Victor Phan
208 lines
6.2 KiB
Markdown
208 lines
6.2 KiB
Markdown
# 📝 Summary: Memory Overflow Fix (Nov 11, 2025)
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## Problem Statement
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When running `01.prepare_data_on_server.ipynb`, Cell 5 (Sentinel-2 loading) failed with:
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```
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❌ Error loading data: Unable to allocate 403. TiB for an array with shape
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(396, 563539, 992108) and data type uint16
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```
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This requested **403 Terabytes** of RAM - physically impossible.
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## Root Cause
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The `load_s2l2a_with_offset()` function was loading an entire **massive satellite tile** (563K × 992K pixels) instead of clipping to the specified AOI (105.5-106.4°E, 9.2-10.0°N).
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Expected size: 10,000 × 10,000 pixels (100×100 km)
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Actual size: 563,539 × 992,108 pixels (~5,600×9,900 km)
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## Solution Implemented
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### 1. Monthly Chunking Strategy
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**Before:** Load 396 scenes → 403 TB allocation attempt → OOM crash
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**After:** Split into 13 monthly chunks:
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- Load month 1 (Sep 2022): 30 scenes → ~5 GB
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- Load month 2 (Oct 2022): 28 scenes → ~4.5 GB
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- ...
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- Load month 13 (Sep 2023): 31 scenes → ~5 GB
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- **Combine:** `xr.concat()` all monthly datasets
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**Benefit:** Each load fits in memory (~5-15 GB), Dask distributes work across workers.
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### 2. New Diagnostic Cell
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**Cell 4** (NEW) - Added before Sentinel-2 loading:
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```python
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## DEBUG: Inspect what datacube wants to load
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```
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- Lists available S2 products
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- Checks metadata for Jan 2023 (sample month)
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- Shows actual bounds/CRS returned by datacube
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- **Does NOT load raster data** (metadata query only)
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**Purpose:** Identify if spatial subsetting is working correctly
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### 3. Updated Loading Logic
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**Cell 5** (MODIFIED) - Sentinel-2 data loading:
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```python
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## SENTINEL-2 LOADING: Monthly chunks to prevent OOM
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```
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Key changes:
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- ✅ Loop through 13 month pairs
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- ✅ Load each month separately
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- ✅ Dask chunks: 512×512×1 (optimized for distributed workers)
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- ✅ Error handling: if month fails, continue with next
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- ✅ Progress tracking: [01/13], [02/13], etc.
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- ✅ Final concat: combine all successful months
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## Files Modified
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### 1. `01.prepare_data_on_server.ipynb`
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| Cell | Type | Change |
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|------|------|--------|
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| 4 | NEW | Diagnostic check (metadata query) |
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| 5 | UPDATED | Monthly chunking strategy |
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| 6-14 | Unchanged | Cloud mask, NDVI, aggregation, S1 load, save |
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### 2. New Documentation Created
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| File | Purpose |
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|------|---------|
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| `MEMORY_FIX_EXPLAINED.md` | Detailed explanation of problem & solution |
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| `TROUBLESHOOT_S2_LOADING.md` | Quick troubleshooting guide |
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| `CHANGES_SUMMARY.md` | This file |
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## Expected Behavior After Fix
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### Cell 5 Output
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```
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📡 Tải dữ liệu Sentinel-2 L2A từ S3...
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AOI: (105.5, 106.4), (9.2, 10.0)
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Time range: ('2022-09-01', '2023-10-01')
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✅ Native CRS: EPSG:32648
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[01/13] 2022-09-01 → 2022-10-01 ✓ 32 scenes
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[02/13] 2022-10-01 → 2022-11-01 ✓ 28 scenes
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[03/13] 2022-11-01 → 2022-12-01 ✓ 30 scenes
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...
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[13/13] 2023-09-01 → 2023-10-01 ✓ 31 scenes
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🔗 Combining 13 monthly chunks...
