This is a personal project to test several daily tasks in the field of data science on the new Apple M1/M1-Pro macs. The project is inspired by
this Medium article by
Dario Radečić.
Check out full repo here
Test Environment
- Python: 3.6.13
- Pandas: 1.1.5
- Numpy: 1.19.2
- PyTorch: 1.10.0
Test Machines and Alias
- MacBook Pro (13’ Mid 2020): MBP-13
- MacBook Air (13’ Late 2020): MBA-M1
- MacBook Pro (14’ Late 2021): MBP-14
- MacBook Air (13’ Mid 2022): MBA-M2
| Spec |
MBP-13 |
MBA M1 |
MBP-14 |
MBA-M2 |
| CPU |
Intel I5 (2.0 GHz) |
M1 (8 Core) |
M1 Pro (8 Core) |
M2 (8 Core) |
| Memory |
16 GB |
8 GB |
16 GB |
16 GB |
| Storage |
512 GB |
256 GB |
512 GB |
512 GB |
| Time |
Mid 2020 |
Late 2020 |
Late 2021 |
Mid 2022 |
Task Description
- Pure Python
- List comprehension with simple math computation (100,000,000 numbers)
- Pandas
- Loading Files: loading a csv large file (4.14 GB)
- Data Manipulation: apply math calculation on columns
- Groupby: groupby and apply math calculations
- Query: query based on column values
- Numpy
- SkLearn
- Decision Tree: fit and grid search
- SVM: fit and grid search (rbf kernel)
- PyTorch
- Simple multilayer perceptron on numerical data & classification task
Result
| Task |
MBP-13 |
MBA M1 |
MBP-14 |
MBA-M2 |
| Pure Python |
182s |
163s |
138s |
128s |
| Pandas - Loading |
149s |
138s |
74s |
49s |
| Pandas - Data Manipulation |
100s |
112s |
91s |
93s |
| Pandas - Groupby |
10s |
9s |
6.96s |
4s |
| Pandas - Query |
177s |
92s |
91s |
99s |
| Numpy |
77s |
151s |
147s |
138s |
| SkLearn - DT (fit) |
14s |
8s |
8s |
4s |
| SkLearn - DT (grid earch) |
92s |
102s |
48s |
56s |
| SkLearn - SVM (fit) |
35s |
27s |
26s |
40s |
| SkLearn - SVM (grid search) |
1089s |
859s |
797s |
945s |
| SkLearn - SVM (grid search - 8 thread parallel) |
328s |
292s |
197s |
290s |
| PyTorch - MLP |
96s |
69s |
66s |
48s |
| Test Time |
Nov. 2021 |
Nov. 2021 |
Nov. 2021 |
Jul. 2022 |
- Note: As noted in the table above, some of the tests are not done at the same time, which means there might be system updates on the OS level that impact task performances.
Get Started
- Fork the repo here
- Virtual Environment - Highly recommanded
- Install conda or Anaconda
conda create -n "py36" python=3.6 ipythonconda activate py36- More detail about virtual environment can be found here
- Jupyter Notebook
conda install -c conda-forge notebook
- Scikit
pip install -U scikit-learn
- Pandas
- Pytorch
conda install pytorch torchvision -c pytorch
- tqdm
conda install -c conda-forge tqdm
Other Related Articles
- https://towardsdatascience.com/new-apple-silicon-m1-macbook-air-the-dream-laptop-for-machine-learning-engineers-a1590fbd170f
- https://gist.github.com/markus-beuckelmann/8bc25531b11158431a5b09a45abd6276
- https://wandb.ai/vanpelt/m1-benchmark/reports/Can-Apple-s-M1-help-you-train-models-faster-cheaper-than-NVIDIA-s-V100—VmlldzozNTkyMzg
