Exploration of these EXCELLENT bindings of PyTorch and Rust: https://github.com/LaurentMazare

Goal: Translate a spanish song to english

• cargo new translate and cd into it fully working GPU Hugging Face Translation CLI in Rust

run it: time cargo run -- translate --path lyrics.txt

/*A library that uses Hugging Face to Translate Text
*/
use rust_bert::pipelines::translation::{Language, TranslationModelBuilder};
use std::fs::File;
use std::io::Read;

//build a function that reads a file and returns a string
pub fn read_file(path: String) -> anyhow::Result<String> {
    let mut file = File::open(path)?;
    let mut contents = String::new();
    file.read_to_string(&mut contents)?;
    Ok(contents)
}

//build a function that reads a file and returns an array of the lines of the file
pub fn read_file_array(path: String) -> anyhow::Result<Vec<String>> {
    let mut file = File::open(path)?;
    let mut contents = String::new();
    file.read_to_string(&mut contents)?;
    let array = contents.lines().map(|s| s.to_string()).collect();
    Ok(array)
}


//build a function that reads a file and translates it
pub fn translate_file(path: String) -> anyhow::Result<()> {
    let model = TranslationModelBuilder::new()
        .with_source_languages(vec![Language::Spanish])
        .with_target_languages(vec![Language::English])
        .create_model()?;
    let text = read_file_array(path)?;
    //pass in the text to the model
    let output = model.translate(&text, None, Language::English)?;
    for sentence in output {
        println!("{}", sentence);
    }
    Ok(())
}

• cargo new stress cd into stress
• To test CPU for PyTorch do: cargo run -- cpu
• To test GPU for PyTorch do: cargo run -- gpu
• To monitor CPU/Memory run htop
• To monitor GPU run nvidia-smi -l 1
• To use threaded GPU load test use: cargo run -- tgpu

A repo to show how GPUs work with Rust and PyTorch. export TORCH_CUDA_VERSION=cu117

The cd into pytorch-gpu-util and run cargo run -- gpu

One tip is to look into your build to ensure the crate actually downloaded the cuda version:

 ls -l /workspaces/rust-pytorch-gpu-template/pytorch-gpu-util/target/debug/build/torch-sys-0893541a21a2091d/out/libtorch/libtorch/lib | grep cuda
-rw-rw-rw- 1 codespace codespace   1235152 Jan 16 22:18 libc10_cuda.so
-rw-rw-rw- 1 codespace codespace    828800 Jan 16 22:18 libc10d_cuda_test.so
-rw-rw-rw- 1 codespace codespace    687320 Jan 16 22:20 libcudart-e409450e.so.11.0
-rw-rw-rw- 1 codespace codespace   7221084 Jan 16 22:18 libgloo_cuda.a
-rw-rw-rw- 1 codespace codespace   3769170 Jan 16 22:18 libtensorpipe_cuda.a
-rw-rw-rw- 1 codespace codespace 382610744 Jan 16 22:19 libtorch_cuda_cpp.so
-rw-rw-rw- 1 codespace codespace 753941192 Jan 16 22:20 libtorch_cuda_cu.so
-rw-rw-rw- 1 codespace codespace 219665888 Jan 16 22:20 libtorch_cuda_linalg.so
-rw-rw-rw- 1 codespace codespace      7496 Jan 16 22:20 libtorch_cuda.so

Ensure this variable is set: export TORCH_CUDA_VERSION=cu117 cd into pytorch-mnist and run cargo run -- conv.

• clone this repo: https://github.com/LaurentMazare/diffusers-rs
• Follow these setup instructions: https://github.com/LaurentMazare/diffusers-rs#clip-encoding-weights

After all the weights are downloaded run:

cargo run --example stable-diffusion --features clap -- --prompt "A very rusty robot holding a fire torch to notebooks"

Stable Diffusion 2.1 Pegging GPU

Rusty Robot Torching Notebooks

Ideas From Jeremy Wall:

I believe the easiest way is to use the #[link] attribute https://doc.rust-lang.org/reference/items/external-blocks.html#the-link-attribute. But you can also force it with rustc's -l argument https://doc.rust-lang.org/rustc/command-line-arguments.html#-l-link-the-generated-crate-to-a-native-library You can just set the RUSTFLAGS env variable to set that flag for cargo builds