Why?
100% anecdotal --
The Keras interface for tensorflow makes it easy & fast to make "good enough" models. That is often a driving factor
The State of Machine Learning Frameworks
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Re: The State of Machine Learning Frameworks
#12Why?
Well, you could read the article :^) As a summary, though: PyTorch has become dominant in research because of its API (both its stability + having eager mode). TF has become dominant in industry because A. it came out several years before PyTorch and industry is slow to move, B. It supported a lot of production use cases (mobile, serving, removing Python overhead) that PyTorch didn't for a long time.
If only articles were as concise as your summary is I would enjoy reading them but as long as they are many pages long I have no time to read beyond the titles, abstracts, conclusions and comments.
BTW I've also read (here on HN) PyTorch learns much faster than TensorFlow does.
Re: The State of Machine Learning Frameworks
#13I 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…
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.
Re: The State of Machine Learning Frameworks
#14Anyone 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
Jeremy Howard [0] has some takes here [1], mostly negative if I recall correctly.
[0] https://www.fast.ai/about/ [1] https://www.youtube.com/watch?v=J6XcP4JOHmk&t=4152s
Re: The State of Machine Learning Frameworks
#15In my experience in computer vision research it doesn’t matter what you use, yes, immediate mode is slightly more convenient, but research time is influenced much more by your computing power, dataset acqusition/labeling/relabeling power and, last but not the least, by code quality and easy and efficient collaboration - thats why you need tools like DVC/Argoproj. We did get amazing results using Caffe v1 back in the day.
Re: The State of Machine Learning Frameworks
#16Going from doing ML research to data ingestion and analysis to web frameworks to API design, blogging and static site generation is very powerful. The trend seems to be that python will dominate all of these .
Re: The State of Machine Learning Frameworks
#17Earlier quoted context omitted.
Well, you could read the article :^) As a summary, though: PyTorch has become dominant in research because of its API (both its stability + having eager mode). TF has become dominant in industry because A. it came out several years before PyTorch and industry is slow to move, B. It supported a lot of production use cases (mobile, serving, removing Python overhead) that PyTorch didn't for a long time.
If only articles were as concise as your summary is I would enjoy reading them but as long as they are many pages long I have no time to read beyond the titles, abstracts, conclusions and comments. BTW I've also read (here on HN) PyTorch learns much faster than TensorFlow does.
Then it wouldn't be an article, it would be a summary :)
Re: The State of Machine Learning Frameworks
#18It'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.
Re: The State of Machine Learning Frameworks
#19We 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. Being able to debug effortlessly in PyCharm makes life very easy.
2. The API is quite clean and nice and fits in really well with Python and nothing feels hacky. I've developed my own Keras-like framework for experimentation, training and evaluating models quickly and easily and the entire experience has been really enjoyable.
3. The nicest thing though is that as the article points out, a huge percentage of researchers have moved to Pytorch and this allows us to more easily look at other researcher's code and experiment with things easily and incorporate ideas and cutting-edge research into our own work. Even for things that are released in TensorFlow, if it is an important publication that gains attention and traction in the community, you will likely have implementations in Pytorch pop up soon enough.
I do think that TensorFlow still has an edge on the deployment at scale/mobile side of things as pointed out by the article. But Pytorch is a lot younger and they are making a lot of progress with every release in that space.
Re: The State of Machine Learning Frameworks
#20And Amazon is still trying to desperately get people to adopt MXNet.
My former employer pulled in a few AWS data scientists to consult with us on a few projects and based on my interactions it seemed like they were under some directive to strongly discourage anything that wasn't a built-in AWS plug-and-play sagemaker algorithm. It was not a positive experience because of course most of them are half baked.