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The State of Machine Learning Frameworks

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Re: The State of Machine Learning Frameworks

#51

Post by rlpb »

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".

Can you provide any evidence to support that claim?

Here's a report that suggests the exact opposite:

"Don't let the revenue numbers lead you to thinking Red Hat Enterprise Linux (RHEL) is more popular than Ubuntu. By The Cloud Market's Jan. 8, 2019 count of Amazon Web Services (AWS) instances, Ubuntu is used in 314,492 instances, more than any other operating system, while RHEL is used in 22,072 instances."

https://www.zdnet.com/article/inside-ubuntus-financials/

Disclosure: I work for Canonical, but as an engineer; I'm not in marketing or anything and that's not my job. But I do get the impression that Ubuntu is way ahead in general use in the cloud, and is also the generally used base for Docker images (I don't immediately see how to get that statistic out of Docker Hub). I didn't think this statement was controversial.

Re: The State of Machine Learning Frameworks

#52

Post by microtonal »

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 have to move to Keras RNN layers, which they claim to be equivalent. However, when we tried a couple of months back, it made RNN-based training 45% slower. It is all fixable, but it takes time and a lot of testing of all the model variants to see if there are no regressions. It feels quite a bit worse than Python 2 -> 3.

I am a bit saddened by all of this, because I really liked how easy it is to define a graph in Tensorflow in Python, serialize it, and then use its minimalistic C API to use the graph in Go, Rust, or wherever you need it.

How is your experience with PyTorch and backwards API compatibility (I know that they only reached 1.0 fairly recently)?

Re: The State of Machine Learning Frameworks

#54

Post by andbberger »

machinelearning wrote: post #42

Why engineers like Tensorflow: - More code to check-in (Looks more productive) - More infrastructure, e.g. checkpoints, exporters etc. (Looks like they're doing more work) - Fancy visualizations (Allows them to look impressive while presenting loss plots) - Easier to reuse things others have implemented and still get credit for it (TF model zoo, research repo etc.) Why researchers like pytorch: - Way easier to hack t…

Speak for yourself mate.

Why I use tensorflow:

- keras

- I used tensorflow yesterday

Re: The State of Machine Learning Frameworks

#55

Post by minimaxir »

For the majority of production use cases (which tend to get all the AI/ML hype), TensorFlow/Keras is more than powerful enough and accessible enough. If you need to dive down to custom layers/optimizers, PyTorch has value there, but for people looking to get their start in AI/ML, the meme that "TensorFlow sucks" is highly misleading.

Re: The State of Machine Learning Frameworks

#56

Post by jniedrauer »

terranoct wrote: post #40
jniedrauer wrote: post #27

Earlier quoted context omitted.

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 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 consensus in the server market.

Re: The State of Machine Learning Frameworks

#58

Post by mlthoughts2018 »

machinelearning wrote: post #42

Why engineers like Tensorflow: - More code to check-in (Looks more productive) - More infrastructure, e.g. checkpoints, exporters etc. (Looks like they're doing more work) - Fancy visualizations (Allows them to look impressive while presenting loss plots) - Easier to reuse things others have implemented and still get credit for it (TF model zoo, research repo etc.) Why researchers like pytorch: - Way easier to hack t…

> “More code to check-in (Looks more productive)”

Not even bad engineers try to pretend like this is true.

Re: The State of Machine Learning Frameworks

#59

Post by jniedrauer »

rlpb wrote: post #51
jniedrauer wrote: post #27

Earlier quoted context omitted.

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".

Can you provide any evidence to support that claim? Here's a report that suggests the exact opposite: "Don't let the revenue numbers lead you to thinking Red Hat Enterprise Linux (RHEL) is more popular than Ubuntu. By The Cloud Market's Jan. 8, 2019 count of Amazon Web Services (AWS) instances, Ubuntu is used in 314,492 instances, more than any other operating system, while RHEL is used in 22,072 instances." https://…

A more apt comparison would be Ubuntu vs CentOS (and amazon linux and all the other repackaged RHELs). Ubuntu definitely has more market share, but the OP implied that it was "just the way things are done now" which is not true.

The last data seems to be about 3 years old, and Ubuntu was about 1.5-2x the CentOS/amazon linux share. I suspect that's changing with the release of amazon linux 2, but there's no data to back that up.

Amazon itself primarily uses a RHEL based distro, which is what I meant originally.

Re: The State of Machine Learning Frameworks

#60

Post by prostodata »

machinelearning wrote: post #42

Why engineers like Tensorflow: - More code to check-in (Looks more productive) - More infrastructure, e.g. checkpoints, exporters etc. (Looks like they're doing more work) - Fancy visualizations (Allows them to look impressive while presenting loss plots) - Easier to reuse things others have implemented and still get credit for it (TF model zoo, research repo etc.) Why researchers like pytorch: - Way easier to hack t…

PyTorch is simpler, easer to use, consumes less memory and allows for dynamic dynamic computational graphs (dynamic operations during the forward pass).
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