Implement comprehensive Runpod deployment with S3 volume mount architecture for FP32 ML model training on Tesla V100 GPUs. ## Infrastructure Components ### Deployment Scripts (scripts/) - runpod_deploy.sh: Master deployment orchestrator (8-step workflow) - runpod_upload.sh: S3 upload for binaries and test data - upload_env_to_runpod.sh: Secure .env credentials upload - runpod_deploy_test.sh: Prerequisites validation ### Docker Configuration - Dockerfile.runpod: Multi-stage CUDA 12.1 runtime (~2GB, no binaries) - entrypoint.sh: Volume verification and training execution - Architecture: Volume mount (NO S3 downloads in pods) ### S3 Configuration - Bucket: se3zdnb5o4 (Iceland region: eur-is-1) - Endpoint: https://s3api-eur-is-1.runpod.io - Structure: binaries/, test_data/, models/, .env ### OpenTofu Infrastructure (terraform/runpod/) - main.tf: Pod and volume resources - variables.tf: Configuration variables - outputs.tf: Pod connection info - Security: NO credentials in state (uses volume .env) ## Deployment Assets Uploaded ### Training Binaries (77MB) - train_tft_parquet (23M) - TFT-225 features - train_mamba2_parquet (22M) - MAMBA-2 state space - train_dqn (22M) - Deep Q-Network - train_ppo (13M) - Proximal Policy Optimization ### Test Data (13.8 MB) - 9 Parquet files: ES.FUT, NQ.FUT, 6E.FUT, ZN.FUT (180-day datasets) ### Credentials - .env file (1.5 KB, private access, chmod 600) ## Documentation ### Deployment Guides - RUNPOD_DEPLOYMENT_READY_SUMMARY.md: Complete deployment status - RUNPOD_VOLUME_DEPLOYMENT_GUIDE.md: Step-by-step guide (42KB) - RUNPOD_DEPLOYMENT_QUICK_START.md: Quick reference - RUNPOD_UPLOAD_GUIDE.md: S3 upload instructions - RUNPOD_VOLUME_CONFIGURATION_COMPLETE.md: S3 setup report - RUNPOD_S3_PARQUET_UPLOAD_REPORT.md: Data upload verification ### Architecture Documentation - RUNPOD_VOLUME_MOUNT_ARCHITECTURE.md: Volume mount design - RUNPOD_S3_ARCHITECTURE_DIAGRAM.txt: S3 API vs filesystem access - DOCKERFILE_RUNPOD_FINAL_SUMMARY.md: Docker image specification ### Decision Documentation - RUNPOD_DEPLOYMENT_CHECKLIST.md: Go/no-go decision matrix (27KB) - RUNPOD_DEPLOYMENT_DECISION_TREE.md: Decision workflow - FP32_RUNPOD_DEPLOYMENT_READY.md: FP32 deployment readiness ## QAT Enhancements ### Core QAT Infrastructure - ml/src/memory_optimization/qat.rs: Enhanced QAT observer (+226 lines) - ml/src/memory_optimization/auto_batch_size.rs: OOM recovery (+84 lines) - ml/src/tft/qat_tft.rs: QAT TFT wrapper (+154 lines) - ml/src/trainers/tft.rs: QAT training integration (+433 lines) - ml/src/qat_metrics_exporter.rs: NEW - QAT metrics export ### QAT Testing - ml/tests/qat_integration_tests.rs: NEW - Integration test suite - ml/tests/qat_gradient_clipping_test.rs: NEW - Gradient clipping tests - ml/tests/qat_device_consistency_test.rs: Device mismatch tests (+205 lines) - ml/tests/qat_accuracy_validation_test.rs: Accuracy validation - ml/tests/qat_tft_integration_test.rs: TFT QAT integration ### QAT Documentation - ml/docs/QAT_GUIDE.md: Comprehensive QAT guide (+616 lines) - ml/docs/QAT_GRADIENT_CHECKPOINTING_WORKAROUND.md: NEW - Workaround guide - QAT_BLOCKERS_ROOT_CAUSE_ANALYSIS.md: P0 blocker analysis (44KB) - QAT_ACCURACY_VALIDATION_REPORT.md: Accuracy comparison - QAT_GRADIENT_CLIPPING_VALIDATION_REPORT.md: Clipping validation ### QAT Monitoring - config/grafana/dashboards/qat-training-metrics.json: NEW - Grafana dashboard ## AWS CLI Configuration ### Credentials Setup - ~/.aws/credentials: Runpod profile configured - Access Key: user_2xxA3XcIFj16yfL3aBon9niiSpr - Secret Key: (from RUNPOD_S3_SECRET) - ~/.aws/config: Iceland region (eur-is-1) ## Production Readiness ### FP32 Models: ✅ READY FOR DEPLOYMENT - DQN: 15-20s training, ~6MB GPU memory - PPO: 7-10s training, ~145MB GPU memory - MAMBA-2: 2-3 min training, ~164MB GPU memory - TFT-225: 3-5 min training, ~500MB GPU memory - Total GPU Budget: 815MB (fits on 4GB+ Tesla V100) ### QAT Models: 🔴 BLOCKED - 24 tests implemented but DO NOT COMPILE (11 errors) - 3 P0 blockers: device mismatch, gradient checkpointing, OOM recovery - Timeline: 1-2 weeks to fix (13h P0 fixes + validation) ### Wave D Features: ✅ OPERATIONAL - 225 features fully integrated - Feature extraction: 5.10μs/bar (196x faster than target) - Wave D backtest: Sharpe 2.00, Win Rate 60%, Drawdown 15% - Database migration 045: Applied cleanly, zero conflicts ## Cost Analysis ### One-Time Setup - Network Volume: $4/month (50GB SSD) - Upload costs: FREE (S3 API included) ### Per Training Run (TFT-225) - GPU: Tesla V100-PCIE-16GB @ $0.29/hr - Training Time: ~4 hours - Cost per run: $1.16 ### Monthly (20 Training Runs) - Storage: $4.00/month - Training: $23.20/month (20 runs × $1.16) - Total: $27.20/month ## Security ### Credentials Management - ✅ NO credentials in Docker image - ✅ NO credentials in Terraform state - ✅ .env gitignored and not committed - ✅ .env file private on S3 (HTTP 401 on public access) - ✅ Docker Hub repository PRIVATE (jgrusewski/foxhunt) ### Access Control - S3 API: Local client uploads only - Volume mount: Pod filesystem access only - Authentication: AWS CLI with Runpod profile required ## Next Steps 1. ✅ COMPLETE: Build Docker image 2. ⏳ PENDING: Push to Docker Hub 3. ⏳ PENDING: Deploy pod via Runpod console 4. ⏳ PENDING: Validate training on Tesla V100 ## Performance Targets - Build time: 5-10 min - Upload time: ~20 sec (90MB total) - Pod startup: ~30 sec - Training time: 3-5 min (TFT-225) - Total deployment: ~40 min from start to first training run ## Test Status - FP32 tests: 597/608 passing (98.2%) - QAT tests: 0/24 passing (compilation errors) - Overall: 2,062/2,086 passing (98.8% excluding QAT) 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
138 lines
3.8 KiB
Bash
Executable File
138 lines
3.8 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Foxhunt - Runpod Credential Generation Script
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# Generates a secure .env.runpod file with random credentials
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#
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# Usage: ./generate_credentials.sh
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set -euo pipefail
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# Colors for output
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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ENV_FILE=".env.runpod"
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# Print header
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echo "========================================"
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echo "Foxhunt Runpod Credential Generator"
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echo "========================================"
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echo ""
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# Check if .env.runpod already exists
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if [[ -f "${ENV_FILE}" ]]; then
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echo -e "${YELLOW}WARNING: ${ENV_FILE} already exists!${NC}"
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echo ""
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read -p "Overwrite existing file? (yes/no): " CONFIRM
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if [[ "${CONFIRM}" != "yes" ]]; then
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echo "Cancelled. No changes made."
