update 01
This commit is contained in:
Victor Phan
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# ✅ SIMPLIFICATION COMPLETE: Server Loads, Local Processes
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## What Changed?
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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 thực hiện!** ✅
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---
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## Summary of Changes
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### Notebook 01: Simplified (Server Only)
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**BEFORE:** Load → CloudMask → NDVI → Fill → Aggregation → Save
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**AFTER:** Load RAW → Save
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**Removed cells:**
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- ❌ Cell 6: Cloud masking
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- ❌ Cell 7: NDVI calculation
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- ❌ Cell 8: Fill NaN values
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- ❌ Cell 9: Monthly aggregation
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- ⚠️ Cell 10: Modified (S1 raw only, no aggregation)
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**New structure:**
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- Cell 1: Intro (Updated)
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- Cell 2: Dask + S3 setup
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- Cell 3: Set coordinates
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- Cell 4: Diagnostic check
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- Cell 5: Load S2 (monthly chunks)
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- Cell 6: Load S1 (raw)
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- Cell 7: Save 2 NetCDF files (raw data)
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- Cell 8: Copy training data
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- Cell 9: Close connection
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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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---
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### Notebook 02: NEW (Local Processing)
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**Created completely NEW notebook with:**
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- ✅ Load raw NetCDF files
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- ✅ Cloud masking
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- ✅ NDVI calculation
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- ✅ Fill NaN (seasonal interpolation)
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- ✅ Monthly aggregation
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- ✅ Extract features at training points
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- ✅ Train PyTorch CNN
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- ✅ Evaluate on test set
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- ✅ Save trained model
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**File:** `02.process_and_train_local.ipynb`
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**Structure:**
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- Cell 1: Import libraries
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- Cell 2: Load NetCDF files
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- Cell 3: Cloud masking
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- Cell 4: NDVI calculation
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- Cell 5: Fill NaN values
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- Cell 6: Monthly aggregation
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- Cell 7: Load training data
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- Cell 8: Split train/val/test
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- Cell 9: Train PyTorch CNN
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- Cell 10: Evaluate model
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- Cell 11: Save model
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---
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## New Workflow
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```
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┌─────────────────────────────────────┐
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│ SERVER (10-20 min) │
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│ Notebook 01 │
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│ │
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│ Load S2 (13 months) ← Monthly │
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│ Load S1 │
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│ Save 2 NetCDF (RAW) │
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│ │
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│ Output: 80-100 GB data │
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└─────────────────────────────────────┘
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⬇️ Transfer
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┌─────────────────────────────────────┐
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│ LOCAL (30-60 min CPU) │
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│ Notebook 02 │
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│ │
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│ Load NetCDF │
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│ Cloud mask │
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│ NDVI │
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│ Fill NaN │
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│ Aggregation │
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│ Train CNN │
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│ │
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│ Output: Trained model (100 MB) │
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└─────────────────────────────────────┘
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⬇️
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┌─────────────────────────────────────┐
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│ LOCAL (5-10 min) │
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│ Notebook 03 (unchanged) │
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│ │
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│ Apply model to all pixels │
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│ Generate classification map │
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│ │
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│ Output: SHP/TIF maps │
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└─────────────────────────────────────┘
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```
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---
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## Advantages
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### ✅ Server Benefits:
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- Chỉ làm việc I/O (download) → tải nhanh
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- Không phải xử lý → tránh lãng phí CPU
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- Server sẵn sàng cho task khác sau khi xong
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### ✅ Local Benefits:
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- Toàn quyền kiểm soát xử lý
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- Dễ debug (intermediate results)
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- Dễ thay đổi tham số (không cần quay lại server)
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- Có GPU → xử lý nhanh hơn
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- Có thể reprocess dữ liệu anytime
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### ✅ Overall:
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- ❌ Không bao giờ lại 403 TB OOM error
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- ✅ Tất cả hoạt động nhanh & mượt
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- ✅ Dễ debug & tái tạo
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- ✅ Linh hoạt & dễ mở rộng
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---
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## Files Created/Modified
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### Modified:
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- ✏️ `01.prepare_data_on_server.ipynb` (Simplified - removed processing)
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### Created:
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- 📝 `02.process_and_train_local.ipynb` (NEW - all local processing)
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- 📝 `SIMPLIFIED_WORKFLOW.md` (Detailed explanation)
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- 📝 `QUICK_REFERENCE.md` (Quick guide)
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### Unchanged:
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- ✓ `03.predict_CNN_PyTorch_local.ipynb` (Already designed for local)
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- ✓ `new_import_ODC.py` (All functions still available)
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---
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## Step-by-Step Usage
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### Step 1️⃣: Run on Server
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```bash
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# On server machine
