foxhunt/docs/archive/wave_d/reports/DEPLOY_INDEX.md

Runpod Deployment Index

Last Updated: 2025-10-25T18:02:42Z Status: ✅ READY FOR DEPLOYMENT

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Quick Navigation

1. Quick Start (Most Users)

• DEPLOY_01_STATUS.txt - Visual summary of deployment status (1 minute read)
• RUNPOD_DEPLOYMENT_COMMANDS.md - Copy-paste deployment commands (5 minute read)

2. Detailed Documentation

• AGENT_DEPLOY_01_RUNPOD_UPLOAD.md - Full deployment report (10 minute read)
• AGENT_DEPLOY_01_QUICK_SUMMARY.md - Quick reference guide (2 minute read)

3. Configuration Files

• runpod_deployment_manifest.json - Binary metadata with SHA-256 checksums

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File Descriptions

DEPLOY_01_STATUS.txt (1.5 KB)

Purpose: Visual summary of deployment status with ASCII boxes

Contents:

• Binaries uploaded (5/5 with checksums)
• Verification results (6/6 success criteria)
• Production readiness metrics
• Quick start command for TFT training
• Next step: DEPLOY-02 (upload test data)

When to use: Quick status check, sharing with team

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RUNPOD_DEPLOYMENT_COMMANDS.md (15 KB)

Purpose: Copy-paste ready deployment commands for all scenarios

Contents:

1. Prerequisites: AWS CLI setup, Runpod account
2. Option 1: Single model training (TFT recommended)
   • Pod configuration (RTX 4090, $0.44/hr)
   • Complete startup script with error handling
   • Expected output and cost (~$0.015 per run)
3. Option 2: All models sequential training
   • Batch script for 4 models (~5 minutes total)
   • Cost: ~$0.04 for all models
4. Option 3: Automated pod creation via Python API
   • Python script for runpod CLI
   • Auto-termination after training
5. Verification Commands: Post-training validation
6. Cost Optimization Tips: Spot instances (70% cheaper), batch training
7. Troubleshooting: Common errors and solutions

When to use: Creating Runpod pods, deploying to GPU

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AGENT_DEPLOY_01_RUNPOD_UPLOAD.md (18 KB)

Purpose: Complete deployment report with all technical details

Contents:

• Phase 1: Binary compilation results (5 binaries)
• Phase 2: Binary integrity verification (CUDA support, checksums)
• Phase 3: Runpod S3 upload (85.9 MB total, 7.0 MB/s)
• Phase 4: Deployment manifest creation
• Deployment Commands: 3 ready-to-use options
• Success Criteria: 6/6 validation results
• Production Readiness: Test coverage, GPU memory, binary specs
• Recommended GPU Configurations: Per-model requirements
• Next Steps: Immediate and short/medium-term actions
• Appendix: Troubleshooting guide

When to use: Understanding deployment process, debugging issues, technical review

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AGENT_DEPLOY_01_QUICK_SUMMARY.md (2 KB)

Purpose: Quick reference for key results and commands

Contents:

• What was done (5 bullet points)
• Key results table (5 binaries with checksums)
• Quick start commands (download, verify, deploy)
• Production readiness checklist
• Next steps (DEPLOY-02 onwards)

When to use: Quick refresher, sharing results with stakeholders

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runpod_deployment_manifest.json (1.4 KB)

Purpose: Machine-readable deployment metadata

Contents:

{
  "deployment_date": "2025-10-25T18:02:42Z",
  "git_commit": "caf36b41...",
  "binaries": [
    {
      "name": "train_dqn",
      "size": 20857232,
      "sha256": "fedc57ea...",
      "s3_path": "s3://se3zdnb5o4/binaries/train_dqn"
    },
    // ... 4 more binaries
  ],
  "test_pass_rate": "100% (1,337/1,337 ML, 3,196/3,196 total)",
  "production_status": "CERTIFIED",
  "cuda_support": true,
  "models": ["DQN", "PPO", "MAMBA-2", "TFT-FP32"],
  "features": 225
}

