Tools

I think:

• ML research quality is often bounded by engineering skill;
• this is still true in a post-LLM (vibe-coding) world.¹

Here I’ve put together some of the tools, tips, and tricks that have helped me throughout my PhD. You might find more on my GitHub.

ML

• Here is a practical I put together for the AIMS CDT covering:
  • Creating high-quality Python research repositories with minimal effort using cookiecutter.
  • Simplifying the configuration of ML experiments using Hydra.
  • Deep learning at scale, using Ray on Google Cloud TPUs.
• Hydra is the best thing since sliced bread:
  • Slides on why you should use it.
  • Examples of how you can use it.
• I use MLFlow to track experiments and trained models:
  • You can self-host it locally, on premise, or in the cloud.
  • It has better abstractions and a cleaner UI than e.g. WandB.

Misc.

• My personal dotfiles: joncarter1/dotfiles
• A cookiecutter template for ML research: joncarter1/cookiecutter_research
• The Mojo programming language is worth keeping an eye on.
• I’m super excited by Ray, a framework for distributed computing, and enjoy making the occasional contribution when I get time.²

Security

• Buy a pair of YubiKeys:
  • For storing secret keys (e.g. SSH) to avoid storing private keys on any physical machines.
  • For more secure 2FA.

  See this excellent guide on how to get started.

• Use a password manager (e.g. 1Password, LastPass).