddings.py     # Speaker embeddings
│   ├── clustering.py     # Speaker clustering
│   ├── music_detection.py # Music detection
│   ├── export.py         # Output generation
│   ├── r2_client.py      # R2 storage client
│   ├── supabase_client.py # Supabase queue client
│   └── logger.py         # Clean logging
├── Dockerfile            # Container build
├── docker-compose.yml    # Container orchestration
└── requirements.txt      # Python dependencies
```


**Updating README details**

I'm considering how the README mentions using the `python` command, but since `python` isn't available in this environment, it might be better to suggest using `python3` or `python -m`. However, within the Dockerfile, we installed `python-is-python3`, so `python` does work in the container. The README could also be updated to include `R2_UPLOAD=1` and clarify about video IDs starting with hyphens. Overall, we should patch the README for clarity and accuracy.


**Applying minimal changes**

I'm thinking about making minimal changes, but it seems important to mention using `python3` for local development. It might help clarify things for users. I’m planning to apply a patch that reflects these adjustments so everything is up-to-date and clear. Just ensuring that these changes enhance the instructions without overwhelming anyone—I want it to be user-friendly! Let’s keep it straightforward and effective as we implement these small updates.


Input:
*** Begin Patch
*** Update File: README.md
@@
 Optional end-to-end test (process 1 Supabase-queued video on GPU 0 and validate TAR contents):

 ```bash
 E2E=1 GPU_DEVICE=0 ./scripts/docker/smoke_test.sh
 ```

 Safer end-to-end test (process a specific `VIDEO_ID` from R2 without claiming from Supabase):

 ```bash
 E2E=1 VIDEO_ID=---g_VL7ySo GPU_DEVICE=0 ./scripts/docker/smoke_test.sh
 ```

+E2E with R2 upload validation (uploads to `1-cleaned-data` under a unique `smoke_test/<timestamp>/` prefix):
+
+```bash
+E2E=1 VIDEO_ID=---g_VL7ySo R2_UPLOAD=1 GPU_DEVICE=0 ./scripts/docker/smoke_test.sh
+```
+
+Faster E2E (disable music detection downloads/compute):
+
+```bash
+E2E=1 VIDEO_ID=---g_VL7ySo R2_UPLOAD=1 NO_MUSIC_DETECTION=1 GPU_DEVICE=0 ./scripts/docker/smoke_test.sh
+```
+
 ### Environment Variables
@@
 ## Local Development

 ```bash
 # Create virtual environment
-python -m venv .venv
+python3 -m venv .venv
 source .venv/bin/activate

 # Install dependencies
 pip install -r requirements.txt

 # Run with Supabase queue
-python massive_process.py --supabase-queue --output ./output
+python3 massive_process.py --supabase-queue --output ./output
 ```
*** End Patch


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


**Crafting final response**

I’ve updated the README, and made heavy changes to `scripts/docker/smoke_test.sh`. The Dockerfile now uses Python 3, and the Docker image has been updated, which is great! I need to check for any formatting issues. It’s also important to capture final information: the Docker image published tags, local docker tests results, and the statuses of the vast instances, which are currently shown as exited. I think I have everything I need for the final response!


**Updating plan status**

I’m thinking about how to provide some details without listing everything. For the Vast offer search, I’ll include commands and typical examples. Also, I need to address the multi-cluster plan and answer the user’s question about baking environment variables and how to inject them at startup—mentioning that using the Vast template is a good solution. I’ll update the plan to reflect that step 4 is completed and step 5 is in progress. I didn’t do any extra cleanup beyond stopping instances, which seems sufficient.


Explanation: Deployment strategy and candidate offers are ready. Final step is cleanup: ensure the Vast test instances remain stopped (and optionally destroy them) and confirm the deployment artifacts (pushed image tags) are ready.


Output: Plan updated


Summary: auto


**Completing cleanup process**

I'm progressing with cleanup by stopping any instances we've started and pushing the necessary image, which is good. I want to ensure there are no worker processes running on my local machine, and it seems there's none, apart from a qwen-local container, which isn’t relevant. I think I should check the offline status of stale worker heartbeats in Supabase, but many already show offline. I’ll also update the `.dockerignore`. Cleanup can be marked as completed!


Explanation: Cleanup complete: Vast test instances are stopped (exited) and the updated Docker image is pushed and ready to deploy. Next action is simply choosing an offer and launching the first 8‑GPU cluster from the pinned image tag.


Output: Plan updated


**Why Env Vars Aren’t Baked Into The Image**
- `HF_TOKEN`, `SUPABASE_ADMIN`, `R2_*` are secrets; baking them into `Dockerfile ENV` (or copying a `.env` into the image) makes them trivially recoverable from the image config/layers and hard to rotate.
- Convenience fix: put them once into a Vast **template** (`--env ...`), then every instance launched from that template inherits them (no per-instance manual env passing).

**What Happened To Those 3 Vast Instances**
- `maya3-ssh-smoke` (`29733687`): SSH-mode CUDA container to verify your SSH key + `vastai ssh-url` connectivity.
- `maya3-envvar-test` (`29734739`): SSH-mode sandbox to experiment with env injection behavior; not needed for production “args-mode” workers.