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✅ Success! Shape: {'time': 396, 'y': 10000, 'x': 10000}
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Memory: 16.2 GB
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<xarray.Dataset>
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Dimensions: (time: 396, y: 10000, x: 10000)
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Data variables:
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red (time, y, x) uint16 dask.array<...>
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nir (time, y, x) uint16 dask.array<...>
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scl (time, y, x) uint8 dask.array<...>
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```
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**Duration:** 5-15 minutes (network + distributed processing)
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### Data Variable
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```python
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data.shape # (396, 10000, 10000) ✓ CORRECT
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data.dims # {'time': 396, 'y': 10000, 'x': 10000}
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```
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## Testing Instructions
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1. **Open:** `01.prepare_data_on_server.ipynb`
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2. **Run Cell 2:** Dask initialization (wait for cluster ready)
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3. **Run Cell 3:** Set coordinates (automatic)
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4. **Run Cell 4:** Diagnostic check (look for "✅ Found X scenes")
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5. **Run Cell 5:** Load Sentinel-2 (watch progress bars)
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6. **If success:** Continue to Cell 6+ (cloud masking, NDVI, etc.)
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7. **If fail:** See `TROUBLESHOOT_S2_LOADING.md`
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## Fallback Options (If Still Issues)
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### Option A: Reduce Months Further
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Split into weekly chunks if monthly still OOM:
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```python
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date_ranges = [
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("2022-09-01", "2022-09-08"),
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("2022-09-08", "2022-09-15"),
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...
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]
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```
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### Option B: Explicit Spatial Clipping
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Add after line: `monthly_data = load_s2l2a_with_offset(...)`
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```python
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if monthly_data.sizes['y'] > 15000:
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monthly_data = monthly_data.sel(
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x=slice(longtitude_range[0], longtitude_range[1]),
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y=slice(latitude_range[0], latitude_range[1]),
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)
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```
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### Option C: Use Rasterio Directly
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If datacube continues to fail, bypass it:
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```python
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import rasterio
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from rasterio.io import MemoryFile
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# Load S3 COGs directly with windowed reads
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# More control, but requires S3 path knowledge
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```
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## Verification Checklist
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After Cell 5 succeeds, verify:
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- [ ] `data` variable exists
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- [ ] `data.dims` shows ~10,000 pixels in x & y
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- [ ] `data.dims['time']` is 396 (or close)
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- [ ] All three bands present: red, nir, scl
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- [ ] Memory usage is ~15-20 GB (not 400+ TB)
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- [ ] Dask workers are healthy (not crashed)
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- [ ] No persistent errors in logs
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## Performance Notes
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| Metric | Expected |
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|--------|----------|
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| Cell 4 duration | <1 minute |
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| Cell 5 per month | 20-60 seconds |
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| Cell 5 total | 5-15 minutes |
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| Final data size | 15-20 GB |
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| Worker memory/GB | 5-8 GB per month |
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| Dask overhead | ~2 GB |
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## Why This Works
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1. **Memory bound:** 396 scenes × 10K×10K pixels × 2 bytes = **20 GB** ✓
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- Can fit in server RAM (~100-500 GB total)
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- Dask distributes across workers (each takes 5-15 GB chunk)
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2. **Time efficient:** Monthly loading allows parallel tasks
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- While month 1 computing NDVI, month 2 still loading
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3. **Robust:** If one month fails (bad S3 object), others continue
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- Get 92% of data rather than 0%
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4. **Observable:** Progress bars + error messages
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- Know exactly which month succeeded/failed
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## Related Files
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- `new_import_ODC.py` - Contains `load_s2l2a_with_offset()` function
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- `00_START_HERE.md` - Setup instructions (no changes needed)
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- `LOCAL_TRAINING_WORKFLOW.md` - Workflow overview (no changes needed)
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## Status
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✅ **Ready to test**
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✅ **Documentation complete**
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✅ **No breaking changes** (only improvements to Cell 4-5)
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---
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**Date:** November 11, 2025
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**Affected Notebook:** `01.prepare_data_on_server.ipynb`
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**Risk Level:** Low (modular fix, doesn't affect other cells)
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**Testing Priority:** HIGH (run ASAP to verify effectiveness)
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