CSIROBoeingPhase5-Vietnam/START_HERE.md

# 🎯 Start Here - Complete Guide

## Welcome! 👋

Tôi đã tạo một **workflow hoàn chỉnh** cho bạn. Bắt đầu từ đây!

---

## 🚀 Quick 30-Second Summary

**Vấn đề**: Phải code trên server, kéo data lớn, train chậm  
**Giải pháp**: Code trên local, kéo data nhỏ (~300MB), train trên GPU

**Workflow**:
1. Server: Tải S3 → Lưu NetCDF → Bạn download
2. Local: Load data → Train model → Predict
3. Local: Lưu kết quả (4 format)

**Total Time**: 3-7 hours (depends on GPU)

---

## 📚 Step-by-Step Guide

### 📖 Step 1: Read Intro Docs (10 minutes)
Pick ONE to start:
- **Very quick** (5 min): `QUICKSTART_PYTORCH.md`
- **Quick** (10 min): `PYTORCH_WORKFLOW_SUMMARY.md`
- **Complete** (20 min): `README_PYTORCH_WORKFLOW.md`

### 🔧 Step 2: Setup Python (15 minutes)
Follow: `PYTORCH_REQUIREMENTS.txt`

```bash
# Create env
python -m venv pytorch_env
source pytorch_env/bin/activate

# Install PyTorch
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Install dependencies
pip install numpy xarray netcdf4 geopandas scikit-learn matplotlib
```

### 🖥️ Step 3: Server - Prepare Data (1-3 hours)
Run notebook on SERVER:
```
01.prepare_data_on_server.ipynb
```
Output: data_for_training/ folder (~300 MB)

### ⬇️ Step 4: Download Data (30 minutes)
```bash
scp -r user@server:path/data_for_training ./
```

### 💻 Step 5: Local - Train Model (30 min - 2 hours)
Run notebook on LOCAL:
```
02.train_CNN_PyTorch_local.ipynb
```
Output: model_cnn_pytorch_full.pt

### 🎯 Step 6: Local - Predict (10-30 minutes)
Run notebook on LOCAL:
```
03.predict_CNN_PyTorch_local.ipynb
```
Output: land_use_prediction.{nc, tif, png, json}

---

## 📁 What You Get

### After Step 3 (Server):
```
data_for_training/
├── average_ndvi.nc      (100-150 MB)
├── average_vv.nc        (50-100 MB)
├── average_vh.nc        (50-100 MB)
└── train_data/          (training points)
```

### After Step 5 (Local):
```
model_cnn_pytorch_full.pt      (100 MB - trained model)
training_history.png            (training curves)
```

### After Step 6 (Local):
```
land_use_prediction.nc          (classification map - NetCDF)
land_use_prediction.tif         (classification map - GeoTIFF)
prediction_map.png              (visualization)
prediction_metadata.json        (model info + accuracy)
```

---

## ⏱️ Time Estimates

| Phase | GPU | CPU |
|-------|-----|-----|
| Data prep (server) | 1-2h | 2-4h |
| Training | 30-60m | 90-150m |
| Prediction | 5-10m | 15-30m |
| **Total** | **~2-3h** | **~4-6h** |

---

## 📖 Documentation Files

All documentation organized:

| File | Purpose | Time | When to Read |
|------|---------|------|--------------|
| **THIS FILE** | Overview | 5 min | Start here! |
| `QUICKSTART_PYTORCH.md` | Quick guide | 5 min | First |
| `PYTORCH_REQUIREMENTS.txt` | Setup | 5 min | Before starting |
| `PYTORCH_INSTALLATION.md` | GPU setup | 10 min | If GPU issues |
| `LOCAL_TRAINING_WORKFLOW.md` | Full workflow | 15 min | For details |
| `README_PYTORCH_WORKFLOW.md` | Project index | 20 min | Reference |
| `PYTORCH_WORKFLOW_SUMMARY.md` | Summary | 10 min | Overview |

---

## 🎯 Choose Your Path

### 🏃 I'm in a hurry (15 min read)
1. Read: `QUICKSTART_PYTORCH.md`
2. Read: `PYTORCH_REQUIREMENTS.txt`
3. Start: Notebook 01 on server

### 🚶 I want to understand everything (1 hour read)
1. Read: `PYTORCH_WORKFLOW_SUMMARY.md`
2. Read: `LOCAL_TRAINING_WORKFLOW.md`
3. Read: `README_PYTORCH_WORKFLOW.md`
4. Start: Notebook 01 on server

### 🤔 I have specific questions
- **GPU issues**: Check `PYTORCH_INSTALLATION.md`
- **Setup issues**: Check `PYTORCH_REQUIREMENTS.txt`
- **Workflow questions**: Check `LOCAL_TRAINING_WORKFLOW.md`
- **Model architecture**: Check `README_PYTORCH_WORKFLOW.md`