When to use: Automated scripts, CI/CD pipelines, verification tools

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Deployment Workflow

Step 1: Compile Binaries (COMPLETE ✅)

• Status: All 5 binaries compiled
• Location: target/release/examples/train_*
• CUDA support: Verified (CUDA 12.9)
• See: AGENT_DEPLOY_01_RUNPOD_UPLOAD.md (Phase 1)

Step 2: Upload to Runpod S3 (COMPLETE ✅)

• Status: All 5 binaries uploaded
• Location: s3://se3zdnb5o4/binaries/
• Total size: 85.9 MB
• Upload speed: 7.0 MB/s average
• See: AGENT_DEPLOY_01_RUNPOD_UPLOAD.md (Phase 3)

Step 3: Upload Test Data (PENDING ⏳)

• Next agent: DEPLOY-02
• Files to upload:
  • ES_FUT_180d.parquet (2.9 MB)
  • NQ_FUT_180d.parquet (4.4 MB)
  • 6E_FUT_180d.parquet (2.8 MB)
  • ZN_FUT_90d.parquet (2.8 MB)
• Command:

  aws s3 cp test_data/*.parquet s3://se3zdnb5o4/test_data/ \
    --profile runpod \
    --endpoint-url https://s3api-eur-is-1.runpod.io
  

Step 4: Create Runpod Pod (PENDING ⏳)

• Next agent: DEPLOY-03
• Use: RUNPOD_DEPLOYMENT_COMMANDS.md (Option 1)
• GPU: NVIDIA RTX 4090 (24GB VRAM)
• Cost: $0.44/hr (~$0.015 per 2-minute TFT run)

Step 5: Run Training (PENDING ⏳)

• Next agent: DEPLOY-04
• Expected time: ~2 minutes (TFT), ~5 minutes (all 4 models)
• Expected cost: ~$0.015 (TFT), ~$0.04 (all models)

Step 6: Validate Checkpoints (PENDING ⏳)

• Next agent: DEPLOY-05
• Download checkpoints from s3://se3zdnb5o4/models/
• Run local inference tests
• Verify model accuracy (RMSE <0.05 for 225 features)

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Binary Details

Binary	Size	Purpose	GPU Memory	Training Time
train_dqn	19.9 MB	Deep Q-Network	~6 MB	15-20 sec
train_ppo	12.5 MB	Proximal Policy Opt.	~145 MB	7-10 sec
train_mamba2_dbn	13.3 MB	MAMBA-2 (DBN data)	~164 MB	2-3 min
train_mamba2_parquet	19.7 MB	MAMBA-2 (Parquet)	~164 MB	2-3 min
train_tft_parquet	20.6 MB	TFT (Parquet, cache opt.)	~525-550 MB	2 min

Total GPU Memory (All Models): 840-865 MB (fits on RTX 4090/3060/A4000)

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Cost Estimates

Single Model Training (TFT)

• GPU: NVIDIA RTX 4090 (24GB VRAM)
• Training time: ~2 minutes
• Cost per run: $0.015 ($0.44/hr * 2/60 hr)
• Monthly cost (daily retraining): $0.45 ($0.015 * 30 days)

Batch Training (All 4 Models)

• GPU: NVIDIA RTX 4090 (24GB VRAM)
• Training time: ~5 minutes (DQN 20s + PPO 10s + MAMBA-2 3min + TFT 2min)
• Cost per run: $0.04 ($0.44/hr * 5/60 hr)
• Monthly cost (daily retraining): $1.20 ($0.04 * 30 days)

Cost Optimization (Spot Instances)

• Community Cloud (Spot): $0.13/hr (70% cheaper)
• TFT training: $0.004 per run (73% savings)
• Batch training: $0.011 per run (72% savings)
• Monthly cost (daily batch): $0.33 (72% savings)

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Production Readiness Checklist

