Global State#
NumPy has a few import-time, compile-time, or runtime options which change the global behaviour. Most of these are related to performance or for debugging purposes and will not be interesting to the vast majority of users.
Performance-Related Options#
Number of Threads used for Linear Algebra#
NumPy itself is normally intentionally limited to a single thread
during function calls, however it does support multiple Python
threads running at the same time.
Note that for performant linear algebra NumPy uses a BLAS backend
such as OpenBLAS or MKL, which may use multiple threads that may
be controlled by environment variables such as OMP_NUM_THREADS
depending on what is used.
One way to control the number of threads is the package
threadpoolctl
Madvise Hugepage on Linux#
When working with very large arrays on modern Linux kernels, you can experience a significant speedup when transparent hugepage is used. The current system policy for transparent hugepages can be seen by:
cat /sys/kernel/mm/transparent_hugepage/enabled
When set to madvise NumPy will typically use hugepages for a performance
boost. This behaviour can be modified by setting the environment variable:
NUMPY_MADVISE_HUGEPAGE=0
or setting it to 1 to always enable it. When not set, the default
is to use madvise on Kernels 4.6 and newer. These kernels presumably
experience a large speedup with hugepage support.
This flag is checked at import time.
Interoperability-Related Options#
The array function protocol which allows array-like objects to hook into the NumPy API is currently enabled by default. This option exists since NumPy 1.16 and is enabled by default since NumPy 1.17. It can be disabled using:
NUMPY_EXPERIMENTAL_ARRAY_FUNCTION=0
See also numpy.class.__array_function__ for more information.
This flag is checked at import time.
Debugging-Related Options#
Relaxed Strides Checking#
The compile-time environment variable:
NPY_RELAXED_STRIDES_DEBUG=0
can be set to help debug code written in C which iteraters through arrays
manually. When an array is contiguous and iterated in a contiguous manner,
its strides should not be queried. This option can help find errors where
the strides are incorrectly used.
For details see the memory layout documentation.
Warn if no memory allocation policy when deallocating data#
Some users might pass ownership of the data pointer to the ndarray by
setting the OWNDATA flag. If they do this without setting (manually) a
memory allocation policy, the default will be to call free. If
NUMPY_WARN_IF_NO_MEM_POLICY is set to "1", a RuntimeWarning will
be emitted. A better alternative is to use a PyCapsule with a deallocator
and set the ndarray.base.