41–50 of 91 posts
It's a community-driven project, a Python take of Torch http://torch.ch/ . Several folks involved in development and use so far (a non-exhaustive list): * Facebook * Twitter * NVIDIA * SalesForce * ParisTech * CMU * Digital Reasoning * INRIA * ENS The maintainers work at Facebook AI Research
This project aside, I'm in love with that setup UI on the homepage telling you exactly how to get started given your current setup.
Just a personal (anti-)preference I guess
Guess there's no escaping Python. I had hoped Lua(jit) might emerge as a scientific programming alternative but with Torch now throwing its hat into the Python ring I sense a monoculture in the making. Bit of a shame really because Lua is a nice language and was an interesting alternative.
If only Mike Pall created a transpiler infrastructure layer on top of LuaJIT.
Every time I decide I'm going to get into Python frameworks again, and I start looking at code, and I see people making everything object-oriented, I bail Just a personal (anti-)preference I guess
It's a community-driven project, a Python take of Torch http://torch.ch/ . Several folks involved in development and use so far (a non-exhaustive list): * Facebook * Twitter * NVIDIA * SalesForce * ParisTech * CMU * Digital Reasoning * INRIA * ENS The maintainers work at Facebook AI Research
Not only that, but it appears to use the same core c libray (TH) as Lua torch.
Guess there's no escaping Python. I had hoped Lua(jit) might emerge as a scientific programming alternative but with Torch now throwing its hat into the Python ring I sense a monoculture in the making. Bit of a shame really because Lua is a nice language and was an interesting alternative.
The Python that you write when using these frameworks just the glue code / scripts. All you're doing is calling the framework's functions. Most of it gets thrown away (as researchers). The stuff that doesn't is self-contained and usually short. You're not writing 100k+ line codebases.
Lua may be faster for certain tasks (data processing), but the time it takes for does tasks is usually a rounding error in deep learning. Not to mention you can still code in C/C++ with pytorch.
If there is a monoculture in machine learning, it would be the deep learning monoculture.
Only a few months ago people saying that the deep learning library ecosystem was starting to stabilize. I never saw that as the case. The latest frontier for deep learning libraries is ensuring efficient support for dynamic computation graphs. Dynamic computation graphs arise whenever the amount of work that needs to be done is variable. This may be when we're processing text, one example being a few words while anot…
Earlier quoted context omitted.
A very large portion of performance problems can be mitigated with the use of cython and the new asyncio stuff. asyncio success story: https://magic.io/blog/asyncpg-1m-rows-from-postgres-to-pytho... cython: http://scikit-learn.org/stable/developers/performance.html
Luajit is at least 10x faster than python and easily obviates the need to mess around with cython. That's an easy win for Lua. Let's be honest: Torch has decided that if you cannot beat them, join them. It is about network effects. Not about Python better than Lua intrinsically.
Guess there's no escaping Python. I had hoped Lua(jit) might emerge as a scientific programming alternative but with Torch now throwing its hat into the Python ring I sense a monoculture in the making. Bit of a shame really because Lua is a nice language and was an interesting alternative.