The State of Machine Learning Frameworks • Hacker News Board

Re: The State of Machine Learning Frameworks

#31

Post by [deleted] » Thu, Oct 10, 2019, 5:11 PM UTC

[deleted]

Re: The State of Machine Learning Frameworks

#32

Post by pmiller2 » Thu, Oct 10, 2019, 5:12 PM UTC

rossdavidh wrote: post #23

...because in research, the person doing the programming gets to choose, whereas in industry, their boss does. (ducks)

More like people who have long since left the company get to choose.

Re: The State of Machine Learning Frameworks

#33

Post by ChefboyOG » Thu, Oct 10, 2019, 5:13 PM UTC

_coveredInBees wrote: post #19

We use Pytorch extensively in our startup. We tackle a lot of new research problems as consultants/partners to help develop products or devise new algorithms/models to solve tasks for our customers. We have never regretted our choice to pick Pytorch. I found the article pretty spot on when comparing Tensorflow and Pytorch. The things that have appealed to me about Pytorch are: 1. Extremely easy to debug and work with…

Agreed, but projects like Cortex are narrowing the gap around deployment imo https://github.com/cortexlabs/cortex

Re: The State of Machine Learning Frameworks

#34

Post by king_magic » Thu, Oct 10, 2019, 5:19 PM UTC

Anecdotally, I've dumped TensorFlow in favor of PyTorch for almost all new work I'm doing at my organization (industry focused). Biggest gripes with TensorFlow are overly complex APIs, instability from release to release, constantly broken code in Google's repos, and poor documentation. Maybe TF 2.0 will be better, but for me, the PyTorch ship has already sailed, and I am sailing on it.

Re: The State of Machine Learning Frameworks

#35

Post by krastanov » Thu, Oct 10, 2019, 5:20 PM UTC

A funny reason I am stuck with Tensorflow: I would love to use PyTorch, but for the moment only Tensorflow has support for complex numbers.

pytorch bug tracker: https://github.com/pytorch/pytorch/issues/755

Re: The State of Machine Learning Frameworks

#36

Post by probably_wrong » Thu, Oct 10, 2019, 5:20 PM UTC

swampthinker wrote: post #8

If I were to start a theoretical computer vision company today, which would I be better off using?

My very biased opinion: you start with PyTorch because it's easy to develop and debug, and there's no point in having the fastest tools for a model that you can't train properly.

Once your model is running, and if/when you start hitting performance bottlenecks, then you consider migrating your model to TensorFlow.

Re: The State of Machine Learning Frameworks

#37

Post by [deleted] » Thu, Oct 10, 2019, 5:20 PM UTC

amrrs wrote: post #25

bonoboTP wrote: post #13

Earlier quoted context omitted.

And simply TensorFlow was there earlier, so people implemented stuff in it. I think there's more inertia in industry, whereas researchers may more easily switch frameworks between two papers.

And Keras was there too which made a lot of people us Keras with TF. I doubt if TF would have risen to this popularity hadn't it been to Keras.

[deleted]

Re: The State of Machine Learning Frameworks

#38

Post by acgan » Thu, Oct 10, 2019, 5:22 PM UTC

probably_wrong wrote: post #36

swampthinker wrote: post #8

If I were to start a theoretical computer vision company today, which would I be better off using?

My very biased opinion: you start with PyTorch because it's easy to develop and debug, and there's no point in having the fastest tools for a model that you can't train properly. Once your model is running, and if/when you start hitting performance bottlenecks, then you consider migrating your model to TensorFlow.

Yup, this echoes the philosophy at e.g. Tesla. Development speed matters more than performance at first.

Re: The State of Machine Learning Frameworks

#39

Post by olavgg » Thu, Oct 10, 2019, 5:22 PM UTC

barbecue_sauce wrote: post #28

onlyyimte wrote: post #18

It's only a matter of time until PyTorch will also dominate industry. It's always like this. Think how Ubuntu took over the server market because amateurs were preferring it instead of Redhat/CentOS. And when they became professionals or were in a position to decide, they also put Ubuntu on the server because this is what they knew best.

When did Ubuntu take over the server market?

With the introduction of cloud

https://www.phoronix.com/scan.php?page=news_item&px=Canonica...

https://www.flickr.com/photos/42204979@N00/5266983786/sizes/... https://thecloudmarket.com/stats

Re: The State of Machine Learning Frameworks

#40

Post by terranoct » Thu, Oct 10, 2019, 5:22 PM UTC

jniedrauer wrote: post #27

onlyyimte wrote: post #18

It's only a matter of time until PyTorch will also dominate industry. It's always like this. Think how Ubuntu took over the server market because amateurs were preferring it instead of Redhat/CentOS. And when they became professionals or were in a position to decide, they also put Ubuntu on the server because this is what they knew best.

I'm not sure that's a great example, given that AWS mostly runs on RHEL-based OSs and Debian is still preferred for Docker. Ubuntu did not "take over the server market".

RHEL is popular for solutions like running a datacenter mostly because it has a nice enterprise support story. It's what the E in that acronym is for, after all. Ubuntu, meanwhile, is quite popular among us mere mortals who have to fix our own boxen.

Debian is popular for Docker images exactly because many of the people trying Docker were already familiar with Ubuntu. Those users quickly ended up wanting smaller images, making Debian an obvious thing to try out since Ubuntu is basically Debian with bells on.

Ubuntu fought a sea of distros and came out as what's very nearly an industry standard, if not an official one. The 90s were a fricking mess by comparison. Slackware on floppies.

(And now I need "Slackware on floppies" dubbed over the "Jesus wept" scene from Hellraiser.)