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I've been out of the ML game for a little while now (2+ years) but previously was under the impression everyone loved Keras. This thread says otherwise. The on

I've been out of the ML game for a little while now (2+ years) but previously was under the impression everyone loved Keras. This thread says otherwise. The only mentioned alternative so far is `ggml`, which I have heard mentions of but am not familiar with. What are other alternatives & their corresponding pros and cons? Now, I am familiar with: - Tensorflow (complex API right?) - Keras (simple API?)

andy99 3y ago
Huggingface I would consider a high level framework, that uses pytorch as a backend (they may now be using rust/candle as well?) Mojo is another ML specific python dialect. I think most people including me associate Keras with being very simple and high level, which isn't really correct now based on the article. ggml is somewhat specialized, I was giving it as an example of the kind of framework I think we'll see more of, that is specialized, dependency free, written in a compiled language and directly optimized for speed. As use cases consolidate around specific architecture like LLMs, a faster and simpler low lever framework becomes preferable to a swiss army knife with abstraction upon abstraction like pytorch.
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uoaei 3y ago
Depends what kind of ML. If you were just spinning up models to shove data through, sure. If you wanted to craft a model to your particular needs, you will have a much better time with Torch, even 5 years ago when online tutorials were filled with TF/Keras. I think Keras more or less died when the TF 2.0 transition was botched so badly. Many moved to Torch and never looked back. FWIW I actually prefer JAX but my work is atypical compared to the usual "predict churn" or whatever application.
the__alchemist 3y ago
My guess; perspective from a noob who's been using TF (Which I think absorbed Keras's API??) and OpenVINO: The Data Science/Python crew prefer PyTorch, and it's become self-perpetuating by WOM. So, people new are being recommended to use Torch, and it spreads that way.
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iskander 3y ago
Keras started out great but grew clumsily, making custom use-cases cumbersome and error prone and also became inextricably linked with TensorFlow (which was a big headache compared with PyTorch). It seems like this rewrite cleans up the meandering mess of Keras's middle period, or at least I'd be willing to give it a try.
jasonjmcghee 3y ago
It's been a few years since I did Keras dev full time, but debugging was a nightmare. PyTorch- as long as you don't compile and you run on CPU, it's pretty much like debugging standard code. Debugging CUDA is harder. Edit: Seems like `run_eagerly` solves a lot of the pain of debugging Keras code.

andy99 3y ago

Huggingface I would consider a high level framework, that uses pytorch as a backend (they may now be using rust/candle as well?) Mojo is another ML specific python dialect. I think most people including me associate Keras with being very simple and high level, which isn't really correct now based on the article. ggml is somewhat specialized, I was giving it as an example of the kind of framework I think we'll see more of, that is specialized, dependency free, written in a compiled language and directly optimized for speed. As use cases consolidate around specific architecture like LLMs, a faster and simpler low lever framework becomes preferable to a swiss army knife with abstraction upon abstraction like pytorch.

deleted 3y ago
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uoaei 3y ago

Depends what kind of ML. If you were just spinning up models to shove data through, sure. If you wanted to craft a model to your particular needs, you will have a much better time with Torch, even 5 years ago when online tutorials were filled with TF/Keras. I think Keras more or less died when the TF 2.0 transition was botched so badly. Many moved to Torch and never looked back. FWIW I actually prefer JAX but my work is atypical compared to the usual "predict churn" or whatever application.

the__alchemist 3y ago

My guess; perspective from a noob who's been using TF (Which I think absorbed Keras's API??) and OpenVINO: The Data Science/Python crew prefer PyTorch, and it's become self-perpetuating by WOM. So, people new are being recommended to use Torch, and it spreads that way.

deleted 3y ago

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iskander 3y ago

Keras started out great but grew clumsily, making custom use-cases cumbersome and error prone and also became inextricably linked with TensorFlow (which was a big headache compared with PyTorch). It seems like this rewrite cleans up the meandering mess of Keras's middle period, or at least I'd be willing to give it a try.

jasonjmcghee 3y ago

It's been a few years since I did Keras dev full time, but debugging was a nightmare. PyTorch- as long as you don't compile and you run on CPU, it's pretty much like debugging standard code. Debugging CUDA is harder. Edit: Seems like `run_eagerly` solves a lot of the pain of debugging Keras code.