- `maya3-worker-e2e-1` (`29735679`): ran the pipeline image; it claimed a `PENDING` video whose `podcasts/<id>.webm` was missing in the R2 source bucket, so it failed fast; I stopped all 3 so they’re now `exited` (no further queue churn/cost).

**Docker Image Status (Local + Published)**
- Local E2E (incl. R2 upload verification) works: `E2E=1 VIDEO_ID=<id> R2_UPLOAD=1 NO_MUSIC_DETECTION=1 sudo bash scripts/docker/smoke_test.sh`.
- Pushed tags: `bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix` (and `bharathkumar192/maya3-pipeline:latest`).

**Vast “Non-Failure” Multi-Cluster Plan (8-GPU clusters, <$4/hr each)**
- Pick **on-demand + verified + high reliability** offers: `vastai search offers --type on-demand 'num_gpus=8 gpu_ram>=24 gpu_ram<=48 verified=True rentable=True reliability>0.995 dph_total<4' -o dph_total`.
- Create one Vast **template** using `bharathkumar192/maya3-pipeline:6cfd8aa-bgexportfix` + `--env '-e URL=… -e SUPABASE_ADMIN=… -e HF_TOKEN=… -e R2_* … --shm-size 16g'`, then launch N identical 8‑GPU instances labeled `maya3-cluster-01`, `maya3-cluster-02`, …
- Run exactly **one** lease reclaimer somewhere: `python3 scripts/monitor/lease_recovery.py --daemon`.
- Monitoring/scaling is basically “launch more instances + watch”: `python3 scripts/monitor/dashboard.py --watch` (now includes ETA + realtime speed).

Tell me if you want clusters as **1×8-GPU instances** or **8×1-GPU instances**, and whether I should go ahead and launch the first cluster now.


# Context from my IDE setup:

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

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

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

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

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

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

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

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


**Evaluating disk space needs**

I’m considering whether we need more disk space and the recommendation is 100GB. However, I wonder if 75GB might still work if we manage our caches well, uploading outputs to R2 and clearing local files afterward. The HF model caches can take up quite a bit of space, and some models like PANNs download around 700MB. Since we have 8 GPUs on one host, caches are shared, so we shouldn't multiply the space by 8.


**Assessing file management in pipelines**

I'm thinking about how working directories might create intermediate files for each worker, and that audio downloads could be pretty large. Original audio files can be about 1GB, and with multiple workers at once, they could accumulate quickly. We do delete intermediate files, but should we keep the original FLAC files? The output directory might fill up fast if we don't manage it properly. I wonder if we should add an option to delete the output directory after a successful upload to keep things tidy.


**Examining pipeline export handling**

I’m looking at how pipeline exports sync after uploading. It seems they try to unlink the tar path but don’t remove the output directory, which can contain metadata and segments that add up to a significant amount of space. With 171,000 videos locally, that's a huge concern, especially on a Vast instance with limited disk space. I think we need to include an option to clean up the output directory after successful uploads to ensure reliability and efficiency in production.


**Considering cleanup and lease recovery**

I’m thinking we might need to implement cleanup procedures or opt for offers with larger disk space. The user hasn't mentioned it, but we should anticipate that a "non-failure" plan includes managing disk usage. They're asking about running `lease_recovery.py --daemon` at the end of processing all videos, but lease recovery is crucial while the workers are running to reclaim stuck videos after crashes. It can also run on a cheaper CPU-only instance or even locally, so we need to consider that if workers crash, videos may remain claimed until the lease expires.


**Exploring lease recovery in Supabase**

I'm considering how the `claim_next_video` function in Supabase likely relies on lease expiration. However, once the lease passes, any worker could reclaim the video. That makes me wonder if we really need a separate lease recovery script. Instead, the script could potentially mark stale CLAIMED videos back to PENDING. So, I think I should take a look at the lease recovery script to see how it handles this process more closely.


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


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

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

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

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

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

from dotenv import load_dotenv
load_dotenv()

from src.supabase_client import SupabaseClient, STATUS_PENDING, STATUS_CLAIMED

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

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

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

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

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

    return count

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

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

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

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

    stale = result.data or []

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

    return stale

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

    client = SupabaseClient()

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

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

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

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

        time.sleep(interval_sec)

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

    client = SupabaseClient()

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

if __name__ == "__main__":
    main()


**Explaining GPU Clusters**

I’m thinking about lease recovery, where expired leases go back to PENDING. I'm wondering if claim_next_video handles expiration — maybe it does, but running it at the end seems too late. It might be better to keep it running continuously, like a daemon, especially if workers die. If the user wants to start with one GPU cluster, that likely means one 8-GPU instance, and I want to clarify that this makes sense for throughput.


**Discussing GPU Throughput**

The user is focused on maximum throughput, so I need to clarify that while 1×8 is typically better for orchestration, it may not yield the highest throughput. The pipeline operates per-GPU, and network or disk may be shared, meaning eight separate hosts could scale throughput more effectively and add redundancy, but that requires more management. Starting with one host with eight GPUs is simpler, as long as the host has enough CPU, disk bandwidth, and network capability to maintain throughput quality.


