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The State of Machine Learning Frameworks

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Re: The State of Machine Learning Frameworks

#101

Post by kuu »

lalaland1125 wrote: post #65
rayalez wrote: post #7

Anyone has any opinions on TF2.0? They've released it recently, and it seems like it should be much closer to PyTorch now, but I don't know enough to evaluate it properly. https://www.youtube.com/watch?v=EqWsPO8DVXk

TF2.0 (and in particular their recommended tf.keras) is simply a clone of the Pytorch API in most respects. There is no reason to use it vs just using Pytorch, especially as Pytorch now support easy model exporting for running in production.

Uhm I would think that the tf.keras API is a clone of Keras (I mean, Keras was made by a Google Engineer!!)... which came before Pytorch

Re: The State of Machine Learning Frameworks

#102

Post by smg »

option wrote: post #84
microtonal wrote: post #52

We are considering to move to PyTorch, we really dislike how the Tensorflow 1.x -> 2.0 transition is handled. For years a lot of stuff has been added to tf.contrib , some things were only in tf.contrib and now that it's dropped in TF a lot of project (including ours) have to do quite large rewrites. Since the last few 1.x iterations, Tensorflow has been complaining that the older RNN layers are deprecated and that we…

that’s what we did. We had a popular open source project using TF 1.x For it’s successor we chose Pytorch instead of TF 2 and have been very happy with this decision

Could you please share your open source project?

Re: The State of Machine Learning Frameworks

#104

Post by snendroid-ai »

I'm using Keras from last 3 years. Most of the time where I have to deal with core TF code is when I have to write some custom layers. I totally agree on a part where hacking together TF code seems nightmare (well, initially.. but not once you know what you're doing), where PyTorch more looks like blissful experience (I have not tried PyT yet, just speaking from reading all these comments). I'm genuinely curious about how one can use the trained PyTorch models in production? For example, I got 6 TF based translation models + 1 classification model running on single AWS instance with TensorFlow Serving with 1 GPU and 8 CPU cores. These 7 models are deployed to take advantage of all the resources of this instance and everything runs smoothly. Now considering I got these same models in PyTorch, what are my options to do the same?

Re: The State of Machine Learning Frameworks

#105

Post by joshvm »

pillefitz wrote: post #26
amrrs wrote: post #9

I think tensorflow dominates industry purely because of its capability of exporting the model into a coreml Android model or easy of moving it to production in a GCP environment or in whatever form. Pytorch might have to build a good production pipeline around it to catch up in this game. With fastai module that's built on Pytorch learning and developing Deep Learning solutions have become a lot easier. So there's a…

You can do the same with PyTorch, right? Just export the model as .onnx and import it with whatever inference engine you like.

I've done this with OpenVino and it works well enough. There are some gotchas with Onnx, some layers break or have weird bugs (upsample in particular used to be a problem).

Re: The State of Machine Learning Frameworks

#106

Post by phillipcarter »

What I really liked about this article is near the end, where it identifies two things:

* Automatic differentiation of higher-order differentiation being important, and how there's clearly room to disrupt there

* Increasing hardware diversity seems to mean that both frameworks will run into a brick wall as-is

Exciting space. It'll be fascinating to see how dramatically, or not, things change in the coming years.

Re: The State of Machine Learning Frameworks

#107

Post by bananocurrency »

jniedrauer wrote: post #56
terranoct wrote: post #40

Earlier quoted context omitted.

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 imag…

> Ubuntu fought a sea of distros and came out as what's very nearly an industry standard, if not an official one. I think you may be living in a bubble. I've been running devops for various shops for half a decade and I've only once used Ubuntu, because it was already being used by an acquisition. I won't deny that Ubuntu is popular. It's certainly got the lions share of the desktop market. But there is no such conse…

https://blog.digitalocean.com/where-do-droplets-form/ I don't think this feels like a bubble.

Re: The State of Machine Learning Frameworks

#108

Post by lalaland1125 »

kuu wrote: post #101
lalaland1125 wrote: post #65

Earlier quoted context omitted.

TF2.0 (and in particular their recommended tf.keras) is simply a clone of the Pytorch API in most respects. There is no reason to use it vs just using Pytorch, especially as Pytorch now support easy model exporting for running in production.

Uhm I would think that the tf.keras API is a clone of Keras (I mean, Keras was made by a Google Engineer!!)... which came before Pytorch

tf.keras contains a deprecated API that was the original keras as well as a new API that basically is a clone of Pytorch. You can tell the difference because only the new API works nicely with eager mode.
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