MastersThesis/src/README.md

# Running Notebooks in Background

## Quick: Check Ray Tune Progress

**Current run:** PaddleOCR hyperparameter optimization via Ray Tune + Optuna.
- 64 trials searching for optimal detection/recognition thresholds
- 2 CPU workers running in parallel (Docker containers on ports 8001-8002)
- Notebook: `paddle_ocr_raytune_rest.ipynb` → `output_raytune.ipynb`
- Results saved to: `~/ray_results/trainable_paddle_ocr_2026-01-18_17-25-43/`

```bash
# Is it still running?
ps aux | grep papermill | grep -v grep

# View live log
tail -f papermill.log

# Count completed trials (64 total)
find ~/ray_results/trainable_paddle_ocr_2026-01-18_17-25-43/ -name "result.json" ! -empty | wc -l

# Check workers are healthy
curl -s localhost:8001/health | jq -r '.status'
curl -s localhost:8002/health | jq -r '.status'
```

---

## Option 1: Papermill (Recommended)

Runs notebooks directly without conversion.

```bash
pip install papermill
nohup papermill <notebook>.ipynb output.ipynb > papermill.log 2>&1 &
```

Monitor:
```bash
tail -f papermill.log
```

## Option 2: Convert to Python Script

```bash
jupyter nbconvert --to script <notebook>.ipynb
nohup python <notebook>.py > output.log 2>&1 &
```

**Note:** `%pip install` magic commands need manual removal before running as `.py`

## Important Notes

- Ray Tune notebooks require the OCR service running first (Docker)
- For Ray workers, imports must be inside trainable functions

## Example: Ray Tune PaddleOCR

```bash
# 1. Start OCR service
cd src/paddle_ocr && docker compose up -d ocr-cpu

# 2. Run notebook with papermill
cd src
nohup papermill paddle_ocr_raytune_rest.ipynb output_raytune.ipynb > papermill.log 2>&1 &

# 3. Monitor
tail -f papermill.log
```