Numba
cudf
Our great sponsors
Numba | cudf | |
---|---|---|
124 | 23 | |
9,432 | 7,274 | |
1.8% | 2.9% | |
9.9 | 9.9 | |
7 days ago | 2 days ago | |
Python | C++ | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
Numba
-
Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
-
Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
-
Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
-
Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
-
PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
-
Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
-
This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
-
Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
cudf
-
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.
-
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.
-
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.
-
[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
-
Story of my life
To put Data Analytics on GPU Steroids, Try RAPIDS cudf https://rapids.ai/
-
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.
-
[D] [R] Large-scale clustering
try https://rapids.ai/
-
[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
What are some alternatives?
NetworkX - Network Analysis in Python
chia-plotter
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
wif500 - Try to find the WIF key and get a donation 200 btc
Dask - Parallel computing with task scheduling
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
cupy - NumPy & SciPy for GPU
rmm - RAPIDS Memory Manager
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
CUDA.jl - CUDA programming in Julia.
SymPy - A computer algebra system written in pure Python
mpire - A Python package for easy multiprocessing, but faster than multiprocessing