FLAML
pyomo
FLAML | pyomo | |
---|---|---|
9 | 14 | |
3,679 | 1,844 | |
1.3% | 1.5% | |
7.9 | 10.0 | |
27 days ago | 5 days ago | |
Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 or later |
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
pyomo
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pyomo VS timefold-solver - a user suggested alternative
2 projects | 4 Jan 2024
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[P] Advice needed for what tool/algorithm is appropriate
Pyomo: We tried pyomo still using the same matrix representation as above (5-minutes timeslot interval), but still encountered the same difficulty of expressing program durations as constraint. I seem to not able to make a condition inside the constraint declaration such that if this matrix entry is 1, then do this operation.
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pyomo VS casadi - a user suggested alternative
2 projects | 5 Sep 2023
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Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
Alternative: pyomo
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Are there any mathematical optimizations modeling libraries made for Common Lisp?
I’m looking for something similar to Pyomo for Python. Something that connects on the backend to something like GLPK, CBC, IPOPT. Using Google, I’ve only been able to find a few linear programming libraries. If anyone could point me the right direction, it would be greatly appreciated!
- What software is used in the field these days?
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Operations research packages
Pyomo, it even has its own book. Additionally, CVXOPT focuses on convex optimization, PuLP on linear programming (it has lots of interfaces for other solvers).
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flopt: powerful optimization modeling tool
There are some optimization modeling tools, Pulp andScipy are known for linear programming (LP) modeling, CVXOPT and Pyomo for quadratic programming (QP).
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[Request] As a little side project, I want to map out the most efficient path to take when mowing my lawn. How might I go about doing this?
To rephrase this in math terms, you're looking for the least expensive possible path that covers every node in your yard. As for tools, if you don't mind programming in python, maybe try this: http://www.pyomo.org/.
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Integer vs. Linear Programming in Python
For modelling libraries in general-purpose languages, Gurobi's python bindings have the best reputation. But of course Gurobi is very expensive (I have heard about $50k for a fully unrestricted license, plus $10k yearly for support). On the open-source side, besides Google's OR-Tools, there is Pyomo [1] and PuLP [2] in Python (as the article mentions). In Julia, there is JuMP [3], whose development community is extremely enthusiastic.
Traditionally, however, mathematical models were encoded in domain-specific languages. The most prominent one is AMPL [4] which is proprietary. The glpk [5] people have developed a very neat open source clone of AMPL: the GNU MathProg language. For a more modern take on AMPL-type modelling DSLs, look at ZIMPL [6], which is open source as well.
[1] http://www.pyomo.org/
[2] https://coin-or.github.io/pulp/
[3] https://jump.dev/JuMP.jl/stable/
[4] https://ampl.com
[5] https://www.gnu.org/software/glpk/
[6] https://zimpl.zib.de/
What are some alternatives?
autogluon - Fast and Accurate ML in 3 Lines of Code
pulp - A python Linear Programming API
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
PySCIPOpt - Python interface for the SCIP Optimization Suite
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.
or-tools - Google's Operations Research tools:
ML-For-Beginners - 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Bonmin - Basic Open-source Nonlinear Mixed INteger programming
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
do-mpc - Model predictive control python toolbox
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
acados - Fast and embedded solvers for nonlinear optimal control