tune-sklearn
spock
tune-sklearn | spock | |
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4 | 12 | |
462 | 115 | |
- | 1.7% | |
0.0 | 7.0 | |
6 months ago | 6 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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tune-sklearn
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LightGBM vs. XGBoost: Which distributed version is faster?
Of course not! :)
The Ray ecosystem is actually chalk full of integrations, from XGBoost Ray (https://docs.ray.io/en/master/xgboost-ray.html), to PyTorch on Ray (https://docs.ray.io/en/master/using-ray-with-pytorch.html), and of course hyperparameter search with Ray Tune for a variety of libraries, including Sklearn (https://github.com/ray-project/tune-sklearn).
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[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?
If you mainly use scikit-learn, you should consider using tune-sklearn.
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[P] Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret
Just wanted to share a not widely known feature of PyCaret. By default, PyCaret's tune_model uses the tried and tested RandomizedSearchCV from scikit-learn. However, not everyone knows about the various advanced options tune_model() currently allows you to use such as cutting edge hyperparameter tuning techniques like Bayesian Optimization through libraries such as tune-sklearn, Hyperopt, and Optuna.
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[D] Here are 3 ways to Speed Up Scikit-Learn - Any suggestions?
You might want to try out tune-sklearn as it seems like it works for catboost as well. I am trying it use tune-sklearn to speed up my scikit-learn hyperparameter tuning.
spock
- Managing complex configurations any other way would be highly illogical
- [D] Alternatives to fb Hydra?
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Why you should use Data Classes in Python
(Note: I wrote a library called spock that was originally based on dataclasses and then shifted to attrs. In the end attrs was just the better and more fully fledged library for what I needed so I’ve always preferred attrs over dataclasses since then)
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Is Spock-Config the only tool that integrates object-oriented config files and command-line interfaces?
Spock-Config allows one to create OO configuration files. That's how I roll. I currently use PYdantic settings and it's great. But it does not offer command-line re-configuration of what you have in the OO config file.
- My first Python project: reference finder
- Python 3.11 will now have tomllib - Support for Parsing TOML in the Standard Library
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Spock - Managing complex configurations any other way would be highly illogical...
Check out more in the docs or on GitHub
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[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?
We wrote Spock which actually sits in the middle ground between Hydra and OmegaConf (I’m of the same opinion that Hydra does a little too much feature wise). You can do hierarchical composition within the markdown of any JSON, YAML, or TOML files by simply using the config argument. No code needed to merge. Docs are here if you’re interested.
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[D] Tools to avoid writing tons of scripts
Spock
What are some alternatives?
auto-sklearn - Automated Machine Learning with scikit-learn
gin-config - Gin provides a lightweight configuration framework for Python
guildai - Experiment tracking, ML developer tools
dvc - 🦉 ML Experiments and Data Management with Git
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
strictyaml - Type-safe YAML parser and validator.
reference-finder - Matches PDFs to sentences in text or docx file
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
traitlets - A lightweight Traits like module
tomli - A lil' TOML parser