# Runpod Deployment Index

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

---

## 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

---

## 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**:
```json
{
  "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

---

## 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:
  ```bash
  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)

---

## 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)

---

## 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)

---

## 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

---

## 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`

---

## Troubleshooting

### Quick Diagnostics
```bash
# 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)

---

## Next Steps

### Immediate (DEPLOY-02): Upload Test Data
**Estimated Time**: 10 minutes
**Command**:
```bash
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**:
```bash
# 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
```

---

## 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**