Productivity Tricks in Machine Learning

In machine learning research, one often needs to run many experiments in parallel e.g. hyperparameter search. In this post, we gather some useful tricks in one place for better productivity.

Environment setup

• Miniconda
• JupyterLab
• Modify ~/.bashrc
• Modify ~/.vimrc

Server

• passwd: change user password on the server
• SSH access to server via JupyterLab see also here
  • On server: $ jupyter lab --no-browser --port=8000
  • On local: $ ssh -N -L localhost:8001:loacalhost:8000 user@remote_host
  • -N: specifies SSH that no remote commands to be executed
  • -L: port forwarding
  • Kill port: fuser -k 8000/tcp
• xvfb: fake-screen on the server
  • xvfb-run -a -s “-screen 0 1400x900x24 +extension RANDR" --python file.py
  • Note: it requires nvidia driver installed with flag --no-opengl-files` and CUDA installed with flag –no-opengl-libs`
• Get user’s memory utilization:
  • ps -U user_name --no-headers -o rss | awk '{sum+=$1} END {print int(sum/1024/1024) "GB"}'

Some tools

• htop : CPU monitoring
• gpustat : GPU monitoring. On top of nvidia-sim with better visualization
• terminator : group all opened terminals in one place
• flake8 : check your Python code quality with PEP8

Misc

GitHub repo clean-up about large historical files: reference link

• Find large items (20 largest): git verify-pack -v .git/objects/pack/pack-{hash}.idx | sort -k 3 -n | tail -n 20
• View the large pack object: git rev-list --objects --all | grep {hash}{hash} path/file.ext
• Branch filtering: git filter-branch --index-filter 'git rm --cached --ignore-unmatch ./path/*.ext' --tag-name-filter cat -- --all
• Push: git push origin --force --all

GitHub repo image host: use imgur, so you don’t need to upload it to the repo.

Use keyboard shortcuts as much as possible: GNOME, Browser (Chrome), Jupyterlab …

Experiment parallelization:

• Make hyperparameter configuration as a dictionary (one could use some function to generate a list of dict for sweeping)
• Use ProcessPoolExecutor or existing libraries (e.g. Ray, lagom) to execute a run function in parallel, each for one configuration dictionary.
• Discouraged to use command line arguments (xargs), because this would increase the code complexity and also less convenient to change settings for hyperparameter, parallelizations etc.