The State of Machine Learning Frameworks in 2019

24 pointsposted 4 days ago
by jxmorris12
(thegradient.pub)

15 Comments

Scene_Cast2

2 hours ago

I found out that in the embedded world (think microcontrollers without an MMU), Tensorflow lite is still the only game in town (pragmatically speaking) for vendor-supported hardware acceleration.

leviliebvin

2 hours ago

I recently tried to port my model to JAX. Got it all working the "JAX WAY", and I believe I did everything correct, with one neat top level .jit() applied to the training step. Unfortunately I could not replicate the performance boost of torch.compile(). I have not yet delved under the hood to find the culprit, but my model is fairly simple so I was sort of expecting JAX JIT to perform just as well if not better than torch.compile().

Have anyone else had similiar experiences?

yberreby

41 minutes ago

JAX code usually ends up being way faster than equivalent torch code for me, even with torch.compile. There are common performance killers, though. Notably, using Python control flow (if statements, loops) instead of jax.lax primitives (where, cond, scan, etc).

CaptainOfCoit

4 hours ago

> In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves.

Seems they were pretty spot on! https://trends.google.com/trends/explore?date=all&q=pytorch,...

But to be fair, it was kind of obvious around ~2023 without having to look at metrics/data, you just had to look at what the researchers publishing novel research used.

Any similar articles that are a bit more up to date, maybe even for 2025?

fleahunter

an hour ago

Interesting point about the shift towards PyTorch. It really has been fascinating to see how preferences in frameworks can impact the entire research landscape. I remember back in 2017, I felt like I was constantly hearing about TensorFlow everywhere, and then out of nowhere, PyTorch just started gaining this insane momentum. It was almost like watching a sports team come out of nowhere to win the championship!

In my experience, a lot of it comes down to the community and the ease of use. Debugging in PyTorch feels way more intuitive, and I wonder if that’s why so many people are gravitating toward it. I’ve seen countless tutorials and workshops pop up for PyTorch compared to TensorFlow recently, which speaks volumes to how quickly things can change.

But then again, TensorFlow's got its enterprise backing, and I can't help but think about the implications of that. How long can PyTorch ride this wave before it runs into pressure from industry demands? And as we look toward 2025, do you think we'll see a third contender emerge, or will it continue to be this two-horse race?

bonoboTP

19 minutes ago

TensorFlow was an overengineered Google-style mess and they constantly made breaking changes.

All the graph building and session running was way too complex, with too much global state and variable sharing was complicated and based on naming and variable scopes and name scopes and so on.

It was an okay try, but that design simply didn't work so well for quick prototyping, iterating, debugging that's crucial in research.

PyTorch was much closer to just writing straightforward numpy code. TensorFlow 2 then tried to catch up with "eager mode", but in the background it was still a graph and tracing often broke and you had to write the code very carefully and with limitations.

In the end, Pytorch also developed proper production and serving tools as well as graph compilation, so now there's basically no reason to go to TensorFlow. Not even Google researchers use it (they use jax). I guess some industries still use it but at some point I expect Google to shut down TF and focus on the JAX ecosystem with some kind of conversion tools for TF.

CaptainOfCoit

an hour ago

> But then again, TensorFlow's got its enterprise backing, and I can't help but think about the implications of that. How long can PyTorch ride this wave before it runs into pressure from industry demands?

PyTorch has a huge collection of companies, organizations and other entities backing it, it's not gonna suddenly disappear soon, that much is clear. Take a look at https://pytorch.org/foundation/ for a sample

kenjackson

28 minutes ago

The thing about Tensorflow in 2017 is that everyone acknowledged how difficult it was to use. While it was almost the only game in town, no one was happy. Those are probably the areas where an upstart can come in and disrupt.

Legend2440

3 hours ago

It’s still all pytorch.

Unless you’re working at Google, then maybe you use JAX.

mattnewton

2 hours ago

JAX is quite popular in many labs outside of Google doing large scale training runs, because up until recently the parallelism ergonomics were way better. PyTorch core is catching up (maybe already witn the latest release, haven’t used it yet) and there are a lot of PyTorch using projects to study though.

oceansky

4 hours ago

In 2019 I delivered a instance segmentation project and I used Mask RCNN and tensorflow.

Nowadays it looks like yolo absolutely dominates this segment. Any data scientists can chime in?

bonoboTP

2 hours ago

SAM (Segment Anything Model) by Meta is a popular go-to choice for off the shelf segmentation.

But the exciting new research is moving beyond the narrow task of segmentation. It's not just about having new models that get better scores but building larger multimodal systems, broader task definitions etc.

deepsquirrelnet

3 hours ago

I haven’t used RCNN, but trained a custom YOLOv5 model maybe 3-4 years ago and was very happy with the results.

I think people have continued to work on it. There’s no single lab or developer, it mostly appears that the metrics for comparison are usually focused on the speed/MAP plane.

One nice thing is that even with modest hardware, it’s low enough latency to process video in real time.