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PyTorch 2.0

pytorch.org

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Re: PyTorch 2.0

#11

Post by belval »

posharma wrote: post #8
belval wrote: post #7

> We believe that this is a substantial new direction for PyTorch – hence we call it 2.0. torch.compile is a fully additive (and optional) feature and hence 2.0 is 100% backward compatible by definition. How about just calling it PyTorch 1.14 if it's backward compatible? Version numbering shouldn't be used as a marketing gimmick.

Is this really the biggest problem that needs to be solved in AI?

Not sure I understand that question, is versioning the biggest problem no, but it costs nothing to keep semver and prevent production headaches later.

If you meant inference speed then yeah it's a very big problem so it's good that they are addressing it.

Re: PyTorch 2.0

#14

Post by staunch »

chazeon wrote: post #12

How is PyTorch compares to JAX and its stack?

PyTorch and JAX are both open-source libraries for developing machine learning models, but they have some important differences. PyTorch is a more general-purpose library that provides a wide range of functionalities for developing and training machine learning models. It also has strong support for deep learning and is used by many researchers and companies in production environments.

JAX, on the other hand, is designed specifically for high-performance machine learning research. It is built on top of the popular NumPy library and provides a set of tools for creating, optimizing, and executing machine learning algorithms with high performance. JAX also integrates with the popular Autograd library, which allows users to automatically differentiate functions for training machine learning models.

Overall, the choice between PyTorch and JAX will depend on the specific requirements and goals of the project. PyTorch is a good choice for general-purpose machine learning development and is widely used in industry, while JAX is a better choice for high-performance research and experimentation.

https://chat.openai.com/chat

Re: PyTorch 2.0

#15

Post by mi_lk »

belval wrote: post #11
posharma wrote: post #8

Earlier quoted context omitted.

Is this really the biggest problem that needs to be solved in AI?

Not sure I understand that question, is versioning the biggest problem no, but it costs nothing to keep semver and prevent production headaches later. If you meant inference speed then yeah it's a very big problem so it's good that they are addressing it.

what exact production headaches you are expecting by bump the number from 1.13 -> 2.0, while all existing codes keep working as before?

And how is it different from bumping 1.13 to 1.14, even if they named it 1.14?

Re: PyTorch 2.0

#16

Post by whimsicalism »

posharma wrote: post #8
belval wrote: post #7

> We believe that this is a substantial new direction for PyTorch – hence we call it 2.0. torch.compile is a fully additive (and optional) feature and hence 2.0 is 100% backward compatible by definition. How about just calling it PyTorch 1.14 if it's backward compatible? Version numbering shouldn't be used as a marketing gimmick.

Is this really the biggest problem that needs to be solved in AI?

No? What would have given you that impression?

Oh, I see. You were trying to be dismissive.

Re: PyTorch 2.0

#17

Post by whimsicalism »

staunch wrote: post #14
chazeon wrote: post #12

How is PyTorch compares to JAX and its stack?

PyTorch and JAX are both open-source libraries for developing machine learning models, but they have some important differences. PyTorch is a more general-purpose library that provides a wide range of functionalities for developing and training machine learning models. It also has strong support for deep learning and is used by many researchers and companies in production environments. JAX, on the other hand, is desi…

I was reading this and thinking it was a pretty terrible answer - glad it is just generated by an AI and not you personally so I'm not insulting you.

JAX is basically numpy on steroids and lets you do a lot of non-standard things (like a differentiable physics simulation or something) that would be harder with Pytorch.

They are both "high-performance."

Pytorch is more geared towards traditional deep learning and has the utilities and idioms to support it.

Re: PyTorch 2.0

#18

Post by cube2222 »

staunch wrote: post #14
chazeon wrote: post #12

How is PyTorch compares to JAX and its stack?

PyTorch and JAX are both open-source libraries for developing machine learning models, but they have some important differences. PyTorch is a more general-purpose library that provides a wide range of functionalities for developing and training machine learning models. It also has strong support for deep learning and is used by many researchers and companies in production environments. JAX, on the other hand, is desi…

It's funny, cause already after the first sentence it felt like ChatGPT, probably because I've played with it a lot these past few days, and expectedly I found a disclaimer at the end.

That said, the answer isn't really useful, as it's very generic, without anything concrete (other than the mention of Autograd) imo.

Though a follow up question might improve on that.

Re: PyTorch 2.0

#19

Post by pdntspa »

belval wrote: post #7

> We believe that this is a substantial new direction for PyTorch – hence we call it 2.0. torch.compile is a fully additive (and optional) feature and hence 2.0 is 100% backward compatible by definition. How about just calling it PyTorch 1.14 if it's backward compatible? Version numbering shouldn't be used as a marketing gimmick.

They're saying it represents a change in direction and is a pretty big feature, traditionally that's been a good reason to increment a major version number.
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