Personally, at the nexus of Phoenix/Beam/NX there is some fantastic stuff to be done. Any single-user use case or batch-style workflow (training) is still likely to be better served with Python, cos it’s just got such a lead. But if you’re doing anything that looks like compute as a service, this is fantastic, fantastic news. Personally I plan to build financial market yield curve fitting algos as a service. Fitting a 7-paramater yield curve using some functional forms takes many seconds even with AVX-enabled Numpy, and that’s too long when data is streaming in at you dozens of times a second. Obviously python can do GPU but you can’t be managing access to it as a service easily. Python is really a stone age runtime honestly. With GPU enabled Nx we can get be just as fast as Python 100 milliseconds and pass it back to the user “semi-real time”. And have the data streaming into the same stack with all the advantages BEAM offers for that. I’ve been struggling to get a scalable Elixir to python solution that would “fit” the BEAM paradigm for some time, with Pyrlang being my prime candidate, but it’s so dodgily maintained. This is going to allow me to throw that frustrating architecture out and just do Elixir. I can’t thank @josevalim enough.
vegabook
marsell
Miners and traders are not the same thing, my man. 
(and I’m aware of the lengths miners go to, including custom ASICs)
goofansu
I don’t know much about IoT, but I think it will be great in IoT with Nerves for edge computing.
josevalim
Nx supports two pluggable parts:
-
Backends - this is responsible for dispatching functions to the tensor wherever it is allocated at (memory, GPU, etc)
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Compilers - this allows you to receive a graph (expression tree) which you compile down somewhere
Backends are, in a way, what controls the eager execution mode. Compilers are the lazy mode. The only backend is pure Elixir. So you do have eager mode, but it will be slow. The compilers we have today is the evaluating compiler (so it invokes the backend) and the XLA compiler.
In other words, as soon as we have a fast backend (I have been thinking about libtorch’s Aten bindings), we should have fast eager (backend) and lazy (compile) modes.
vegabook
With apologies for the colon (my target audience tends to be numerical Python), I’m pitching Nx into my financial coding network:
zakimedina
Thank you for quick response! (Elixir community rocks!) … If anything I am NOW MORE EXCITED!
My response to ATen bindings for libtorch(Pytorch) on Elixir - YES PLEASE with fries! (by fries I mean some kind of OpenAI Gym integration!
In all honesty, this would be a super big deal. I know there is already a Rust binding with PyTorch, why not Elixir!
I believe catering to both Pytorch and TensorFlow deep learning frameworks will ultimately expand the Elixir community to both Academia, Companies with existing Elixir code and those on the fence about making the jump to Elixir.
lucaong
Repeating an opinion that I wrote on another topic, I am SUPER excited about Nx. I think that Elixir is a great language to express numeric computation such as linear algebra. The problem used to be that such a great expressivity was not backed by the necessary low-level support to achieve good performance, but Nx promises to fix this.
Machine learning seems to be the most obvious application (not only neural networks, rather a good chunk of the whole field), but linear algebra and multi-dimensional vector computations have much wider applications than that: think signal processing, information theory, graphics, cryptography, graph algorithms, control theory, etc.
I would personally love to implement some ideas for recommender systems that I previously experimented with in other languages. I am also eager to see what the community will come up with 
LukeWood
Agreed big time.
I’m a huge Elixir advocate & have lots of experience w/ ML. Despite these facts… I can’t see myself venturing away from the python stack until there is an incredibly compelling case to do so.
The ecosystem built around python for ML is absolutely massive and it’ll take awhile for anyone to come close to matching that ecosystem.
I’d like to try NX out for a research project - but I worry about things like distributed training, unexpected bottlenecks, etc.
josevalim
It is important to set our expectations and goals accordingly. I believe it is completely unrealistic for Nx success to be measured as winning data scientists hearts or by matching Python’s ecosystem.
As I mentioned in the podcast, I would already consider a success if we change the answer to “Can Elixir be used for Machine Learning?” from a clear “No” to a “Maybe”. Maybe you are doing a prototype in Elixir and you can quickly hook an existing model instead of having to learn another language and tooling. Or maybe you are integrating ML models with Nerves to run on the edge. I believe those are attainable and it will already be a big success, simply because it was not possible to do any of this a month ago. Once we get to a “Maybe”, we can start discussing if, when, and how to make that a “Yes”.
As we saw on Elixir’s early days, the best way to adopt the language was to try it on small, well-defined problems, and get a feel for it. I don’t think Nx is going to be any different. Try it on small and reasonably well-defined problems, so you can get an initial look and give us feedback, and decide on how to move forward accordingly. Hopefully this will happen enough times for us to continue improving.
LukeWood
Thanks for pointing out that podcast Jose - very enlightening as to the goals of nx.
FWIW I’d classify myself as a “maybe” for the appropriate research project - in particular if I am doing another research project w/ non-deep neural nets I think that would be an excellent starting point. I have a planned follow up to spectral-neural-nets - so maybe as a first step I will implement the FFT in nx and see how it goes.
I’ll be sure to post on the forums w/ some thoughts if I end up doing so.