travisoliphant

9 ms·

31.
by travisoliphant 10y ago
For some plugins integration just means visual integration (i.e. window-manager integration). And, to be clear, this one is not poorly-implemented. It's based on a very powerful web-application toolkit called PhosphorJS written b
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by travisoliphant 10y ago
Everyone has their own story around this. For me, the reason I did not go with Octave when I chose to migrate from Matlab to Python was that I wanted to be part of a general-purpose language community. Octave had the nice advantage of b
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by travisoliphant 10y ago
Good luck, William. You have my full support! Thank you for all the great work on Sage and associated tools!
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by travisoliphant 11y ago
Bokeh can be used with both Python 2.7 and Python 3. Additional dependencies are needed to work with Python 2.7. In fact, the "write call-back functions in Python" capability uses a Python to JS compiler called Flexx -- http:&
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by travisoliphant 11y ago
There are a lot of smart people that like Python. There are a lot of smart people that like R too. Bokeh is designed to be useful from both R and Python (and other languages as well). Here is a recent presentation showing Bokeh (from Pyth
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by travisoliphant 11y ago
You might take a look at Bokeh ( http://bokeh.pydata.org ) and either the PyData stack or R (Bokeh can be used from R as well: https://github.com/bokeh/rbokeh ). Bokeh inside a Jupyter notebook with widgets
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by travisoliphant 12y ago
Yes, LLVM is a great approach for doing code-gen from arbitrary specialized VMs. And the Python interface to it makes it easy to experiment. We no longer use llvmpy for Numba (we use a simpler interface llvmlite) and so llvmpy could use
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by travisoliphant 12y ago
The numba code-base implements quite a bit of this. We actually moved away from the AST approach and went back to the byte-code approach because the AST approach quickly becomes unwieldy as the number of Visitors that you apply grows. Co
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by travisoliphant 12y ago
This is a great tutorial about first-generation Numba. The author learned a lot about LLVM and llvmpy while working with several of our devs. If you are interested in the "Further work" in his article, come join the Numba proj
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by travisoliphant 12y ago
This is an interesting idea. However, their timelines are too short. It makes me feel like they don't really understand what it's going to take. A good start would be the C++ library libdynd which was designed to potentially be u
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by travisoliphant 12y ago
The performance numbers they show are fairly cherry-picked and are not really showing a broad-based comparison of Numba's capability. In addition, Numba is still under active development and the version they used is a few versions ol
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by travisoliphant 12y ago
Interesting post. It seems that everyone wants to write their own jit rather than cooperate with the on-going open source efforts. 1) Support for recursion is in a PR for Numba that has just not yet been merged. 2) optional simplificatio
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by travisoliphant 12y ago
The link to the "non-store" download page which we tell everyone about is: http://continuum.io/downloads . Yes, we are a company and do sell things, but our commitment to open source is backed by many, many years o
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by travisoliphant 13y ago
This is a nice post, and the Python packaging community is making strides in the right direction. However, it's not there yet. Armin could bring himself to say this in this post: "It's there, it kinda works, and it's pr
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by travisoliphant 13y ago
You can also try conda's environments which are a system-level concept and is an even lighter-weight approach to some of the problems people are using Docker to solve. If you just need an independent software environment, conda gives
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by travisoliphant 13y ago
Yes, thank you!. You have the correct link.
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by travisoliphant 13y ago
A lot of people in the PyData ecosystem using NumPy and Pandas are using conda and conda packcages ( http://conda.pydata.org ). Conda packages are easy to build and many exist at repo.continuum.io and on channels at http:/&
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by travisoliphant 13y ago
Conda ( http://conda.pydata.org ) is a more general packaging format than wheels and we use it to distribute the Scientific Python Stack (including complex C-dependencies) with ease. It is more general than wheels and solves thi
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by travisoliphant 13y ago
Using conda to build and deploy binary dependencies. Create system-level virtual-environments with ease. Anaconda has jump-started providing binary packages for several platforms. Getting a conda package from can be as simple as `conda bu
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by travisoliphant 13y ago
I'm looking forward to hearing from Brian Granger (IPython) and Peter.
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by travisoliphant 13y ago
And by free, he also means free to re-distribute (attribution license). It comes with a very nice package manager (equivalent to brew, yum, apt-get, etc --- but with integrated virtual environments). This package manager BSD-licensed and
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by travisoliphant 13y ago
This is 100% true. Hadoop gets way too much attention given the other useful solutions that exist out there. I have known people to use Disco successfully on several hundred node cluster. You can also interact Disco with IPython parall
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by travisoliphant 13y ago
It's hard to see how you could have read the article given your comment. This is certainly not a copy of some original presentation. The article may not be motivated well and certainly can be critiqued on many levels. But it describ
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by travisoliphant 13y ago
But, the upshot is that Numba produces code that is either faster or roughly the same speed as C or Cython.
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by travisoliphant 13y ago
Yes, there are several. See slide 15 of this talk http://www.slideshare.net/teoliphant/numba-siam-2013 and also the github repo: https://github.com/teoliphant/speed Note that the array-expressions previously in numbapro only have been
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by travisoliphant 13y ago
Numba's original purpose was not to speed up concise, optimal NumPy code. The fact that it can actually do that is an extra bonus. Numba's purpose was and still remains best at allowing you to still write the "hard-to vectorize" algorithm
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by travisoliphant 13y ago
Right now Numba is still pre 1.0 and so there will be issues for specific cases. The best way to get them solved is to provide your test-case so that we can grow the test suite. Right now, I agree that Cython and pure C++ is still the bes
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by travisoliphant 13y ago
Numba is trying to let you make a different choice for different sections of the same program so that you can do different styles of programming in code meant to be a library than you might do in other sections of code meant to be more "oca
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by travisoliphant 13y ago
Memoryviews are the step-child of NumPy arrays. The buffer-protocol was the real intent and the memory view the forgotten "example" until a few brave and noble Python devs rescued it from obscurity. Memory-views are not that useful to o
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by travisoliphant 13y ago
I'd love to see how Numba helps you. Give it a try: http://numba.pydata.org Slide deck: https://speakerdeck.com/pyconslides/numba-a-dynamic-python-c... IPython notebook examples: https://www.wakari.io/nb/travis/numba https://www.

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