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joshgev | 3 years ago
I want to see a DSL that can be used to describe models elegantly and then export them either to a shared object or to something that can be run with a runtime (in this case IREE). Things like ONNX and TorchScript promised this but I've had little luck getting these to work well enough to trust them in large scale production deployments.
I understand that PyTorch is an awesome tool for researchers, but it doesn't necessarily fit into a prod environment.
plonk|3 years ago
You need to write some infrastructure around PyTorch to make it work. Something like a key/mapping in each checkpoint that says which architecture to choose with which parameters.
It sure could be easier, but is saving the model's code into the checkpoint enough? Things like the data pre-processing expected by the model would also have to be included for it to really be self-contained.
raihansaputra|3 years ago
dr_zoidberg|3 years ago
Admittedly we're on a reasonably easy situation: we just have to deploy models (some from scikit-learn, some from Keras, some from PyTorch) to various users who mainly run a specific version of python under Windows and Linux, with CPU and GPU support.
btwillard|3 years ago
mccoyb|3 years ago
Why should I look at Aesara’s representation of multi-dimensional array programs when I might already use JAX’s?
Does Aesara support a staging transformation that allows me to construct programs in your representation from a subset of Python?
I’m personally interested in the answers to these questions, given what I know about IREE, JAX, and XLA — as a user in the space, I haven’t been able to determine how Aesara would actually benefit me over JAX.
Note that I know that Aesara can use JAX as a backend — but I’m trying to ascertain what one extra layer buys me.
ioedward|3 years ago
Some of the largest deployments of ML are using PyTorch models, e.g. OpenAI, Meta, Microsoft.