amkkma

34 ms·

1. 121.▲
   by amkkma 6y ago

   Nobody needs tensorflow. That's just a kludge around the fact that python is terrible for ML.
2. 122.▲
   by amkkma 6y ago

   https://www.reddit.com/r/Julia/comments/keuumi/bayesian_neur... See the paper. It allows for the ecosystem to have a combinatorial explosion of possibilities as each domain expert works on their own pack
3. 123.▲
   by amkkma 6y ago

   It's definitely not possible. These have to be rewritten with the specific autodiff /ML framework in mind. Even then, you're not going to have fast custom types to be used on the GPU without dropping into C++
4. 124.▲
   by amkkma 6y ago

   >If you find yourself writing a simulation, and you need to optimize away billions of function calls, you can use the C++ API provided by TensorFlow. I prefer to just use Julia and get that for free. Also can write custom cuda kernels in
5. 125.▲
   by amkkma 6y ago

   >there's nothing stopping you from composing things in Python. In Julia you can compose a custom distribution, with a bayesian model with an ODE with a neural network with unit number types with custom Julia written CUDA kernels and
6. 126.▲
   by amkkma 6y ago

   Then you haven't looked at Julia's ecosystem. It may not be quite as mature, but it's getting there quickly. It's also far more interoperable because of Julia's multiple dispatch and abstract types. For example, the
7. 127.▲
   by amkkma 6y ago

   Yea, Julia compiles natively to GPUs. See here: https://juliagpu.org/
8. 128.▲
   by amkkma 6y ago

   Julia can do GPU kernels (Intel, AMD, Nvidia) also, they just aren't all implemented in pure Julia (or tuned) yet, hence the CUDA lib calls. https://juliagpu.org/ https://juliagpu.org/2020-09-28-gemmker
9. 129.▲
   by amkkma 6y ago

   And what do you call this: https://news.ycombinator.com/reply?id=25823541&goto=threads%...
10. 130.▲
    by amkkma 6y ago

    Are the Cython and Pythran codes running in parallel? To do that with Julia: https://docs.julialang.org/en/v1/manual/multi-threading/ Or https://github.com/JuliaFolds/FLoops.jl
11. 131.▲
    by amkkma 6y ago

    Sorry- missed that
12. 132.▲
    by amkkma 6y ago

    gotcha
13. 133.▲
    Genie: The highly productive Julia web framework(genieframework.com)

    2 points by amkkma 6y ago|0 comments
14. 134.▲
    by amkkma 6y ago

    What about traits or multiple inheritance?
15. 135.▲
    by amkkma 6y ago

    So can Julia: https://github.com/plotly/Dash.jl https://www.youtube.com/watch?v=uLhXgt_gKJc https://github.com/GenieFramework/Genie.jl
16. 136.▲
    by amkkma 6y ago

    Julia has plenty of plotting solutions that are better for stats than matplotlib: https://github.com/JuliaPlots/AlgebraOfGraphics.jl https://github.com/queryverse/VegaLite.jl https://
17. 137.▲
    by amkkma 6y ago

    Thanks. How's that relate to Gelman's BDA or statistical rethinking?
18. 138.▲
    by amkkma 6y ago

    But does it aid in understanding regular models, as they might have a bayesian interpretation?
19. 139.▲
    by amkkma 6y ago

    Julia is perfectly fine for non numerical programming, better in ways than python inheritance spaghetti. See: https://github.com/GenieFramework/Genie.jl Multiple dispatch + parametric types is a very general and power
20. 140.▲
    by amkkma 6y ago

    >some ODE support Best ODE/SDE etc support of any programming language. Including composing them with machine learning and AD, GPU, multithreading, DSLs and symbolic simplification etc https://sciml.ai/
21. 141.▲
    by amkkma 6y ago

    Probably the usual complaints about package load and compilation time. These aren't fundamental issues with the language and will be solved by tiered compilation (there's already an interpreter mode, just have to integrate that wi
22. 142.▲
    by amkkma 6y ago

    What? I used to write python, and to me, MD is first and foremost a conceptual improvement. Makes writing code so much sweeter, more expressive and sensical. It also heavily improves composability. It's LESS performant if not done car
23. 143.▲
    by amkkma 6y ago

    I don't see anywhere about a better type system or multiple dispatch. Try being less salty In what language are you defining these custom arrays or types? certainly not in python, or they'll be too slow to be worthwhile.
24. 144.▲
    by amkkma 6y ago

    Which is extremely limited compared to Zygote which can do custom types, dicts, custom arrays, complex type system and multiple dispatch uses etc
25. 145.▲
    by amkkma 6y ago

    Even if structs worked, they wouldn't be part of a good type system, don't work with abstract interfaces/inheritance,traits, multiple dispatch, fast union types, zero allocation immutables or any myriad of other things
26. 146.▲
    by amkkma 6y ago

    >Fantastic comment, always brought up, except that this same >subset of Python (really how to use CPU efficiently) is ?about the same that anyone writing about Julia performance is >preaching. This is completely and demonstrably fa
27. 147.▲
    by amkkma 6y ago

    Another one that's forward looking: https://github.com/tkf/Mutabilities.jl
28. 148.▲
    A quick introduction to data parallelism in Julia(juliafolds.github.io)

    160 points by amkkma 6y ago|15 comments
29. 149.▲
    Julia mixed precision GEMM codegen meets and exceeds CUBLAS(juliagpu.org)

    4 points by amkkma 6y ago|0 comments
30. 150.▲
    by amkkma 6y ago

    https://genieframework.com/ (A Django like MVC framework, with templating and a julia -> JS compiler for the frontend) https://www.youtube.com/watch?v=xPUOCQ2SJF0&list=PLP8iPy9hna... (JuliaCon 2020