MLIR is getting used internally more and more inside TensorFlow, but also by separate team in different projects, like IREE for example (https://github.com/google/iree ). The TensorFlow lite converter has been replaced by the new MLIR-based one, similarly for the Edge TPU.
But more than that, what makes me confident is the traction we are getting outside Google. First, we landed MLIR in LLVM last month: https://github.com/llvm/llvm-project/commit/0f0d0ed1c78f1a80...
The LLVM Fortran frontend (f18/flang) which will merge soon in the LLVM monorepo is using MLIR for their own IR. It'll be exiting to develop a non-ML MLIR-based frontend within LLVM! In particular Flang is opening an HPC perspective that could be leveraged by other DSLs later. They are adding an OpenMP dialect to MLIR right now: https://llvm.discourse.group/t/rfc-openmp-dialect-in-mlir/39...
Intel has been actively porting their nGraph/PlaidML framework to be based on MLIR (search for "The Stripe dialect" and "nGraph Dialect" here: https://mlir.llvm.org/talks/ ).
I'm less familiar with S4TF, but I know they recently got some nice new hires, including https://twitter.com/DaveAbrahams/status/1207690883782467584
But yes, I think S4TF has had zero interest among people actually doing ML and is probably dead. It might take Google a while to actually kill it off, though.
From conversations I’ve had with people doing ML, if they know about S4TF at all they actively dislike certain aspects (for example that it’s based on a statically typed language), or are dismissive of others (like support for differentiation, which is a neat parlor trick but saves a tiny bit of effort).
Imagine a future where you could tell if your shit's broken by recompiling it, and your "deployment" would be just putting some RPC in front of your existing code.
If it's not too forward, do you know at the moment if FastAI will continue to invest in Swift? I know development was a bit stalled due to the work needed for FastAI 2.0, but I was wandering if it'll resume, or if Richard Wei and Chris Lattner leaving changes anything?
Cheers and thanks for FastAI!
PD: Was working a bit on SwiftCV, added the videoio and highgui modules. Was wondering if I should PR it to fastai's or vvmnnnkv's version of the repo?
(Most data scientists on Windows, including me, use SSH to connect to a GPU server running Linux for training models.)
Anyone that cares about bash on Windows can install mingw or WSL, preferably WSL2.
Tablets don't have CLIs by default.
Even if UNIX clones ruled the world, chsh is a thing.
All of them much more mature than Swift, outside Apple's eco-system, including being able to target CUDA.
Also C++17/C++20 isn't that bad.
I learned C++ARM at the age of 16, and many Portuguese universities teach it at first year students, surely newcomers can grok it.
Also Portuguese universities seem to be out of sync with the rest of the world if they teach C++. In the US most people with a CS degree haven't been exposed to C++. It's either Java or something functional. C++ typically doesn't even come up. Which is pretty puzzling to me, because most of the programs I use daily (including this very browser) are C++ programs.
Also, I actually learned C++ during high school, back when C++ARM was the only "standard", and most C compilers still did a mix of K&R and C89.
Nowadays many high schools do stuff with Raspeberry PI and Arduinos (Processing is just C++).
And you have books like these available https://www.amazon.de/f%C3%BCr-Kids-Grundlagen-Spieleprogram...
I think a compiled subset of python is the best way to make that happen. All of the JITs (Numba, PyTorch, Jax, etc) already handle a decent portion of the language, so it should be doable.
Last I checked, Swift is still years away from 1st class Windows support and adequate tooling. Without those in place, Swift+Tensorflow is still a very niche product for Google and I cannot think of a good enough reason why they should heavily invest into it.
Python Tensorflow, Julia, ML.NET, Tensorflow for C++, Pytorch, DL4J do all pretty well on Windows.
As far as I understand, the MLIR project predates its machine learning application and was originally intended as a new IR for clang. In that capacity it makes a lot of sense. MLIR is also currently experimental in Tensorflow, although I have no idea how mature the implementation is.
Similarly, there has been significant investment into Swift for Tensorflow, so it's probably here to stay. On the other hand, from a language design perspective Swift is not a particularly good choice for automatic differentiation and translation into Tensorflow graphs (imperative, exposing many details of the underlying machine, etc.). Without a lot of investment into this project it might just be overtaken by a better engineered competitor, or more likely, fail to gain sufficient mind-share over the "good enough" python solution that already exists.