Cargo.toml

[package]
name = "fastnn"
version = "0.3.0"
edition = "2021"
rust-version = "1.87"
description = "A deep learning library in Rust, with CUDA kernels for the parts that matter"
license = "MIT"
readme = "README.md"
repository = "https://github.com/CanReader/FastNN"
keywords = ["deep-learning", "neural-network", "cuda", "gpu", "machine-learning"]
categories = ["science", "mathematics"]
exclude = ["docs/", ".github/"]

[lib]
name = "fastnn"
path = "src/lib.rs"

[dependencies]
rand = "0.8"        # weight init, dropout masks, shuffling
rand_distr = "0.4"  # normal distribution
rayon = "1.8"       # data parallelism on the CPU paths
libm = "0.2"        # erff, for exact GELU
ureq = "2.9"        # dataset download
flate2 = "1.0"      # dataset decompression

[build-dependencies]
cc = { version = "1.0", features = ["parallel"] }

[dev-dependencies]
criterion = { version = "0.5", features = ["html_reports"] }

[features]
default = []
# Compile cuda/kernels.cu with nvcc and link cudart/cublas/curand. Off by
# default so `cargo add fastnn` builds without the CUDA toolkit; without it,
# cuda/stubs.c supplies the symbols and everything runs on the CPU.
cuda = []

[[example]]
name = "simple_mlp"
path = "examples/simple_mlp.rs"

[[example]]
name = "mnist_mlp"
path = "examples/mnist_mlp.rs"

[[example]]
name = "mnist_cnn"
path = "examples/mnist_cnn.rs"

[[example]]
name = "char_lm"
path = "examples/char_lm.rs"

[[example]]
name = "finetune"
path = "examples/finetune.rs"

[[example]]
name = "gan"
path = "examples/gan.rs"

[[example]]
name = "vae"
path = "examples/vae.rs"

[[example]]
name = "reinforce"
path = "examples/reinforce.rs"

[[example]]
name = "multi_task"
path = "examples/multi_task.rs"

[[bench]]
name = "tensor_ops"
harness = false
path = "benches/tensor_ops.rs"

[profile.release]
opt-level = 3
lto = true
codegen-units = 1