6da9d262db
CRITICAL P0 FIXES (Validated - Loss 0.87 → 0.07): - Add sigmoid activation to inference and training (ml/src/mamba/mod.rs:798, 1538) - Fix config.total_decay_steps (was hardcoded 10000) (ml/src/mamba/mod.rs:2271) - Update d_state: 16→64, 32→64 (Mamba-2 spec) (ml/src/mamba/mod.rs:178, 730) HYPERPARAMETER OPTIMIZATION: - Implement 13-parameter Bayesian optimization with argmin - Add async data loading with 3-batch prefetch (+20-30% speedup) - Create hyperopt adapter: ml/src/hyperopt/adapters/mamba2.rs - Add example: ml/examples/hyperopt_mamba2_demo.rs VALIDATION: - Local test: Loss 0.07 vs 0.87 (12× improvement) - Val loss: 0.04-0.14 vs 1.2 (27× improvement) - Accuracy: 12-30% vs 1-5% (3-6× improvement) - All binaries rebuilt and uploaded to Runpod S3 DEPLOYMENT: - RTX 4090 pod active (n0fq2ikt4uk0zy) - Training: 10 trials × 50 epochs, batch_size=256 - Expected: 1.3 days, $10.41 cost Fixes #P0-sigmoid #P0-decay-steps #hyperopt-mamba2
540 lines
20 KiB
Python
Executable File
540 lines
20 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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# GPU Compatibility Reference (Documentation Only)
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# CRITICAL CHANGE (2025-10-28): Removed CUDA 13+ GPU filtering
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#
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# Previously filtered H100, L40S, RTX 6000 Ada as "CUDA 13.0+ incompatible"
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# because we believed driver 580+ could not run CUDA 12.9 binaries.
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#
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# CONFIRMED SAFE: NVIDIA driver 580+ is backward compatible with CUDA 12.x
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# - Driver 580 natively supports CUDA 12.9 binaries (no forward compat package needed)
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# - PTX JIT compilation works correctly (verified via NVIDIA docs)
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# - Our CUDA 12.9 binaries work on both driver 550 (CUDA 12.x) and 580+ (CUDA 13.x)
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# - /usr/local/cuda/compat path in Dockerfile provides additional forward compat
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#
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# Sources:
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# - NVIDIA CUDA Compatibility Docs (Minor Version Compatibility)
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# - Perplexity AI verification (2025-10-28)
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# - Medium article: "CUDA Hell" (driver backward compatibility)
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#
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# Result: ALL GPUs with driver 525+ can run our CUDA 12.9 binaries
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# Known compatible GPUs (reference only - not used for filtering)
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KNOWN_COMPATIBLE_GPU_TYPES = [
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'RTX A4000',
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'RTX A5000',
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'RTX A6000',
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'Tesla V100',
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'RTX 4090',
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'A100',
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'H100', # CUDA 13 GPU - backward compatible with CUDA 12.9
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'L40S', # CUDA 13 GPU - backward compatible with CUDA 12.9
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'RTX 6000 Ada', # CUDA 13 GPU - backward compatible with CUDA 12.9
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]
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# Deprecated: No longer used (kept for git history)
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INCOMPATIBLE_GPU_TYPES = []
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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(allow_cuda13=False):
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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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Args:
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allow_cuda13: [DEPRECATED] No longer used - all GPUs supported via backward compatibility
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"""
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print(" Querying GPU types and pricing...")
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# Deprecation warning if allow_cuda13 was explicitly used
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if allow_cuda13:
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print(" ⚠️ Note: --allow-cuda13 flag is deprecated (all GPUs now supported)")
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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 (SIMPLIFIED - no CUDA version filtering):
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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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#
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# CRITICAL: No longer filtering by CUDA version!
