FLAML
river
FLAML | river | |
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9 | 17 | |
3,679 | 4,775 | |
1.3% | 1.3% | |
7.9 | 9.1 | |
27 days ago | 6 days ago | |
Jupyter Notebook | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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.
FLAML
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AutoGen: Enabling Next-Gen GPT-X Applications
I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
4. FLAML
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
- [D] If there’s one practical tip you wish should have been drilled deeply into you when you first started out learning about deep learning, what would it be?
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what is the future of ML.NET?
Improved AutoML - Again, with collaboration from Microsoft Research, we used FLAML to update our existing AutoML solutions. What does this mean for you? You're using the latest techniques but all you need is a problem to solve and some data to get started.
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/FLAML/blob/main/notebook/flaml_automl.ipynb
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[N] Fast AutoML with Microsoft's FLAML + Ray Tune
Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & seamlessly parallelizable algorithms across a cluster.
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[R] FLAML - Fast and Lightweight AutoML library
Looks nice but I wonder if this is practical for non-tiny problems. The papers are a bit hard to follow but it looks like training is restarted with every new architecture choice. As for the library itself, the only large neural net example is a finetune of an NLP model that only searches over ADAM's optimizer params - which could be useful but it's a stretch to call that AutoML.
- Flaml – Cost-effective hyperparameter optimization AutoML
river
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🔍Underrated Open Source Projects You Should Know About đź§
River is a Python library for online machine learning. Online machine learning can dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction, content personalization.
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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Unexpected Expected Thriller: A Tale of Coding Curiosity
Today, I'm going to take you on a thrilling coding adventure inspired by a LinkedIn code snippet, where I tangled with FastAPI, River, Watchdog, and Tenacity. Ready? Buckle up!
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
Complimentary: river and skorch
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What are your favorite tools or components in the Kafka ecosystem?
River - https://github.com/online-ml/river (Online machine learning, best used with Bytewax for Kafka integration)
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Show HN: Want something better than k-means? Try BanditPAM
Hey, great work. Do you think this algorithm would be amenable to be done online? I'm the author of River (https://riverml.xyz) where we're looking for good online clustering algorithms.
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Python's “Disappointing” Superpowers
If you don't know Rust, but know Python, you can install Python libraries written in Rust with pip. Like, pip install polars or pip install robyn. In this case you follow the two bottom links. But then you don't write your own libraries and stuff so.. I guess that's not what you want.
But, if you want to learn Rust, you probably wouldn't start out with pyo3. You first install Rust with https://rustup.rs/ and then check out the official book, and the book rust by example, that you can find here https://www.rust-lang.org/learn - and maybe write some code on the Rust playground https://play.rust-lang.org/ - then, you use pyo3 to build Python libraries in Rust, and then use maturin https://www.maturin.rs/ to build and publish them to Pypi.
But if you still prefer to begin with Rust by writing Python libraries (it's a valid strategy if you are very comfortable with working with multiple stacks), the Maturin link has a tutorial that setups a program that is half written in python, half written in Rust, https://www.maturin.rs/tutorial.html (well the pyo3 link I sent also has one too. You should refer to the documentation of both, because you will use the two together)
After learning Rust, the next step is looking for libraries that you could leverage to make Python programs ultra fast. Here https://github.com/rayon-rs/rayon is an obvious choice, see some examples from the Rust cookbook https://rust-lang-nursery.github.io/rust-cookbook/concurrenc... - when you create a parallel iterator, it will distribute the processing to many threads (by default, one per core). The rust cookbook, by the way, is a nice reference to see the most used crates (Rust libraries) in the Rust ecosystem.
Anyway there are some posts about pyo3 on the web, like this blog post https://boring-guy.sh/posts/river-rust/ (note: it uses an outdated version of pyo3, and doesn't seem to use maturin which is a newer tool). This post was written by the developers of https://github.com/online-ml/river - another Python library written in Rust
- [D] Is it possible to update random forest parameters with new data instead of retraining on all data?
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If ChatGPT that could browse to the internet, what would you ask it to do?
Oh they definitely can be incrementally updated, there is just added complexity. Online learning has been used with more classical machine learning methods in real-time analytics for a while now. River is a library that handles that.
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[D] Good online learning-to-rank models
We have both bandits and FTRL implemented in River (https://riverml.xyz) if that helps.
What are some alternatives?
autogluon - Fast and Accurate ML in 3 Lines of Code
alibi-detect - Algorithms for outlier, adversarial and drift detection
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
python-tidal - Python API for TIDAL music streaming service
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
wayfire - A modular and extensible wayland compositor
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
PySyft - Perform data science on data that remains in someone else's server
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
edl - Inofficial Qualcomm Firehose / Sahara / Streaming / Diag Tools :)
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
makinage - Stream Processing Made Easy