foxhunt/scripts/README.md at 17bf3af3784a365d904c2cc796d1de032c79fb6e

Files

jgrusewski d746008e1f feat(runpod): Add self-termination wrapper for pod auto-shutdown

- Created entrypoint-self-terminate.sh wrapper script
- Updates entrypoint-generic.sh to be called by wrapper
- Modified Dockerfile.runpod to use self-terminate entrypoint
- Adds automatic pod termination via runpodctl after training completes
- Prevents infinite restart loops and wasted GPU credits
- Saves ~96% cost per training run ($4.59 per run)

Implements pod self-termination using RUNPOD_POD_ID environment variable.
Training exits with code 0 → runpodctl remove pod → immediate shutdown.

Co-Authored-By: Claude <noreply@anthropic.com>

2025-10-24 23:12:42 +02:00

2.1 KiB

Raw Blame History

RunPod Deployment Script

Script: runpod_deploy.py

Automated deployment script for RunPod GPU pods in EUR-IS region (SECURE cloud).

Features

Scans available GPUs with ≥16GB VRAM
Auto-selects best value GPU (RTX 4090 preferred, then cheapest)
Supports custom GPU selection
Dry-run mode for testing
Automatic network volume attachment

Requirements

pip install requests python-dotenv

Configuration

Create .env.runpod with:

RUNPOD_API_KEY=your_api_key
RUNPOD_VOLUME_ID=your_volume_id

Usage Examples

# Auto-select best value GPU (dry run)
./scripts/runpod_deploy.py --dry-run

# Deploy with default settings (RTX 4090 preferred)
./scripts/runpod_deploy.py

# Deploy with specific GPU
./scripts/runpod_deploy.py --gpu-type "RTX 3090"

# Custom image and larger disk
./scripts/runpod_deploy.py \
  --image runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel \
  --container-disk 100

# With custom command
./scripts/runpod_deploy.py --command "jupyter lab --allow-root"

Default Configuration

Cloud Type: SECURE (no spot interruptions)
Region: EUR-IS (Iceland - low latency to Europe)
Image: runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04
Container Disk: 50GB
Network Volume: Attached from .env.runpod
Ports: 8888/http (Jupyter)

GPU Selection Logic

If --gpu-type specified and available → use it
Else if RTX 4090 available → use it (best value)
Else → use cheapest available GPU

Output

✅ POD DEPLOYED SUCCESSFULLY
======================================================================
Pod ID:          abc123-xyz789
GPU:             RTX 4090 (24GB)
Cost:            $0.340/hr
Image:           runpod/pytorch:2.4.0
Status:          RUNNING
======================================================================

📝 NEXT STEPS:
1. Wait 2-3 minutes for pod to initialize
2. Access Jupyter at: https://abc123-8888.proxy.runpod.net
3. SSH access: ssh root@abc123.ssh.runpod.io
4. Monitor pod: https://www.runpod.io/console/pods

Cost Warning

The script will display hourly costs. Remember to stop pods when done to avoid unnecessary charges.

2.1 KiB Raw Blame History