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>
424 lines
15 KiB
Python
Executable File
424 lines
15 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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RunPod Deployment Script - FIXED VERSION
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Scans for best value GPU in EUR-IS region and deploys a pod on SECURE cloud.
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KEY FIX: Uses REST API for availability-aware deployment instead of GraphQL global counts.
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"""
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import os
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import sys
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import argparse
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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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RUNPOD_VOLUME_ID = os.getenv('RUNPOD_VOLUME_ID')
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RUNPOD_CONTAINER_REGISTRY_AUTH_ID = os.getenv('RUNPOD_CONTAINER_REGISTRY_AUTH_ID')
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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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if not RUNPOD_VOLUME_ID:
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print("ERROR: RUNPOD_VOLUME_ID not found in .env.runpod")
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sys.exit(1)
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# EUR-IS datacenters to scan
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# CRITICAL FIX: Volume se3zdnb5o4 is in EUR-IS-1 ONLY!
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# If pod deploys to EUR-IS-2 or EUR-IS-3, volume will NOT be accessible
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EUR_IS_DATACENTERS = ['EUR-IS-1'] # ONLY EUR-IS-1 for volume mounting!
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# REST API endpoint (NEW - supports datacenter filtering)
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REST_API_URL = "https://rest.runpod.io/v1/pods"
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# GraphQL endpoint (still needed for listing GPU types and pricing)
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GRAPHQL_ENDPOINT = "https://api.runpod.io/graphql"
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# GraphQL query to fetch GPU types with global availability and pricing
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GPU_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_graphql(query, variables=None):
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"""Execute GraphQL query against RunPod API."""
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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 = {"query": query}
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if variables:
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payload["variables"] = variables
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try:
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response = requests.post(GRAPHQL_ENDPOINT, json=payload, headers=headers, timeout=30)
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response.raise_for_status()
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result = response.json()
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# Check for GraphQL errors
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if 'errors' in result:
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error_msg = result['errors'][0].get('message', 'Unknown error')
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print(f"ERROR: GraphQL errors: {result['errors']}")
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return None
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return result
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except requests.exceptions.RequestException as e:
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print(f"ERROR: Failed to query RunPod GraphQL API: {e}")
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return None
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def get_available_gpu_types():
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"""
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Query available GPU types with ≥16GB VRAM that have SOME availability in SECURE cloud.
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NOTE: This returns GLOBAL secure cloud availability, not EUR-IS specific.
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The actual datacenter filtering happens during deployment via REST API.
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"""
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print(" Querying GPU types and pricing...")
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data = query_graphql(GPU_QUERY)
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if not data:
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return []
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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 SOMEWHERE in secure cloud - not necessarily EUR-IS)
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# 3. Has pricing information
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available_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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available_gpus.append({
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'id': gpu.get('id', ''),
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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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'global_available': secure_count # This is GLOBAL, not EUR-IS specific
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})
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if available_gpus:
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# Sort by price (cheapest first)
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available_gpus.sort(key=lambda x: x['price'])
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print(f" ✅ Found {len(available_gpus)} GPU type(s) with global secure cloud availability")
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else:
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print(f" ⚠️ No GPU types found with ≥16GB VRAM in secure cloud")
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return available_gpus
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def deploy_pod_rest_api(gpu, image, command, container_disk, datacenters, dry_run=False):
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"""
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Deploy a pod using the REST API with datacenter-specific availability filtering.
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This is the KEY FIX: REST API checks EUR-IS datacenter availability at deployment time,
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not relying on global GraphQL counts.