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# Driver 580+ is backward compatible with CUDA 12.9 binaries (verified 2025-10-28)
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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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gpu_name = gpu.get('displayName', 'Unknown')
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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_name,
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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 ≥16GB VRAM")
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else:
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print(f" ⚠️ No GPU types found with ≥16GB VRAM")
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return available_gpus
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def sanitize_command(command):
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"""
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Sanitize deployment command to ensure it works with Docker ENTRYPOINT chain.
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CRITICAL: RunPod's dockerStartCmd sets Docker CMD, NOT ENTRYPOINT.
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The ENTRYPOINT chain (entrypoint-self-terminate.sh → entrypoint-generic.sh)
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MUST execute first for pod auto-termination to work.
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This function:
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1. Detects and strips /bin/bash -c wrappers (which bypass entrypoint)
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2. Removes chmod +x commands (entrypoint-generic.sh handles this)
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3. Removes tee redirection (container logs capture everything)
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4. Returns clean binary path + arguments
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Args:
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command: Raw command string from user
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Returns:
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Sanitized command string suitable for dockerStartCmd
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"""
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import re
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if not command:
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return command
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# Strip leading/trailing whitespace
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cmd = command.strip()
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# Detect /bin/bash -c wrapper
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if cmd.startswith('/bin/bash -c'):
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print(" ⚠️ WARNING: Detected /bin/bash -c wrapper - stripping to preserve entrypoint chain")
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# Extract command from quotes
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match = re.search(r'/bin/bash -c ["\'](.+)["\']', cmd)
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if match:
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cmd = match.group(1)
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print(f" Extracted: {cmd[:80]}...")
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# Remove chmod +x prefix (entrypoint-generic.sh handles this)
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cmd = re.sub(r'chmod \+x [^\s]+ && ', '', cmd)
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# Remove tee redirection suffix (container logs capture everything)
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cmd = re.sub(r' 2>&1 \| tee [^\s]+$', '', cmd)
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return cmd.strip()
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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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# NOTE: Auto-termination is now handled by entrypoint-self-terminate.sh wrapper script
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# inside the Docker container, not via the RunPod API "terminateAfter" field (which causes HTTP 400).
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# The wrapper script terminates the pod after training completes to prevent restart loops.
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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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"minRAMPerGPU": 8,
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"minVCPUPerGPU": 2
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}
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# Sanitize and add Docker command if specified
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# CRITICAL ARCHITECTURE:
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# 1. dockerStartCmd sets Docker CMD (NOT ENTRYPOINT)
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# 2. Docker execution order: ENTRYPOINT args... + CMD args...
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# 3. Our ENTRYPOINT: /entrypoint.sh (→ entrypoint-self-terminate.sh → entrypoint-generic.sh)
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# 4. entrypoint-generic.sh calls: exec "$@" (passes CMD to binary)
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# 5. entrypoint-self-terminate.sh captures exit code and terminates pod on success
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#
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# MUST AVOID (sanitized automatically):
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# - /bin/bash -c "..." wrappers (override ENTRYPOINT, bypass auto-termination)
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# - chmod commands (entrypoint-generic.sh handles this)
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# - Shell redirections like tee (container logs capture everything)
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#
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# Command is pre-sanitized in main() before reaching here
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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: entrypoint-self-terminate.sh (after training)")
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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 (DQN 1 epoch smoke test)
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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 TFT training command
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./runpod_deploy.py --command "/runpod-volume/binaries/train_tft_parquet --parquet-file /runpod-volume/test_data/ES_FUT_180d.parquet --epochs 50 --use-gpu"
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# Custom DQN training with different dataset
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./runpod_deploy.py --command "/runpod-volume/binaries/train_dqn --parquet-file /runpod-volume/test_data/NQ_FUT_180d.parquet --epochs 100 --output-dir /runpod-volume/models"
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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: DQN smoke test (1 epoch, small dataset, proven working)
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- Command parameter accepts FULL command including binary path
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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='/runpod-volume/binaries/train_dqn --parquet-file /runpod-volume/test_data/ES_FUT_small.parquet --epochs 1 --output-dir /runpod-volume/models',
|
|
help='Full training command including binary path (default: DQN 1-epoch smoke test)'
|
|
)
|
|
parser.add_argument(
|
|
'--container-disk',
|
|
type=int,
|
|
default=50,
|
|
help='Container disk size in GB (default: 50)'
|
|
)
|
|
parser.add_argument(
|
|
'--dry-run',
|
|
action='store_true',
|
|
help='Show deployment plan without actually deploying'
|
|
)
|
|
parser.add_argument(
|
|
'--allow-cuda13',
|
|
action='store_true',
|
|
help='[DEPRECATED] No longer needed - all GPUs supported via backward compatibility'
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Sanitize command to ensure entrypoint chain works
|
|
if args.command:
|
|
args.command = sanitize_command(args.command)
|
|
|
|
print("🔍 Querying available GPU types (global secure cloud)...")
