ultimate-utils (uutils)

A comprehensive Python utility library for machine learning research, spanning PyTorch training infrastructure, statistical analysis, meta-learning, LLM evaluation, and experiment management.

Built and maintained by Brando Miranda (Stanford, MIT, UIUC).

pip install ultimate-utils

import uutils
uutils.hello()

What's Inside

Notifications vs. Interactive Agents

The messaging modules above (emailing, discord_uu, whatsapp_uu) are one-way programmatic notification senders — small libraries you import from scripts, schedulers, and watchers to push alerts like "job finished", "GPU idle", or "deploy complete". They are not chat agents; there is no conversation loop, no inbound message handling, and no LLM attached.

If you want an interactive AI assistant that lives inside WhatsApp / Discord / Telegram / iMessage (reads your messages, replies, executes tasks), that is a different category of tool. Projects like OpenClaw (hosted via myclaw.ai) exist for that use case — they bundle Baileys, grammY, Discord.py, AppleScript bridges, etc., and wire them into an agent loop.

Rule of thumb:

Today Brando's default notification channel is email (uutils.emailing); Discord and WhatsApp modules are available but optional. For the interactive-agent use case, see issue #41. The executable plan lives at experiments/01_self_hosted_openclaw/cc_prompt.md here, with the canonical home being ~/agents-config/experiments/01_self_hosted_openclaw/cc_prompt.md (mirror it back into agents-config when convenient).

Installation

Note: PyTorch must be installed separately (with CUDA if you need GPU support).

Development install (recommended)

conda create -n uutils python=3.11 -y
conda activate uutils
pip install -e ~/ultimate-utils

pip install from PyPI

pip install ultimate-utils

Verify installation

python -c "import uutils; uutils.hello()"
python -c "import uutils; uutils.torch_uu.gpu_test_torch_any_device()"

Quick Examples

Seed everything for reproducibility

from uutils import seed_everything
seed_everything(42)

Save and load with dill

from uutils import save_with_dill, load_with_dill
save_with_dill(my_object, '~/data/my_object.pkl')
obj = load_with_dill('~/data/my_object.pkl')

Plot with error bands

from uutils.plot import plot_with_error_bands
plot_with_error_bands(x, y, yerr, xlabel='Steps', ylabel='Loss', title='Training Loss')

Statistical testing with effect size

from uutils.stats_uu.effect_size import stat_test_with_effect_size_as_emphasis
stat_test_with_effect_size_as_emphasis(group1_data, group2_data)

W&B logging

from uutils.logging_uu.wandb_logging.common import setup_wandb, log_2_wandb
setup_wandb(args)
log_2_wandb(metrics_dict, step=step)

Publishing to PyPI

# Bump version in setup.py, then:
cd ~/ultimate-utils && bash scripts/publish_to_pypi.sh

Citation

If you use ultimate-utils in your research, please cite:

@software{miranda2024uutils,
  author       = {Brando Miranda},
  title        = {ultimate-utils: A Comprehensive Utility Library for Machine Learning Research},
  year         = {2024},
  publisher    = {PyPI},
  url          = {https://github.com/brando90/ultimate-utils},
  note         = {Available at \url{https://pypi.org/project/ultimate-utils/}}
}

You can also find the author's publications on Google Scholar.

Related Publications

This library has supported research in the following publications (among others):

• Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Not Yet More Human than Human
• Does MAML Only Work via Feature Re-use? A Data Set Centric Perspective

License

Apache-2.0