MastersThesis/src

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/

# 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'

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Option 1: Papermill (Recommended)

Runs notebooks directly without conversion.

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

Monitor:

tail -f papermill.log

Option 2: Convert to Python Script

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

# 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