d746008e1f
- 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>
124 lines
3.2 KiB
Python
Executable File
124 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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RunPod GPU Scanner
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Queries RunPod API to show available SECURE cloud GPUs with ≥16GB VRAM.
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"""
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import os
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import sys
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import requests
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from dotenv import load_dotenv
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# Load environment variables from .env.runpod
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env_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), '.env.runpod')
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load_dotenv(env_path)
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RUNPOD_API_KEY = os.getenv('RUNPOD_API_KEY')
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if not RUNPOD_API_KEY:
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print("ERROR: RUNPOD_API_KEY not found in .env.runpod")
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sys.exit(1)
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GRAPHQL_ENDPOINT = "https://api.runpod.io/graphql"
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# GraphQL query to fetch GPU types
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QUERY = """
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{
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gpuTypes {
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id
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displayName
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memoryInGb
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secureCloud
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communityCloud
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lowestPrice(input: {gpuCount: 1}) {
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uninterruptablePrice
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}
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}
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}
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"""
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def query_runpod_api():
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"""Query RunPod GraphQL API for GPU types."""
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {RUNPOD_API_KEY}"
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}
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payload = {
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"query": QUERY
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}
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try:
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response = requests.post(GRAPHQL_ENDPOINT, json=payload, headers=headers, timeout=10)
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response.raise_for_status()
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return response.json()
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except requests.exceptions.RequestException as e:
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print(f"ERROR: Failed to query RunPod API: {e}")
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sys.exit(1)
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def filter_and_sort_gpus(data):
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"""Filter for secure cloud GPUs with ≥16GB VRAM, sorted by price."""
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gpu_types = data.get('data', {}).get('gpuTypes', [])
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# Filter criteria:
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# 1. memoryInGb >= 16
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# 2. secureCloud > 0 (available in secure cloud)
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# 3. Has pricing information
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filtered_gpus = []
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for gpu in gpu_types:
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memory = gpu.get('memoryInGb', 0)
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secure_count = gpu.get('secureCloud', 0)
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lowest_price = gpu.get('lowestPrice', {})
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price = lowest_price.get('uninterruptablePrice') if lowest_price else None
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if memory >= 16 and secure_count > 0 and price is not None:
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filtered_gpus.append({
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'name': gpu.get('displayName', 'Unknown'),
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'vram': memory,
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'price': float(price),
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'available': secure_count
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})
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# Sort by price (cheapest first)
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filtered_gpus.sort(key=lambda x: x['price'])
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return filtered_gpus
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def display_gpus(gpus):
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"""Display GPU information in clean format."""
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if not gpus:
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print("No secure cloud GPUs found with ≥16GB VRAM in EUR-IS region.")
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return
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print("\n" + "="*70)
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print("RUNPOD SECURE CLOUD GPUs (≥16GB VRAM) - EUR-IS REGION")
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print("="*70)
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print(f"{'GPU Name':<30} {'VRAM':<10} {'Price/hr':<12} {'Available':<10}")
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print("-"*70)
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for gpu in gpus:
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name = gpu['name']
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vram = f"{gpu['vram']}GB"
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price = f"${gpu['price']:.3f}"
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available = f"{gpu['available']} pods"
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print(f"{name:<30} {vram:<10} {price:<12} {available:<10}")
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print("="*70)
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print(f"Total GPUs found: {len(gpus)}")
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print()
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def main():
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"""Main execution function."""
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print("Querying RunPod API...")
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# Query API
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data = query_runpod_api()
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# Filter and sort GPUs
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gpus = filter_and_sort_gpus(data)
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# Display results
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display_gpus(gpus)
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if __name__ == "__main__":
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main()
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