Item	Status	Notes
✅ Binaries compiled	COMPLETE	5/5 FP32 models
✅ CUDA support verified	COMPLETE	CUDA 12.9 linked
✅ Binaries uploaded to S3	COMPLETE	85.9 MB total
✅ Deployment manifest	COMPLETE	SHA-256 checksums
✅ Test pass rate 100%	COMPLETE	1,337/1,337 ML, 3,196/3,196 total
✅ P0 bugs fixed	COMPLETE	3/3 (TFT, MAMBA-2, PPO)
⏳ Test data uploaded	PENDING	DEPLOY-02
⏳ Runpod pod template	PENDING	DEPLOY-03
⏳ Training validated	PENDING	DEPLOY-04
⏳ Checkpoints verified	PENDING	DEPLOY-05

Overall Status: 6/10 complete (60%), ready for next phase

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Key Resources

Runpod Console

• URL: https://www.runpod.io/console/pods
• Purpose: Create/manage GPU pods
• Required: API key, payment method

Runpod S3 Bucket

• Bucket: s3://se3zdnb5o4/
• Region: eur-is-1
• Endpoint: https://s3api-eur-is-1.runpod.io
• Contents:
  • binaries/ - 5 training binaries (85.9 MB)
  • test_data/ - Parquet data files (pending DEPLOY-02)
  • models/ - Trained model checkpoints (pending DEPLOY-04)
  • runpod_deployment_manifest.json - Deployment metadata

AWS CLI Profile

• Profile name: runpod
• Region: eur-is-1
• Verify: aws configure list-profiles | grep runpod

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Troubleshooting

Quick Diagnostics

# Verify AWS profile
aws configure list-profiles | grep runpod

# List binaries on S3
aws s3 ls s3://se3zdnb5o4/binaries/ \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io

# Download manifest
aws s3 cp s3://se3zdnb5o4/runpod_deployment_manifest.json /tmp/manifest.json \
  --profile runpod \
  --endpoint-url https://s3api-eur-is-1.runpod.io

# Verify checksums
cat /tmp/manifest.json | jq -r '.binaries[] | "\(.name): \(.sha256)"'

Common Issues

See RUNPOD_DEPLOYMENT_COMMANDS.md (Troubleshooting section) for:

• Binary download fails (AWS credentials)
• Out of GPU memory (use RTX 4090 or gradient checkpointing)
• Test data not found (upload test_data/*.parquet first)
• Checkpoint not saved (create /workspace/models directory)

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Next Steps

Immediate (DEPLOY-02): Upload Test Data

Estimated Time: 10 minutes Command:

aws s3 cp test_data/ES_FUT_180d.parquet s3://se3zdnb5o4/test_data/ \
  --profile runpod --endpoint-url https://s3api-eur-is-1.runpod.io
# Repeat for NQ_FUT, 6E_FUT, ZN_FUT

Short-Term (DEPLOY-03): Create Pod Template

Estimated Time: 15 minutes See: RUNPOD_DEPLOYMENT_COMMANDS.md (Option 1)

Short-Term (DEPLOY-04): Run TFT Training

Estimated Time: 5 minutes (2 min training + 3 min setup) Expected Cost: $0.015

Short-Term (DEPLOY-05): Validate Checkpoints

Estimated Time: 10 minutes Command:

# Download checkpoint
aws s3 cp s3://se3zdnb5o4/models/tft_final_*.safetensors \
  ./models/tft_final.safetensors \
  --profile runpod --endpoint-url https://s3api-eur-is-1.runpod.io

# Run inference test
cargo run -p ml --example test_tft_inference --release -- \
  --checkpoint ./models/tft_final.safetensors \
  --test-file test_data/ES_FUT_small.parquet

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Summary

✅ DEPLOY-01 COMPLETE: All 5 FP32 training binaries compiled, verified, and uploaded to Runpod S3 (85.9 MB total). Deployment manifest created with SHA-256 checksums. Production certified with 100% test pass rate (1,337/1,337 ML tests, 3,196/3,196 workspace tests). Ready for immediate GPU deployment with zero blockers.

Next Agent: DEPLOY-02 (Upload test data to Runpod S3)

Files to Read:

1. Quick start: RUNPOD_DEPLOYMENT_COMMANDS.md
2. Full details: AGENT_DEPLOY_01_RUNPOD_UPLOAD.md
3. Status check: DEPLOY_01_STATUS.txt

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End of Index