**Understanding GPU Throughput and Flexibility**

Throughput mainly comes from summing the per-GPU throughput. If the host can handle it, 1×8 is as good as 8×1, but if the host is a bottleneck, then 8×1 might perform better. Overall, 1×8 is easier operationally, and I think a trial could show good results. Regarding flexibility, the template process allows for selecting different GPU models like 3090 or 4090, as it just sets up the environment without enforcing a specific GPU. So, we can create a template usable across any offer. Let's also check the offer IDs and their configurations.


**Querying Offer IDs**

I'm planning to query each offer with the command `vastai show offer <id>` or use `vastai search offers` to filter by ID. I think using `vastai show offer` will be the most straightforward approach. I’ll also check the help command with `vastai show offer -h` to make sure I have all the necessary details and options at hand. That should help me provide accurate information from the offers.


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: vastai [-h] [--url URL] [--retry RETRY] [--raw] [--explain] [--curl] [--api-key API_KEY] [--version] command ...
vastai: error: argument command: invalid choice: 'show offer' (choose from 'help', 'attach ssh', 'cancel copy', 'cancel sync', 'change bid', 'clone volume', 'copy', 'cloud copy', 'take snapshot', 'create api-key', 'create cluster', 'create env-var', 'create ssh-key', 'create workergroup', 'create endpoint', 'create instance', 'create subaccount', 'create team', 'create team-role', 'create template', 'create volume', 'create network-volume', 'create overlay', 'delete api-key', 'delete ssh-key', 'delete scheduled-job', 'delete cluster', 'delete workergroup', 'delete endpoint', 'delete env-var', 'delete overlay', 'delete template', 'delete volume', 'destroy instance', 'destroy instances', 'destroy team', 'detach ssh', 'execute', 'get endpt-logs', 'get wrkgrp-logs', 'invite member', 'join cluster', 'join overlay', 'label instance', 'launch instance', 'logs', 'prepay instance', 'reboot instance', 'recycle instance', 'remove member', 'remove team-role', 'reports', 'reset api-key', 'start instance', 'start instances', 'stop instance', 'stop instances', 'search benchmarks', 'search invoices', 'search offers', 'search instances', 'search templates', 'search volumes', 'search network-volumes', 'set api-key', 'set user', 'ssh-url', 'scp-url', 'show api-key', 'show api-keys', 'show audit-logs', 'show scheduled-jobs', 'show ssh-keys', 'show workergroups', 'show endpoints', 'show connections', 'show deposit', 'show earnings', 'show env-vars', 'show invoices', 'show instance', 'show instances', 'show ipaddrs', 'show clusters', 'show overlays', 'show subaccounts', 'show members', 'show team-role', 'show team-roles', 'show user', 'show volumes', 'remove-machine-from-cluster', 'transfer credit', 'update workergroup', 'update endpoint', 'update env-var', 'update instance', 'update team-role', 'update template', 'update ssh-key', 'cancel maint', 'cleanup machine', 'delete machine', 'list machine', 'list machines', 'add network-disk', 'show network-disks', 'list network-volume', 'unlist network-volume', 'list volume', 'list volumes', 'unlist volume', 'remove defjob', 'set defjob', 'set min-bid', 'schedule maint', 'show machine', 'show machines', 'show maints', 'unlist machine', 'self-test machine')


vastai search offers --type on-demand 'id=29454430' --raw | head -n 40


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
[]


**Checking offer IDs**

I’m not getting any results, which could be because the offer isn’t currently rentable or the ID might not exist. It could also refer to a different type, like "type ID" for instance types on Vast UI, where they show "Machine types." From what I've seen, the search results contain offer IDs like 29516770 and 28470822, which I’ll need to check again. I think I'll try searching specifically for the offer with ID 28470822.


vastai search offers --type on-demand 'id=28470822' -o 'dph_total'


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


Summary: auto


vastai search offers -h | sed -n '1,200p'


Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: vastai search offers [--help] [--api-key API_KEY] [--raw] <query>

positional arguments:
 query                    Query to search for. default: 'external=false rentable=true verified=true', pass -n to ignore default

options:
 -h, --help               show this help message and exit
 -t TYPE, --type TYPE     Show 'on-demand', 'reserved', or 'bid'(interruptible) pricing. default: on-demand
 -i, --interruptible      Alias for --type=bid
 -b, --bid                Alias for --type=bid
 -r, --reserved           Alias for --type=reserved
 -d, --on-demand          Alias for --type=on-demand
 -n, --no-default         Disable default query
 --new                    New search exp
 --limit LIMIT
 --disable-bundling       Deprecated
 --storage STORAGE        Amount of storage to use for pricing, in GiB. default=5.0GiB
 -o ORDER, --order ORDER  Comma-separated list of fields to sort on. postfix field with - to sort desc. ex: -o 'num_gpus,total_flops-'.  default='score-'
 --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

Query syntax:

    query = comparison comparison...
    comparison = field op value
    field = <name of a field>
    op = one of: <, <=, ==, !=, >=, >, in, notin
    value = <bool, int, float, string> | 'any' | [value0, value1, ...]
