cudf
cuDF - GPU DataFrame Library (by rapidsai)
modin
Modin: Scale your Pandas workflows by changing a single line of code (by modin-project)
| cudf | modin | |
|---|---|---|
| 23 | 11 | |
| 7,291 | 9,476 | |
| 2.9% | 1.3% | |
| 9.9 | 9.6 | |
| about 4 hours ago | 6 days ago | |
| C++ | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
cudf
Posts with mentions or reviews of cudf.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-17.
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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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Why we dropped Docker for Python environments
Perhaps the largest for package size is the NVIDIA developed rapids toolkit https://rapids.ai/ . Even still adding things like pandas and some geospatial tools, you rapidly end up with an image well over a gigabyte, despite following cutting edge best practice with docker and python.
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Introducing TeaScript C++ Library
Yes sure, that is how OpenMP does; but on the other side: you seem to already do some basic type inference, and building an AST, no? Then you know as well the size and type of your vectors, and can execute actions in parallel if there is enough data to be worth parallelizing. Is there anyone who don't want their code to execute faster if it is possible? Those that do work in big data domain do use threads and vectorized instructions without user having to type in any directive; just import different library. Example, numpy or numpy with cuda backend, or similar GPU accelerated libraries like cudf.
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[D] Can we use Ray for distributed training on vertex ai ? Can someone provide me examples for the same ? Also which dataframe libraries you guys used for training machine learning models on huge datasets (100 gb+) (because pandas can't handle huge data).
Not the answer about Ray: you could use rapids.ai. I'm using it for for dataframe manipulation on GPU
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Story of my life
To put Data Analytics on GPU Steroids, Try RAPIDS cudf https://rapids.ai/
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Artificial Intelligence in Python
You can scope out https://rapids.ai/. Nvidia's AI toolkits. They have some handy notebooks to poke at to get you started.
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[D] [R] Large-scale clustering
try https://rapids.ai/
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[P] Looking for state of the art clustering algorithms
As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).
- Integrating multiple point clouds?
- Buka | Sains Data GPU RAPIDS
modin
Posts with mentions or reviews of modin.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-15.
- 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?
When comparing cudf and modin you can also consider the following projects:
Numba - NumPy aware dynamic Python compiler using LLVM
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
chia-plotter
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
wif500 - Try to find the WIF key and get a donation 200 btc
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
rmm - RAPIDS Memory Manager
PandasGUI - A GUI for Pandas DataFrames
CUDA.jl - CUDA programming in Julia.
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.