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HandWiki. Dask. Encyclopedia. Available online: https://encyclopedia.pub/entry/30459 (accessed on 23 September 2026).
HandWiki. Dask. Encyclopedia. Available at: https://encyclopedia.pub/entry/30459. Accessed September 23, 2026.
HandWiki. "Dask" Encyclopedia, https://encyclopedia.pub/entry/30459 (accessed September 23, 2026).
HandWiki. (2022, October 20). Dask. In Encyclopedia. https://encyclopedia.pub/entry/30459
HandWiki. "Dask." Encyclopedia. Web. 20 October, 2022.
Dask
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The content is sourced from: https://handwiki.org/wiki/Software:Dask

Dask is a flexible open-source Python library for parallel computing. Dask scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, Scikit-learn and NumPy. It also exposes low-level APIs that help programmers run custom algorithms in parallel. Dask was created by Matthew Rocklin in December 2014 and has over 9.8k stars and 500 contributors on GitHub. Dask is used by retail, financial, governmental organizations, as well as life science and geophysical institutes. Walmart, Wayfair, JDA, GrubHub, General Motors, NVIDIA, Harvard Medical School, Capital One and NASA are among the organizations that use Dask.

multi-core python life science

References

  1. "Scalable computing with Dask". https://ulhpc-tutorials.readthedocs.io/en/latest/python/advanced/dask-ml/. 
  2. "DataFrame - Dask documentation". https://docs.dask.org/en/stable/dataframe.html. 
  3. "Bag - Dask documentation". https://docs.dask.org/en/stable/bag.html. 
  4. "Array - Dask documentation". https://docs.dask.org/en/stable/array.html. 
  5. "DASK". https://www.nvidia.com/en-us/glossary/data-science/dask/#:~:text=Dask%20%2B%20NVIDIA%3A%20Driving%20Accessible%20Accelerated%20Analytics&text=Seeing%20the%20power%20and%20accessibility,GPUs%20and%20GPU%2Dbased%20systems.. 
  6. Eswaramoorthy, Pavithra. "What is Dask?". coiled.io. https://coiled.io/blog/what-is-dask/. 
  7. "Dask-ML". https://ml.dask.org/. 
  8. "Parallel computing with Dask". https://docs.xarray.dev/en/latest/user-guide/dask.html. 
  9. "Delayed - Dask documentation". https://docs.dask.org/en/latest/delayed.html. 
  10. "Futures - Dask documentation". https://docs.dask.org/en/latest/futures.html. 
  11. "Specification - Dask documentation". https://docs.dask.org/en/latest/spec.html. 
  12. "Dask scheduler - Dask documentation". https://docs.dask.org/en/stable/deploying.html. 
  13. "Computing with scikit-learn". https://scikit-learn.org/stable/computing/parallelism.html. 
  14. "Parallel Prediction and Transformation - Dask documentation". https://ml.dask.org/meta-estimators.html#parallel-meta-estimators. 
  15. "Incremental Hyperparameter Optimization - Dask documentation". https://ml.dask.org/hyper-parameter-search.html#hyperparameter-incremental. 
  16. "API Reference - Dask documentation". https://ml.dask.org/modules/api.html#api. 
  17. "Distributed XGBoost with Dask". https://xgboost.readthedocs.io/en/stable/tutorials/dask.html. 
  18. "How Distributed LightGBM Works. Dask". https://lightgbm.readthedocs.io/en/latest/Parallel-Learning-Guide.html#dask. 
  19. Rocklin, Matthew. "Dask and Pandas and XGBoost". http://matthewrocklin.com/blog/work/2017/03/28/dask-xgboost. 
  20. "Skorch documentation". https://skorch.readthedocs.io/en/stable/. 
  21. "SciKeras documentation". https://www.adriangb.com/scikeras/stable/. 
  22. "Dask Usage at Blue Yonder". https://tech.blueyonder.com/dask-usage-at-blue-yonder/. 
  23. "Matthew Rocklin - Bio". https://matthewrocklin.com/bio.html. 
  24. Bowne-Anderson, Hugo. "Search at Grubhub and User Intent". coiled.io. https://coiled.io/blog/grubhub-science-thursday/. 
  25. Eswaramoorthy, Pavithra. "Who Uses Dask?". coiled.io. https://coiled.io/blog/who-uses-dask/. 
  26. McEntee Ryan, McCarty Mike. "Dask & RAPIDS: The Next Big Thing for Data Science & ML". capitalone.com. https://www.capitalone.com/tech/machine-learning/dask-and-rapids-data-science-and-machine-learning-at-capital-one/. 
  27. Patel, Harshil. "Which library should I use? Apache Spark, Dask, and Pandas Performance Compared (With Benchmarks)". censius.ai. https://censius.ai/blogs/apache-spark-vs-dask-vs-pandas. 
  28. "Adapting Dask to Data Intensive Geoscience Research". https://coiled.wistia.com/medias/kgu2m47eg9. 
  29. "Met Office". https://www.metoffice.gov.uk/. 
  30. "Pangeo". https://pangeo.io/. 
  31. "Forecasting with HEAVY.AI and Prophet". https://docs.heavy.ai/data-science/additional-examples/forecasting-with-omnisci-and-prophet. 
  32. "Dask - the simple way. Tsfresh documentation". https://tsfresh.readthedocs.io/en/latest/text/large_data.html. 
  33. "scikit-learn". https://scikit-learn.org/stable/. 
  34. "Scale Python with Dask on GPUs". https://rapids.ai/dask.html. 
  35. "Dask Executor - Apache Airflow Documentation". https://airflow.apache.org/docs/apache-airflow/stable/executor/dask.html. 
  36. "Deployment: Dask. Prefect Docs.". https://docs.prefect.io/core/advanced_tutorials/dask-cluster.html. 
  37. "Dask". https://dask.org/. 
  38. "Anaconda". https://www.anaconda.com/. 
  39. "The Blaze Ecosystem". http://blaze.pydata.org/. 
  40. "DARPA". https://www.darpa.mil/. 
  41. "GitHub, Dask, 2014". https://github.com/dask/dask/commit/05488db498c1561d266c7b676b8a89021c03a9e7. 
  42. "Dask History.". https://www.youtube.com/watch?v=5nJVg8j11h0&t=20s. 
  43. "Xarray". https://xarray.pydata.org/en/stable/. 
  44. "Image processing in Python". https://scikit-image.org/. 
  45. "pandas". https://pandas.pydata.org/. 
  46. Rocklin, Matthew. "Funding Dask, a brief history". https://matthewrocklin.com/blog/work/2020/01/08/founding-1. 
  47. "Coiled: Python for Data Science on the Cloud with Dask". https://coiled.io/. 
  48. Wiggers, Kyle. "Data and AI operations startup Coiled nabs $21M". VentureBeat. https://venturebeat.com/2021/05/18/data-and-ai-operations-startup-coiled-nabs-21m/. 
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