pathml
Dask
pathml | Dask | |
---|---|---|
2 | 32 | |
364 | 12,055 | |
3.0% | 1.1% | |
8.0 | 9.6 | |
about 1 month ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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pathml
- Hilo Semanal de Consultas IT - Asesoría Técnica, Desarrollo Profesional y Aprendizaje
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Dask – a flexible library for parallel computing in Python
We have been using dask to support our computational pathology workflows [1], where the images are so big that they cannot be loaded in memory, let alone analyzed (standard pathology whole slide images are ~1GB; some microscopy techniques generate images >1TB). We divide each image into a bunch of smaller tiles and process each tile independently. The dask.distributed scheduler lets us scale up by distributing the tile processing across a cluster.
Benefits of dask.distributed: easy to get up and running, and has support for spinning up clusters on lots of different computing platforms (local machines, HPC cluster, k8s, etc.)
One difficulty is optimizing performance - there are so many configuration details (job size, number of workers, worker resources, etc. etc.) that it's been hard to know what is best.
[1] https://github.com/Dana-Farber-AIOS/pathml
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?
mpire - A Python package for easy multiprocessing, but faster than multiprocessing
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
slideflow - Deep learning library for digital pathology, with both Tensorflow and PyTorch support.
Numba - NumPy aware dynamic Python compiler using LLVM
Keras - Deep Learning for humans
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.
pytorch-ssim - pytorch structural similarity (SSIM) loss
NetworkX - Network Analysis in Python
cudf - cuDF - GPU DataFrame Library
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
legate.pandas - An Aspiring Drop-In Replacement for Pandas at Scale
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python