MarkDana · January 21, 2025 16:07 · Dec 7, 2021 · Dec 7, 2021
diff --git a/m1-max-numpy-setup.md b/m1-max-numpy-setup.md
@@ -65,6 +65,8 @@ Except for the above optimal one, I also tried several other installations
 
 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:
@@ -86,16 +88,16 @@ for _ in range(runtimes):
 
 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 this [post](https://towardsdatascience.com/m1-macbook-pro-vs-intel-i9-macbook-pro-ultimate-data-science-comparison-dde8fc32b5df). Results in this post is also reported in following, as `M1` and `i9–9880H`.
+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 |
-+-------+-----------+------------+-------------+-----------+--------------------+----+----------+
-| mysvd |  1.02300  |   4.29386  |   4.13854   |  4.75812  |      12.57879      |  / |     /    |
-+-------+-----------+------------+-------------+-----------+--------------------+----+----------+
-| dario |     21    |     41     |      39     |    323    |         40         | 33 |    23    |
-+-------+-----------+------------+-------------+-----------+--------------------+----+----------+
++-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+
+|  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    |
++-------+-----------+------------+-------------+-----------+--------------------+----+----------+----------+
 ```
 
diff --git a/m1-max-numpy-setup.md b/m1-max-numpy-setup.md
@@ -0,0 +1,101 @@
+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.
+
+### 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 this [post](https://towardsdatascience.com/m1-macbook-pro-vs-intel-i9-macbook-pro-ultimate-data-science-comparison-dde8fc32b5df). Results in this post is also reported in following, as `M1` and `i9–9880H`.
+
+### 3. Results:
+```
++-------+-----------+------------+-------------+-----------+--------------------+----+----------+
+|  sec  | np_veclib | np_default | np_openblas | np_netlib | np_openblas_source | M1 | i9–9880H |
++-------+-----------+------------+-------------+-----------+--------------------+----+----------+
+| mysvd |  1.02300  |   4.29386  |   4.13854   |  4.75812  |      12.57879      |  / |     /    |
++-------+-----------+------------+-------------+-----------+--------------------+----+----------+
+| dario |     21    |     41     |      39     |    323    |         40         | 33 |    23    |
++-------+-----------+------------+-------------+-----------+--------------------+----+----------+
+```
+
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