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The stack is very volatile and unmaintainable because it doesn't need to be maintainable. Exactly why we have unmaintainable software in other domains. During t

by rfoo 4y ago

The stack is very volatile and unmaintainable because it doesn't need to be maintainable. Exactly why we have unmaintainable software in other domains. During the last 10 years there are ALWAYS totally new model architecture with new operations (or in case of CV new bizarre uses of Conv). By the time you get your performant perfectly maintainable masterpiece ready it's not needed anymore. The stack optimizes for flexibility and iteration speed naturally, just like why people use Rails. In fact I'd love to see that Transformer really dominates. We can then start to converge on software. And compute-wise transformers are really simple, too!

nunobrito 4y ago
Still a poor excuse. Had they written this in Java and things wouldn't be so difficult both on performance and maintainability. Never understood why people think that indented languages are any simpler when in fact they bring all kinds of trouble for getting things done.
- revelio 4y ago
  There's a Java ML library called Tribuo that might be worth looking at.
  - dopidopHN 4y ago
    Thanks, the boring aspect of Java is appealing here.
- rfoo 4y ago
  There's deeplearning4j (from Theano days!), go figure why it didn't take off.
wootland 4y ago
Does this mean it would be easy to move off Python all together? It seems like the problem stems from everyone using pytorch at the base layer. How realistic is it recreate those apis in another, more modern language. Coding in Rust, Go... then distributing a single binary vs. pip hell seems like it would be worth it.
- sroussey 4y ago
  Go would be interesting for the reason you could send an executable. I’d love for JS/TS to dominate as well. Use ‘bun bun’ to send an executable if need be, but also use in in web backends.
- rfoo 4y ago
  Check https://pytorch.org/tutorials/advanced/cpp_frontend.html https://pytorch.org/tutorials/advanced/cpp_frontend.html You can easily build a standalone binary (well, it would be GiB+ if you use CUDA... but that's the cost of statically linking cu*), had you coded your model and training loop in C++. It then happily runs everywhere as long as a NVIDIA GPU driver is available (don't need to install CUDA). Protip: Your AI research team REALLY DON'T WANT TO DO THIS BECAUSE THEY LOVE PYTHON. Having Python, even with the dependency management shit, is a feature, not a bug. (if you want Rust / Go and don't want to wrapping libtorch/tf then you have a lot of work to do but yeah it's possible. also there are model compiler guys [1] where the promise is model.py in model.o out you just link it with your code) [1] https://mlc.ai https://mlc.ai
colinsane 4y ago
> The stack optimizes for flexibility and iteration speed naturally “unmaintainable” (as in “i’m spending an hour each day sorting out which dep update broke my project”) usually gets in the way of the former point.

by rfoo 4y ago

nunobrito 4y ago

Still a poor excuse. Had they written this in Java and things wouldn't be so difficult both on performance and maintainability. Never understood why people think that indented languages are any simpler when in fact they bring all kinds of trouble for getting things done.

revelio 4y ago
There's a Java ML library called Tribuo that might be worth looking at.
- dopidopHN 4y ago
  Thanks, the boring aspect of Java is appealing here.
rfoo 4y ago
There's deeplearning4j (from Theano days!), go figure why it didn't take off.

wootland 4y ago

Does this mean it would be easy to move off Python all together? It seems like the problem stems from everyone using pytorch at the base layer. How realistic is it recreate those apis in another, more modern language. Coding in Rust, Go... then distributing a single binary vs. pip hell seems like it would be worth it.

sroussey 4y ago
Go would be interesting for the reason you could send an executable. I’d love for JS/TS to dominate as well. Use ‘bun bun’ to send an executable if need be, but also use in in web backends.
rfoo 4y ago
Check https://pytorch.org/tutorials/advanced/cpp_frontend.html https://pytorch.org/tutorials/advanced/cpp_frontend.html You can easily build a standalone binary (well, it would be GiB+ if you use CUDA... but that's the cost of statically linking cu*), had you coded your model and training loop in C++. It then happily runs everywhere as long as a NVIDIA GPU driver is available (don't need to install CUDA). Protip: Your AI research team REALLY DON'T WANT TO DO THIS BECAUSE THEY LOVE PYTHON. Having Python, even with the dependency management shit, is a feature, not a bug. (if you want Rust / Go and don't want to wrapping libtorch/tf then you have a lot of work to do but yeah it's possible. also there are model compiler guys [1] where the promise is model.py in model.o out you just link it with your code) [1] https://mlc.ai https://mlc.ai

colinsane 4y ago

> The stack optimizes for flexibility and iteration speed naturally “unmaintainable” (as in “i’m spending an hour each day sorting out which dep update broke my project”) usually gets in the way of the former point.