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"""
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from datetime import datetime, timedelta
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pod_name = "foxhunt-training"
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# Calculate auto-termination time (3 hours from now - safety buffer for training)
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# This prevents infinite restart loops when training completes
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terminate_time = (datetime.utcnow() + timedelta(hours=3)).strftime("%Y-%m-%dT%H:%M:%SZ")
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# Build REST API request payload
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deployment_payload = {
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"cloudType": "SECURE", # CRITICAL: Only secure cloud
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"dataCenterIds": datacenters, # CRITICAL: EUR-IS specific datacenters
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"dataCenterPriority": "availability", # Try datacenters in order, prefer available
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"gpuTypeIds": [gpu['id']], # GPU type to deploy
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"gpuTypePriority": "availability", # Use available GPU
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"gpuCount": 1,
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"name": pod_name,
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"imageName": image,
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"containerDiskInGb": container_disk,
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"volumeInGb": 0, # Using network volume instead
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"networkVolumeId": RUNPOD_VOLUME_ID,
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"volumeMountPath": "/runpod-volume", # CRITICAL: WHERE to mount the volume
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"ports": ["8888/http", "22/tcp"],
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"env": {},
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"interruptible": False, # On-demand (non-spot)
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"terminateAfter": terminate_time, # FIX: Auto-terminate to prevent restart loops
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"minRAMPerGPU": 8,
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"minVCPUPerGPU": 2
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}
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# Add Docker command if specified
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# RunPod expects dockerStartCmd as an array of strings (shell arguments)
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# Split the command string into proper arguments
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if command:
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# Split command into arguments (respects quoted strings)
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import shlex
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deployment_payload["dockerStartCmd"] = shlex.split(command)
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# Add Docker registry authentication if configured
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if RUNPOD_CONTAINER_REGISTRY_AUTH_ID:
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deployment_payload["containerRegistryAuthId"] = RUNPOD_CONTAINER_REGISTRY_AUTH_ID
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print("\n" + "="*70)
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print("DEPLOYMENT PLAN")
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print("="*70)
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print(f"Pod Name: {pod_name}")
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print(f"GPU: {gpu['name']} ({gpu['vram']}GB VRAM)")
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print(f"Datacenters: {', '.join(datacenters)} (tries in order)")
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print(f"Price: ${gpu['price']:.3f}/hr (estimate)")
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print(f"Docker Image: {image}")
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print(f"Container Disk: {container_disk}GB")
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print(f"Network Volume: {RUNPOD_VOLUME_ID} → /runpod-volume")
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print(f"Ports: 8888/http (Jupyter), 22/tcp (SSH)")
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print(f"Auto-Terminate: {terminate_time} (prevents restart loops)")
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if command:
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print(f"Command: {command}")
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print("="*70)
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if dry_run:
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print("\nDRY RUN: Skipping actual deployment")
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print("\nPayload would be:")
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import json
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print(json.dumps(deployment_payload, indent=2))
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return None
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print("\nDeploying pod via REST API (checks EUR-IS availability)...")
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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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try:
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response = requests.post(REST_API_URL, json=deployment_payload, headers=headers, timeout=60)
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# Debug output
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print(f" HTTP Status: {response.status_code}")
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# SUCCESS: HTTP 200 (OK) or HTTP 201 (Created)
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if response.status_code in [200, 201]:
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try:
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pod_data = response.json()
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# Validate pod_data structure
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if not isinstance(pod_data, dict):
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print(f" ⚠️ Unexpected response format: {type(pod_data)}")
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print(f" Raw response: {response.text[:500]}")
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return None
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# Check if pod was actually created
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if 'id' not in pod_data:
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print(f" ⚠️ Pod data missing 'id' field")
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print(f" Raw response: {response.text[:500]}")
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return None
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print(f" ✅ Pod created successfully! ID: {pod_data['id']}")
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return pod_data
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except ValueError as e:
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print(f" ⚠️ Failed to parse JSON response: {e}")
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print(f" Raw response: {response.text[:500]}")
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return None
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elif response.status_code == 400:
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try:
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error_data = response.json()
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error_msg = error_data.get('error', 'Unknown error')
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print(f" ⚠️ Deployment failed: {error_msg}")
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# Check if it's an availability issue
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if 'not available' in error_msg.lower() or 'no machines' in error_msg.lower():
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print(f" 💡 GPU not available in EUR-IS datacenters at this time")
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except ValueError:
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print(f" ⚠️ Deployment failed: {response.text[:200]}")
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return None
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else:
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print(f" ❌ Unexpected response (status {response.status_code}): {response.text[:200]}")
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return None
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except requests.exceptions.RequestException as e:
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print(f" ⚠️ Deployment failed: {str(e)[:100]}")
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return None
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def display_deployment_result(pod_data):
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"""Display deployment results and connection info."""
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print("\n" + "="*70)
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print("✅ POD DEPLOYED SUCCESSFULLY")
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print("="*70)
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print(f"Pod ID: {pod_data['id']}")
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# Extract GPU info safely
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machine = pod_data.get('machine', {})
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gpu_info = machine.get('gpuType', {}) if machine else {}
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print(f"GPU: {gpu_info.get('displayName', 'N/A')}")
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print(f"GPU Count: {pod_data.get('gpu', {}).get('count', 'N/A')}")
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print(f"Cost: ${pod_data.get('costPerHr', 'N/A')}/hr")
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# Extract datacenter
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datacenter = machine.get('dataCenterId', 'N/A')
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print(f"Datacenter: {datacenter}")
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print(f"Image: {pod_data.get('imageName', pod_data.get('image', 'N/A'))}")
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print(f"Container Disk: {pod_data['containerDiskInGb']}GB")
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print(f"Status: {pod_data.get('desiredStatus', 'RUNNING')}")
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print("="*70)
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print("\n📝 NEXT STEPS:")
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print("1. Wait 2-3 minutes for pod to initialize")
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print(f"2. Access Jupyter at: https://{pod_data['id']}-8888.proxy.runpod.net")
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print(f"3. SSH access: ssh root@{pod_data['id']}.ssh.runpod.io")
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print("4. Monitor pod: https://www.runpod.io/console/pods")
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print(f"\n⚠️ IMPORTANT: Pod will cost ${pod_data.get('costPerHr', 'N/A')}/hr - remember to stop when done!")
