JoelHBierman

Are there any known methods of getting to build Numpy/Scipy from source using Accelerate to avoid the mismatch? (I think support might have been dropped altogether.)

One thing I noticed while investigating this a bit: If you run the command `np.show_config()`, you can see that the Numpy binaries from Conda-forge and pypi are built using different...

That's both interesting and unfortunate. Interesting in the sense that whatever the computational backend for the Conda-forge build is, it appears to be different than the pypi build. (Of course,...

That does make some sense. And by this, I don't mean that it's ideal, just that it appears consistent about what we know about the chips. The main differences in...

Of all the possible explanations I can think of, that makes the most sense to me. Do you know if there are any published benchmarks for specific open source scientific...

@ TiborGY I would not know how to monitor the health of my SSD, so I cannot speak to that. I have a 16GB M1 Mac mini sitting on my...

Some good news for building numpy using the Accelerate framework! From the numpy 1.21.0 release notes at https://numpy.org/doc/stable/release/1.21.0-notes.html : "With the release of macOS 11.3, several different issues that numpy...

Ah I see. The limited benchmarking I did that showed better performance from the mini forge build must be due to something else then. I would look into this more...

If I run `conda list`, it tells me that I installed the `py39h1f3b974_0` NumPy 1.21.0 build from conda-forge.

I have rerun the benchmarks at: https://markus-beuckelmann.de/blog/boosting-numpy-blas.html. The results are: pypi numpy 1.21.3: ``` Dotted two 4096x4096 matrices in 0.74 s. Dotted two vectors of length 524288 in 0.26 ms....