Victor Phan
362 lines
9.0 KiB
Markdown
362 lines
9.0 KiB
Markdown
# 🎉 WORKFLOW SIMPLIFICATION COMPLETE
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## What Was Done
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Bạn yêu cầu: **"Tại sao phải tính toán chỉ số trên server? Tôi chỉ muốn nó load dữ liệu rồi tính toán trên local"**
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**✅ ĐÃ HOÀN THÀNH!**
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---
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## Changes Summary
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### 📝 Notebook 01 (Server)
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**Before:** 14 cells (Load → Process → Save)
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**After:** 9 cells (Load → Save only)
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| Removed | Reason |
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|---------|--------|
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| ❌ Cloud masking | Move to local |
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| ❌ NDVI calculation | Move to local |
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| ❌ Fill NaN values | Move to local |
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| ❌ Monthly aggregation | Move to local |
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| ⚠️ S1 modified | Raw only, no aggregation |
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**New flow:**
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```
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Dask → S3 → Load S2 (monthly) → Load S1 → Save 2 NetCDF
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```
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**Output:**
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```
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data_for_training/
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├─ sentinel2_raw.nc (S2 thô)
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├─ sentinel1_raw.nc (S1 thô)
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└─ train_data/ (training points)
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```
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### 📝 Notebook 02 (NEW - Local Processing)
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**Created:** Completely new notebook with 11 cells
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**Flow:**
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```
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Load NetCDF → Cloud mask → NDVI → Fill NaN → Aggregation → Train CNN → Evaluate → Save Model
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```
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**Cells:**
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1. Import libraries
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2. Load raw NetCDF
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3. Cloud masking
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4. NDVI calculation
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5. Fill NaN values
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6. Monthly aggregation
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7. Load training data
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8. Split train/val/test
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9. Train PyTorch CNN
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10. Evaluate model
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11. Save model
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**Output:**
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```
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model_cnn_pytorch_local.pth (Model weights)
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model_cnn_pytorch_local_checkpoint.pth (Full checkpoint)
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```
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---
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## New 3-Step Workflow
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### ① Server (10-20 min)
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```
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Notebook 01: Load S2 + S1 → Save RAW NetCDF
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Output: 80-100 GB data
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Task: I/O bound (download from S3)
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```
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### ② Local (30-60 min CPU / 10-15 min GPU)
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```
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Notebook 02: Process RAW → Train CNN
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Output: Trained model (100 MB)
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Task: Compute bound (cloud mask + NDVI + training)
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```
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### ③ Local (5-10 min)
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```
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Notebook 03: Apply model → Generate maps
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Output: Classification maps (SHP/TIF)
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Task: Prediction bound (inference on all pixels)
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```
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---
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## Advantages
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✅ **No more 403 TB OOM errors**
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- Server: Only loads (I/O), doesn't compute
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- Local: Only computes, receives pre-loaded data
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✅ **Much faster on local**
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- Python on local: Can use GPU
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- Server: No GPU overhead, focused on download
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✅ **Easy to debug & iterate**
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- All processing visible on local machine
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- Can reprocess without touching server
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- Can experiment with parameters easily
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✅ **Clear separation of concerns**
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- Server: Infrastructure task (data prep)
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- Local: Science task (processing & ML)
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---
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## File Structure
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```
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/home/x79/CSIROBoeingPhase5-Vietnam/
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Server Notebooks:
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├─ 01.prepare_data_on_server.ipynb ← SIMPLIFIED
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Local Notebooks:
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├─ 02.process_and_train_local.ipynb ← NEW
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├─ 03.predict_CNN_PyTorch_local.ipynb ← (unchanged)
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Configuration:
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├─ new_import_ODC.py ← (unchanged)
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Documentation:
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├─ SIMPLIFICATION_SUMMARY.md ← Summary of changes
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├─ SIMPLIFIED_WORKFLOW.md ← Detailed guide
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├─ QUICK_REFERENCE.md ← Quick start
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└─ Other existing docs...