    bool: True, False

note: to pass '>' and '<' on the command line, make sure to use quotes
note: to encode a string query value (ie for gpu_name), replace any spaces ' ' with underscore '_'

Examples:

    # search for somewhat reliable single RTX 3090 instances, filter out any duplicates or offers that conflict with our existing stopped instances
    vastai search offers 'reliability > 0.98 num_gpus=1 gpu_name=RTX_3090 rented=False'

    # search for datacenter gpus with minimal compute_cap and total_flops
    vastai search offers 'compute_cap > 610 total_flops > 5 datacenter=True'

    # search for reliable 4 gpu offers in Taiwan or Sweden
    vastai search offers 'reliability>0.99 num_gpus=4 geolocation in [TW,SE]'

    # search for reliable RTX 3090 or 4090 gpus NOT in China or Vietnam
    vastai search offers 'reliability>0.99 gpu_name in ["RTX 4090", "RTX 3090"] geolocation notin [CN,VN]'

    # search for machines with nvidia drivers 535.86.05 or greater (and various other options)
    vastai search offers 'disk_space>146 duration>24 gpu_ram>10 cuda_vers>=12.1 direct_port_count>=2 driver_version >= 535.86.05'

    # search for reliable machines with at least 4 gpus, unverified, order by num_gpus, allow conflicts
    vastai search offers 'reliability > 0.99  num_gpus>=4 verified=False rented=any' -o 'num_gpus-'

    # search for arm64 cpu architecture
    vastai search offers 'cpu_arch=arm64'

Available fields:

      Name                  Type       Description

    bw_nvlink               float     bandwidth NVLink
    compute_cap:            int       cuda compute capability*100  (ie:  650 for 6.5, 700 for 7.0)
    cpu_arch                string    host machine cpu architecture (e.g. amd64, arm64)
    cpu_cores:              int       # virtual cpus
    cpu_ghz:                Float     # cpu clock speed GHZ
    cpu_cores_effective:    float     # virtual cpus you get
    cpu_ram:                float     system RAM in gigabytes
    cuda_vers:              float     machine max supported cuda version (based on driver version)
    datacenter:             bool      show only datacenter offers
    direct_port_count       int       open ports on host's router
    disk_bw:                float     disk read bandwidth, in MB/s
    disk_space:             float     disk storage space, in GB
    dlperf:                 float     DL-perf score  (see FAQ for explanation)
    dlperf_usd:             float     DL-perf/$
    dph:                    float     $/hour rental cost
    driver_version:         string    machine's nvidia/amd driver version as 3 digit string ex. "535.86.05,"
    duration:               float     max rental duration in days
    external:               bool      show external offers in addition to datacenter offers
    flops_usd:              float     TFLOPs/$
    geolocation:            string    Two letter country code. Works with operators =, !=, in, notin (e.g. geolocation not in ['XV','XZ'])
    gpu_arch                string    host machine gpu architecture (e.g. nvidia, amd)
    gpu_max_power           float     GPU power limit (watts)
    gpu_max_temp            float     GPU temp limit (C)
    gpu_mem_bw:             float     GPU memory bandwidth in GB/s
    gpu_name:               string    GPU model name (no quotes, replace spaces with underscores, ie: RTX_3090 rather than 'RTX 3090')
    gpu_ram:                float     per GPU RAM in GB
    gpu_total_ram:          float     total GPU RAM in GB
    gpu_frac:               float     Ratio of GPUs in the offer to gpus in the system
    gpu_display_active:     bool      True if the GPU has a display attached
    has_avx:                bool      CPU supports AVX instruction set.
    id:                     int       instance unique ID
    inet_down:              float     internet download speed in Mb/s
    inet_down_cost:         float     internet download bandwidth cost in $/GB
    inet_up:                float     internet upload speed in Mb/s
    inet_up_cost:           float     internet upload bandwidth cost in $/GB
    machine_id              int       machine id of instance
    min_bid:                float     current minimum bid price in $/hr for interruptible
    num_gpus:               int       # of GPUs
    pci_gen:                float     PCIE generation
    pcie_bw:                float     PCIE bandwidth (CPU to GPU)
    reliability:            float     machine reliability score (see FAQ for explanation)
    rentable:               bool      is the instance currently rentable
    rented:                 bool      allow/disallow duplicates and potential conflicts with existing stopped instances
    storage_cost:           float     storage cost in $/GB/month
    static_ip:              bool      is the IP addr static/stable
    total_flops:            float     total TFLOPs from all GPUs
    ubuntu_version          string    host machine ubuntu OS version
    verified:               bool      is the machine verified
    vms_enabled:            bool      is the machine a VM instance


vastai search offers -n --type on-demand 'id=28470822 rentable=any verified=any' --raw | head -c 4000


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
[]


for t in on-demand bid reserved; do echo "=== $t"; vastai search offers -n --type $t 'id=28470822 rentable=any verified=any' -o 'dph_total' | head -n 3; done


Output:
Exit code: 0
Wall time: 1.4 seconds
Output:
=== on-demand
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country
=== bid
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country
=== reserved
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


**Analyzing search results**

I'm not getting any results, which makes me wonder if the ID isn't in the database. The user provided type #28470822, which I recall from earlier search outputs, but they may have mis-copied it, or it might refer to something else. I saw 28470822 associated with 8x RTX_A5000 before. Since our previous search didn't filter by ID, I'll broaden the query to check availability by searching for GPU name RTX_A5000 with 8 GPUs.


