Dominant existing ML libraries (mostly in Python/Python or C++) have been developed with all these supports and Rust is no exception. Still, constant-generics (good array support), stable std::Sims and native GPU, asynchronous etc.
Is tracking most of the signals in this area and a simple search over crates.Io will tell you that we have a lot of things to cover, so when in comes to production Rust is not there yet! I think the experimental phase is getting into its final stage, once Rust pushes the immediate requirements such as const-generic, At, std::Sims, GPU support.
I think the community is getting bigger and considering the collective efforts of the authors and contributors of the aforementioned crates, the number of ML specialists and enthusiasts is approx. Where we can all get together to do interesting things by learning from and assessing existing ones (in particular in Python) to create our own curated Rust ecosystem.
DL frontiers are pushing more and more into systems and compiler so that harder computations, graph level optimizations, differentiation (aka differentiable programming), efficient code gen and kernel generations etc. Basically, we can train (mostly vision tasks now) using any DL frameworks (TensorFlow, PyTorch, Monet) or bridge some with Onyx, then compile using TVM on varieties of supported pieces of hardware, and for inference, we can use our beloved Rust.
Another very interesting project which uses Rust for inference is tract which has good support for TF and Onyx ops. I should mention that Google’s TFL ite, Tencent’s CNN or Feather CNN, Xiaomi’s MACE and Microsoft’s ELL are all trying to push their own solutions, but frankly, they’re still limited to certain well-known tasks and are painful to use for varieties of other tasks.
I’d say, first read the source code of any major DL framework and try to catch up on the compiler development. Then you’ll see the pieces are moving fast and haven’t even converged to a relatively complete solution.
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Rust's performance, low-level control, and zero-cost high-level abstractions make it a compelling alternative to more established ecosystems for MachineLearning. While the Rust ML ecosystem is still young and best described as experimental, several ambitious projects and building blocks have emerged.
In the last couple of months I have witnessed the lurking interest in the Rust community about machine learning, here on Reddit, on Discord and on GitHub projects. · Simple Linear Regression from scratch in Rust Posted on December 13, 2018, As one of the oldest and easiest MachineLearning algorithms, implementing Simple Linear Regression can be an eye-opening and rewarding experience for anyone new to MachineLearning, Deep Learning and AI.
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This episode is clearly not providing you with an exhaustive list of the benefits of Rust, nor its capabilities. · To us, Rust seems to be a worthy alternative to the big players in the field of MachineLearning, namely C++, Python and LA.
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Joe232 December 4, 2018, 4:48am #1Is Rust good for deep learning and artificial intelligence, just like Python? (AI is a field wide enough that maybe Rust is better than Python for some problems, say, tree search.
Joe232 December 4, 2018, 11:55am #5One question, is there a way to use a bit of python in deep learning and then use Rust for the most part (if that makes any sense)? I don’t know its exact status, but if it works for every model, you may simply create and train your network with python, and then export it and load on Rust side.
It’s worth noting that googles own evaluation for the future of TensorFlow did include Rust as a strong possibility (but since Chris latter, of LLVM and swift, was lead on the new team it was not really a surprise when they picked swift). Rust : We believe that Rust supports all the ingredients necessary to implement the techniques in this paper: it has a strong static side, and its traits system supports zero-cost abstractions which can be provably eliminated by the compiler.
IMO, with respect to rust and AI, it’s not productive to focus on ad hoc data exploration and jellylike experience. One problem with this though is that you kinda want professional mathematicians involved in the community… and the number of mathematicians that can code is tiny, and of that number most of them prefer (or were trained on) Matlab, python, Haskell… picking up rust is a tall order.
Reality, very few companies have the capital to support research and development on new tech that is this involved (e.g., teams of 10s of PhDs in comp-sci and math building foundational software). There are no other companies at the scale of IBM, Google, Facebook, Salesforce, Uber, and Microsoft that are investing in software, platforms, compilers, or mathematical libraries for Rust in AI/ml… at least not yet.
And unfortunately, building foundational AI/ml software is not really something that can happen with a few people working on personal time (at least not within a reasonable time-frame). Jbowles: Rust, technically, is a great choice for building ML/AI software… but it all comes down to ecosystem and community.
But I didn’t want to use something like Python where it is much slower compared to Rust and its syntax is awful IMO. I just don’t like its indentation rules and the lack of semicolons that end lines.
