taco
Dask
| taco | Dask | |
|---|---|---|
| 2 | 32 | |
| 1,208 | 11,999 | |
| 1.1% | 0.6% | |
| 0.0 | 9.6 | |
| 18 days ago | 6 days ago | |
| C++ | Python | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
taco
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The Distributed Tensor Algebra Compiler (2022)
I agree! Much of this work was done as part of the overarching TACO project (https://github.com/tensor-compiler/taco), in an attempt to distribute sparse tensor computations (https://rohany.github.io/publications/sc2022-spdistal.pdf). MLIR recently (~mid 2022) began implementing the ideas from TACO into a "sparse tensor" dialect, so perhaps some of these ideas could make it into there. I'm working with MLIR these days, and if I could re-do the project now I would probably utilize and targetb the MLIR linalg infrastructure!
- Qué tire la primer piedra, aquien no le ha pasado así....?
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
blitz - Blitz++ Multi-Dimensional Array Library for C++
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Grassmann.jl - ⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
Numba - NumPy aware dynamic Python compiler using LLVM
CuTeLib - CUDA Template Library provides simple, typesafe, performant constructs for C++ CUDA projects
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
MegEngine - MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
NetworkX - Network Analysis in Python
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
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
theme-ui - Build consistent, themeable React apps based on constraint-based design principles
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python