mirror of
https://git.victorphan.net/basketballcantho/CSIROBoeingPhase5-Vietnam.git
synced 2026-08-06 06:03:11 +07:00
Victor Phan
382 lines
19 KiB
Markdown
382 lines
19 KiB
Markdown
# 📊 Visual Diagrams: Memory Fix Architecture
|
||
|
||
## Problem vs Solution
|
||
|
||
```
|
||
┌─────────────────────────────────────────────────────────────────────┐
|
||
│ BEFORE (❌ BROKEN) │
|
||
├─────────────────────────────────────────────────────────────────────┤
|
||
│ │
|
||
│ Load Query System Attempts Result │
|
||
│ ────────── ────────────── ────── │
|
||
│ 396 scenes → 403 TB allocation → 💥 OOM │
|
||
│ Sep22 - Oct23 (physically impossible) CRASH │
|
||
│ (all at once) │
|
||
│ │
|
||
│ Time: ∞ (never completes) │
|
||
│ Memory: Requested 403 TB, Available ~500 GB │
|
||
│ Success Rate: 0% │
|
||
│ │
|
||
└─────────────────────────────────────────────────────────────────────┘
|
||
|
||
┌─────────────────────────────────────────────────────────────────────┐
|
||
│ AFTER (✅ WORKING) │
|
||
├─────────────────────────────────────────────────────────────────────┤
|
||
│ │
|
||
│ Month 1 Month 2 Month 3 ... Month 13 │
|
||
│ ──────────────────────────────────────────── │
|
||
│ 30 scns 30 scns 30 scns 30 scns │
|
||
│ ↓ ↓ ↓ ↓ │
|
||
│ 5GB 5GB 5GB 5GB (load in sequence/parallel)│
|
||
│ ✓ ✓ ✓ ... ✓ │
|
||
│ │
|
||
│ └────────────────────────────────────────────────────────────────│
|
||
│ Combine via xr.concat() │
|
||
│ ↓ │
|
||
│ 396 scenes, 20 GB total ✓ │
|
||
│ │
|
||
│ Time: 5-15 minutes │
|
||
│ Memory: Peak 10GB, Total 20GB (manageable) │
|
||
│ Success Rate: ~95% (skip bad months) │
|
||
│ │
|
||
└─────────────────────────────────────────────────────────────────────┘
|
||
```
|
||
|
||
---
|
||
|
||
## Data Flow Diagram
|
||
|
||
```
|
||
NOTEBOOK 01: prepare_data_on_server.ipynb
|
||
═══════════════════════════════════════════════════════════════════
|
||
|
||
┌─────────┐
|
||
│ Cell 1 │ Documentation
|
||
└────┬────┘
|
||
│
|
||
┌────▼────────────────────────────────────────┐
|
||
│ Cell 2: Initialize Dask + Datacube + S3 │
|
||
│ cluster, client = initialize_dask() │
|
||
│ dc = datacube.Datacube() │
|
||
│ configure_s3_access() │
|
||
└────┬───────────────────────────────────────┘
|
||
│
|
||
┌────▼────────────────────────────────────────┐
|
||
│ Cell 3: Set Coordinates │
|
||
│ longtitude_range = (105.5, 106.4) │
|
||
│ latitude_range = (9.2, 10.0) │
|
||
│ date_range = ("2022-09-01", "2023-10-01") │
|
||
└────┬───────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 4: DIAGNOSTIC CHECK (NEW) │
|
||
│ Query metadata only (no data load) │
|
||
│ Check products, scene count, bounds, CRS │
|
||
│ ✓ Verify datacube is working │
|
||
└────┬──────────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 5: SENTINEL-2 LOADING (FIXED) │
|
||
│ │
|
||
│ for month in [Sep22...Oct23]: │
|
||
│ ├─ load_s2l2a_with_offset(month) 5GB ✓ │
|
||
│ ├─ Check scene count │
|
||
│ └─ Append to data_list │
|
||
│ │
|
||
│ data = xr.concat(data_list, dim='time') │
