Re: [Numpy-discussion] docs.scipy.org down

2017-03-13 Thread Ralf Gommers
On Tue, Mar 14, 2017 at 8:16 AM, Ryan May wrote: > Is https://docs.scipy.org/ being down known issue? > It is. Is being worked on; tracking issue is https://github.com/numpy/numpy/issues/8779 Thanks for reporting, Ralf ___

[Numpy-discussion] docs.scipy.org down

2017-03-13 Thread Ryan May
Is https://docs.scipy.org/ being down known issue? Ryan -- Ryan May ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] caching large allocations on gnu/linux

2017-03-13 Thread Francesc Alted
2017-03-13 18:11 GMT+01:00 Julian Taylor : > On 13.03.2017 16:21, Anne Archibald wrote: > > > > > > On Mon, Mar 13, 2017 at 12:21 PM Julian Taylor > > > > > wrote: > > > > Should it be agreed

Re: [Numpy-discussion] caching large allocations on gnu/linux

2017-03-13 Thread Julian Taylor
On 13.03.2017 16:21, Anne Archibald wrote: > > > On Mon, Mar 13, 2017 at 12:21 PM Julian Taylor > > > wrote: > > Should it be agreed that caching is worthwhile I would propose a very > simple implementation. We only

Re: [Numpy-discussion] caching large allocations on gnu/linux

2017-03-13 Thread Anne Archibald
On Mon, Mar 13, 2017 at 12:21 PM Julian Taylor < jtaylor.deb...@googlemail.com> wrote: Should it be agreed that caching is worthwhile I would propose a very > simple implementation. We only really need to cache a small handful of > array data pointers for the fast allocate deallocate cycle that

Re: [Numpy-discussion] float16/32: wrong number of digits?

2017-03-13 Thread Anne Archibald
On Mon, Mar 13, 2017 at 12:57 PM Eric Wieser wrote: > > `float(repr(a)) == a` is guaranteed for Python `float` > > And `np.float16(repr(a)) == a` is guaranteed for `np.float16`(and the same > is true up to `float128`, which can be platform-dependent). Your code >

Re: [Numpy-discussion] float16/32: wrong number of digits?

2017-03-13 Thread Eric Wieser
> `float(repr(a)) == a` is guaranteed for Python `float` And `np.float16(repr(a)) == a` is guaranteed for `np.float16`(and the same is true up to `float128`, which can be platform-dependent). Your code doesn't work because you're deserializing to a higher precision format than you serialized to.

[Numpy-discussion] caching large allocations on gnu/linux

2017-03-13 Thread Julian Taylor
Hi, As numpy often allocates large arrays and one factor in its performance is faulting memory from the kernel to the process. This has some cost that is relatively significant. For example in this operation on large arrays it accounts for 10-15% of the runtime: import numpy as np a =