mirror of https://github.com/tracel-ai/burn.git
116 lines
3.8 KiB
TOML
116 lines
3.8 KiB
TOML
[package]
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authors = ["nathanielsimard <nathaniel.simard.42@gmail.com>"]
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categories = ["science", "no-std", "embedded", "wasm"]
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description = "Flexible and Comprehensive Deep Learning Framework in Rust"
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edition = "2021"
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keywords = ["deep-learning", "machine-learning", "tensor", "pytorch", "ndarray"]
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license = "MIT OR Apache-2.0"
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name = "burn-core"
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readme = "README.md"
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repository = "https://github.com/tracel-ai/burn/tree/main/burn-core"
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version = "0.12.0"
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[features]
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default = [
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"std",
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"burn-dataset?/default",
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"burn-ndarray?/default",
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"burn-candle?/default",
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"burn-wgpu?/default",
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"burn-fusion?/default",
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"burn-tch?/default",
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"burn-dataset?/default",
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"burn-common/default",
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"burn-tensor/default",
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]
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std = [
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"flate2",
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"log",
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"rand/std",
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"rmp-serde",
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"serde/std",
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"serde_json/std",
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"bincode/std",
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"half/std",
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"burn-ndarray?/std",
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"burn-candle?/std",
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"burn-wgpu?/std",
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"burn-fusion?/std",
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"burn-common/std",
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"burn-tensor/std",
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]
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dataset = ["burn-dataset"]
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sqlite = ["burn-dataset?/sqlite"]
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sqlite-bundled = ["burn-dataset?/sqlite-bundled"]
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wasm-sync = ["burn-tensor/wasm-sync", "burn-common/wasm-sync"]
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# Backend
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autodiff = ["burn-autodiff"]
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fusion = ["burn-fusion", "burn-wgpu?/fusion"]
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## Backend features
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cuda = ["burn-candle?/cuda"]
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accelerate = ["burn-candle?/accelerate", "burn-ndarray?/blas-accelerate"]
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openblas = ["burn-ndarray?/blas-openblas"]
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openblas-system = ["burn-ndarray?/blas-openblas-system"]
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blas-netlib = ["burn-ndarray?/blas-netlib"]
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ndarray = ["burn-ndarray"]
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tch = ["burn-tch"]
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candle = ["burn-candle"]
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wgpu = ["burn-wgpu"]
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# Serialization formats
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experimental-named-tensor = ["burn-tensor/experimental-named-tensor"]
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test-tch = ["tch"] # To use tch during testing, default uses ndarray.
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test-wgpu = ["wgpu"] # To use wgpu during testing, default uses ndarray.
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[dependencies]
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# ** Please make sure all dependencies support no_std when std is disabled **
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burn-common = { path = "../burn-common", version = "0.12.0", default-features = false }
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burn-dataset = { path = "../burn-dataset", version = "0.12.0", optional = true, default-features = false }
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burn-derive = { path = "../burn-derive", version = "0.12.0" }
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burn-tensor = { path = "../burn-tensor", version = "0.12.0", default-features = false }
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# Backends
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burn-ndarray = { path = "../burn-ndarray", version = "0.12.0", optional = true, default-features = false }
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burn-wgpu = { path = "../burn-wgpu", version = "0.12.0", optional = true }
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burn-autodiff = { path = "../burn-autodiff", version = "0.12.0", optional = true }
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burn-fusion = { path = "../burn-fusion", version = "0.12.0", optional = true }
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burn-tch = { path = "../burn-tch", version = "0.12.0", optional = true }
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burn-candle = { path = "../burn-candle", version = "0.12.0", optional = true }
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derive-new = { workspace = true }
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libm = { workspace = true }
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log = { workspace = true, optional = true }
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rand = { workspace = true, features = ["std_rng"] } # Default enables std
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# Using in place of use std::sync::Mutex when std is disabled
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spin = { workspace = true, features = ["mutex", "spin_mutex"] }
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# The same implementation of HashMap in std but with no_std support (only alloc crate is needed)
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hashbrown = { workspace = true, features = ["serde"] } # no_std compatible
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# Serialize Deserialize
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flate2 = { workspace = true, optional = true }
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serde = { workspace = true, features = ["derive"] }
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bincode = { workspace = true }
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half = { workspace = true }
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rmp-serde = { workspace = true, optional = true }
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serde_json = { workspace = true, features = ["alloc"] } #Default enables std
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[dev-dependencies]
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tempfile = { workspace = true }
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burn-dataset = { path = "../burn-dataset", version = "0.12.0", features = [
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"fake",
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] }
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burn-ndarray = { path = "../burn-ndarray", version = "0.12.0", default-features = false }
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burn-autodiff = { path = "../burn-autodiff", version = "0.12.0" }
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