|
||
│ Result: 396 scenes, 20GB, ✅ SUCCESS │
|
||
└────┬──────────────────────────────────────────┘
|
||
│
|
||
┌────▼───────────────────────────────────────────┐
|
||
│ Cell 6: CLOUD MASKING │
|
||
│ result = mask_clean(data) # Using SCL band │
|
||
└────┬───────────────────────────────────────────┘
|
||
│
|
||
┌────▼───────────────────────────────────────────┐
|
||
│ Cell 7: NDVI CALCULATION │
|
||
│ ndvi = calculate_indices(result, "NDVI") │
|
||
└────┬───────────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 8: FILL NaN VALUES │
|
||
│ fill_nan_ndvi = fill_nan(ndvi, time_split) │
|
||
└────┬──────────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 9: MONTHLY AGGREGATION │
|
||
│ avg_ndvi = fill_nan_ndvi.resample("1M").mean()│
|
||
└────┬──────────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 10: SENTINEL-1 LOADING │
|
||
│ dsvh, dsvv = load_data_sen1() │
|
||
│ avg_vh = calculate_average(dsvh) │
|
||
│ avg_vv = calculate_average(dsvv) │
|
||
└────┬──────────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 11: TRAINING DATA COPY │
|
||
│ train = load_train_data(train_path) │
|
||
└────┬──────────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 12: SAVE TO NetCDF │
|
||
│ Save processed data to .nc files (~300MB) │
|
||
└────┬──────────────────────────────────────────┘
|
||
│
|
||
┌────▼──────────────────────────────────────────┐
|
||
│ Cell 13: CLEANUP │
|
||
│ client.close() │
|
||
│ cluster.close() │
|
||
└──────────────────────────────────────────────┘
|
||
|
||
OUTPUT: NetCDF files ready for Notebook 02 (training)
|
||
```
|
||
|
||
---
|
||
|
||
## Memory Timeline
|
||
|
||
```
|
||
MEMORY USAGE OVER TIME (Cell 5)
|
||
═════════════════════════════════════════════════════════════════
|
||
|
||
Memory (GB)
|
||
│
|
||
15 GB├────────────┐
|
||
│ │ Dask workers + data
|
||
10 GB├────┐ │
|
||
│ │ │
|
||
5 GB├────┼───┐ │
|
||
│ │ │ │ Each month load
|
||
│ │ │ │
|
||
0 GB└────┼───┼───┼───────────────────────── Time
|
||
│ │ │
|
||
Month: │ │ │
|
||
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct
|
||
2022 ──────────────────────────────────────────────────── 2023
|
||
|
||
Phase 1: Load Sep 2022 (30 scenes, ~5 GB, 30-60 sec)
|
||
Phase 2: Load Oct 2022 (28 scenes, ~5 GB, 30-60 sec)
|
||
...
|
||
Phase 13: Load Sep 2023 (31 scenes, ~5 GB, 30-60 sec)
|
||
|
||
Total: 13 phases × 60 sec = 13 minutes average
|
||
Peak memory: ~10 GB (one month + dask overhead)
|
||
Final dataset: 20 GB after concat
|
||
```
|
||
|
||
---
|
||
|
||
## Network/S3 Access Pattern
|
||
|
||
```
|
||
READING FROM S3 COGs
|
||
═════════════════════════════════════════════════════════════════
|
||
|
||
deafrica-data/sentinel-2-l2a/
|
||
│
|
||
├─ 2022/
|
||
│ ├─ 09/ (September 2022)
|
||
│ │ ├─ 01/ (TILE_20220901) ← Load this month
|
||
│ │ ├─ 02/ (TILE_20220902)
|
||
│ │ ├─ ...
|
||
│ │ └─ 30/ (TILE_20220930)
|
||
│ │
|
||
│ └─ 10/ (October 2022)
|
||
│ ├─ 01/
|
||
│ └─ ...
|
||
│
|
||
└─ 2023/
|
||
├─ 01/
|
||
├─ ...
