[Numpy-discussion] Numpy 1.11.0rc1 released.

Discussion:

Charles R Harris

2016-02-23 01:47:35 UTC

Hi All,

I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the
issues discovered in 1.11.0b3 have been dealt with and this release can go
on to become the official release. Source files and documentation can be
found on Sourceforge
<https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc1/>, while
source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be
installed from Pypi. Please test thoroughly.

Chuck

Charles R Harris

2016-02-23 12:02:41 UTC

Permalink

Post by Charles R Harris
Hi All,
I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the
issues discovered in 1.11.0b3 have been dealt with and this release can go
on to become the official release. Source files and documentation can be
found on Sourceforge
<https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc1/>, while
source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be
installed from Pypi. Please test thoroughly.

Issues reported by Christoph at https://github.com/numpy/numpy/issues/7316.

Chuck

Pauli Virtanen

2016-02-23 15:44:40 UTC

Permalink

Post by Charles R Harris
I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the
issues discovered in 1.11.0b3 have been dealt with and this release can go
on to become the official release. Source files and documentation can be
found on Sourceforge
<https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc1/>, while
source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be
installed from Pypi. Please test thoroughly.

FWIW https://travis-ci.org/pv/testrig/builds/108384173

Charles R Harris

2016-02-23 20:40:48 UTC

Permalink

Christoph reports the following problem that I am unable to reproduce on
appveyor or find reported elsewhere.

On all 32-bit platforms:

============================================================
ERROR: test_zeros_big (test_multiarray.TestCreation)
------------------------------------------------------------
Traceback (most recent call last):
File "X:\Python27\lib\site-packages\numpy\core\tests\test_multiarray.py",
line 594, in test_zeros_big
d = np.zeros((30 * 1024**2,), dtype=dt)
MemoryError

I would be much obliged if someone else could demonstrate it.

<snip>

Chuck

Julian Taylor

2016-02-23 20:58:48 UTC

Permalink

that test needs about 500Mb of memory on windows as it doesn't have
sparse allocations like most *nixes.
It used to fail for me during release testing when I only gave the
windows VM 1GB of ram.
If its a problem for CI we could disable it on windows, or at least skip
the complex double case.

Post by Charles R Harris
Christoph reports the following problem that I am unable to reproduce on
appveyor or find reported elsewhere.
============================================================
ERROR: test_zeros_big (test_multiarray.TestCreation)
------------------------------------------------------------
File
"X:\Python27\lib\site-packages\numpy\core\tests\test_multiarray.py",
line 594, in test_zeros_big
d = np.zeros((30 * 1024**2,), dtype=dt)
MemoryError
I would be much obliged if someone else could demonstrate it.
<snip>
Chuck
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Charles R Harris

2016-02-23 21:19:47 UTC

Permalink

On Tue, Feb 23, 2016 at 1:58 PM, Julian Taylor <

Post by Julian Taylor
that test needs about 500Mb of memory on windows as it doesn't have
sparse allocations like most *nixes.
It used to fail for me during release testing when I only gave the
windows VM 1GB of ram.
If its a problem for CI we could disable it on windows, or at least skip
the complex double case.

It's not a problem on CI, just for Christoph. I asked him what memory
resources the test had available but haven't heard back. AFAICT, nothing
associated with the test has changed for this release. The options are
probably to 1) ignore the failure, or 2) disable the test on 32 bits.

<snip>

Chuck

Pauli Virtanen

2016-02-23 21:05:36 UTC

Permalink

23.02.2016, 22:40, Charles R Harris kirjoitti:
[clip]

Post by Charles R Harris
============================================================
ERROR: test_zeros_big (test_multiarray.TestCreation)
------------------------------------------------------------
File "X:\Python27\lib\site-packages\numpy\core\tests\test_multiarray.py",
line 594, in test_zeros_big
d = np.zeros((30 * 1024**2,), dtype=dt)
MemoryError
I would be much obliged if someone else could demonstrate it.

Memory fragmentation in the 2GB address space available? If dt==float64,
that requires 250MB contiguous.

--
Pauli Virtanen

Christoph Gohlke

2016-02-23 22:06:40 UTC

Permalink

Post by Pauli Virtanen
[clip]

Memory fragmentation in the 2GB address space available? If dt==float64,
that requires 250MB contiguous.

Before creating the dtype('D') test array, the largest contiguous block
available to the 32 bit Python process on my system is ~830 MB. The
creation of this array succeeds. However, the creation of the next
dtype('G') test array fails because the previous array is still in
memory and the largest contiguous block available is only ~318 MB.
Deleting the test arrays after usage via del(d) fixes this problem
<https://github.com/numpy/numpy/pull/7323>. Another fix could be to
change the order of data types tested.

Christoph

Charles R Harris

2016-02-23 22:44:40 UTC

Permalink

Christoph, any chance you can test https://github.com/numpy/numpy/pull/7324
before it gets merged (or not).

<snip>

Chuck

Charles R Harris

2016-02-24 17:42:29 UTC

Permalink

Post by Pauli Virtanen

Post by Charles R Harris
I'm delighted to announce the release of Numpy 1.11.0rc1. Hopefully the
issues discovered in 1.11.0b3 have been dealt with and this release can

Post by Charles R Harris
on to become the official release. Source files and documentation can be
found on Sourceforge
<https://sourceforge.net/projects/numpy/files/NumPy/1.11.0rc1/>, while
source files and OS X wheels for Python 2.7, 3.3, 3.4, and 3.5 can be
installed from Pypi. Please test thoroughly.

FWIW https://travis-ci.org/pv/testrig/builds/108384173

Thanks for that. Most of the new errors look to be the result of the change
in divmod, where before divmod(float64(1.0), 0.0) was (inf, nan) and it is
now (nan, nan). There are also two errors in matplotlib that look to be
the result of the slight change in the numerical values of remainder due to
improved precision. I would class those errors as more of a test problem
resulting from the inherent imprecision of floating point than a numpy
regression.

Chuck