autograd-rs
dfdx
| autograd-rs | dfdx | |
|---|---|---|
| 1 | 22 | |
| 0 | 1,812 | |
| - | 0.8% | |
| 6.9 | 5.8 | |
| almost 2 years ago | 10 months ago | |
| Rust | Rust | |
| MIT License | GNU General Public License v3.0 or later |
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autograd-rs
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?
miniboosts - A collection of boosting algorithms written in Rust 🦀
burn - Burn is a next generation Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
corgi - A neural network, and tensor dynamic automatic differentiation implementation for Rust.
executorch - On-device AI across mobile, embedded and edge for PyTorch
tch-rs - Rust bindings for the C++ api of PyTorch.
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]