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>
125 lines
4.1 KiB
Bash
Executable File
125 lines
4.1 KiB
Bash
Executable File
#!/bin/bash
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# Dockerfile.runpod Update Verification Script
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# Date: 2025-10-23
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echo "=========================================="
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echo "Dockerfile.runpod Update Verification"
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echo "=========================================="
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echo ""
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# 1. Check for AWS references
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echo "1. Checking for AWS/S3 references..."
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if ! grep -iq "aws\|s3\|amazon" Dockerfile.runpod 2>/dev/null; then
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echo " ✅ PASS: Zero AWS/S3/Amazon references found"
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else
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echo " ❌ FAIL: Found AWS/S3/Amazon references"
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exit 1
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fi
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echo ""
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# 2. Check for Tesla V100 reference
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echo "2. Checking for Tesla V100 reference..."
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if grep -q "Tesla V100" Dockerfile.runpod 2>/dev/null; then
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V100_COUNT=$(grep -c "Tesla V100" Dockerfile.runpod)
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echo " ✅ PASS: Tesla V100 documented ($V100_COUNT references)"
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else
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echo " ❌ FAIL: Tesla V100 not found in Dockerfile"
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exit 1
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fi
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echo ""
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# 3. Check for private Docker Hub registry
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echo "3. Checking for private Docker Hub registry (jgrusewski/foxhunt)..."
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REGISTRY_COUNT=$(grep -c "jgrusewski/foxhunt" Dockerfile.runpod 2>/dev/null)
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if [ "$REGISTRY_COUNT" -gt 5 ]; then
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echo " ✅ PASS: Private registry documented ($REGISTRY_COUNT references)"
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else
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echo " ❌ FAIL: Private registry not consistently used (found $REGISTRY_COUNT, need >5)"
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exit 1
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fi
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echo ""
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# 4. Check for embedded test data
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echo "4. Checking for embedded test data..."
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if grep -q "COPY test_data/\*.parquet /workspace/test_data/" Dockerfile.runpod; then
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echo " ✅ PASS: Test data COPY instruction found"
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else
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echo " ❌ FAIL: Test data COPY instruction missing"
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exit 1
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fi
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echo ""
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# 5. Verify test data files exist
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echo "5. Verifying test data files exist..."
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TEST_DATA_COUNT=$(ls test_data/*.parquet 2>/dev/null | wc -l)
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if [ "$TEST_DATA_COUNT" -eq 9 ]; then
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echo " ✅ PASS: All 9 Parquet files present"
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ls -1 test_data/*.parquet | sed 's/^/ - /'
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else
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echo " ❌ FAIL: Expected 9 Parquet files, found $TEST_DATA_COUNT"
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exit 1
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fi
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echo ""
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# 6. Check test data size
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echo "6. Checking test data size..."
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TEST_DATA_SIZE=$(du -sm test_data 2>/dev/null | awk '{print $1}')
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if [ "$TEST_DATA_SIZE" -lt 20 ]; then
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echo " ✅ PASS: Test data size is ${TEST_DATA_SIZE}MB (optimal)"
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else
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echo " ⚠️ WARNING: Test data size is ${TEST_DATA_SIZE}MB (larger than expected)"
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fi
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echo ""
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# 7. Check VOLUME directive
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echo "7. Checking VOLUME directive (test_data should NOT be present)..."
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if grep 'VOLUME' Dockerfile.runpod | grep -q 'test_data'; then
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echo " ❌ FAIL: test_data still in VOLUME directive (should be removed)"
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exit 1
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else
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echo " ✅ PASS: test_data removed from VOLUME directive"
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fi
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echo ""
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# 8. Verify entry point
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echo "8. Verifying default entry point..."
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if grep -q 'ENTRYPOINT.*train_tft_parquet' Dockerfile.runpod; then
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echo " ✅ PASS: Default entry point set to train_tft_parquet"
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else
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echo " ❌ FAIL: Entry point not configured correctly"
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exit 1
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fi
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echo ""
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# 9. Check for private registry instructions
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echo "9. Checking for private registry instructions..."
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if grep -q "PRIVATE" Dockerfile.runpod; then
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PRIVATE_COUNT=$(grep -c "PRIVATE" Dockerfile.runpod)
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echo " ✅ PASS: Private registry instructions documented ($PRIVATE_COUNT mentions)"
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else
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echo " ❌ FAIL: Private registry instructions missing"
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exit 1
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fi
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echo ""
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# 10. Final summary
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echo "=========================================="
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echo "✅ ALL CHECKS PASSED"
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echo "=========================================="
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echo ""
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echo "Summary:"
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echo " - Zero AWS/S3 references"
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echo " - Tesla V100 documented as minimum GPU"
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echo " - Docker Hub registry: jgrusewski/foxhunt (PRIVATE)"
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echo " - 9 Parquet files embedded (~${TEST_DATA_SIZE}MB total)"
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echo " - VOLUME directive updated (test_data removed)"
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echo " - Entry point configured for TFT training"
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echo ""
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echo "Next Steps:"
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echo " 1. Build image: docker build -f Dockerfile.runpod -t jgrusewski/foxhunt:latest ."
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echo " 2. Test locally: docker run --gpus all jgrusewski/foxhunt:latest"
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echo " 3. Push to Docker Hub: docker push jgrusewski/foxhunt:latest"
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echo " 4. Set repository to PRIVATE in Docker Hub"
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echo " 5. Deploy on Runpod with Tesla V100"
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echo ""
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