tune-sklearn
hummingbird
tune-sklearn | hummingbird | |
---|---|---|
4 | 9 | |
462 | 3,304 | |
- | 0.5% | |
0.0 | 7.1 | |
6 months ago | 15 days ago | |
Python | Python | |
Apache License 2.0 | MIT 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.
tune-sklearn
-
LightGBM vs. XGBoost: Which distributed version is faster?
Of course not! :)
The Ray ecosystem is actually chalk full of integrations, from XGBoost Ray (https://docs.ray.io/en/master/xgboost-ray.html), to PyTorch on Ray (https://docs.ray.io/en/master/using-ray-with-pytorch.html), and of course hyperparameter search with Ray Tune for a variety of libraries, including Sklearn (https://github.com/ray-project/tune-sklearn).
-
[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?
If you mainly use scikit-learn, you should consider using tune-sklearn.
-
[P] Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret
Just wanted to share a not widely known feature of PyCaret. By default, PyCaret's tune_model uses the tried and tested RandomizedSearchCV from scikit-learn. However, not everyone knows about the various advanced options tune_model() currently allows you to use such as cutting edge hyperparameter tuning techniques like Bayesian Optimization through libraries such as tune-sklearn, Hyperopt, and Optuna.
-
[D] Here are 3 ways to Speed Up Scikit-Learn - Any suggestions?
You might want to try out tune-sklearn as it seems like it works for catboost as well. I am trying it use tune-sklearn to speed up my scikit-learn hyperparameter tuning.
hummingbird
- Treebomination: Convert a scikit-learn decision tree into a Keras model
-
[D] GPU-enabled scikit-learn
If are interested in just predictions you can try Hummingbird. It is part of the PyTorch ecosystem. We get already trained scikit-learn models and translate them into PyTorch models. From them you can run your model on any hardware support by PyTorch, export it into TVM, ONNX, etc. Performance on hardware acceleration is quite good (orders of magnitude better than scikit-learn is some cases)
-
Machine Learning with PyTorch and Scikit-Learn – The *New* Python ML Book
I think Rapids AI's cuML tried to go into this direction (essentially scikit-learn on the GPU): https://docs.rapids.ai/api/cuml/stable/api.html#logistic-reg.... For some reason it never took really off though.
Btw., going on a tangent, you might like Hummingbird (https://github.com/microsoft/hummingbird). It allows you trained scikit-learn tree-based models to PyTorch. I watched the SciPy talk last year, and it's a super smart & elegant idea.
-
Export and run models with ONNX
ONNX opens an avenue for direct inference using a number of languages and platforms. For example, a model could be run directly on Android to limit data sent to a third party service. ONNX is an exciting development with a lot of promise. Microsoft has also released Hummingbird which enables exporting traditional models (sklearn, decision trees, logistical regression..) to ONNX.
-
Supreme Court, in a 6–2 ruling in Google v. Oracle, concludes that Google’s use of Java API was a fair use of that material
And Python.
-
[D] Here are 3 ways to Speed Up Scikit-Learn - Any suggestions?
For inference, you can convert your models to other formats that support GPU acceleration. See Hummingbird https://github.com/microsoft/hummingbird
-
[D] Microsoft library, Hummingbird, compiles trained ML models into tensor computation for faster inference.
The surprising thing is that Hummingbird can be faster than the GPU implementation of LightGBM (and XGBoost) if you use tensor compilers such as TVM. [The paper](https://www.usenix.org/conference/osdi20/presentation/nakandala) describes our findings. We have also open sourced the [benchmark code](https://github.com/microsoft/hummingbird/tree/main/benchmarks) so you try yourself!
-
I learned about Microsoft's Hummingbird library today. 1000x performance??
I took their sample code from Github and tweaked it to spit out times for each model's prediction, as well as increase the number of rows to 5 million. I used Google's Colab and selected GPU for my hardware accelerator. This gives an option to run code on GPU, not that all computations will happen on the GPU.
What are some alternatives?
auto-sklearn - Automated Machine Learning with scikit-learn
onnx - Open standard for machine learning interoperability
guildai - Experiment tracking, ML developer tools
swift - The Swift Programming Language
dvc - 🦉 ML Experiments and Data Management with Git
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
spock - spock is a framework that helps manage complex parameter configurations during research and development of Python applications
cuml - cuML - RAPIDS Machine Learning Library
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
docker - Docker - the open-source application container engine
chemprop - Message Passing Neural Networks for Molecule Property Prediction
ServiceTalk - A networking framework that evolves with your application