How to install numpy on M1 Max, with the most accelerated performance (Apple's vecLib)? Here's the answer as of Dec 6 2021.

---
## Steps
### I. Install miniforge
So that your Python is run natively on arm64, not translated via Rosseta.
1. Download [Miniforge3-MacOSX-arm64.sh](https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh), then
2. Run the script, then open another shell
```bash
$ bash Miniforge3-MacOSX-arm64.sh
```
3. Create an environment (here I use name `np_veclib`)
```bash
$ conda create -n np_veclib python=3.9
$ conda activate np_veclib
```
### II. Install Numpy with BLAS interface specified as vecLib
1. To compile `numpy`, first need to install `cython` and `pybind11`:
```bash
$ conda install cython pybind11
```
2. Compile `numpy` by (Thanks @Marijn's [answer](https://stackoverflow.com/a/66536896/13571357)) - don't use `conda install`!
```bash
$ pip install --no-binary :all: --no-use-pep517 numpy
```
3. An alternative of 2. is to build from source
```bash
$ git clone https://github.com/numpy/numpy
$ cd numpy
$ cp site.cfg.example site.cfg
$ nano site.cfg
```
Edit the copied `site.cfg`: add the following lines:
```
[accelerate]
libraries = Accelerate, vecLib
```
Then build and install:
```bash
$ NPY_LAPACK_ORDER=accelerate python setup.py build
$ python setup.py install
```
4. After either 2 or 3, now test whether numpy is using vecLib:
```bash
>>> import numpy
>>> numpy.show_config()
```
Then, info like `/System/Library/Frameworks/vecLib.framework/Headers` should be printed.
### III. For further installing other packages using conda
Make conda recognize packages installed by pip
```
conda config --set pip_interop_enabled true
```
This must be done, otherwise if e.g. `conda install pandas`, then `numpy` will be in `The following packages will be installed` list and installed again. But the new installed one is from `conda-forge` channel and is slow.

---

## Comparisons to other installations:

### 1. Competitors:
Except for the above optimal one, I also tried several other installations
+ A. `np_default`: `conda create -n np_default python=3.9 numpy`
+ B. `np_openblas`: `conda create -n np_openblas python=3.9 numpy blas=*=*openblas*`
+ C. `np_netlib`: `conda create -n np_netlib python=3.9 numpy blas=*=*netlib*`

The above ABC options are directly installed from conda-forge channel. `numpy.show_config()` will show identical results. To see the difference, examine by `conda list` - e.g. `openblas` packages are installed in B. Note that `mkl` or `blis` is not supported on arm64.
+ D. `np_openblas_source`: First install openblas by `brew install openblas`. Then add `[openblas]` path `/opt/homebrew/opt/openblas` to `site.cfg` and build Numpy from source.
+ `M1` and `i9–9880H` in this [post](https://towardsdatascience.com/m1-macbook-pro-vs-intel-i9-macbook-pro-ultimate-data-science-comparison-dde8fc32b5df).
+ My old `i5-6360U` 2cores on MacBook Pro 2016 13in.

### 2. Benchmarks:
Here I use two benchmarks:
1. `mysvd.py`: My SVD decomposition
```python
import time
import numpy as np
np.random.seed(42)
a = np.random.uniform(size=(300, 300))
runtimes = 10

timecosts = []
for _ in range(runtimes):
    s_time = time.time()
    for i in range(100):
        a += 1
        np.linalg.svd(a)
    timecosts.append(time.time() - s_time)

print(f'mean of {runtimes} runs: {np.mean(timecosts):.5f}s')
```
2. `dario.py`: A benchmark script by [Dario Radečić](https://gist.github.com/daradecic/a2ac0a75d7e5f22c9aa07174dcbbe061/raw/a56ee217e6d3f949b1d1f719a7a134cef130cd9f/macs.py) at the post above.

### 3. Results:
```
+-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+
|  sec  | np_veclib | np_default | np_openblas | np_netlib | np_openblas_source | M1 | i9–9880H | i5-6360U |
+-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+
| mysvd |  1.02300  |   4.29386  |   4.13854   |  4.75812  |      12.57879      |  / |     /    |  2.39917 |
+-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+
| dario |     21    |     41     |      39     |    323    |         40         | 33 |    23    |    78    |
+-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+
```