petite-ad/Cargo.toml at main · leafyoung/petite-ad · GitHub

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[package]
name = "petite-ad"
version = "0.2.1"
edition = "2021"
license = "MIT OR Apache-2.0"
description = "A lightweight automatic differentiation library for learning and educational purposes"
homepage = "https://github.com/leafyoung"
repository = "https://github.com/leafyoung/petite-ad"
readme = "README.md"
keywords = [
    "autodiff",
    "differentiation",
    "gradient",
    "backpropagation",
    "machine-learning",
]
categories = ["algorithms", "mathematics", "science"]

[features]
default = []
serde = ["dep:serde"]
backend-wgpu = ["dep:pollster", "dep:wgpu"]

[dependencies]
serde = { version = "1.0", features = ["derive"], optional = true }
pollster = { version = "0.4", optional = true }
wgpu = { version = "25", optional = true }

[dev-dependencies]
criterion = { version = "0.8.1", default-features = false }
serde_json = "1.0"

[profile.release]
# Optimize for speed and binary size
opt-level = 3
lto = "thin"
codegen-units = 1
strip = true
panic = "abort"

[profile.dev]
# Faster debug builds
codegen-units = 16
lto = false
opt-level = 0
incremental = true

[profile.test]
# Fast test builds with some optimizations
codegen-units = 16
opt-level = 1
lto = false

[profile.bench]
# Maximum performance for benchmarks
inherits = "release"
debug = true

[[bench]]
name = "compute_benchmark"
harness = false