benchmark_TensorFlow_macOS

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benchmark_TensorFlow_macOS

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Python code to benchmark TensorFlow for macOS

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# benchmark_TensorFlow_macOS
A set of Python codes and data to benchmark TensorFlow for macOS on a training task of a large CNN model for image segmentation.  
*I made this set for benchmarking TensorFlow on GPU of M1 SoC in macOS Monterey.*

Installation Instructions of TensorFlow for GPU training in macOS Monterey:  
[Getting Started with tensorflow-metal PluggableDevice (Apple Developer)](https://developer.apple.com/metal/tensorflow-plugin/)

Related article of mine in Medium.com:  
[Training Speed of TensorFlow in macOS Monterey](https://towardsdatascience.com/training-speed-of-tensorflow-in-macos-monterey-3b8020569be1)

## Description
- Train.py : Main code.
- data : contains 512x512 greyscale images for training (0001.png to 0500.png) and validation (0501.png to 0600.png). Images are simple triangles just for benchmark.
- neural_networks : contains an original TF-Keras model. The model has an U-net-like structure with the total parameters of 20-Mega (20,886,706).
- utils : contains helper codes for Train.py - CSV loader, Keras callbacks, and loss and metrics.
- run_BENCHMARK_5times_5min_interval.sh / .bat : Automation scripts to perform the training task five times with five minutes interval.
- package_list : contains package lists of miniconda (miniforge) in which TensorFlow runs on GPU.

## Installation Tips
The following packages are required for this benchmark code.  
**numpy h5py hdf5 joblib opencv graphviz**  
(The other packages will be installed based on their dependencies.)

In my experience, they should be installed to the miniforge3 **BEFORE** installing tensorflow-deps, -macos and -metal.  
As for versions, please refer to my package list "miniforge3_TF26_macOS_Monterey.txt".  
> You may be able to install the packages directly into your env by  
> `conda install --name [env_name] --file miniforge3_TF26_macOS_Monterey.txt`  
> But it may be dependent on machine-specific things, which may be UNSAFE.

## Result

I performed this benchmark code on my MacBook Air M1 and a WIndows workstation. The red circle in the above figure represents comutation time required for all the training images in an epoch. The yellow circle represents time required for validation.  
> Please check my Medium article for more details: [Training Speed of TensorFlow in macOS Monterey](https://towardsdatascience.com/training-speed-of-tensorflow-in-macos-monterey-3b8020569be1)

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