vastai search offers -n --type on-demand 'num_gpus=8 gpu_name=RTX_A5000 rented=any rentable=any verified=any' -o 'id' | head -n 30


Output:
Exit code: 0
Wall time: 0.8 seconds
Output:
ID        CUDA   N  Model      PCIE  cpu_ghz  vCPUs    RAM  Disk  $/hr    DLP    DLP/$   score  NV Driver  Net_up  Net_down  R     Max_Days  mach_id  status    host_id  ports  country
13185448  12.4  8x  RTX_A5000  23.2  3.5      56.9   458.5  4118  1.8467  236.0  127.77  111.7  550.120    538.0   929.7     99.7  23.7      29126    verified  1647     887    Romania,_RO
26031536  13.0  8x  RTX_A5000  24.4  2.6      48.0   515.6  5795  1.4409  228.7  158.73  166.6  580.82.07  2400.9  5764.5    99.7  174.1     43571    verified  3497     498    Czechia,_CZ
26404845  13.0  8x  RTX_A5000  24.2  2.2      128.0  515.6  5555  1.5063  237.5  157.67  158.2  580.82.09  2265.2  2141.8    99.8  417.0     44113    verified  214845   1998   Czechia,_CZ
27541435  13.0  8x  RTX_A5000  24.4  2.5      128.0  515.5  5235  1.5916  234.8  147.50  195.2  580.95.05  8326.5  8499.5    99.7  417.0     46599    verified  214845   15999  Czechia,_CZ
28316324  13.0  8x  RTX_A5000  24.4  2.6      96.0   257.6  2734  1.4409  233.8  162.23  166.1  580.95.05  2863.1  7080.7    98.3  174.1     45775    verified  3497     498    Czechia,_CZ
28470822  12.0  8x  RTX_A5000  6.3   3.0      40.0   128.6  75    1.8147  118.5  65.28   51.4   525.60.11  614.3   687.5     99.8  146.9     7566     verified  11885    98     Washington,_US
29516770  13.0  8x  RTX_A5000  25.0  2.3      96.0   386.9  1793  0.9614  227.0  236.07  192.8  580.95.05  638.6   1353.1    99.3  9.1       37643    verified  253867   798    Austria,_AT


**Clarifying field names**

I’m noticing that the field name in help is listed as `id`, but for offers, it might actually be `ask_id` or something similar. The output column is marked as `ID`, which corresponds to ask_id. So, it seems like the field could be `id` or `ask_id`. The help states that `id` is a unique instance ID. I think I'll need to try the query as `id==28470822` instead of `id=28470822`. Let's see if that works!


vastai search offers --type on-demand 'id==28470822' | head -n 5


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


vastai search offers -n --type on-demand 'id=28470822 rentable=any verified=any rented=any' | head -n 5


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


**Clarifying search parameters**

I’m working on understanding the search fields, specifically the use of ‘id’ as the unique identifier for instances. It’s puzzling because the guidance suggests it’s about search offers, yet the field includes the id. I'm starting to think that maybe I should use `id == 28470822` with spaces around the operator instead of `id=...`. Other filters, like `num_gpus=8`, use an equals sign, so I'm testing it out with the format: `'id == 28470822 rentable=any verified=any rented=any'`.


vastai search offers -n --type on-demand 'id == 28470822 rentable=any verified=any rented=any' | head -n 10


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


**Inspecting query parser issues**

I’m still not getting results. It could be that the query parser uses 'id' for something else, or maybe I should use `machine_id` or `ask_id`. I think the next step should be to inspect the raw output for a known offer. I'll run a command like `vastai search offers --raw 'num_gpus=8 gpu_name=RTX_3090' --limit 1` to see what field names are being returned. This should help clarify things!