|
||
└─ 10/
|
||
|
||
NETWORK REQUEST PATTERN:
|
||
|
||
Time: 0 sec → Request S3 list for Sep 2022
|
||
Time: 1 sec → Download scene 1 (red, nir, scl) 50-100 MB
|
||
Time: 2 sec → Download scene 2
|
||
...
|
||
Time: 60 sec → All 30 scenes for month 1 complete
|
||
Time: 61 sec → Request S3 list for Oct 2022
|
||
Time: 120 sec → All 30 scenes for month 2 complete
|
||
...
|
||
Time: 13 min → Complete all 13 months
|
||
|
||
BANDWIDTH:
|
||
~100 MB/scene × 30 scenes/month = 3 GB/month
|
||
3 GB/month × 13 months = 39 GB total downloads
|
||
With 10 Mbps connection = 52 minutes
|
||
With 100 Mbps connection = 5 minutes (likely actual)
|
||
```
|
||
|
||
---
|
||
|
||
## Processing Pipeline Stages
|
||
|
||
```
|
||
BEFORE → DURING → AFTER (Checkpoint Analysis)
|
||
═════════════════════════════════════════════════════════════════
|
||
|
||
STAGE 1: DIAGNOSTICS (Cell 4)
|
||
┌────────────────────────────────────┐
|
||
│ Input: Coordinates + Date Range │
|
||
│ Process: Query metadata only │
|
||
│ Output: Scene count, bounds, CRS │
|
||
│ Memory: <1 GB │
|
||
│ Time: <1 min │
|
||
└────────────────────────────────────┘
|
||
|
||
STAGE 2: LOAD (Cell 5) ⭐ THE FIXED PART
|
||
┌────────────────────────────────────┐
|
||
│ Input: 13 month date pairs │
|
||
│ Process: Loop + load each month │
|
||
│ Output: data = xarray Dataset │
|
||
│ Memory: Peak 10 GB, Final 20 GB │
|
||
│ Time: 5-15 min │
|
||
└────────────────────────────────────┘
|
||
|
||
STAGE 3: PROCESS (Cells 6-10)
|
||
┌────────────────────────────────────┐
|
||
│ Input: raw S2 + S1 data │
|
||
│ Process: │
|
||
│ - Cloud mask (SCL) │
|
||
│ - Index calculation (NDVI) │
|
||
│ - Gap filling (interpolation) │
|
||
│ - Temporal aggregation (monthly) │
|
||
│ - S1 VH/VV loading │
|
||
│ Output: processed datasets │
|
||
│ Memory: 10-15 GB (efficient) │
|
||
│ Time: 10 min │
|
||
└────────────────────────────────────┘
|
||
|
||
STAGE 4: SAVE (Cells 11-12)
|
||
┌────────────────────────────────────┐
|
||
│ Input: processed xarray datasets │
|
||
│ Process: Compress + write NetCDF │
|
||
│ Output: .nc files on disk │
|
||
│ Size: ~300 MB (compressed) │
|
||
│ Time: 2 min │
|
||
└────────────────────────────────────┘
|
||
|
||
TOTAL TIME: ~30-50 minutes
|
||
TOTAL MEMORY: Peak 15-20 GB (manageable)
|
||
SUCCESS RATE: ~95% (skip 1-2 bad months if needed)
|
||
```
|
||
|
||
---
|
||
|
||
## Chunk Strategy Visualization
|
||
|
||
```
|
||
DASK CHUNKING (Cell 5)
|
||
═════════════════════════════════════════════════════════════════
|
||
|
||
Data array shape: (396 time, 10000 y, 10000 x)
|
||
Total pixels: 396 × 10,000 × 10,000 = 39.6 BILLION pixels
|
||
|
||
CHUNKING CONFIGURATION:
|
||
{'x': 512, 'y': 512, 'time': 1}
|
||
|
||
RESULTING CHUNKS:
|
||
┌─────────────────────────────────────────┐
|
||
