Flux.jl
miri
Flux.jl | miri | |
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22 | 122 | |
4,393 | 3,973 | |
0.4% | 2.7% | |
8.7 | 10.0 | |
3 days ago | about 23 hours ago | |
Julia | Rust | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Flux.jl
- Julia 1.10 Released
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What Apple hardware do I need for CUDA-based deep learning tasks?
If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.
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[D] ClosedAI license, open-source license which restricts only OpenAI, Microsoft, Google, and Meta from commercial use
Flux dominance!
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What would be your programming language of choice to implement a JIT compiler ?
I’m no compiler expert but check out flux and zygote https://fluxml.ai/ https://fluxml.ai/
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Any help or tips for Neural Networks on Computer Clusters
I would suggest you to look into Julia ecosystem instead of C++. Julia is almost identical to Python in terms of how you use it but it's still very fast. You should look into flux.jl package for Julia.
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[D] Why are we stuck with Python for something that require so much speed and parallelism (neural networks)?
Give Julia a try: https://fluxml.ai
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Deep Learning With Flux: Loss Doesn't Converge
2) Flux treats softmax a little different than most other activation functions (see here for more details) such as relu and sigmoid. When you pass an activation function into a layer like Dense(3, 32, relu), Flux expects that the function is broadcast over the layer's output. However, softmax cannot be broadcast as it operates over vectors rather than scalars. This means that if you want to use softmax as the final activation in your model, you need to pass it into Chain() like so:
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“Why I still recommend Julia”
Can you point to a concrete example of one that someone would run into when using the differential equation solvers with the default and recommended Enzyme AD for vector-Jacobian products? I'd be happy to look into it, but there do not currently seem to be any correctness issues in the Enzyme issue tracker that are current (3 issues are open but they all seem to be fixed, other than https://github.com/EnzymeAD/Enzyme.jl/issues/278 which is actually an activity analysis bug in LLVM). So please be more specific. The issue with Enzyme right now seems to moreso be about finding functional forms that compile, and it throws compile-time errors in the event that it cannot fully analyze the program and if it has too much dynamic behavior (example: https://github.com/EnzymeAD/Enzyme.jl/issues/368).
Additional note, we recently did a overhaul of SciMLSensitivity (https://sensitivity.sciml.ai/dev/) and setup a system which amounts to 15 hours of direct unit tests doing a combinatoric check of arguments with 4 hours of downstream testing (https://github.com/SciML/SciMLSensitivity.jl/actions/runs/25...). What that identified is that any remaining issues that can arise are due to the implicit parameters mechanism in Zygote (Zygote.params). To counteract this upstream issue, we (a) try to default to never default to Zygote VJPs whenever we can avoid it (hence defaulting to Enzyme and ReverseDiff first as previously mentioned), and (b) put in a mechanism for early error throwing if Zygote hits any not implemented derivative case with an explicit error message (https://github.com/SciML/SciMLSensitivity.jl/blob/v7.0.1/src...). We have alerted the devs of the machine learning libraries, and from this there has been a lot of movement. In particular, a globals-free machine learning library, Lux.jl, was created with fully explicit parameters https://lux.csail.mit.edu/dev/, and thus by design it cannot have this issue. In addition, the Flux.jl library itself is looking to do a redesign that eliminates implicit parameters (https://github.com/FluxML/Flux.jl/issues/1986). Which design will be the one in the end, that's uncertain right now, but it's clear that no matter what the future designs of the deep learning libraries will fully cut out that part of Zygote.jl. And additionally, the other AD libraries (Enzyme and Diffractor for example) do not have this "feature", so it's an issue that can only arise from a specific (not recommended) way of using Zygote (which now throws explicit error messages early and often if used anywhere near SciML because I don't tolerate it).
So from this, SciML should be rather safe and if not, please share some details and I'd be happy to dig in.