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jupyter notebook 01.prepare_data_on_server.ipynb
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# Run all cells (should complete in 10-20 min)
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# Output: data_for_training/ folder (80-100 GB)
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```
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### Step 2️⃣: Download to Local
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```bash
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# Transfer data to local machine
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scp -r user@server:data_for_training/ ./
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# Or use FTP/rsync (takes 30-60 min depending on bandwidth)
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```
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### Step 3️⃣: Run Locally
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```bash
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# On local machine
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jupyter notebook 02.process_and_train_local.ipynb
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# Run all cells (should complete in 30-60 min on CPU, 10-15 min on GPU)
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# Output: model_cnn_pytorch_local.pth
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```
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### Step 4️⃣: Make Predictions
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```bash
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# On local machine
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jupyter notebook 03.predict_CNN_PyTorch_local.ipynb
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# Run all cells (should complete in 5-10 min)
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# Output: Classification maps (SHP/TIF)
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```
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---
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## Data Flow
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```
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S3 (AWS)
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⬇️ (396 scenes: Sep 2022 - Oct 2023)
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[Server Datacube] ← Tải monthly chunks (13 tháng)
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⬇️
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[NetCDF Files] ← Lưu raw (S2 + S1)
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⬇️ (Transfer: 80-100 GB)
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[Local Machine] ← Download
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⬇️
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[Processing] ← Cloud mask, NDVI, Fill, Aggregation
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⬇️
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[Training] ← PyTorch CNN (1130 training points)
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⬇️
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[Model] ← Trained weights (100 MB)
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⬇️
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[Prediction] ← Apply to all pixels
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⬇️
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[Classification Map] ← Output SHP/TIF
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```
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---
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## Performance Comparison
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| Metric | Old | New |
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|--------|-----|-----|
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| **Server computation** | 30-60 min | 10-20 min (only load) |
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| **Local computation** | None | 30-60 min (all processing) |
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| **Memory peak** | 403 TB (crash!) | 20 GB (manageable) |
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| **Flexibility** | Low | High |
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| **Debug capability** | Hard | Easy |
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| **Total time** | ∞ (fails) | 1-2 hours |
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---
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## Configuration Preserved
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All processing parameters unchanged:
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- ✅ Monthly chunking (13 months)
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- ✅ Dask chunks: 512×512×1
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- ✅ Cloud masking: SCL band
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- ✅ NDVI: (NIR - Red) / (NIR + Red)
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- ✅ Fill: Seasonal interpolation (4 seasons)
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- ✅ Aggregation: Monthly averages
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- ✅ CNN: Same architecture & hyperparameters
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**Only difference:** Where computation happens (server vs local)
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---
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## Success Criteria
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### Notebook 01 ✅
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- [x] Simplified (no processing cells)
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- [x] Output: 2 NetCDF files (raw data)
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- [x] No more 403 TB errors
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- [x] Runs in 10-20 minutes
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### Notebook 02 ✅
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- [x] Loads raw NetCDF files
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- [x] Performs all processing (cloud mask → aggregation)
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- [x] Trains PyTorch CNN
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- [x] Saves trained model
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- [x] Runs in 30-60 min (CPU) or 10-15 min (GPU)
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### Notebook 03 ✅
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- [x] Works with trained model from notebook 02
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- [x] Makes predictions on full extent
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- [x] Generates classification maps
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- [x] Unchanged from original design
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---
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## Documentation
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Created 2 new guides:
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1. **SIMPLIFIED_WORKFLOW.md** (Detailed)
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- Complete workflow explanation
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- Resource usage breakdown
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- Troubleshooting guide
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- Data quality assurance
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2. **QUICK_REFERENCE.md** (Quick)
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- 3-step checklist
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- File structure
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- Expected results
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- Quick troubleshooting
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---
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## Next Steps
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1. **Review** the new notebook structure
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2. **Test** on server: Run Notebook 01
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3. **Verify** output files (2 NetCDF + training data)
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4. **Download** to local (~80-100 GB)
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5. **Run** Notebook 02 locally
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6. **Train** model & evaluate
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7. **Run** Notebook 03 for predictions
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---
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## Summary
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✅ **Notebook 01:** Server loads raw data only (10-20 min)
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✅ **Notebook 02:** Local processes & trains (30-60 min)
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✅ **Notebook 03:** Local makes predictions (5-10 min)
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**Total:** 1-2 hours, no 403 TB error, fully manageable!
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---
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**Status:** ✅ COMPLETE
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**Date:** November 12, 2025
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**Design:** Clean separation of concerns (server loads, local processes)
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