|
|
|
|
# Query available GPUs (global availability) - no CUDA filtering
|
|
gpus = get_available_gpu_types(allow_cuda13=args.allow_cuda13)
|
|
|
|
if not gpus:
|
|
print("\nERROR: No GPUs available with ≥16GB VRAM in SECURE cloud")
|
|
print("\n💡 TIP: This checks global availability.")
|
|
print(" EUR-IS specific availability is checked during deployment via REST API.")
|
|
sys.exit(1)
|
|
|
|
print(f"\n✅ Found {len(gpus)} GPU type(s) to try")
|
|
|
|
# Create priority list: user preference (if specified), then sorted by price
|
|
priority_gpus = []
|
|
|
|
# Add user's preferred GPU if specified
|
|
if args.gpu_type:
|
|
for gpu in gpus:
|
|
if args.gpu_type.lower() in gpu['name'].lower():
|
|
priority_gpus.append(gpu)
|
|
break
|
|
if not priority_gpus:
|
|
print(f"WARNING: Preferred GPU '{args.gpu_type}' not found, trying all GPUs...")
|
|
|
|
# Add remaining GPUs sorted by price (cheapest first)
|
|
# gpus list is already sorted by price at line 121
|
|
for gpu in gpus:
|
|
if gpu not in priority_gpus:
|
|
priority_gpus.append(gpu)
|
|
|
|
# Try each GPU until one succeeds
|
|
attempted_gpus = []
|
|
pod_data = None
|
|
|
|
for gpu in priority_gpus:
|
|
if gpu in attempted_gpus:
|
|
continue
|
|
|
|
attempted_gpus.append(gpu)
|
|
|
|
print(f"\n🎯 Attempting deployment: {gpu['name']} (${gpu['price']:.3f}/hr)...")
|
|
print(f" (Global availability: {gpu['global_available']} in secure cloud)")
|
|
print(f" (Will check EUR-IS specific availability during deployment...)")
|
|
|
|
pod_data = deploy_pod_rest_api(
|
|
gpu,
|
|
args.image,
|
|
args.command,
|
|
args.container_disk,
|
|
EUR_IS_DATACENTERS,
|
|
args.dry_run
|
|
)
|
|
|
|
if pod_data:
|
|
break
|
|
else:
|
|
print(f"❌ {gpu['name']} not available in EUR-IS, trying next option...")
|
|
|
|
# Display results
|
|
if pod_data:
|
|
display_deployment_result(pod_data)
|
|
else:
|
|
print("\n❌ ERROR: Failed to deploy pod on any available GPU in EUR-IS")
|
|
print("\nAttempted GPUs:")
|
|
for gpu in attempted_gpus:
|
|
print(f" - {gpu['name']} ({gpu['vram']}GB VRAM) @ ${gpu['price']:.3f}/hr")
|
|
print("\n💡 TIP: RunPod availability changes frequently. Try again in a few minutes.")
|
|
print(" The script now correctly checks EUR-IS datacenter availability,")
|
|
print(" not just global secure cloud counts.")
|
|
sys.exit(1)
|
|
|
|
if __name__ == "__main__":
|
|
main()
|