Output:
Exit code: 0
Wall time: 0.2 seconds
Output:
usage: vastai execute id COMMAND

positional arguments:
 id                                id of instance to execute on
 COMMAND                           bash command surrounded by single quotes

options:
 -h, --help                        show this help message and exit
 --schedule {HOURLY,DAILY,WEEKLY}  try to schedule a command to run hourly, daily, or monthly. Valid values are HOURLY, DAILY, WEEKLY  For ex. --schedule DAILY
 --start_date START_DATE           Start date/time in format 'YYYY-MM-DD HH:MM:SS PM' (UTC). Default is now. (optional)
 --end_date END_DATE               End date/time in format 'YYYY-MM-DD HH:MM:SS PM' (UTC). Default is 7 days from now. (optional)
 --day DAY                         Day of week you want scheduled job to run on (0-6, where 0=Sunday) or "*". Default will be 0. For ex. --day 0
 --hour HOUR                       Hour of day you want scheduled job to run on (0-23) or "*" (UTC). Default will be 0. For ex. --hour 16
 --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

Examples:
  vastai execute 99999 'ls -l -o -r'
  vastai execute 99999 'rm -r home/delete_this.txt'
  vastai execute 99999 'du -d2 -h'

available commands:
  ls                 List directory contents
  rm                 Remote files or directories
  du                 Summarize device usage for a set of files

Return value:
Returns the output of the command which was executed on the instance, if successful. May take a few seconds to retrieve the results.


Summary: auto


# Context from my IDE setup:

## Open tabs:
- .gitignore: .gitignore
- reset_for_production.py: scripts/reset_for_production.py
- requirements.txt: requirements.txt
- Dockerfile: Dockerfile
- models.py: src/models.py

## My request for Codex:
just answer me these. I need you to give me instructions on how i can deploy instances with my custom image, and how i can pass env variables and how to start, stop, search, create instances, here in chat.


**Providing instructions**

The user prefers direct instructions rather than code changes. I’ll respond with clear guidance and address the earlier question about images and the latest information. I have details from the Vast.ai documentation.

I’ll synthesize the info about using the Vast.ai CLI for tasks like setting the API key, searching offers, and managing instances. I’ll outline sections for setup, search, and create, along with manage lifecycle and update instructions, ensuring it remains concise and clear!


**Clarifying environment variables and instructions**

I want to ensure I'm clear on how to pass environment variables. I should note that when using `--env`, I can quote like `'-e KEY=VALUE -p 8080:8080'` and include options like `-h hostname`. The Python SDK is also useful here, including commands like `pip install vastai-sdk` and initializing with `VastAI(api_key=...)`.

For deploying 10 instances, I can use shell loops with `vastai create instance`, or templates with `create instance <offer_id> --template_hash`. I'll provide a bullet list of commands in monospace format to keep it tidy.


**Providing instructions with caution**

I need to mention the `--onstart` script option, such as `--onstart setup.sh` or with `--onstart-cmd '...'`. It's also important to include `vastai set api-key <key>` for storing configurations.

Since the user is asking about deploying instances, I should be careful to note any potentially destructive commands, like `destroy`. I can include commands like `vastai search offers 'gpu_name=RTX_4090 num_gpus=8 reliability>0.99 rented=False'` and `vastai show instances --raw` for JSON output.

I'll also add guidance on stopping all instances, updating images, and recycling instances while highlighting the importance of GPU priority.


**Structuring the instructions**

I need to mention `vastai recycle instance <id>` and consider the requirement of having the container started; if recycling fails when it's stopped, then we should start it first. It’s possible the failure is due to the container not being running.

I’ll also include how to label instances for cluster management with `vastai label instance <id> "maya3-cluster-01"`.

Here’s a structured answer:

**Setup**
- Install CLI: `pip install --upgrade vastai`; set the key: `vastai set api-key $API_KEY`.

**Search offers**
- `vastai search offers 'reliability>0.98 num_gpus=8 gpu_name=RTX_4090 rented=False' -o 'dph_total'`.

**Create with your image**
- For public image: `vastai create instance <offer_id> --image <your_image> --disk 200 --label "maya3-cluster-01" --env '-e URL=... -e SUPABASE_ADMIN=... -e HF_TOKEN=... -e R2_*...' --ssh --direct --onstart-cmd 'env | grep _ >> /etc/environment; /workspace/maya3_data/.venv/bin/python massive_process.py --supabase-queue ...'`.


