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Top 23 Python Automl Projects
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Ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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nni
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
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igel
a delightful machine learning tool that allows you to train, test, and use models without writing code
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mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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lazypredict
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
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sparseml
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
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AutoViz
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
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Project mention: Ray: Unified framework for scaling AI and Python applications | news.ycombinator.com | 2024-05-03
Project mention: Featuretools – A Python Library for Automated Feature Engineering | news.ycombinator.com | 2023-09-20
I can't find the TimeGPT-1 model.
LICENSE Apache-2
https://github.com/Nixtla/statsforecast/blob/main/LICENSE
Mentions ARIMA, ETS, CES, and Theta modeling
Project mention: Show HN: Web App with GUI for AutoML on Tabular Data | news.ycombinator.com | 2023-08-24Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
Project mention: [Project] AMLTK: A framework for building your own AutoML (AutoSklearn authors) | /r/MachineLearning | 2023-12-09We took some of the lessons learned while building AutoSklearn and AutoPytorch, the good, the bad and the ugly and made a library that to enable the next generation of open-source AutoML tools, to allow them to be research-able but also efficient and scalable. We have some future plans and on-going work with this and we'd like to gather any feedback the community might have!
Project mention: Potential of the Julia programming language for high energy physics computing | news.ycombinator.com | 2023-12-04> Yes, julia can be called from other languages rather easily
This seems false to me. StaticCompiler.jl [1] puts in their limitations that "GC-tracked allocations and global variables do not work with compile_executable or compile_shlib. This has some interesting consequences, including that all functions within the function you want to compile must either be inlined or return only native types (otherwise Julia would have to allocate a place to put the results, which will fail)." PackageCompiler.jl [2] has the same limitations if I'm not mistaken. So then you have to fall back to distributing the Julia "binary" with a full Julia runtime, which is pretty heavy. There are some packages which do this. For example, PySR [3] does this.
There is some word going around though that there is an even better static compiler in the making, but as long as that one is not publicly available I'd say that Julia cannot easily be called from other languages.
[1]: https://github.com/tshort/StaticCompiler.jl
[2]: https://github.com/JuliaLang/PackageCompiler.jl
[3]: https://github.com/MilesCranmer/PySR
Recommendation Framework https://github.com/alibaba/EasyRec
Python Automl related posts
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Show HN: ai() to run any ML and answer with Python type
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Fine-tuning a Mistral Language Model with Anyscale
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MindsDB Docker Extension: Build ML powered apps at a much faster pace
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Using Large Language Models for Hyperparameter Optimization, Zhang et al. 2023 [GPT-4 is quite good at finding the optimal hyperparameters for machine learning tasks]
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[Project] AMLTK: A framework for building your own AutoML (AutoSklearn authors)
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functime: NEW Data - star count:616.0
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functime: NEW Data - star count:601.0
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Index
What are some of the best open-source Automl projects in Python? This list will help you:
Project | Stars | |
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1 | Ray | 31,322 |
2 | best-of-ml-python | 15,633 |
3 | nni | 13,765 |
4 | autokeras | 9,075 |
5 | auto-sklearn | 7,419 |
6 | autogluon | 7,152 |
7 | featuretools | 7,035 |
8 | zenml | 3,682 |
9 | statsforecast | 3,575 |
10 | Merlion | 3,271 |
11 | igel | 3,080 |
12 | mljar-supervised | 2,941 |
13 | keras-tuner | 2,827 |
14 | lazypredict | 2,697 |
15 | Auto-PyTorch | 2,287 |
16 | sparseml | 1,979 |
17 | PySR | 1,934 |
18 | AutoViz | 1,631 |
19 | AutoDL-Projects | 1,546 |
20 | EasyRec | 1,496 |
21 | MLBox | 1,477 |
22 | tods | 1,299 |
23 | SMAC3 | 1,009 |
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