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print()
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def main():
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"""Main execution function."""
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parser = argparse.ArgumentParser(
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description="Deploy a RunPod GPU pod in EUR-IS region (SECURE cloud) - FIXED VERSION",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Auto-select best value GPU with default training command
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./runpod_deploy.py
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# Deploy with specific GPU
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./runpod_deploy.py --gpu-type "RTX 4090"
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# Custom training command (overrides Dockerfile CMD)
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./runpod_deploy.py --command "--parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --epochs 100"
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# Custom image and command
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./runpod_deploy.py --image runpod/pytorch:2.1.0 --command "jupyter lab --ip=0.0.0.0 --allow-root"
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# Dry run (show plan without deploying)
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./runpod_deploy.py --dry-run
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KEY FIXES:
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- Uses REST API for deployment (checks EUR-IS datacenter availability)
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- Properly formats dockerStartCmd as array (RunPod API requirement)
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- Default command: TFT training with Parquet data (overrides Dockerfile CMD)
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"""
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)
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parser.add_argument(
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'--gpu-type',
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help='Preferred GPU type (e.g., "RTX 4090"). Auto-selects best value if not specified.'
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)
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parser.add_argument(
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'--image',
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default='jgrusewski/foxhunt:latest',
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help='Docker image to use (default: jgrusewski/foxhunt:latest)'
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)
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parser.add_argument(
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'--command',
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default='--parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --epochs 50 --learning-rate 0.001',
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help='Training command arguments (default: TFT training with Parquet data)'
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)
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parser.add_argument(
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'--container-disk',
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type=int,
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default=50,
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help='Container disk size in GB (default: 50)'
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)
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parser.add_argument(
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'--dry-run',
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action='store_true',
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help='Show deployment plan without actually deploying'
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)
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args = parser.parse_args()
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print("🔍 Querying available GPU types (global secure cloud)...")
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# Query available GPUs (global availability)
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gpus = get_available_gpu_types()
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if not gpus:
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print("\nERROR: No GPUs available with ≥16GB VRAM in SECURE cloud")
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print("\n💡 TIP: This checks global availability. EUR-IS specific availability")
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print(" is checked during deployment via REST API.")
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sys.exit(1)
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print(f"\n✅ Found {len(gpus)} GPU type(s) to try")
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# Create priority list: user preference (if specified), then sorted by price
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priority_gpus = []
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# Add user's preferred GPU if specified
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if args.gpu_type:
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for gpu in gpus:
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if args.gpu_type.lower() in gpu['name'].lower():
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priority_gpus.append(gpu)
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break
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if not priority_gpus:
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print(f"WARNING: Preferred GPU '{args.gpu_type}' not found, trying all GPUs...")
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# Add remaining GPUs sorted by price (cheapest first)
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# gpus list is already sorted by price at line 121
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for gpu in gpus:
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if gpu not in priority_gpus:
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priority_gpus.append(gpu)
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# Try each GPU until one succeeds
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attempted_gpus = []
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pod_data = None
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for gpu in priority_gpus:
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if gpu in attempted_gpus:
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continue
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attempted_gpus.append(gpu)
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print(f"\n🎯 Attempting deployment: {gpu['name']} (${gpu['price']:.3f}/hr)...")
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print(f" (Global availability: {gpu['global_available']} in secure cloud)")
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print(f" (Will check EUR-IS specific availability during deployment...)")
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pod_data = deploy_pod_rest_api(
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gpu,
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args.image,
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args.command,
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args.container_disk,
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EUR_IS_DATACENTERS,
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args.dry_run
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)
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if pod_data:
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break
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else:
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print(f"❌ {gpu['name']} not available in EUR-IS, trying next option...")
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# Display results
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if pod_data:
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display_deployment_result(pod_data)
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else:
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print("\n❌ ERROR: Failed to deploy pod on any available GPU in EUR-IS")
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print("\nAttempted GPUs:")
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for gpu in attempted_gpus:
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print(f" - {gpu['name']} ({gpu['vram']}GB VRAM) @ ${gpu['price']:.3f}/hr")
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print("\n💡 TIP: RunPod availability changes frequently. Try again in a few minutes.")
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print(" The script now correctly checks EUR-IS datacenter availability,")
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print(" not just global secure cloud counts.")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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