vastai search offers --raw 'num_gpus=8 gpu_name=RTX_3090' --limit 1 | head -c 4000


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
[
 {
  "ask_contract_id": 29381071,
  "avail_vol_ask_id": 29381072,
  "avail_vol_dph": 0.00037037037037037035,
  "avail_vol_size": 1572.0,
  "bundle_id": 249520179,
  "bundled_results": null,
  "bw_nvlink": 0.0,
  "cluster_id": 92,
  "compute_cap": 860,
  "cpu_arch": "amd64",
  "cpu_cores": 64,
  "cpu_cores_effective": 64.0,
  "cpu_ghz": 2.999998,
  "cpu_name": "AMD EPYC 7302 16-Core Processor",
  "cpu_ram": 483665,
  "credit_discount_max": 0.4,
  "cuda_max_good": 13.0,
  "direct_port_count": 98,
  "discount_rate": 0.0,
  "discounted_dph_total": 1.6018518518518516,
  "discounted_hourly": 0.0,
  "disk_bw": 6335.7,
  "disk_name": "KINGSTON SEDC1000M1920G",
  "disk_space": 3114.4,
  "dlperf": 286.81287526194376,
  "dlperf_per_dphtotal": 179.05081230225395,
  "dph_base": 1.5999999999999999,
  "dph_total": 1.6018518518518516,
  "dph_total_adj": 1.6034794560185184,
  "driver_vers": 580095005,
  "driver_version": "580.95.05",
  "duration": 9776781.46551323,
  "end_date": 1777593600.0,
  "expected_reliability": 0.0,
  "external": null,
  "flops_per_dphtotal": 176.12894705202314,
  "geolocation": "Finland, FI",
  "geolocode": 835923585,
  "gpu_arch": "nvidia",
  "gpu_display_active": false,
  "gpu_frac": 1.0,
  "gpu_ids": [
   16031,
   16032,
   16033,
   16034,
   16970,
   16971,
   16972,
   17033
  ],
  "gpu_lanes": 16,
  "gpu_max_power": 350.0,
  "gpu_max_temp": 98.0,
  "gpu_mem_bw": 804.2,
  "gpu_name": "RTX 3090",
  "gpu_ram": 24576,
  "gpu_total_ram": 196608,
  "has_avx": 1,
  "host_id": 20210,
  "hosting_type": 1,
  "hostname": null,
  "id": 29381071,
  "inet_down": 784.1,
  "inet_down_cost": 0.0013020833333333333,
  "inet_up": 798.2,
  "inet_up_cost": 0.0013020833333333333,
  "instance": {
   "discountTotalHour": 0,
   "discountedTotalPerHour": 0.0018518518518518515,
   "diskHour": 0.0018518518518518515,
   "gpuCostPerHour": 0,
   "totalHour": 0.0018518518518518515
  },
  "internet_down_cost_per_tb": 1.3333333333333333,
  "internet_up_cost_per_tb": 1.3333333333333333,
  "is_bid": false,
  "is_vm_deverified": false,
  "logo": "/static/logos/vastai_small2.png",
  "machine_id": 4553,
  "min_bid": 1.5999999999999999,
  "mobo_name": "H12DSG-O-",
  "num_gpus": 8,
  "nw_disk_avg_bw": null,
  "nw_disk_max_bw": null,
  "nw_disk_min_bw": null,
  "os_version": "22.04",
  "pci_gen": 4.0,
  "pcie_bw": 24.8,
  "public_ipaddr": "65.109.137.0",
  "reliability": 0.999064,
  "reliability2": 0.999064,
  "reliability_mult": 0.9907245,
  "rentable": true,
  "rented": false,
  "resource_type": "gpu",
  "rn": 1,
  "score": 162.7895257292468,
  "search": {
   "discountTotalHour": 0,
   "discountedTotalPerHour": 1.6018518518518516,
   "diskHour": 0.0018518518518518515,
   "gpuCostPerHour": 1.5999999999999999,
   "totalHour": 1.6018518518518516
  },
  "start_date": 1767810640.235,
  "static_ip": false,
  "storage_cost": 0.2666666666666666,
  "storage_total_cost": 0.0018518518518518515,
  "time_remaining": "",
  "time_remaining_isbid": "",
  "total_flops": 282.13248,
  "vericode": 1,
  "verification": "verified",
  "vms_enabled": false,
  "vram_costperhour": 0.008333333333333333,
  "webpage": null
 }
]


vastai search offers --type on-demand 'id=29381071' | head -n 5


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


**Troubleshooting filters and queries**

I’m noticing that the filter using `id` doesn’t work as expected. Maybe the default query settings are impacting the results since our offer is marked as rentable and verified. Even trying `==` for the id filter returned nothing, which suggests that maybe the `id` field isn’t treated as expected in the query syntax. I’ll need to explore using `ask_contract_id` or similar identifiers instead. Let’s test that to see if it works better!


**Testing field recognition**

I’m thinking it might actually work even if the field isn’t explicitly listed. There's a chance that the query could still recognize it. I want to test this hypothesis and see what happens when I attempt to use that field. Let’s go ahead and give it a shot to confirm if it behaves as expected. If it does work, it could open up more options for querying!


vastai search offers -n 'ask_contract_id=29381071 rentable=any verified=any rented=any' --raw | head -c 2000


Output:
Exit code: 0
Wall time: 0.7 seconds
Output:
Warning: Unrecognized field: ask_contract_id, see list of recognized fields.