Majority (if not all) people doing real work on AI/ml are research scientists at big companies. I don’t chose python, and I try not to get too annoyed that there really isn’t another choice (Julia is probably the best alternative), instead focusing on what a more diverse programming landscape looks like for ml/AI and what that diversity can bring to expanding thinking about solving certain problems.
Various attempts at deep learning lib sexist in Go, Haskell, Rust, F# … and they all share one thing in common: not enough help, too much work, creators are simply overwhelmed. I think for Rust the path to ml/AI and computational mathematics is through supporting existing community focus to push adoption and gain attention at large corporations currently doing R&D in AI/ml software.
I forgot to mention Amazon above, and they are likely one of the ml/AI companies to find rust adoption for this domain. I initially had high hopes that go would be become a viable alternative to python in the AI/ml landscape (specifically I had planned out a natural language processing framework)… but it was clear around 2016 that the community of go was not really the kind of people who did machine learning work… though there is still a small dedicated community it is nothing like what you see in python.
Jbowles December 4, 2018, 4:07pm #11Lastly, if you are looking to get into the ml/AI/deep- learning area I highly recommend Andrew Track’s book. Joe232 December 4, 2018, 11:41pm #12 Bowles: I think for Rust the path to ml/AI and computational mathematics is through supporting existing community focus to push adoption and gain attention at large corporations currently doing R&D in AI/ml software.
I’m more mostly involved with scientific computing / applied mathematics and would love to use more Rust libraries. I don’t think Rust as it is built is the perfect choice for the messy iterative process scientists go through.
http://www.arewelearningyet.com/scientific-computing mentions a basic Real and Jupiter kernel, but quick interactions at the top level is not Rust strong suit. I don’t know its exact status, but if it works for every model, you may simply create and train your network with python, and then export it and load on Rust side.
To answer the question of the topic starter, there are some solid foundational libraries, such as array, pet graph, the TensorFlow binding, etc. I also got quite an improvement over array by writing custom linear algebra functions using Sims intrinsic.
In fact, a great share of it resembles what I said last year about Rust and its stance in data science. When it comes to TensorFlow, the Python library will always be the most complete and reasonable choice for building the models.
I once heard that there were some third-party initiatives to create high-level abstractions on top of TensorFlow, but cannot testify on their quality. Performance has already been mentioned, but let’s not forget that even the most popular Python libraries for deep learning are either made in other close-to-the-metal languages internally or already take advantage of GPU processing and vectorization, making any potential overhead from the use of Python as the user-facing API close to negligible.
Making graph-based computation fast and efficient is, as also mentioned around here, not as simple as changing the compiler. I find in Rust a greater value here for its type system, allowing us to make fewer mistakes when specifying the various layers of a neural network without compromising performance.
Jbowles February 3, 2019, 2:35pm #19You are correct, if most DL projects are just using c++, FORTRAN, Cuba, etc… then in many cases simple benchmarking is a wash. For standard, boring, vanilla deep learning (DL) or machine learning (ML) projects this is not a problem.
Getting to the “training” or “solver” part of your project is typically the last step in a long process. Additionally, new problems or domains may need slightly different approaches… requiring you to extend on the platform that is already there.
Having to context switch, or pass requirements to another team, in order to update an existing c++ API to support your needs is time-consuming. So from my perspective, democratizing AI is about having mature high quality software in a number of programming languages.
In short, it’s nice to have all parts of your project in the same language: fast, concurrent, parallelize. As it turns out, what everyone is finding necessary: having the same richness of types and expressively from the high level API all the way to the compiler.
Chris Rackauckas (author of Differential Equations projects in Julia) has a good blog post about this, why-numba-and-cython-are-not-substitutes-for-julia, inspired by all the times he had to answer this exact question. It is just as relevant to Rust ’s “zero-cost abstractions” as it is to Julia’s “two-language problem”… (a philosophical side-note: solving the same problem in different programming languages can often yield different and interesting solutions… so restricting DL to python/c++ restricts, ironically, the solution space for ML itself).
In short, the expressiveness of Python must be limited in order to make it fast (see rackauckas’ blog post above for detailed examples). In fact, Rust was one of the languages Google TF “evaluated” for the project , but it shows, and they discuss this, that Rust has all the technical merits for building projects like TF:.
Much of this work could be happening in Rust, it’s just a matter of focus, people, community… but also a mega-evil-corp backer… or something like Julia: they have a small company called JuliaComputing focused on generating money through consulting and applying for grants, and then using revenue to support open-source.