cudf
cuDF - GPU DataFrame Library (by rapidsai)
Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration (by pytorch)
| cudf | Pytorch | |
|---|---|---|
| 27 | 368 | |
| 8,496 | 84,676 | |
| 1.1% | 1.5% | |
| 9.9 | 10.0 | |
| 4 days ago | 5 days ago | |
| C++ | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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 2024-06-14.
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Unleashing GPU Power: Supercharge Your Data Processing with cuDF
cuDF Documentation
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This Week In Python
cudf – GPU DataFrame Library
- cuDF – GPU DataFrame Library
- CuDF – GPU DataFrame Library
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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.
Pytorch
Posts with mentions or reviews of Pytorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-11-11.
- Deprecating PyTorch's official Anaconda channel
-
1MinDocker #6 - Building further
pytorch
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Top 8 OpenSource Tools for AI Startups
Star on GitHub ⭐ - PyTorch
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JAX vs PyTorch: Comparing Two Powerhouses in ML Frameworks
Developed by the Facebook AI Research (FAIR) lab, PyTorch is an open-source machine learning framework used to build efficient machine learning models. In contrast to JAX, PyTorch is based on an imperative programming paradigm. It is a popular library and is used by many companies to build their machine learning models.
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Is Open Source AI Bull?
Software Frameworks. These are the libraries and frameworks on which the system source code is built. One needs access to not only the frameworks (many of these are open source software already, such as PyTorch and Tensorflow) but also the specific versioning used in the system source code and the training source code. Details matter.
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Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
PyTorch is a tool for building deep learning models, launched by Meta in 2016. It is often used in image recognition, natural language processing, and reinforcement learning. PyTorch is essential for researchers, data scientists, and machine learning engineers.
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How to Learn Generative AI: A Step-by-Step Guide
Use TensorFlow and PyTorch to experiment with building neural networks.
- What's new in C++26 (part 1)
What are some alternatives?
When comparing cudf and Pytorch you can also consider the following projects:
Numba - NumPy aware dynamic Python compiler using LLVM
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
chia-plotter
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
wif500 - Try to find the WIF key and get a donation 200 btc
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
rmm - RAPIDS Memory Manager
flax - Flax is a neural network library for JAX that is designed for flexibility.
CUDA.jl - CUDA programming in Julia.
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
mpire - A Python package for easy multiprocessing, but faster than multiprocessing
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more