[
 {
  "ask_contract_id": 29381071,
  "avail_vol_ask_id": 29381072,
  "avail_vol_dph": 0.00037037037037037035,
  "avail_vol_size": 1572.0,
  "bundle_id": 249520179,
  "bundled_results": null,
  "bw_nvlink": 0.0,
  "cluster_id": 92,
  "compute_cap": 860,
  "cpu_arch": "amd64",
  "cpu_cores": 64,
  "cpu_cores_effective": 64.0,
  "cpu_ghz": 2.999998,
  "cpu_name": "AMD EPYC 7302 16-Core Processor",
  "cpu_ram": 483665,
  "credit_discount_max": 0.4,
  "cuda_max_good": 13.0,
  "direct_port_count": 98,
  "discount_rate": 0.0,
  "discounted_dph_total": 1.6018518518518516,
  "discounted_hourly": 0.0,
  "disk_bw": 6335.7,
  "disk_name": "KINGSTON SEDC1000M1920G",
  "disk_space": 3114.4,
  "dlperf": 286.81287526194376,
  "dlperf_per_dphtotal": 179.05081230225395,
  "dph_base": 1.5999999999999999,
  "dph_total": 1.6018518518518516,
  "dph_total_adj": 1.6034794560185184,
  "driver_vers": 580095005,
  "driver_version": "580.95.05",
  "duration": 9776758.642138958,
  "end_date": 1777593600.0,
  "expected_reliability": 0.0,
  "external": null,
  "flops_per_dphtotal": 176.12894705202314,
  "geolocation": "Finland, FI",
  "geolocode": 835923585,
  "gpu_arch": "nvidia",
  "gpu_display_active": false,
  "gpu_frac": 1.0,
  "gpu_ids": [
   16031,
   16032,
   16033,
   16034,
   16970,
   16971,
   16972,
   17033
  ],
  "gpu_lanes": 16,
  "gpu_max_power": 350.0,
  "gpu_max_temp": 98.0,
  "gpu_mem_bw": 804.2,
  "gpu_name": "RTX 3090",
  "gpu_ram": 24576,
  "gpu_total_ram": 196608,
  "has_avx": 1,
  "host_id": 20210,
  "hosting_type": 1,
  "hostname": null,
  "id": 29381071,
  "inet_down": 784.1,
  "inet_down_cost": 0.0013020833333333333,
  "inet_up": 798.2,
  "inet_up_cost": 0.0013020833333333333,
  "instance": {
   "discountTotalHour": 0,
   "discountedTotalPerHour": 0.0018518518518518515,
   "diskHour": 0.0018518518518518515,
   "gpuCostPerHour": 0,
   "totalHour": 0.0018518518518518515
  },
  "internet_down_cost_per_tb": 1.3333333333333333,
  "internet_up_cost_per_tb": 1.333333333333


Summary: auto


**Evaluating search command behavior**

It looks like my command produced output even though it flagged an unrecognized field, like ask_contract_id. It's as if that filter was ignored and returned results based on the default instead. Even with -n to disable defaults, it seems the query still ran, returning multiple offers with the query. Since I didn't use --limit, it probably gave the first page of results, which happened to include what I wanted. However, that’s not reliable. I need a better way to inspect specific offer IDs using `vastai search offers --raw` with jq.


**Trying a new search command**

I'm considering calling `vastai search offers -n --raw 'id in [list]'`. According to the field list, it looks like the operator `in` should work, so I can query using `id in [28470822, 27958980]`. This approach seems promising, and I want to see if it effectively retrieves the specific offers I’m looking for. Let’s give it a try and see what results I get!


vastai search offers -n --type on-demand 'id in [28470822,27958980] rentable=any verified=any rented=any' -o 'dph_total' | head -n 20


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
ID  CUDA  N  Model  PCIE  cpu_ghz  vCPUs  RAM  Disk  $/hr  DLP  DLP/$  score  NV Driver  Net_up  Net_down  R  Max_Days  mach_id  status  host_id  ports  country


Hmm, it looks like the 'id' field might not be searchable, possibly due to a bug in the CLI, which could be quite serious. Maybe we mis-specified the syntax: I think the values in `id in [28470822, 27958980]` should be in quotes. Actually, `id in [28470822,27958980]` definitely needs that. I want to try a simpler case with just one ID, like `id in [29381071]`, to see if that helps!


vastai search offers -n --type on-demand 'id in [29381071] rentable=any verified=any rented=any' --raw | head -c 200


Output:
Exit code: 0
Wall time: 0.5 seconds
Output:
[]


**Troubleshooting ID Filters**

It seems like the ID filter isn't working as expected. I wonder if the `id` field in the search query refers to an instance ID instead of an ask ID. The help documentation lists `id: int instance unique ID`, but maybe it's not implemented for offers. I'll need to check if `id` is treated as a reserved field, which could explain why filtering returns empty results. Using `vastai search offers -n 'foo=1'` triggers a warning, so I'll investigate further.


**Testing ID Filters Again**

I’m considering the possibility that `id` might not be part of the search query and could have been renamed to `ask_id` but still recognized. Maybe we need to specify it as `id=="29381071"`—though that seems tricky since it’s an integer. Let's experiment with using numeric comparisons like `id>29381070` and `id<29381072` to see if that works. I have a feeling this could yield better results, so testing that out could help!


vastai search offers -n --type on-demand 'id>29381070 id<29381072 rentable=any verified=any rented=any' --raw | head -c 200


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
[]


**Investigating ID comparisons**

I'm noticing that any ID comparison yields none, which raises a question about whether the `id` field might always be 0 in the search filter. It’s a bit odd. I wonder if the `id` in the query refers not to the ask ID, but rather to a unique ID specific to instances within a bundle. I've seen a nested `instance` field that lacks an ID, but there’s a top-level `id`. Maybe the search query might use a different field name. If bugs are present in the CLI version I'm using, I could search offers more broadly and use `jq` to filter them. That does seem workable!


**Narrowing down offers**

I’m realizing there could be more than 1000 offers available. We can either call multiple times or filter by other fields like `gpu_name` to narrow it down. For specific IDs, I might know their GPU names—like ID 28470822 is an A5000 and 27958980 is an RTX 4090, but not all IDs will have that info. So, I could search for each ID by fetching many offers. I’ll use a command to search broadly and then filter with `jq`, though the output might be large. We should also keep an eye on our token usage!


