Dask

Dask: Library for dynamic task scheduling. Dask is open source and freely available. It is developed in coordination with other community projects like Numpy, Pandas, and Scikit-Learn.

Keywords for this software

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References in zbMATH (referenced in 24 articles )

Showing results 1 to 20 of 24.
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  1. Jack D. Sundberg, Siona S. Benjamin, Lauren M. McRae, Scott C. Warren: Simmate: a framework for materials science (2022) not zbMATH
  2. Johannes N. Hansen: nd - A Framework for the Analysis of n-dimensional Earth Observation Data (2022) not zbMATH
  3. Dutta, R., Schoengens, M., Pacchiardi, L., Ummadisingu, A., Widmer, N., Künzli, P., Onnela, J.-P., Mira, A: ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation (2021) not zbMATH
  4. Riley X. Brady; Aaron Spring: climpred: Verification of weather and climate forecasts (2021) not zbMATH
  5. Wu-Jung Lee, Emilio Mayorga, Landung Setiawan, Imran Majeed, Kavin Nguyen, Valentina Staneva: Echopype: A Python library for interoperable and scalable processing of water column sonar data for biological information (2021) arXiv
  6. Andrew R. Bennett; Joseph J. Hamman; Bart Nijssen: MetSim: A Python package for estimation and disaggregation of meteorological data (2020) not zbMATH
  7. Figueras i Ventura, J., Lainer, M., Schauwecker, Z., Grazioli, J., Germann, U.: Pyrad: A Real-Time Weather Radar Data Processing Framework Based on Py-ART (2020) not zbMATH
  8. Grübel, Julia; Kleinert, Thomas; Krebs, Vanessa; Orlinskaya, Galina; Schewe, Lars; Schmidt, Martin; Thürauf, Johannes: On electricity market equilibria with storage: modeling, uniqueness, and a distributed ADMM (2020)
  9. Julian Blank, Kalyanmoy Deb: pymoo: Multi-objective Optimization in Python (2020) arXiv
  10. Martí Bosch: DetecTree: Tree detection from aerial imagery in Python (2020) not zbMATH
  11. Michela Paganini, Jessica Zosa Forde: dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration (2020) arXiv
  12. Muammar El Khatib, Wibe A de Jong: ML4Chem: A Machine Learning Package for Chemistry and Materials Science (2020) arXiv
  13. P.E. Hadjidoukas, A. Bartezzaghi, F. Scheidegger, R. Istrate, C.Bekas, A.C.I. Malossi: torcpy: Supporting task parallelism in Python (2020) not zbMATH
  14. Peter Michael Habelitz, Janis Keuper: PHS: A Toolbox for Parallel Hyperparameter Search (2020) arXiv
  15. Tobias Stål, Anya M. Reading: A Grid for Multidimensional and Multivariate Spatial Representation and Data Processing (2020) not zbMATH
  16. Yunus Sevinchan; Benjamin Herdeanu; Jeremias Traub: dantro: a Python package for handling, transforming, and visualizing hierarchically structured data (2020) not zbMATH
  17. Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
  18. Eshan D. Mitra, Ryan Suderman, Joshua Colvin, Alexander Ionkov, Andrew Hu, Herbert M. Sauro, Richard G. Posner, William S. Hlavacek: PyBioNetFit and the Biological Property Specification Language (2019) arXiv
  19. Mattia Almansi, Renske Gelderloos, Thomas W. N. Haine, Atousa Saberi. Ali H. Siddiqui: OceanSpy: A Python package to facilitate ocean model data analysis and visualization (2019) not zbMATH
  20. Toccaceli, Paolo; Gammerman, Alexander: Combination of inductive Mondrian conformal predictors (2019)

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