FLAML
IronPython
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| FLAML | IronPython | |
|---|---|---|
| 9 | 7 | |
| 3,663 | 2,365 | |
| 3.0% | 0.9% | |
| 8.3 | 7.4 | |
| 12 days ago | 13 days ago | |
| Jupyter Notebook | Python | |
| MIT License | 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.
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
IronPython
- IronPython – Python 3 for .NET
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Does Python plan to add JIT or get rid of the GIL?
Sadly, both Jython and IronPython have stalled somewhat. IronPython is slowly working towards Python 3 support. Jython has a roadmap towards Python 3 support, but their version 3 repo is not in active development.
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How are Python and C related? I've read that Python is 'made from C'. Does that mean that Python is just an abstraction of lots of large C functions?
The program I linked is the Python interpreter you're probably using, but the abstract set of rules that make up Python are not in any particular way tied to C. You could also use a Java program or a C# program to interpret your Python code. They've even made Python in Python itself, although it uses a more restricted version of Python so it's easier to compile it.
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Will we ever get a new CLR language to replace C# Like Kotlin did for Java?
It’s up to date https://github.com/IronLanguages/ironpython3/releases/tag/v3.4.0-beta1
- what is the future of ML.NET?
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A Decade Later, .NET Developers Still Fear Being 'Silverlighted' by Microsoft -- Visual Studio Magazine
I mean, there is active work on the repo for it. They even just released a new alpha version yesterday for Python 3.4 support.
- IronPython 3.4.0-alpha1 is available
What are some alternatives?
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
MicroPython - MicroPython - a lean and efficient Python implementation for microcontrollers and constrained systems
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
PySec - OWASP Python Security Project
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.
Grumpy - Grumpy is a Python to Go source code transcompiler and runtime.
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
CLPython - An implementation of Python in Common Lisp
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
pyRevit - Rapid Application Development (RAD) Environment for Autodesk Revit®
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
ANSYS-WB-Batch-Script - Python scripts to use in ANSYS Workbench Batch mode. No Python package installation needed. Supports Parametric input/output. Uses only in-build ANSYS tools.