candle

Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use. Try our online demos: whisper, LLaMA2, yolo.

Get started

Make sure that you have candle-core correctly installed as described in Installation.

Let's see how to run a simple matrix multiplication. Write the following to your myapp/src/main.rs file:

use candle_core::{Device, Tensor};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let device = Device::Cpu;

    let a = Tensor::randn(0f32, 1., (2, 3), &device)?;
    let b = Tensor::randn(0f32, 1., (3, 4), &device)?;

    let c = a.matmul(&b)?;
    println!("{c}");
    Ok(())
}

cargo run should display a tensor of shape Tensor[[2, 4], f32].

Having installed candle with Cuda support, simply define the device to be on GPU:

- let device = Device::Cpu;
+ let device = Device::new_cuda(0)?;

For more advanced examples, please have a look at the following section.

Check out our examples

Check out our examples:

Whisper: speech recognition model.
LLaMA and LLaMA-v2: general LLM.
Falcon: general LLM.
Bert: useful for sentence embeddings.
StarCoder: LLM specialized to code generation.
Stable Diffusion: text to image generative model.
DINOv2: computer vision model trained using self-supervision (can be used for imagenet classification, depth evaluation, segmentation).
Quantized LLaMA: quantized version of the LLaMA model using the same quantization techniques as llama.cpp.
yolo-v3 and yolo-v8: object detection and pose estimation models. Run them using the following commands:

cargo run --example whisper --release
cargo run --example llama --release
cargo run --example falcon --release
cargo run --example bert --release
cargo run --example bigcode --release
cargo run --example stable-diffusion --release -- --prompt "a rusty robot holding a fire torch"
cargo run --example dinov2 --release -- --image path/to/myinput.jpg
cargo run --example quantized --release
cargo run --example yolo-v3 --release -- myimage.jpg
cargo run --example yolo-v8 --release -- myimage.jpg # for pose estimation, add --task pose

In order to use CUDA add --features cuda to the example command line. If you have cuDNN installed, use --features cudnn for even more speedups.

There are also some wasm examples for whisper and llama2.c. You can either build them with trunk or try them online: whisper, llama2.

For LLaMA2, run the following command to retrieve the weight files and start a test server:

cd candle-wasm-examples/llama2-c
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/model.bin
wget https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/tokenizer.json
trunk serve --release --public-url /candle-llama2/ --port 8081

And then head over to http://localhost:8081/candle-llama2.

Features

Simple syntax, looks and feels like PyTorch.
- Model training.
- Embed user-defined ops/kernels, such as flash-attention v2.
Backends.
- Optimized CPU backend with optional MKL support for x86 and Accelerate for macs.
- CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
- WASM support, run your models in a browser.
Included models.
- LLMs: LLaMA v1 and v2, Falcon, StarCoder.
- Whisper (multi-lingual support).
- Stable Diffusion.
- Computer Vision: DINOv2, EfficientNet, yolo-v3, yolo-v8.
File formats: load models from safetensors, npz, ggml, or PyTorch files.
Serverless (on CPU), small and fast deployments.
Quantization support using the llama.cpp quantized types.

How to use

Cheatsheet:

	Using PyTorch	Using Candle
Creation	`torch.Tensor([[1, 2], [3, 4]])`	`Tensor::new(&[[1f32, 2.], [3., 4.]], &Device::Cpu)?`
Creation	`torch.zeros((2, 2))`	`Tensor::zeros((2, 2), DType::F32, &Device::Cpu)?`
Indexing	`tensor[:, :4]`	`tensor.i((.., ..4))?`
Operations	`tensor.view((2, 2))`	`tensor.reshape((2, 2))?`
Operations	`a.matmul(b)`	`a.matmul(&b)?`
Arithmetic	`a + b`	`&a + &b`
Device	`tensor.to(device="cuda")`	`tensor.to_device(&Device::new_cuda(0)?)?`
Dtype	`tensor.to(dtype=torch.float16)`	`tensor.to_dtype(&DType::F16)?`
Saving	`torch.save({"A": A}, "model.bin")`	`candle::safetensors::save(&HashMap::from([("A", A)]), "model.safetensors")?`
Loading	`weights = torch.load("model.bin")`	`candle::safetensors::load("model.safetensors", &device)`

Structure

candle-core: Core ops, devices, and Tensor struct definition
candle-nn: Tools to build real models
candle-examples: Examples of using the library in realistic settings
candle-kernels: CUDA custom kernels
candle-datasets: Datasets and data loaders.
candle-transformers: transformers-related utilities.
candle-flash-attn: Flash attention v2 layer.

FAQ

Why should I use Candle?

Candle's core goal is to make serverless inference possible. Full machine learning frameworks like PyTorch are very large, which makes creating instances on a cluster slow. Candle allows deployment of lightweight binaries.

Secondly, Candle lets you remove Python from production workloads. Python overhead can seriously hurt performance, and the GIL is a notorious source of headaches.

Finally, Rust is cool! A lot of the HF ecosystem already has Rust crates, like safetensors and tokenizers.

Other ML frameworks

dfdx is a formidable crate, with shapes being included in types. This prevents a lot of headaches by getting the compiler to complain about shape mismatches right off the bat. However, we found that some features still require nightly, and writing code can be a bit daunting for non rust experts.

We're leveraging and contributing to other core crates for the runtime so hopefully both crates can benefit from each other.
burn is a general crate that can leverage multiple backends so you can choose the best engine for your workload.
tch-rs Bindings to the torch library in Rust. Extremely versatile, but they bring in the entire torch library into the runtime. The main contributor of tch-rs is also involved in the development of candle.

Common Errors

Missing symbols when compiling with the mkl feature.

If you get some missing symbols when compiling binaries/tests using the mkl or accelerate features, e.g. for mkl you get:

  = note: /usr/bin/ld: (....o): in function `blas::sgemm':
          .../blas-0.22.0/src/lib.rs:1944: undefined reference to `sgemm_' collect2: error: ld returned 1 exit status

  = note: some `extern` functions couldn't be found; some native libraries may need to be installed or have their path specified
  = note: use the `-l` flag to specify native libraries to link
  = note: use the `cargo:rustc-link-lib` directive to specify the native libraries to link with Cargo

or for accelerate:

Undefined symbols for architecture arm64:
            "_dgemm_", referenced from:
                candle_core::accelerate::dgemm::h1b71a038552bcabe in libcandle_core...
            "_sgemm_", referenced from:
                candle_core::accelerate::sgemm::h2cf21c592cba3c47 in libcandle_core...
          ld: symbol(s) not found for architecture arm64

This is likely due to a missing linker flag that was needed to enable the mkl library. You can try adding the following for mkl at the top of your binary:

extern crate intel_mkl_src;