# Burn WGPU Backend
[Burn](https://github.com/tracel-ai/burn) WGPU backend
[](https://crates.io/crates/burn-wgpu)
[](https://github.com/tracel-ai/burn-wgpu/blob/master/README.md)
This crate provides a WGPU backend for [Burn](https://github.com/tracel-ai/burn) using the
[wgpu](https://github.com/gfx-rs/wgpu).
The backend supports Vulkan, Metal, DirectX11/12, OpenGL, WebGPU.
## Usage Example
```rust
#[cfg(feature = "wgpu")]
mod wgpu {
use burn_autodiff::Autodiff;
use burn_wgpu::{Wgpu, WgpuDevice};
use mnist::training;
pub fn run() {
let device = WgpuDevice::default();
training::run::<Autodiff<Wgpu<f32, i32>>>(device);
}
}
```
> ⚠️ **Warning**
> When using one of the `wgpu` backends, you may encounter compilation errors related to recursive type evaluation. This is due to complex type nesting within the `wgpu` dependency chain.
> To resolve this issue, add the following line at the top of your `main.rs` or `lib.rs` file:
> ```rust
> #![recursion_limit = "256"]
> ```
> The default recursion limit (128) is often just below the required depth (typically 130-150) due to deeply nested associated types and trait bounds.
## Configuration
You can set `BURN_WGPU_MAX_TASKS` to a positive integer that determines how many computing tasks are
submitted in batches to the graphics API.
## Alternative SPIR-V backend
When targeting Vulkan, the `spirv` feature flag can be enabled to enable the SPIR-V compiler
backend, which performs significantly better than WGSL. This is especially true for matrix
multiplication, where SPIR-V can make use of TensorCores and run at `f16` precision. This isn't
currently supported by WGSL. The compiler can also be selected at runtime by setting the
corresponding generic parameter to either `SpirV` or `Wgsl`.
## Platform Support
| Metal | No | Yes | No | Yes | No | No | Yes | No |
| Vulkan | Yes | Yes | Yes | Yes | Yes | Yes | Yes | No |
| OpenGL | No | Yes | Yes | Yes | Yes | Yes | Yes | No |
| WebGpu | No | Yes | No | No | No | No | No | Yes |
| Dx11/Dx12 | No | Yes | No | No | Yes | No | No | No |