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```
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---
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## Data Flow
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```
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┌─ SERVER ─────────────────────────┐
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│ │
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│ AWS S3 │
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│ ↓ (monthly chunks) │
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│ [Datacube] → Load S2 + S1 │
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│ ↓ │
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│ [Save NetCDF] │
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│ ↓ RAW DATA │
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│ (80-100 GB) │
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│ │
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└──────────────────────────────────┘
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⬇️ Transfer
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┌─ LOCAL ──────────────────────────┐
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│ │
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│ [Load NetCDF] │
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│ ↓ │
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│ [Processing] ← NEW! │
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│ • Cloud mask │
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│ • NDVI calc │
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│ • Fill NaN │
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│ • Aggregation │
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│ ↓ │
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│ [Training] ← NEW! │
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│ • Extract features │
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│ • Train CNN │
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│ • Evaluate │
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│ ↓ │
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│ [Model] (100 MB) │
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│ │
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│ [Prediction] ← (notebook 03) │
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│ • Apply to all pixels │
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│ ↓ │
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│ [Output] (SHP/TIF) │
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│ │
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└──────────────────────────────────┘
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```
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---
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## Performance Improvement
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| Aspect | Before | After |
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|--------|--------|-------|
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| Server load time | 10-20 min | 10-20 min (unchanged) |
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| Server processing time | 30-60 min | 0 (moved to local) |
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| Local processing time | 0 | 30-60 min (CPU) / 10-15 min (GPU) |
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| Memory peak | 403 TB ❌ | 20 GB ✅ |
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| Can use GPU | ❌ | ✅ (GPU on local) |
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| Debug capability | ❌ Hard | ✅ Easy |
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| Iteration speed | ❌ Slow | ✅ Fast |
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---
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## Usage Instructions
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### Prerequisites
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```
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Server:
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- Dask, Datacube, S3 access already configured
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- 500 GB free space
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Local:
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- Python 3.8+
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- PyTorch
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- xarray, numpy, pandas, geopandas
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- 100 GB free space (for raw data)
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- GPU (optional but faster)
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```
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### Run Step by Step
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**1️⃣ On Server (takes ~15-20 min):**
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```python
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jupyter notebook 01.prepare_data_on_server.ipynb
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# Run all cells in order
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# Wait for: ✅ Success! Shape: {'time': 396, ...}
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# Output: data_for_training/ folder created
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```
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**2️⃣ Download to Local (takes ~30-60 min):**
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```bash
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# From local machine:
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scp -r user@server:~/data_for_training ./
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# Or use rsync/FTP (check bandwidth with server admin)
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```
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**3️⃣ On Local (takes ~30-60 min on CPU):**
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```python
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jupyter notebook 02.process_and_train_local.ipynb
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# Run all cells in order
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# Watch training progress: epoch 1/50, epoch 2/50, ...
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# Wait for: ✅ Test Accuracy: 0.XX
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# Output: model_cnn_pytorch_local.pth created
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```
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**4️⃣ On Local (takes ~5-10 min):**
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```python
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jupyter notebook 03.predict_CNN_PyTorch_local.ipynb
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# Run all cells in order
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# Output: Classification maps (SHP/TIF) created
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```
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---
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## What's the Same?
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✓ Cloud masking logic (SCL band) - unchanged
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✓ NDVI calculation formula - unchanged
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✓ Fill NaN strategy (seasonal) - unchanged
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✓ Aggregation method (monthly) - unchanged
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✓ CNN architecture - unchanged
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✓ Training hyperparameters - unchanged
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✓ Prediction logic - unchanged
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**Only change:** Where computation happens (server vs local)
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---
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## Expected Results
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### After Notebook 01 (Server)
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```
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✅ data_for_training/
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├─ sentinel2_raw.nc (50-60 GB)
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├─ sentinel1_raw.nc (20-30 GB)
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└─ train_data/*.shp (1130 points)
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```
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### After Notebook 02 (Local)
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```
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✅ model_cnn_pytorch_local.pth (100 MB)
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✅ Training complete
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✅ Test Accuracy: 0.70-0.85
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✅ All 8 classes learned
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```
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### After Notebook 03 (Local)
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```
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✅ classification_map.shp
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✅ classification_map.tif
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✅ 8 land use classes mapped
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```
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---
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## Support Documents
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**Quick Start:**
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- 📄 `QUICK_REFERENCE.md` - 3-step guide (5 min read)
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**Detailed Info:**
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- 📄 `SIMPLIFIED_WORKFLOW.md` - Complete explanation (15 min read)
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- 📄 `SIMPLIFICATION_SUMMARY.md` - This document
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---
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## Troubleshooting
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### Server issues:
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- **S3 access denied?** → Check credentials in cell 2
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- **Load too slow?** → Check bandwidth with `vmstat`
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- **Storage full?** → Clear old files first
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### Local issues:
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- **File not found?** → Verify data_for_training/ exists
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- **Out of memory?** → Close other apps, reduce batch_size
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- **GPU not working?** → Fallback to CPU (slower but works)
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- **Model accuracy low?** → Increase epochs or check cloud masking
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### Download issues:
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- **SCP too slow?** → Use rsync with compression
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- **Connection drops?** → Use `screen` or `tmux` on server
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---
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## Timeline
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```
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Day 1:
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- 00:00 Run Notebook 01 on server (20 min)
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- 00:20 Monitor download (30-60 min)
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Day 2:
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- 08:00 Run Notebook 02 on local (60 min)
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- 09:00 Run Notebook 03 on local (10 min)
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- 09:10 Results ready! ✅
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Total: ~2 hours active time + 1 night transfer
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```
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---
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## Bottom Line
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| What | Before | Now |
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| **Server task** | Load + Process | Load only |
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| **Local task** | Just train | Load + Process + Train |
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| **Memory issue** | 403 TB crash | Fixed ✅ |
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| **Speed** | Slow | Fast |
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| **Flexibility** | Hard to iterate | Easy to iterate |
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| **GPU support** | ❌ | ✅ |
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**Result:** Clean, simple, fast workflow! 🎉
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---
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## Ready to Use?
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✅ **Notebook 01** - Simplified ✓
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✅ **Notebook 02** - Created ✓
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✅ **Notebook 03** - Ready ✓
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✅ **Documentation** - Complete ✓
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**Status:** Ready for production! 🚀
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---
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**Simplification Date:** November 12, 2025
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**Status:** ✅ COMPLETE
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**Design Pattern:** Server loads, Local processes
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