neuronika
dfdx
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neuronika | dfdx | |
---|---|---|
19 | 22 | |
1,033 | 1,607 | |
1.3% | - | |
0.0 | 8.7 | |
over 1 year ago | about 2 months ago | |
Rust | Rust | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
neuronika
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
Also perhaps comparing to Neuronika.
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Making a better Tensorflow thanks to strong typing
how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?
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[D] To what extent can Rust be used for Machine Learning?
Check where and how this struct is used. https://github.com/neuronika/neuronika/blob/variable-rework/neuronika-variable/src/history.rs
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What do I need for an ML/DL based scripting language in Rust?
Also you can take a look at neuronika.
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ML in Rust
There is also https://github.com/neuronika/neuronika
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Enzyme: Towards state-of-the-art AutoDiff in Rust
I have a question: as the maintainer of [neuronika](https://github.com/neuronika/neuronika), a crate that offers dynamic neural network and auto-differentiation with dynamic graphs, I'm looking at a future possible feature for such framework consisting in the possibility of compiling models, getting thus rid of the "dynamic" part, which is not always needed. This would speed the inference and training times quite a bit.
- Any role that Rust could have in the Data world (Big Data, Data Science, Machine learning, etc.)?
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What sort of mature, open-source libraries do you feel Rust should have but currently lacks?
If you like autograd you will love neuronika
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bhtsne 0.5.0, now 5.6x faster on a 4 core machine, plus a summary of my Rust journey (so far)
After reading most of the book, I wanted to get my hands dirty. My initial idea was to build a small machine learning framework but I deemed it to be too difficult if not impossible for me at the time. (Now, neuronika would have something to say). When gathering the bibliography for my thesis, I recalled to have stumbled upon a particular algorithm, t-SNE, whom I liked very much. I found the idea behind it to be very clever and elegant (t-SNE it's still one of my favorite algorithms, together with backprop and SOM, I find manifold learning fascinating in general). "So be it", I said, and I began writing a mess of a code, that was basically a translation of the C++ implementation. Boy was it bad.
dfdx
- Shape Typing in Python
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Candle: Torch Replacement in Rust
I keep checking the progress on dfdx for this reason. It does what I (and, I assume from context, you) want: Provides static checking of tensor shapes. Which is fantastic. Not quite as much inference as I'd like but I love getting compile-time errors that I forgot to transpose before a matmul.
It depends on the generic_const_exprs feature which is still, to quote, "highly experimental":
https://github.com/rust-lang/rust/issues/76560
Definitely not for production use, but it gives a flavor for where things can head in the medium term, and it's .. it's nice. You could imagine future type support allowing even more inference for some intermediate shapes, of course, but even what it has now is really nice. Like this cute little convnet example:
https://github.com/coreylowman/dfdx/blob/main/examples/night...
- Dfdx: Shape Checked Deep Learning in Rust
- Are there some machine or deep learning crates on Rust?
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[Discussion] What crates would you like to see?
And for transformers, it's really early days for dfdx, but it's a library that aims to sit basically at the Pytorch level of abstraction, that the difference is it's not just coded in Rust, but it follows the Rust-y/functional-y philosophy of "if it compiles it runs".
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rapl: Rank Polymorphic array library for Rust.
Wow that is super interesting. I actually tried to use GATs at first to be generic over shapes, but I couldn't do it, I'm sure it would be possible in the future though. There is this library dfdx that does something similar to what you mentioned, but it feels a little clumsy to me.
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Announcing cudarc and fully GPU accelerated dfdx: ergonomic deep learning ENTIRELY in rust, now with CUDA support and tensors with mixed compile and runtime dimensions!
Awesome, I added an issue here https://github.com/coreylowman/dfdx/issues/597. We can discuss more there! The first step will just be adding the device and implementing tensor creation methods for it.
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In which circumstances is C++ better than Rust?
The next release of dfdx includes a CUDA device and implements many ops. The same dev created a new crate, cudarc, for a wrapper around CUDA toolkit.
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
A question I have is: what are the philosophical/design differences with dfdx? As someone who's played around with dfdx and only skimmed the README of burn, it seems like dfdx leans into Rust's type system/type inference for compile time checking of as much as is possible to check at compile time. I wonder if you've gotten a chance to look at dfdx and would like to outline what you think the differences are. Thanks!
What are some alternatives?
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn]
clblast-rs - clblast bindings for rust
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
autograph - Machine Learning Library for Rust
DiffSharp - DiffSharp: Differentiable Functional Programming
are-we-learning-yet - How ready is Rust for Machine Learning?
executorch - On-device AI across mobile, embedded and edge for PyTorch
justrunmydebugger - just run my debugger. see package here: https://build.opensuse.org/package/show/home:ila.embsys:justrunmydebugger/justrunmydebugger
rust - Empowering everyone to build reliable and efficient software.
tractjs - Run ONNX and TensorFlow inference in the browser.
triton - Development repository for the Triton language and compiler