On Sun, 2021-03-14 at 17:15 +1100, Juan Nunez-Iglesias wrote:
> 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
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
On Sat, 2021-03-13 at 00:33 +0100, PIERRE AUGIER wrote:
> Hi,
>
> I tried to compile Numpy with `pip install numpy==1.20.1 --no-binary
> numpy --force-reinstall` and I can reproduce the regression.
>
> Good news, I was able to reproduce the difference with only Numpy
> 1.20.1.
>
> Arrays
On 2021/03/12 1:33 PM, PIERRE AUGIER wrote:
arr.copy() or np.copy(arr) do not give the same result, with arr obtained from a
Pandas dataframe with arr = df.values. It's strange because type(df.values) gives
so I would expect arr.copy() and np.copy(arr) to give
exactly the same result.
Hi,
I tried to compile Numpy with `pip install numpy==1.20.1 --no-binary numpy
--force-reinstall` and I can reproduce the regression.
Good news, I was able to reproduce the difference with only Numpy 1.20.1.
Arrays prepared with (`df` is a Pandas dataframe)
arr = df.values.copy()
or
arr =
On Fri, 2021-03-12 at 21:36 +0100, PIERRE AUGIER wrote:
> Hi,
>
> I'm looking for a difference between Numpy 0.19.5 and 0.20 which
> could explain a performance regression (~15 %) with Pythran.
>
> I observe this regression with the script
>