**Expanding on instance deployment instructions**

I need to provide a clear pattern for creating instances, such as using `vastai create instance <offer_id> --image <repo>/<image>:<tag> --disk 200 --label "..." --env '-e KEY=VALUE -p 2222:22' --ssh --direct --onstart-cmd '...'`. It’s also helpful to mention using a private registry with `--login '-u USER -p TOKEN docker.io'`.

For instance lifecycle, I can include commands like `vastai show instances`, `stop/start`, and `recycle` for updating images. I’ll also summarize creating templates and briefly mention the Python SDK as an optional reference.

Since the user requested instructions primarily, I’ll keep it concise but ensure all necessary details are included. I want to remember to clarify what deploying instances with a custom image means—building, tagging, and pushing it to a registry, then using it in the `create instance` command.

I’ll also clarify the difference between `reboot` and `recycle`, plus give a heads-up about using `vastai search offers` for finding machines.

Lastly, I’ll update my plan to reflect these new instructions. I think I’ll create three steps:
1. Summarize commands for deploying the custom image.
2. Explain environment variables and onstart scripts.
3. Explain lifecycle commands and templates.

I’ll mark step one as in progress and ensure that the final answer doesn’t leave any plan incomplete.


**Updating the plan and providing instructions**

Since a plan exists, I need to keep it updated and make sure it reflects our current tasks. I’ll call `update_plan` now to set the step to in progress.

I can keep it simple by having just one step: “Provide Vast.ai deployment instructions.” When I finish drafting my answer, I’ll mark this step as completed. This way, I only need one additional tool call after the answer.

Now, let’s proceed to update the plan with that single step.


Explanation: Pivoting from hands-on recycling to providing the requested Vast.ai deployment/lifecycle instructions in chat.


Output: Plan updated


**Crafting API Instructions**

I need to craft the final answer with clear instructions. I should cover the setup of API key, the search for offers, and steps for creating instances with custom images and environment variables. It’s important to include commands for starting, stopping, listing, rebooting, recycling, destroying, and updating instances. I'll also mention how to get the SSH URL and logs. Regarding environment variables, I should provide proper quoting examples and instructions regarding private registries. I’ll organize this into concise sections to enhance clarity.


**Crafting API Instructions**

I want to lay out the setup steps clearly. First, I'll start with setting up the API with `pip install --upgrade vastai` and `vastai set api-key $VAST_API_KEY`. Then, for finding machines, I’ll use a specific search command.

Next, I'll explain how to create an instance with your image, including environment variables and private image logins. I also want to recommend reusing a configuration with templates.

Finally, I'll cover lifecycle operations like listing, starting, stopping instances, updating the image tag, and accessing logs remotely. I’ll ensure clarity and conciseness throughout!


**Crafting Instance Instructions**

I should mention the process for creating 10 instances. I can suggest running a loop using a command like `for i in {01..10}; do vastai create instance ... --label "maya3-cluster-$i"; done`, making sure to use different offer IDs for each instance.

Additionally, I want to include a note about using `--cancel-unavail` to fail if scheduling fails.

For checking the current image, I can mention using `vastai show instances` to see the Image column or `vastai show instance <id> --raw` for detailed output. However, I'll avoid using their specific secrets and stick to placeholders.


Output: Plan updated


**Setup**
- Install + auth: `pip install --upgrade vastai` then `vastai set api-key $VAST_API_KEY`

**Check what image an instance is running**
- All instances: `vastai show instances` (look at the **Image** column)
- Single instance (JSON): `vastai show instance <id> --raw` (look for `image_uuid`)

**Search for machines (offers)**
- Example: `vastai search offers 'reliability>0.98 num_gpus=8 gpu_name=RTX_4090 rented=False' -o 'dph_total'`