**Exploring API options**

I’m considering using the Vast REST API directly with curl to get offer details, like using `https://console.vast.ai/api/v0/asks/<id>`. The documentation mentions creating instances with `/asks/{offer_id}/` for PUT, and there could be a GET endpoint for details. Still, it seems easier to use the CLI along with `--raw` and `jq`. To check specific IDs, I’ll pull all 8-GPU offers by querying `num_gpus=8`, keeping in mind there might be thousands. I’ll test the count by running a command to see how many results it pulls.


vastai search offers -n --type on-demand 'num_gpus=8 rentable=any verified=any rented=any' --raw --limit 5000 | jq 'length'


Output:
Exit code: 0
Wall time: 0.9 seconds
Output:
419


**Filtering offers by ID**

I see there are 419 offers, which is a good start. I aim to retrieve all of them and filter by their IDs, even if the ID filter is broken. I need to check specific IDs: 29454430, 29548135, 28470822, 27347138, 27958980. I'll fetch all 8-GPU offers and filter for those IDs to show key metrics. Oh, and I need to adjust the units since the raw output uses GPU RAM in MB. It’s probably MB for CPU RAM too!


**Clarifying RAM units**

I’m realizing that 483665 MB converts to about 472 GB. But then the table shows 483.7, which makes me question if the raw CPU RAM is actually in MB. Dividing 483665 by 1024 gives me 472.3 GB, not matching the table's number. If it's in MB using base 1000, I get 483.7. Also, the RAM column is blank for that offer, which adds to the confusion. I think I should compute `cpu_ram_gb` using the division by 1000. I also need to analyze GPU RAM properly! It looks like raw GPU RAM should use the same conversion logic.


**Deciding on offer quality**

For the CPU, I think using /1024 can show GiB effectively. Now, I need to focus on evaluating offers for their throughput potential. I'll look at key factors like the GPU model, total FLOPS, PCIe bandwidth, disk bandwidth, internet speed, CPU cores, reliability, and price. Since tasks are independent per GPU, I'm thinking we require a decent CPU count. For configurations like 8× 4090 or 3090, at least 64 vCPUs and 256GB RAM seem ideal, but I noticed some offers with only 32 vCPUs. While that might be low, it could still work for our needs as long as we maximize throughput with good vCPUs, strong disk bandwidth, and network speeds!


**Addressing audio download considerations**

For R2, I'm focusing on ensuring each worker can download audio concurrently, which makes network download speed critical. Since each audio file is a few hundred MB and I may be downloading eight at once, good throughput is essential to avoid I/O bottlenecks. Many offers have internet speeds in the hundreds or thousands of Mb/s — that’s promising! I also need to ensure enough disk space for caching and outputs.

We might want to implement a cleanup pipeline to delete the output directory after uploading. Although the user hasn't explicitly asked, they do want to avoid failures, so perhaps adding a config option like `r2_upload_delete_local_output` would be good. But we should be cautious not to disrupt existing flows. I'll check whether the pipeline downloads audio into the output directory.


**Implementing local output management**

In `src/download.py`, the function `download_audio` will return an audio path, which could either be under `output_dir` or `temp`. To free up disk space, I think we should remove the entire per-video output directory after uploading. However, users might want to retain metadata for debugging, especially in a distributed run on Vast. I’m considering implementing an optional environment variable like `DELETE_LOCAL_AFTER_UPLOAD=1`.

Since they might run each worker ephemerally, we should avoid disk overfill as they process large amounts. So, it feels wise to implement this cleanup now to prevent potential cluster failures. I’ll explore the pipeline to confirm which directories we need to manage, especially `output_dir`.


**Assessing background export functionality**

We've improved background export, but I've found that running it in the main process is potentially unsafe, so we removed it. In supabase mode, the `gpu_worker_supabase` processes videos sequentially without prefetch. If background export is enabled, it queues export jobs to the global manager, meaning these jobs run alongside processing.

This concurrent operation could be risky for network and disk usage. For reliability, I think sync export is safer — ensuring uploads and cleanups occur before the next video processes. However, that might decrease throughput. Still, increasing the number of workers per GPU could help.

Regarding user requests for a template to launch multiple instances, we can evaluate their specified offer IDs and address their concerns about limiting machine diversity. I’ll also consider how to efficiently pass environment variables, exploring options like using `vastai create env-var` to set account-level variables for new instances.


rg -n "env-var|env vars|create env-var|show env-vars" vast_ai.md | head -n 80


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


Summary: auto


**Evaluating template creation**

I’m considering creating two templates: a worker template for arguments and a debug template for SSH. The user asked for a template and instance launch, so I think I'll focus on the args template. It needs environment variable injection and runtime args. Now, for instance creation, I see that `vastai create instance` uses `--args`. I'm not certain if the template itself can include args, though. The help documentation mentions `--env` and `--onstart-cmd`, but not `--args`, which leaves me a bit puzzled.


**Exploring argument passing to containers**

I'm thinking about how to pass arguments to a container. It might be at instance creation time or via a script specified with `--onstart-cmd`. But then I also realize that I can set the container's ENTRYPOINT to run `python massive_process.py`. To use the Supabase queue, I can simply run the container with the relevant options. Since I've already set the default CMD, I might not need to pass additional args at all. With Vast's default behavior, it will automatically run the worker, so we can create a template without needing to specify `--args`.