I kinda gave up on Julia for deep learning since it’s so buggy. I am using PyTorch now. Not great but at least it works!
(I also remember getting frustrated by frequent uninterruptible kernel hangs in Jupyter, but that might have been a skill issue on my part. But it was definitely a friction I don't encounter with python. When I was developing in Julia I remember feeling anxiety/dread about hitting enter on new cells, double and triple checking my code lest I initiate an uninterruptible error and have to restart my kernel and lose all my compilation progress, meaning I'll have to wait a long time again to run code and generate new graphs.)
Tiny example (which blends Julia-the-language and Julia-the-ecosystem, for better and worse): I just timed reading the most recent CSV I generated in real life, a relatively small 14k rows x 19 columns. 10ms in Julia+CSV+DataFrames, 37ms in Python+Pandas...ie much faster in Julia but also not a pain point either way.
- Lack of multi-GPU support,
- some other weird bugs related to autograd which i never fully figured out,
- and the killer one: none of my coauthors used Julia, so I decided to just go with PyTorch.
PyTorch has been just fine, and it's nice to not have to reinvent to wheel for every new model architecture.
Also, it's now pretty easy to call Python from Julia (and vice versa) [1]. I haven't used it for deep learning, but I've been using it to implement my algorithms in Julia while making use of Jax-based libraries from Python so it's certainly quite smooth and ergonomic.
sometimes that grad student is a brilliantly productive programmer + the libraries reach escape velocity and build a community, and then you get areas where Julia is state of the art like in differential equation solving, or generally other areas of "classical" scientific computing.
in other cases the grad student is merely a very good programmer, and they just sort of float along being "almost but not quite there" for a long time, maybe abandoned depending on the maintainer's career path.
the latter case is pretty common in the machine learning ecosystem. a lot of people get excited about using a fast language for ML, see that Julia can do what they want in a really cool way, and then run into some breaking problem or missing feature ("will be fixed eventually") after investing some time in a project.
The Julia repl is incredibly nice though, I do miss that.
Edit: I see — I think you mean exporting lowered StableHLO code in a shape polymorphic format —- from the docs: https://jax.readthedocs.io/en/latest/export/shape_poly.html
This is not the thing I usually think when someone says dynamic shape support.
In this model, you have to construct a static graph initially —- then you’re allowed to specify a restricted set of input shapes to be symbolic, to avoid the cost of lowering — but you’ll still incur the cost of compilation for any new shapes which the graph hasn’t been specialized for (because those shapes affect the array memory layouts, which XLA needs to know to be aggressive)
https://jax.readthedocs.io/en/latest/export/export.html#supp...
Edit: I think you need to look here as well, the Exported objects do in fact serialize a function and support shape polymorphism:
https://jax.readthedocs.io/en/latest/export/shape_poly.html#...
My definition might be wrong, but I often think of full dynamic shape support as implying something dynamic about the computation graph.
For instance, JAX supports a scan primitive — whose length must be statically known. With full dynamic shape support, this length might be unknown — which would mean one could express loops with shape dependent size.
As far as I can tell, shape polymorphic exports may sort of give you that — but you incur the cost of compilation, which will not be negligible with XLA.
I work with edge devices, so I have also been experimenting with IREE for deployment, that can handle dynamic shapes (at times, it stopped working for a version but may be working again in the development branch).