---

## ✅ Checklist Before Starting

- [ ] Read at least `QUICKSTART_PYTORCH.md`
- [ ] Python 3.8+ installed
- [ ] Virtual environment ready
- [ ] ~500 MB free disk space
- [ ] Understand the 3-step workflow

---

## 🎓 Key Concepts

### What is this workflow?

**Phase 1: Server** (1-3 hours)
- Download satellite images from S3
- Process: remove clouds, calculate vegetation indices
- Save as compact NetCDF files (300 MB)

**Phase 2: Local Machine** (30 min - 2 hours)
- Load NetCDF files
- Train CNN model using PyTorch
- Use GPU for 10-100x speedup

**Phase 3: Local Machine** (10-30 minutes)
- Use trained model to classify entire region
- Create classification map (1080×1080 pixels)
- Export to multiple formats

### Why this approach?

| Aspect | Benefit |
|--------|---------|
| **Data** | Download once (~300 MB), use many times |
| **Training** | GPU on your machine (faster + no server wait) |
| **Development** | Code locally (easier debugging) |
| **Flexibility** | Tune hyperparameters quickly |
| **Privacy** | Data stays mostly local |

---

## 🚀 Start Command

### If on server now:
```bash
jupyter notebook 01.prepare_data_on_server.ipynb
```

### If on local machine now:
```bash
# After downloading data_for_training/ folder
jupyter notebook 02.train_CNN_PyTorch_local.ipynb
```

---

## 🎁 Bonus Features

- ✅ **GPU Auto-detection** - Automatically uses GPU if available
- ✅ **Early Stopping** - Prevents overfitting
- ✅ **Learning Rate Scheduling** - Automatic adjustment
- ✅ **Visualization** - Training curves + maps
- ✅ **Metadata** - Saves model info + accuracy
- ✅ **Multiple Formats** - NetCDF, GeoTIFF, PNG, JSON

---

## 💡 Pro Tips

1. **Save bandwidth**: Run server notebook once, download data, use for multiple experiments
2. **Experiment locally**: Change hyperparameters easily, retrain quickly
3. **Batch predictions**: Handle large regions by batch processing
4. **GPU matters**: 10-100x faster than CPU for training

---

## ❓ FAQ

**Q: Can I run everything on server?**  
A: Yes, but slower (CPU-only). Better to follow 3-step workflow.

**Q: Can I run everything local?**  
A: Notebooks 02-03 yes, notebook 01 needs server (data on S3).

**Q: How much data will I download?**  
A: ~300 MB for data_for_training folder.

**Q: Do I need GPU?**  
A: Not required, but 10-100x faster with GPU.

**Q: Can I use CPU only?**  
A: Yes, will take 2-3x longer.

**Q: What if I have NVIDIA GPU?**  
A: Install with CUDA 11.8 or 12.1 for best performance.

---

## 📞 Need Help?

1. **Installation issues**: → `PYTORCH_INSTALLATION.md`
2. **Dependencies**: → `PYTORCH_REQUIREMENTS.txt`
3. **Workflow questions**: → `LOCAL_TRAINING_WORKFLOW.md`
4. **GPU setup**: → `PYTORCH_INSTALLATION.md`
5. **Model details**: → `README_PYTORCH_WORKFLOW.md`

---

## 🎉 Ready to Start?

Pick ONE action now:

### Option A: Fast Track (5 min)
```
1. Read: QUICKSTART_PYTORCH.md
2. Go to: Step 3 (run notebook 01)
```

### Option B: Full Understanding (1 hour)
```
1. Read: PYTORCH_WORKFLOW_SUMMARY.md
2. Read: LOCAL_TRAINING_WORKFLOW.md
3. Read: README_PYTORCH_WORKFLOW.md
4. Setup: PYTORCH_REQUIREMENTS.txt
5. Go to: Step 3 (run notebook 01)
```

### Option C: Hands-On Learning (1-2 hours)
```
1. Setup environment
2. Run all 3 notebooks in order
3. Read docs as you go
4. Experiment with hyperparameters
```

---

## 🚀 Next Action

**You are here!** ← You've read this file

**Next**: Choose your path above and pick ONE file to read next

**Then**: Follow the steps

**Finally**: Enjoy your trained CNN model! 🎉

---

## 📊 Success Looks Like

After completing all steps:
- ✅ Trained CNN model on local machine
- ✅ Model accuracy: 75-85%
- ✅ Classification map: 1080×1080 pixels
- ✅ Results exported in 4 formats
- ✅ Everything completed locally (no server wait)

---

**Status**: Ready to Go 🟢  
**Version**: 1.0  
**Last Updated**: November 2025  

---

## 🎯 One Last Thing

The best part? **No more uploading code to server!** 🎉

- 💻 Code on your machine
- 📊 Use server data
- ⚡ Train with GPU
- 🎨 Visualize instantly
- 🚀 Iterate quickly

Enjoy! 🚀