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exit 0
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fi
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echo ""
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fi
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# Prompt for Databento API key
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echo -e "${BLUE}Enter your Databento API key:${NC}"
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echo "(Get from: https://databento.com/)"
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read -p "> " DATABENTO_API_KEY
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if [[ -z "${DATABENTO_API_KEY}" ]]; then
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echo "ERROR: Databento API key is required"
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exit 1
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fi
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echo ""
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echo "Generating random credentials..."
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echo ""
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# Generate random credentials
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POSTGRES_PASSWORD=$(openssl rand -base64 32)
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VAULT_TOKEN=$(openssl rand -base64 32)
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JWT_SECRET=$(openssl rand -base64 64)
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# Create .env.runpod file
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cat > "${ENV_FILE}" <<EOF
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# Foxhunt Runpod Credentials
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# Generated: $(date -u +"%Y-%m-%d %H:%M:%S UTC")
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#
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# IMPORTANT: Keep this file secure!
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# - DO NOT commit to version control
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# - DO NOT share via Slack/Discord/email
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# - Store backup in 1Password or AWS Secrets Manager
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#
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# Upload to Runpod volume BEFORE deploying pods:
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# 1. Deploy volume: tofu apply -target=runpod_network_volume.foxhunt_volume
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# 2. Upload this file to volume as '.env' (via Runpod console or S3 API)
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# 3. Deploy pod: tofu apply
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# Database credentials
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POSTGRES_PASSWORD="${POSTGRES_PASSWORD}"
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# Vault token
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VAULT_TOKEN="${VAULT_TOKEN}"
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# Databento API key (for market data)
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DATABENTO_API_KEY="${DATABENTO_API_KEY}"
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# JWT secret (for authentication)
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JWT_SECRET="${JWT_SECRET}"
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EOF
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# Set secure permissions
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chmod 600 "${ENV_FILE}"
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# Print success message
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echo -e "${GREEN}✓ Credentials generated successfully!${NC}"
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echo ""
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echo "File created: ${ENV_FILE}"
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echo "Permissions: $(stat -c %a "${ENV_FILE}" 2>/dev/null || stat -f %Lp "${ENV_FILE}" 2>/dev/null)"
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echo ""
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# Print credential details (lengths only, not values)
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echo "Credential details:"
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echo " - POSTGRES_PASSWORD: ${#POSTGRES_PASSWORD} chars"
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echo " - VAULT_TOKEN: ${#VAULT_TOKEN} chars"
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echo " - DATABENTO_API_KEY: ${#DATABENTO_API_KEY} chars"
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echo " - JWT_SECRET: ${#JWT_SECRET} chars"
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echo ""
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# Validate credentials
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echo "Running validation..."
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if ./validate_credentials.sh "${ENV_FILE}" 2>/dev/null; then
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echo ""
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echo -e "${GREEN}✓ All validations passed!${NC}"
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else
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echo ""
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echo -e "${YELLOW}⚠ Some validations failed. Review output above.${NC}"
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fi
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echo ""
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echo "========================================"
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echo "Next Steps"
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echo "========================================"
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echo ""
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echo "1. Backup credentials securely:"
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echo " - Store ${ENV_FILE} in 1Password, LastPass, or AWS Secrets Manager"
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echo " - DO NOT commit to git (already gitignored)"
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echo ""
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echo "2. Deploy Runpod volume:"
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echo " cd /home/jgrusewski/Work/foxhunt/terraform/runpod"
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echo " tofu init"
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echo " tofu apply -target=runpod_network_volume.foxhunt_volume"
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echo ""
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echo "3. Upload ${ENV_FILE} to volume:"
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echo " - Go to: https://www.runpod.io/console/volumes"
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echo " - Click on 'foxhunt-data-volume'"
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echo " - Upload ${ENV_FILE} as '.env'"
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echo ""
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echo "4. Deploy pod:"
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echo " tofu apply"
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echo ""
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echo "5. Verify credentials loaded:"
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echo " ssh root@<PUBLIC_IP>"
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echo " echo \$POSTGRES_PASSWORD # Should print password"
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echo " docker-compose ps # All services should be 'Up'"
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echo ""
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echo "========================================"
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