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Slicing, also with arbitrary step size, and negative indices to mean
elements from the end of the axis.
Views and subviews of arrays; iterators that yield subviews.
Status and Lookout
Still iterating on and evolving the crate
The crate is continuously developing, and breaking changes are expected
during evolution from version to version. We adopt the newest stable
rust features if we need them.
Performance:
Prefer higher order methods and arithmetic operations on arrays first,
then iteration, and as a last priority using indexed algorithms.
Efficient floating point matrix multiplication even for very large
matrices; can optionally use BLAS to improve it further.
Crate Feature Flags
The following crate feature flags are available. They are configured in
your Cargo.toml.
std
Rust standard library (enabled by default)
This crate can be used without the standard library by disabling the
default std feature. To do so, use this in your Cargo.toml:
The geomspace linspace logspace range std var var_axis and std_axis
methods are only available when std is enabled.
serde
Enables serialization support for serde 1.x
rayon
Enables parallel iterators, parallelized methods and par_azip!.
Implies std
approx
Implementations of traits from version 0.5 of the [approx] crate.
blas
Enable transparent BLAS support for matrix multiplication.
Uses blas-src for pluggable backend, which needs to be configured
separately (see below).
matrixmultiply-threading
Enable the threading feature in the matrixmultiply package
portable-atomic-critical-section
Whether portable-atomic should use critical-section
How to use with cargo
[dependencies]
ndarray = "0.16.0"
How to enable BLAS integration
Blas integration is an optional add-on. Without BLAS, ndarray uses the
matrixmultiply crate for matrix multiplication for f64 and f32
arrays (and it's always enabled as a fallback since it supports matrices of
arbitrary strides in both dimensions).
Depend and link to blas-src directly to pick a blas provider. Ndarray
presently requires a blas provider that provides the cblas-sys interface. If
further feature selection is wanted or needed then you might need to depend directly on
the backend crate's source too. The backend version must be the one that
blas-src also depends on.
An example configuration using system openblas is shown below. Note that only
end-user projects (not libraries) should select provider:
[dependencies]
ndarray = { version = "0.16.0", features = ["blas"] }
blas-src = { version = "0.10", features = ["openblas"] }
openblas-src = { version = "0.10", features = ["cblas", "system"] }
Using system-installed dependencies can save a long time building dependencies.
An example configuration using (compiled) netlib is shown below anyway:
[dependencies]
ndarray = { version = "0.16.0", features = ["blas"] }
blas-src = { version = "0.10.0", default-features = false, features = ["netlib"] }
When this is done, your program must also link to blas_src by using it or
explicitly including it in your code:
extern crate blas_src;
The following versions have been verified to work together. For ndarray 0.15 or later,
there is no tight coupling to the blas-src version, so version selection is more flexible.
ndarray
blas-src
openblas-src
netlib-src
0.16
0.10
0.10
0.8
0.15
0.8
0.10
0.8
0.15
0.7
0.9
0.8
0.14
0.6.1
0.9.0
0.13
0.2.0
0.6.0
BLAS on MSRV
Although ndarray currently maintains an MSRV of 1.64.0, this is separate from the MSRV (either stated or real) of the various BLAS providers.
As of the time of writing, openblas currently supports MSRV of 1.71.1.
So, while ndarray and openblas-src are compatible, they can only work together with toolchains 1.71.1 or above.