Getting Started

tenferro supports direct CPU tensor computation, eager scalar-loss AD, traced execution, transform AD, einsum, linear algebra, and CUDA execution through the feature-gated CubeCL backend.

Installation

The tenferro crate re-exports everything you need. Use a local checkout while the crates are still evolving:

[dependencies]
tenferro = { path = "/path/to/tenferro-rs/tenferro" }

This uses the default cpu-faer backend. To use the LAPACK/BLAS CPU backend, disable default features and enable cpu-blas:

[dependencies]
tenferro = { path = "/path/to/tenferro-rs/tenferro", default-features = false, features = ["cpu-blas"] }

Exactly one CPU backend must be enabled: cpu-faer or cpu-blas. The cpu-blas backend needs a BLAS/LAPACK provider. Link one from the system toolchain, or enable src-openblas to build against OpenBLAS:

[dependencies]
tenferro = { path = "/path/to/tenferro-rs/tenferro", default-features = false, features = ["src-openblas"] }

Or switch to crates.io once published:

[dependencies]
tenferro = "..."

First CPU Program

use tenferro::{CpuBackend, Tensor};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let mut backend = CpuBackend::new();

    let a = Tensor::from_vec(vec![2, 2], vec![1.0_f64, 3.0, 2.0, 4.0]);
    let b = Tensor::from_vec(vec![2, 2], vec![5.0_f64, 7.0, 6.0, 8.0]);

    let c = a.matmul(&b, &mut backend)?;

    assert_eq!(c.shape(), &[2, 2]);
    assert_eq!(c.as_slice::<f64>().unwrap(), &[19.0, 43.0, 22.0, 50.0]);

    Ok(())
}

Next Steps