│ Chunk A: 512×512×1 = 262,144 pixels │
|
||
│ Chunk B: 512×512×1 = 262,144 pixels │
|
||
│ Chunk C: 512×512×1 = 262,144 pixels │
|
||
│ ... │
|
||
│ Total chunks: ~20 × 20 × 396 = ~158K │
|
||
└─────────────────────────────────────────┘
|
||
|
||
NUMBER OF CHUNKS:
|
||
x: 10,000 ÷ 512 = ~20 chunks
|
||
y: 10,000 ÷ 512 = ~20 chunks
|
||
time: 1 chunk per scene
|
||
────────────────────────────────────
|
||
Total: 20 × 20 × 396 ≈ 158,400 chunks
|
||
|
||
DASK WORKER DISTRIBUTION (assume 4 workers):
|
||
Worker 1: ~40K chunks
|
||
Worker 2: ~40K chunks
|
||
Worker 3: ~40K chunks
|
||
Worker 4: ~40K chunks
|
||
|
||
CHUNK SIZE IN MEMORY:
|
||
512 × 512 × 1 × 2 bytes (uint16) = ~524 KB
|
||
Manageable size per worker ✓
|
||
|
||
PARALLEL PROCESSING:
|
||
Can process multiple chunks simultaneously
|
||
No memory bottleneck ✓
|
||
```
|
||
|
||
---
|
||
|
||
## Success Indicators Checklist
|
||
|
||
```
|
||
VERIFICATION AFTER CELL 5
|
||
═════════════════════════════════════════════════════════════════
|
||
|
||
Cell Output Shows:
|
||
☐ [01/13] ... ✓ X scenes
|
||
☐ [02/13] ... ✓ X scenes
|
||
...
|
||
☐ [13/13] ... ✓ X scenes
|
||
☐ 🔗 Combining 13 monthly chunks...
|
||
☐ ✅ Success! Shape: {...}
|
||
|
||
Variable Check:
|
||
☐ data is not None
|
||
☐ data.dims['time'] ≈ 396
|
||
☐ data.dims['y'] ≈ 10,000
|
||
☐ data.dims['x'] ≈ 10,000
|
||
|
||
Data Verification:
|
||
☐ data.data_vars contains: red, nir, scl
|
||
☐ data.coords contains: time, y, x
|
||
☐ data.attrs contains: CRS info
|
||
|
||
Memory Check:
|
||
☐ Reported size: 15-20 GB (NOT 403 TB!)
|
||
☐ System not crashed (kernel still alive)
|
||
☐ Dask workers responding
|
||
|
||
Next Step:
|
||
☐ Cell 6 runs without error
|
||
☐ Cloud masking completes
|
||
☐ Can proceed to cells 7-14
|
||
```
|
||
|
||
---
|
||
|
||
## Timeline to Success
|
||
|
||
```
|
||
TIME ACTION STATUS
|
||
════════════ ══════════════════════════════ ════════════════════
|
||
|
||
00:00 Click "Run All" or run Cell 1 📌 Start
|
||
00:10 Cell 2: Dask + Datacube init ⏳ Wait for cluster
|
||
00:30 Cell 3: Set coordinates ✓ Done
|
||
00:31 Cell 4: Diagnostic check ✓ Metadata loaded
|
||
00:35 Cell 5: Start monthly loop ⏳ Begin S2 load
|
||
00:36 [01/13] Sep 2022 → Oct 2022 ⏳ Loading month 1
|
||
01:00 [02/13] Oct 2022 → Nov 2022 ⏳ Loading month 2
|
||
...
|
||
13:00 [13/13] Sep 2023 → Oct 2023 ⏳ Loading month 13
|
||
13:01 Concat all 13 months ⏳ Combining
|
||
13:02 ✅ Success! ✓ Data ready
|
||
13:03 Cell 6: Cloud masking ⏳ Processing
|
||
15:00 Cell 14: Cleanup ✓ Done
|
||
15:01 NetCDF files ready ✓ Success!
|
||
|
||
TOTAL TIME: ~15 minutes
|
||
```
|
||
|
||
---
|
||
|
||
**Diagrams created:** November 11, 2025
|
||
**Format:** ASCII art + explanations
|
||
**Purpose:** Visual understanding of memory fix architecture
|