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chore: update main README links to crate-specific READMEs (#1415)

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Eric Kalosa-Kenyon 2024-03-05 12:11:46 -05:00 committed by GitHub
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@ -331,7 +331,7 @@ implementation details. It is fully optimized with the
[performance characteristics mentioned earlier](#performance), as it serves as our research [performance characteristics mentioned earlier](#performance), as it serves as our research
playground for a variety of optimizations. playground for a variety of optimizations.
See the [WGPU Backend README](./burn-wgpu/README.md) for more details. See the [WGPU Backend README](./crates/burn-wgpu/README.md) for more details.
</details> </details>
@ -345,7 +345,7 @@ Based on [Candle by Hugging Face](https://github.com/huggingface/candle), a mini
for Rust with a focus on performance and ease of use, this backend can run on CPU with support for for Rust with a focus on performance and ease of use, this backend can run on CPU with support for
Web Assembly or on Nvidia GPUs using CUDA. Web Assembly or on Nvidia GPUs using CUDA.
See the [Candle Backend README](./burn-candle/README.md) for more details. See the [Candle Backend README](./crates/burn-candle/README.md) for more details.
> _Disclaimer:_ This backend is not fully completed yet, but can work in some contexts like > _Disclaimer:_ This backend is not fully completed yet, but can work in some contexts like
> inference. > inference.
@ -362,7 +362,7 @@ PyTorch doesn't need an introduction in the realm of deep learning. This backend
[PyTorch Rust bindings](https://github.com/LaurentMazare/tch-rs), enabling you to use LibTorch C++ [PyTorch Rust bindings](https://github.com/LaurentMazare/tch-rs), enabling you to use LibTorch C++
kernels on CPU, CUDA and Metal. kernels on CPU, CUDA and Metal.
See the [LibTorch Backend README](./burn-tch/README.md) for more details. See the [LibTorch Backend README](./crates/burn-tch/README.md) for more details.
</details> </details>
@ -376,7 +376,7 @@ This CPU backend is admittedly not our fastest backend, but offers extreme porta
It is our only backend supporting _no_std_. It is our only backend supporting _no_std_.
See the [NdArray Backend README](./burn-ndarray/README.md) for more details. See the [NdArray Backend README](./crates/burn-ndarray/README.md) for more details.
</details> </details>
@ -416,7 +416,7 @@ Of note, it is impossible to make the mistake of calling backward on a model tha
that does not support autodiff (for inference), as this method is only offered by an Autodiff that does not support autodiff (for inference), as this method is only offered by an Autodiff
backend. backend.
See the [Autodiff Backend README](./burn-autodiff/README.md) for more details. See the [Autodiff Backend README](./crates/burn-autodiff/README.md) for more details.
</details> </details>
@ -455,7 +455,7 @@ Of note, we plan to implement automatic gradient checkpointing based on compute
bound operations, which will work gracefully with the fusion backend to make your code run even bound operations, which will work gracefully with the fusion backend to make your code run even
faster during training, see [this issue](https://github.com/tracel-ai/burn/issues/936). faster during training, see [this issue](https://github.com/tracel-ai/burn/issues/936).
See the [Fusion Backend README](./burn-fusion/README.md) for more details. See the [Fusion Backend README](./crates/burn-fusion/README.md) for more details.
</details> </details>