mirror of https://github.com/tracel-ai/burn.git
43 lines
1.5 KiB
TOML
43 lines
1.5 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 = "Tensor library with user-friendly APIs and automatic differentiation support"
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edition.workspace = true
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keywords = ["deep-learning", "machine-learning", "tensor", "pytorch", "ndarray"]
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license.workspace = true
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name = "burn-tensor"
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readme.workspace = true
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repository = "https://github.com/tracel-ai/burn/tree/main/crates/burn-tensor"
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version.workspace = true
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[features]
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default = ["std", "repr"]
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doc = ["default"]
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experimental-named-tensor = []
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export_tests = ["burn-tensor-testgen"]
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std = ["rand/std", "half/std", "num-traits/std", "burn-common/std", "burn-common/rayon"]
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repr = []
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[dependencies]
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burn-common = { path = "../burn-common", version = "0.14.0", default-features = false}
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burn-tensor-testgen = { path = "../burn-tensor-testgen", version = "0.14.0", optional = true }
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derive-new = { workspace = true }
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half = { workspace = true, features = ["bytemuck"] }
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num-traits = { workspace = true }
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rand = { workspace = true }
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rand_distr = { workspace = true } # use instead of statrs because it supports no_std
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bytemuck = { workspace = true }
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# The same implementation of HashMap in std but with no_std support (only needs alloc crate)
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hashbrown = { workspace = true } # no_std compatible
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# Serialization
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serde = { workspace = true }
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[dev-dependencies]
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rand = { workspace = true, features = ["std", "std_rng"] } # Default enables std
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[package.metadata.docs.rs]
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features = ["doc"]
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