Dask VS PyMC

Compare Dask vs PyMC and see what are their differences.

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Dask PyMC
32 3
11,982 8,142
1.5% 1.2%
9.7 9.4
4 days ago 5 days ago
Python Python
BSD 3-clause "New" or "Revised" License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.

Dask

Posts with mentions or reviews of Dask. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-15.

PyMC

Posts with mentions or reviews of PyMC. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-06.
  • PYMC Release: v5.0.0
    1 project | news.ycombinator.com | 12 Dec 2022
  • An Astronomer's Introduction to NumPyro
    1 project | news.ycombinator.com | 17 Aug 2022
    I believe the pymc versions were resolved into developing version 4 of pymc. Development at https://github.com/pymc-devs/pymc

    It still depends on theano now evolved and renamed

  • What is Probabilistic Programming?
    4 projects | /r/learnmachinelearning | 6 Sep 2021
    This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing

What are some alternatives?

When comparing Dask and PyMC you can also consider the following projects:

Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

statsmodels - Statsmodels: statistical modeling and econometrics in Python

Numba - NumPy aware dynamic Python compiler using LLVM

stan - Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

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.

NetworkX - Network Analysis in Python

SymPy - A computer algebra system written in pure Python

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

pyro - Deep universal probabilistic programming with Python and PyTorch

Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python

SciPy - SciPy library main repository