**Create an instance with your custom image**
- Public image:
  `vastai create instance <offer_id> --image <repo>/<image>:<tag> --disk 200 --label "maya3-01" --ssh --direct --env '-e URL=... -e SUPABASE_ADMIN=... -e HF_TOKEN=... -e R2_ACCESS_KEY_ID=... -e R2_SECRET_ACCESS_KEY=... -e R2_ENDPOINT_URL=... -e R2_BUCKET=... -e R2_PREFIX=... -p 22:22' --onstart-cmd 'env | grep _ >> /etc/environment; echo started'`
- Private registry image: add `--login '-u <user> -p <token> docker.io'` (or your registry)

**Start / stop / restart**
- List: `vastai show instances` / IDs only: `vastai show instances -q`
- Stop/start one: `vastai stop instance <id>` / `vastai start instance <id>`
- Stop/start all: `vastai stop instances $(vastai show instances -q)` / `vastai start instances $(vastai show instances -q)`
- Reboot (restart container): `vastai reboot instance <id>`
- Recycle (freshly pull image + recreate container): `vastai recycle instance <id>`

**Change the image tag on an existing instance**
- `vastai update instance <id> --image <repo>/<image>:<new_tag>` then `vastai recycle instance <id>`

**Templates (recommended for repeating the same env/onstart)**
- Create: `vastai create template "maya3" <repo>/<image>:<tag> --env '-e ...' --onstart-cmd '...'`
- Use: `vastai create instance <offer_id> --template_hash <hash_id> --disk 200 --label "maya3-01"`

**Logs / remote**
- Logs: `vastai logs <id> --tail 5000`
- SSH: `ssh $(vastai ssh-url <id>)`


# Context from my IDE setup:

## Open tabs:
- .gitignore: .gitignore
- reset_for_production.py: scripts/reset_for_production.py
- requirements.txt: requirements.txt
- Dockerfile: Dockerfile
- models.py: src/models.py

## My request for Codex:
1. can you reset supabase with all videos to pending
2. reset analytics table as well to empty values


**Considering script execution**

I’m looking at environment variables like URL and SUPABASE_ADMIN, which need to be set, probably in a .env file. I can run the script, but it'll require confirmation, though we can make it quicker by piping 'RESET'. I need to be careful not to delete R2 since the user didn't ask for that. So, maybe it’s better to modify the script with a `--skip-r2` flag. I also need to check for any AGENTS instructions related to scripts in the repo.


**Checking AGENTS instructions**

I see that the AGENTS.md file is supposed to be included in the developer message, but we haven't received it. The user provided some instructions for a specific path, and we should still follow them like AGENTS instructions. I should also search for any actual AGENTS.md file within the scope; it could exist. I can quickly check for it using the command `find .. -name AGENTS.md`, though it's not absolutely necessary. I'll keep things minimal and proceed with the environment settings.


**Planning script execution**

I can run commands without needing approvals, which is great. Even though it's simple, the task involves two operations, so maybe having a mini plan isn't a bad idea. I can list it like this: 1) run the Supabase reset script without deleting R2, and 2) verify counts afterward. It's essential to be careful about secrets, as the output might reveal credentials, but thankfully the script only prints counts and messages without exposing keys. The user's goal is to reset Supabase and analytics to empty values, so let's proceed with caution!


**Executing script functions**

I’m considering whether to run functions directly in an interactive Python session or just use Python to call the necessary functions from the script file. The easiest approach might be to call `reset_videos_to_pending` and `clear_analytics_tables`, while skipping the deletion of the R2 folder, without modifying the script. The user wants this reset done now, which is fine. However, I wonder about confirmation; we could log the actions and inform the user via chat since there’s no approval policy in place. Let's proceed with the reset as the user requested!


**Updating script for safety**

I need to ensure the handling of environment variables while calling functions directly. If I reset videos, it only affects those not already in a pending status. Since the user wants to clear other statuses too, I might need to include `lease_expires_at` in the update statement. The script uses the Supabase Python library to handle updates, but I should also be cautious about secrets and overall functionality.

To improve safety, I’ll modify the script to include a `--skip-r2` flag to prevent accidental deletions. I'll adjust the argparse flags accordingly and run the script with this new option for a safer execution. After running, it’ll be good to check counts as well.


**Implementing code patch**

The user has asked for two tasks, and I can go ahead and execute them. However, as a coding agent, I should implement a patch to update the script properly. Following the guidelines, I need to use `apply_patch` for any code modifications. So, I’ll proceed to patch the `scripts/reset_for_production.py` file accordingly to ensure everything aligns with the user's requests and the necessary safety precautions. Let's get this done!


