Hi Pierre,
If you’re able to compile NumPy locally and you have reliable benchmarks, you
can write a script that tests the runtime of your benchmark and reports it as a
test pass/fail. You can then use “git bisect run” to automatically find the
commit that caused the issue. That will help narro
Any idea why the most recent version isn't available on the main anaconda
channel. conda-forge and building are not options for a number of reasons.
I posted a package request there but double digit days have gone by it just
got a thumbs up and package-request tag
https://github.com/ContinuumIO/
No, because the array of 100 elements will only have the overhead once,
while the 100 arrays will each have the overhead repeated.
Think about the overhead like a book cover on a book. It takes additional
space, but provides storage for the book, information to help you find it,
etc. Each book on
On Sat, Mar 13, 2021 at 4:18 PM wrote:
> So is it right that 100 arrays of one element is smaller than one array
> with size of 100 elements?
>
No, typically the opposite is true.
--
Robert Kern
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So is it right that 100 arrays of one element is smaller than one array with size of 100 elements?14.03.2021, 00:06, "Todd" :Ideally float64 uses 64 bits for each number while float16 uses 16 bits. 64/16=4. However, there is some additional overhead. This overhead makes up a large portion of sma
On Sat, Mar 13, 2021 at 4:02 PM wrote:
> Dear colleagues!
>
> Size of np.float16(1) is 26
> Size of np.float64(1) is 32
> 32 / 26 = 1.23
>
Note that `sys.getsizeof()` is returning the size of the given Python
object in bytes. `np.float16(1)` and `np.float64(1)` are so-called "numpy
scalar object
Ideally float64 uses 64 bits for each number while float16 uses 16 bits.
64/16=4. However, there is some additional overhead. This overhead makes
up a large portion of small arrays, but becomes negligible as the array
gets bigger.
On Sat, Mar 13, 2021, 16:01 wrote:
> Dear colleagues!
>
> Size
Dear colleagues! Size of np.float16(1) is 26Size of np.float64(1) is 3232 / 26 = 1.23 Since memory is limited I have a question after this code: import numpy as np import sys a1 = np.ones(1, dtype='float16') b1 = np.ones(1, dtype='float64') div_1 = sys.getsizeof(b1) / sys.getsizeof(a1)
