bagua
modin
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| bagua | modin | |
|---|---|---|
| 6 | 11 | |
| 865 | 9,465 | |
| 0.0% | 1.2% | |
| 4.8 | 9.6 | |
| 9 months ago | about 20 hours ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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bagua
modin
- The Distributed Tensor Algebra Compiler (2022)
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A Polars exploration into Kedro
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
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Modern Polars: an extensive side-by-side comparison of Polars and Pandas
Yeah, tried Polars a couple of times: the API seems worse than Pandas to me too. eg the decision only to support autoincrementing integer indexes seems like it would make debugging "hmmm, that answer is wrong, what exactly did I select?" bugs much more annoying. Polars docs write "blazingly fast" all over them but I doubt that is a compelling point for people using single-node dataframe libraries. It isn't for me.
Modin (https://github.com/modin-project/modin) seems more promising at this point, particularly since a migration path for standing Pandas code is highly desirable.
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Polars: The Next Big Python Data Science Library... written in RUST?
If anyone wants a faster version of pandas it’s not hard to find, modin for example uses multiple cores to speed it up, so if you have 4 cores it’s about 4 times faster than pandas, and has the same API as pandas.
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Working with more than 10gb csv
Modin should fit. It implements Pandas APIs with e.g. Ray as backend. https://github.com/modin-project/modin
- Modern Python Performance Considerations
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I made a video about efficient memory use in pandas dataframes!
If you really want speed you should try modin.pandas which makes pandas multi-threaded.
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Almost no one knows how easily you can optimize your AI models
I am guessing XGB is fairly optimised as it is. If you would want to use the sklearn libraries with pandas, look into Modin
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TIL about modin.pandas which significantly speeds up pandas if you import modin.pandas instead of pandas.
Source
- How to Speed Up Pandas with 1 Line of Code
What are some alternatives?
machin - Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
optuna - A hyperparameter optimization framework
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
PERSIA - High performance distributed framework for training deep learning recommendation models based on PyTorch.
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
PandasGUI - A GUI for Pandas DataFrames
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.