tune-sklearn
dvc
| tune-sklearn | dvc | |
|---|---|---|
| 4 | 109 | |
| 462 | 13,139 | |
| - | 0.8% | |
| 0.0 | 9.6 | |
| 6 months ago | 5 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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.
dvc
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My Favorite DevTools to Build AI/ML Applications!
Collaboration and version control are crucial in AI/ML development projects due to the iterative nature of model development and the need for reproducibility. GitHub is the leading platform for source code management, allowing teams to collaborate on code, track issues, and manage project milestones. DVC (Data Version Control) complements Git by handling large data files, data sets, and machine learning models that Git can't manage effectively, enabling version control for the data and model files used in AI projects.
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Why bad scientific code beats code following "best practices"
What you’re describing sounds like DVC (at a higher-ish—80%-solution level).
https://dvc.org/
See pachyderm too.
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First 15 Open Source Advent projects
10. DVC by Iterative | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
- ML Experiments Management with Git
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Git Version Controlled Datasets in S3
I was using DVC (https://dvc.org/) for some time to help solve this but it was getting hard to manage the storage connections and I would run into cache issues a lot, but this solves it using git-lfs itself.
- Ask HN: How do your ML teams version datasets and models?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
DVC (Data Version Control):
- Evaluate and Track Your LLM Experiments: Introducing TruLens for LLMs
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
What are some alternatives?
auto-sklearn - Automated Machine Learning with scikit-learn
MLflow - Open source platform for the machine learning lifecycle
guildai - Experiment tracking, ML developer tools
lakeFS - lakeFS - Data version control for your data lake | Git for data
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
spock - spock is a framework that helps manage complex parameter configurations during research and development of Python applications
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
git-submodules - Git Submodule alternative with equivalent features, but easier to use and maintain.