Pytorch is widely accepted as the de facto ML framework in both research and industry. TensorFlow comes second in industry. Jax is hardly used at all, but uses the same backend as TensorFlow.
Triton is a python-like language to define ML math operations that run efficiently on hardware accelerators like GPUs or TPUs. OpenAI open sourced it. If there's a particular math operation you have a unique need for in your model, and it hasn't already been implemented by some other library, and it's important for efficiency, you'd probably write it in triton these days. It'll be compiled to an intermediate representation, then to an efficient runtime.
The course linked deals with "MLSys", or "ml systems". That means using GPUs and other hardware accelerators efficiently to run ML math operations on one or more computers.
95% of working ML engineers will never need to write Triton, and will be more than satisfied with PyTorch. Many more ML engineers will, nevertheless, write Triton code, because it is interesting, fun, easy, and people are impressed when you tell them you did.
Hosting pytorch models efficiently is currently awkward, because there's no clear winner in the ecosystem. ONNX is a way of representing model graphs in a framework-agnostic way. Other systems can interpret ONNX graphs to do inference. So sometimes, when someone wants to host a pytorch model, they turn it into an ONNX model and run it with an efficient runtime on CPUs or GPUs.
> Triton is a python-like language to define ML math operations that run efficiently on hardware accelerators like GPUs or TPUs. OpenAI open sourced it.
This is incorrect. Triton has literally no path to TPU and it has always been open source because it was Philippe Tillet's PhD project (OAI simply hired Philippe).
> 95% of working ML engineers will never need to write Triton, and will be more than satisfied with PyTorch.
Maybe 95% of hobbyist ML engineers but professional ML engineers are absolutely writing Triton day-to-day (eg FB has an army of such people). Even if you're not writing Triton you're still using Triton through inductor.
> because it is interesting, fun, easy, and people are impressed when you tell them you did
Professionals write Triton not for any of the reasons you mentioned but for the same reason they wrote CUDA kernels prior: it's a path to peak performance for their specific workloads (where stock PyTorch kernels have mediocre performance).
Triton is a python-like language to define ML math operations that run efficiently on hardware accelerators like GPUs or TPUs. OpenAI open sourced it. If there's a particular math operation you have a unique need for in your model, and it hasn't already been implemented by some other library, and it's important for efficiency, you'd probably write it in triton these days. It'll be compiled to an intermediate representation, then to an efficient runtime.
The course linked deals with "MLSys", or "ml systems". That means using GPUs and other hardware accelerators efficiently to run ML math operations on one or more computers.
95% of working ML engineers will never need to write Triton, and will be more than satisfied with PyTorch. Many more ML engineers will, nevertheless, write Triton code, because it is interesting, fun, easy, and people are impressed when you tell them you did.
Hosting pytorch models efficiently is currently awkward, because there's no clear winner in the ecosystem. ONNX is a way of representing model graphs in a framework-agnostic way. Other systems can interpret ONNX graphs to do inference. So sometimes, when someone wants to host a pytorch model, they turn it into an ONNX model and run it with an efficient runtime on CPUs or GPUs.