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The content in the article is a bit dated. Jupyter + Numba + Dask is the direction scientific computing (in Python) is taking. Ipyparallel is not really scalabl

The content in the article is a bit dated. Jupyter + Numba + Dask is the direction scientific computing (in Python) is taking. Ipyparallel is not really scalable in my experience.

commandlinefan 8y ago
I thought so, too - enough that I checked the date, but the date on the article is yesterday. Maybe it was written a while ago and just now uploaded. I thought it was strange to refer to IPython as a standalone interpreter outside of Jupyter... I mean, you can, but I don't think anybody does anymore.
- xapata 8y ago
  > I don't think anybody does anymore Counter-example: I use IPython in the terminal, outside of Jupyter.
  - leephillips 8y ago
    So do I, and many others. Nowadays you can type "jupyter console", which gets you almost the same thing; and JupyterLab has console components.
  - newen 8y ago
    Me too! I actually hook up an Ipython terminal to a Jupyter notebook and in order to do REPL type work in the terminal. Much better to work in a REPL terminal and also save my work in the notebook.
semi-extrinsic 8y ago
This is an extremely limited view of "scientific computing" that seems to only focus on analytics, which is a tiiiny part of sci comp. Your "stack" does nothing for solving/including ODEs, PDEs, DAEs, Fourier analysis, numerical integration, automatic differentiation, linear equation system solvers, preconditioners, nonlinear equation system solvers, the entire field of optimization, inverse problems, statistical methods, Monte Carlo simulations, molecular dynamics, PIC methods, geometric integration, lattice quantum field theory, molecular dynamics, ab initio methods, density functional theory, finite difference/volume/element methods, lattice Boltzmann methods, boundary integral methods, mesh generation methods, error estimation, uncertainty quantification... Those are just off the top of my head, the list goes on and on.
- biridir 8y ago
  I completely agree that there are many scientific libraries in python which scale up. I was addressing the article which showed a more advanced way to use python with the purpose of making it applicable to large datasets. If you were to implement a method from scratch or scale up to a larger dataset then you'll end up with using numba, numpy and dask. This is completely from a lower level programming perspective to implement and integrate methods rather than pipelining methods from higher level scientific libraries. Just for some context: https://www.scipy.org/about.html https://www.scipy.org/about.html https://www.scipy.org/topical-software.html https://www.scipy.org/topical-software.html
  - semi-extrinsic 8y ago
    I have yet to see a situation where Numba makes real sense, as compared to just dropping down into C(++) or Fortran when you need to do the heavy lifting. Can you give me a good example?

The content in the article is a bit dated. Jupyter + Numba + Dask is the direction scientific computing (in Python) is taking. Ipyparallel is not really scalable in my experience.

commandlinefan 8y ago

I thought so, too - enough that I checked the date, but the date on the article is yesterday. Maybe it was written a while ago and just now uploaded. I thought it was strange to refer to IPython as a standalone interpreter outside of Jupyter... I mean, you can, but I don't think anybody does anymore.

xapata 8y ago
> I don't think anybody does anymore Counter-example: I use IPython in the terminal, outside of Jupyter.
- leephillips 8y ago
  So do I, and many others. Nowadays you can type "jupyter console", which gets you almost the same thing; and JupyterLab has console components.
- newen 8y ago
  Me too! I actually hook up an Ipython terminal to a Jupyter notebook and in order to do REPL type work in the terminal. Much better to work in a REPL terminal and also save my work in the notebook.

semi-extrinsic 8y ago

This is an extremely limited view of "scientific computing" that seems to only focus on analytics, which is a tiiiny part of sci comp. Your "stack" does nothing for solving/including ODEs, PDEs, DAEs, Fourier analysis, numerical integration, automatic differentiation, linear equation system solvers, preconditioners, nonlinear equation system solvers, the entire field of optimization, inverse problems, statistical methods, Monte Carlo simulations, molecular dynamics, PIC methods, geometric integration, lattice quantum field theory, molecular dynamics, ab initio methods, density functional theory, finite difference/volume/element methods, lattice Boltzmann methods, boundary integral methods, mesh generation methods, error estimation, uncertainty quantification... Those are just off the top of my head, the list goes on and on.

biridir 8y ago
I completely agree that there are many scientific libraries in python which scale up. I was addressing the article which showed a more advanced way to use python with the purpose of making it applicable to large datasets. If you were to implement a method from scratch or scale up to a larger dataset then you'll end up with using numba, numpy and dask. This is completely from a lower level programming perspective to implement and integrate methods rather than pipelining methods from higher level scientific libraries. Just for some context: https://www.scipy.org/about.html https://www.scipy.org/about.html https://www.scipy.org/topical-software.html https://www.scipy.org/topical-software.html
- semi-extrinsic 8y ago
  I have yet to see a situation where Numba makes real sense, as compared to just dropping down into C(++) or Fortran when you need to do the heavy lifting. Can you give me a good example?