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Top 22 Python Xgboost Projects
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mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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.
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m2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Check out: https://github.com/BayesWitnesses/m2cgen
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mars
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
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Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.
Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.
So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.
Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.
Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.
[0] - https://github.com/kserve/kserve
[1] - https://github.com/triton-inference-server/server
[2] - https://github.com/triton-inference-server/client
[3] - https://github.com/triton-inference-server/model_analyzer
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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.
AutoViz: A Python library that automatically visualizes any dataset in just one line of code.
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awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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MLForecast
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MLServer
An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.
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Auto_ViML
Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
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Hyperactive
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
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Project mention: Show HN: A gallery of dev tool marketing examples | news.ycombinator.com | 2023-10-07
Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/.
Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase, Posthog, Auth0).
So past year and a half, I’ve been screenshoting examples of how companies that are good at dev marketing do things like pricing, landing page design, ads, videos, blog conversion ideas. And for each example I added a note as to why I thought it was good.
Now, it is ~140 examples organized by tags so you can browse all or get stuff for a particular topic.
Hope it is helpful to some dev tool founders and marketers in here.
wdyt?
Also, I am always looking for new companies/marketing ideas to add to this, so if you’d like to share good examples I’d really appreciate it.
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alpha-zero-boosted
A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)
Project mention: DeepMind has open-sourced the heart of AlphaGo and AlphaZero | news.ycombinator.com | 2023-02-15> I came up with a nifty implementation in Python that outperforms the naive impl by 30x, allowing a pure python MCTS/NN interop implementation. See https://www.moderndescartes.com/essays/deep_dive_mcts/
Great post!
Chasing pointers in the MCTS tree is definitely a slow approach. Although typically there are < 900 "considerations" per move for alphazero. I've found getting value/policy predictions from a neural network (or GBDT[1]) for the node expansions during those considerations is at least an order of magnitude slower than the MCTS tree-hopping logic.
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Python-Schema-Matching
A python tool using XGboost and sentence-transformers to perform schema matching task on tables.
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I’ll push back on this. I think most of us want some external validation that what we’re creating is useful and valuable.
I spent 2 solid years working on https://improve.ai and it’s gotten very little uptake. It’s taken a long time to emotionally get over putting that much blood, sweat, and money into something that almost no one values.
Going forward will be failing much faster and not giving my life energy to products that aren’t showing traction.
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quick-deploy
Optimize, convert and deploy machine learning models as fast inference API using Triton and ORT. Currently support Hugging Face transformers, PyToch, Tensorflow, SKLearn and XGBoost models.
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Rating-Correlations
Predicts chess960 or crazyhouse ratings given bullet or blitz and others for either Lichess.org or Chess.com servers.
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SaaSHub
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2023-10-07.
Python Xgboost related posts
- Show HN: A gallery of dev tool marketing examples
- How to structure/manage a machine learning experiment? (medical imaging)
- How to grow a developer blog to 3M annual visitors? with Jakub Czakon (Neptune.ai)
- [D] Is there any all in one deep learning platform or software
- New Data Scientist, want to get into MLOps, where to start?
- Does a fully sentient (Or at least as sentient as you and me) AI with free will have a soul?
- [D] The hype around Mojo lang
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A note from our sponsor - Onboard AI
getonboard.dev | 9 Dec 2023
Index
What are some of the best open-source Xgboost projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | mljar-supervised | 2,839 |
| 2 | m2cgen | 2,658 |
| 3 | mars | 2,656 |
| 4 | kserve | 2,613 |
| 5 | AutoViz | 1,508 |
| 6 | MLBox | 1,460 |
| 7 | awesome-gradient-boosting-papers | 962 |
| 8 | mlforecast | 596 |
| 9 | MLServer | 496 |
| 10 | Auto_ViML | 473 |
| 11 | Hyperactive | 473 |
| 12 | FastTreeSHAP | 471 |
| 13 | neptune-client | 426 |
| 14 | xgboost_ray | 122 |
| 15 | tempo | 110 |
| 16 | alpha-zero-boosted | 78 |
| 17 | Python-Schema-Matching | 20 |
| 18 | python-ranker | 19 |
| 19 | dmatrix2np | 15 |
| 20 | quick-deploy | 6 |
| 21 | Language_Identifier | 1 |
| 22 | Rating-Correlations | 1 |
SaaSHub - Software Alternatives and Reviews
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