Input:
*** Begin Patch
*** Update File: scripts/reset_for_production.py
@@
 Usage:
-    python scripts/reset_for_production.py [--dry-run]
+    python scripts/reset_for_production.py [--dry-run] [--skip-r2]
 """
@@
 def main():
     parser = argparse.ArgumentParser(description='Reset Supabase and R2 for fresh production run')
     parser.add_argument('--dry-run', action='store_true', help='Show what would be done without making changes')
+    parser.add_argument('--skip-r2', action='store_true', help='Skip deleting R2 cleaned/trail/ prefix')
     args = parser.parse_args()
@@
     # 3. Delete R2 folder
-    print("\n" + "=" * 60)
-    print("STEP 3: Delete R2 cleaned/trail/ folder")
-    print("=" * 60)
-    delete_r2_folder(args.dry_run)
+    if args.skip_r2:
+        print("\n" + "=" * 60)
+        print("STEP 3: Skip R2 deletion (--skip-r2)")
+        print("=" * 60)
+    else:
+        print("\n" + "=" * 60)
+        print("STEP 3: Delete R2 cleaned/trail/ folder")
+        print("=" * 60)
+        delete_r2_folder(args.dry_run)
*** End Patch


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


Summary: auto


printf 'RESET\n' | python3 scripts/reset_for_production.py --skip-r2


Output:
Exit code: 0
Wall time: 3.3 seconds
Output:
============================================================
PRODUCTION RESET SCRIPT
============================================================

⚠  WARNING: This will reset ALL data!
Type 'RESET' to confirm:
============================================================
STEP 1: Reset Videos to PENDING
============================================================

📊 Current video status counts:
   PENDING: 172502
   CLAIMED: 56
   COMPLETED: 9
   FAILED: 44
   SKIPPED: 0

🔄 Resetting all videos to PENDING...
   ✅ Reset 109 videos to PENDING

============================================================
STEP 2: Clear Analytics Tables
============================================================

📊 Analytics tables:
   worker_heartbeats: 56 rows
   worker_metrics: 28 rows
   processing_events: 52 rows
   error_logs: 43 rows

🗑  Clearing analytics tables...
   ✅ Cleared worker_heartbeats
   ✅ Cleared worker_metrics
   ✅ Cleared processing_events
   ✅ Cleared error_logs

============================================================
STEP 3: Skip R2 deletion (--skip-r2)
============================================================

============================================================
DONE!
============================================================

✅ All reset operations completed.

📝 Next steps:
   1. Run the pipeline WITHOUT --r2-upload-prefix to upload directly to 1-cleaned-data/
   2. Or use: --r2-upload-prefix '' (empty string)


python3 scripts/reset_for_production.py --dry-run --skip-r2


Output:
Exit code: 0
Wall time: 2.1 seconds
Output:
============================================================
PRODUCTION RESET SCRIPT
============================================================

⚠  DRY RUN MODE - No changes will be made

============================================================
STEP 1: Reset Videos to PENDING
============================================================

📊 Current video status counts:
   PENDING: 172611
   CLAIMED: 0
   COMPLETED: 0
   FAILED: 0
   SKIPPED: 0

🔍 [DRY RUN] Would reset all videos to PENDING

============================================================
STEP 2: Clear Analytics Tables
============================================================

📊 Analytics tables:
   worker_heartbeats: 0 rows
   worker_metrics: 0 rows
   processing_events: 0 rows
   error_logs: 0 rows

🔍 [DRY RUN] Would delete all rows from analytics tables

============================================================
STEP 3: Skip R2 deletion (--skip-r2)
============================================================

============================================================
DONE!
============================================================


- Ran `python3 scripts/reset_for_production.py --skip-r2` and confirmed the reset:
  - Videos: reset `109` non-pending videos back to `PENDING` (now `PENDING: 172611`, others `0`).
  - Analytics: cleared `worker_heartbeats`, `worker_metrics`, `processing_events`, `error_logs` (all now `0` rows).
- Added `--skip-r2` flag to `scripts/reset_for_production.py` so you can reset Supabase without touching R2.