- Flux: The Elegant Machine Learning Stack
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Jax vs. Julia (Vs PyTorch)
> In his item #1, he links to https://discourse.julialang.org/t/loaderror-when-using-inter... The issue is actually a Zygote bug, a Julia package for auto-differentiation, and is not directly related to Julia codebase (or Flux package) itself. Furthermore, the problematic code is working fine now, because DiffEqFlux has switched to Enzyme, which doesn't have that bug. He should first confirm whether the problem he is citing is actually a problem or not.
> Item #2, again another Zygote bug.
If flux chose a buggy package as a dependency, that's on them, and users are well justified in steering clear of Flux if it has buggy dependencies. As of today, the Project.toml for both Flux and DiffEqFlux still lists Zygote as a dependency. Neither list Enzyme.
https://github.com/FluxML/Flux.jl/blob/master/Project.toml
miri
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Rust: Box Is a Unique Type
>While we are many missing language features away from this being the case, the noalias case is also magic descended upon box itself, with no user code ever having access to it.
I'm not sure why the author thinks there's magic behind Box. Box is not a special case of `noalias`. Run this snippet with miri and you'll see the same issue: https://play.rust-lang.org/?version=stable&mode=debug&editio...
`Box` _does_ have an expectation that its inner pointer is not aliased to another Box (even if used for readonly operations). See: https://github.com/rust-lang/miri/issues/1800#issuecomment-8...)
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Bytecode VMs in Surprising Places
Miri [0] is an interpreter for the mid-level intermediate representation (MIR) generated by the Rust compiler. MIR is input for more processing steps of the compiler. However miri also runs MIR directly. This means miri is a VM. Of course it's not a bytecode VM, because MIR is not a bytecode AFAIK. I still think that miri is a interesting example.
And why does miri exist?
It is a lot slower. However it can check for some undefined behavior.
[0]: https://github.com/rust-lang/miri
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RFC: Rust Has Provenance
Provenance is a dynamic property of pointer values. The actual underlying rules that a program must follow, even when using raw pointers and `unsafe`, are written in terms of provenance. Miri (https://github.com/rust-lang/miri) represents provenance as an actual value stored alongside each pointer's address, so it can check for violations of these rules.
Lifetimes are a static approximation of provenance. They are erased after being validated by the borrow checker, and do not exist in Miri or have any impact on what transformations the optimizer may perform. In other words, the provenance rules allow a superset of what the borrow checker allows.
- Mir: Strongly typed IR to implement fast and lightweight interpreters and JITs
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Running rustc in a browser
There has been discussion of doing this with MIRI, which would be easier than all of rustc.
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Piecemeal dropping of struct members causes UB? (Miri)
This issue has been fixed: https://github.com/rust-lang/miri/issues/2964
- Erroneous UB Error with Miri?
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I've incidentally created one of the fastest bounded MPSC queue
Actually, I've done more advanced tests with MIRI (see https://github.com/rust-lang/miri/issues/2920 for example) which allowed me to fix some issues. I've also made the code compatible with loom, but I didn't found the time yet to write and execute loom tests. That's on the TODO-list, and I need to track it with an issue too.
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Interested in "secure programming languages", both theory and practice but mostly practice, where do I start?
He is one of the big brains behind Miri, which is a interpreter that runs on the MIR (compiler representation between human code and asm/machine code) and detects undefined behavior. Super useful tool for language safety, pretty interesting on its own.
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Formal verification for unsafe code?
I would also run your tests in Miri (https://github.com/rust-lang/miri) to try to cover more bases.
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
cons-list - Singly-linked list implementation in Rust
Knet.jl - Koç University deep learning framework.
sanitizers - AddressSanitizer, ThreadSanitizer, MemorySanitizer
tensorflow - An Open Source Machine Learning Framework for Everyone
rust - Empowering everyone to build reliable and efficient software.
Transformers.jl - Julia Implementation of Transformer models
Rust-Full-Stack - Rust projects here are easy to use. There are blog posts for them also.
Torch.jl - Sensible extensions for exposing torch in Julia.
rfcs - RFCs for changes to Rust
Lux.jl - Explicitly Parameterized Neural Networks in Julia
nomicon - The Dark Arts of Advanced and Unsafe Rust Programming