Stats

Basic Dask repo stats
1
8,215
9.4
6 days ago

dask/dask is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.

Dask Alternatives

Similar projects and alternatives to Dask based on common topics and language

  • GitHub repo Scrapy

    Scrapy, a fast high-level web crawling & scraping framework for Python.

  • GitHub repo Ray

    An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

  • GitHub repo Poetry

    Python dependency management and packaging made easy.

  • GitHub repo Nim

    Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).

  • GitHub repo FrameworkBenchmarks

    Source for the TechEmpower Framework Benchmarks project

  • GitHub repo mpi4jax

    Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:

  • GitHub repo orange

    🍊 :bar_chart: :bulb: Orange: Interactive data analysis

NOTE: The number of mentions on this list indicates mentions on common posts. Hence, a higher number means a better Dask alternative or higher similarity.

Posts

Posts where Dask has been mentioned. We have used some of these posts to build our list of alternatives and similar projects - the last one was on 2021-04-16.
  • Why is Python popular despite being accused of being slow?
    Not everyone has the same "parallelism" needs. I have used mpi4py to distribute scientific computations using numpy over thousands of cores on hundreds of servers with much less effort than doing the same thing in C / C++ and almost no performance penalty (I could batch my data in big enough chunks). Today there are higher level distributed computing packages like dask that are even easier to use.
  • Too much data to preprocess to work with pandas — is pyspark.sql a feasible alternative?
    reddit.com/r/PySpark | 2021-02-25
    I haven't used it myself I have to admit, but I think dask could fit your workflow. Spark might add a little bit too much overhead if you're not used to it and you're not using a distributed system but of course it would also work.
  • [D] Jax (or other libraries) when not using GPUs/TPUs but CPUs.