Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Code for testing various M1 Chip benchmarks with TensorFlow.
| Date | Stars |
|---|---|
| 2026-07-24 | 528 |
| 2026-07-25 | 528 |
| 2026-07-28 | 528 |
| 2026-07-30 | 528 |
| 2026-07-31 | 528 |
| 2026-08-06 | 529 |
Today
+1 stars today
This week
+1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.19%/day
# M1, M1 Pro, M1 Max Machine Learning Speed Test Comparison
This repo contains some sample code to benchmark the new M1 MacBooks (M1 Pro and M1 Max) against various other pieces of hardware.
It also has steps below to setup your M1, M1 Pro, M1 Max, M1 Ultra or M2 Mac to run the code.
## Who is this repo for?
**You:** have a new M1, M1 Pro, M1 Max, M1 Ultra or M2 Mac and would like to get started doing machine learning and data science on it.
**This repo:** teaches you how to install the most common machine learning and data science packages (software) on your machine and make sure they run using sample code.
## Machine Learning Experiments Conducted
All experiments were run with the same code. For Apple devices, TensorFlow environments were created with the steps below.
| Notebook Number | Experiment |
| ----- | ----- |
| [00](https://github.com/mrdbourke/m1-machine-learning-test/blob/main/00_cifar10_tinyvgg_benchmark.ipynb) | TinyVGG model trained on CIFAR10 dataset with TensorFlow code. |
| [01](https://github.com/mrdbourke/m1-machine-learning-test/blob/main/01_food101_effnet_benchmark.ipynb) | EfficientNetB0 Feature Extractor on Food101 dataset with TensorFlow code.
| [02](https://github.com/mrdbourke/m1-machine-learning-test/blob/main/02_random_forest_benchmark.ipynb) | `RandomForestClassifier` from Scikit-Learn trained with random search cross-validation on California Housing dataset. |
## Results
See the [results directory](https://github.com/mrdbourke/m1-machine-learning-test/tree/main/results).
## Steps (how to test your Apple Silicon machine)
1. Create an environment and install dependencies ([see below](https://github.com/mrdbourke/m1-machine-learning-test#how-to-setup-a-tensorflow-environment-on-m1-m1-pro-m1-max-using-miniforge-shorter-version))
2. Clone this repo
3. Run various notebooks (results come at the end of the notebooks)
## How to setup a TensorFlow environment on M1, M1 Pro, M1 Max, M1 Ultra, M2 using Miniforge (shorter version)
If you're experienced with making environments and using the command line, follow this version. If not, see the longer version below.
1. Download and install Homebrew from https://brew.sh. Follow the steps it prompts you to go through after installation.
2. [Download Miniforge3](https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh) (Conda installer) for macOS arm64 chips (M1, M1 Pro, M1 Max).
3. Install Miniforge3 into home directory.
```bash
chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate
```
4. Restart terminal.
5. Create a directory to setup TensorFlow environment.
```bash
mkdir tensorflow-test
cd tensorflow-test
```
6. Make and activate Conda environment. **Note:** Python 3.8 is the most stable for using the following setup.
```bash
conda create --prefix ./env python=3.8
conda activate ./env
```
7. Install TensorFlow dependencies from Apple Conda channel.
```bash
conda install -c apple tensorflow-deps
```
8. Install base TensorFlow (Apple's fork of TensorFlow is called `tensorflow-macos`).
```bash
python -m pip install tensorflow-macos
```
9. Install Apple's `tensorflow-metal` to leverage Apple Metal (Apple's GPU framework) for M1, M1 Pro, M1 Max GPU acceleration.
```bash
python -m pip install tensorflow-metal
```
10. (Optional) Install TensorFlow Datasets to run benchmarks included in this repo.
```bash
python -m pip install tensorflow-datasets
```
11. Install common data science packages.
```bash
conda install jupyter pandas numpy matplotlib scikit-learn
```
12. Start Jupyter Notebook.
```bash
jupyter notebook
```
13. Import dependencies and check TensorFlow version/GPU access.
```python
import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
import matplotlib.pyplot as plt
# Check for TensorFlow GPU access
print(f"TensorFlow has access to the following devices:\n{tf.config.list_physical_devices()}")
# See TensorFlow versioExcerpt of 13,052 characters
Read on GitHub
Daniel Bourke
58
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f18d14cc100e918d, topic:tensorflow