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README.md

Running PyTorch on the Apple M1's GPU

We will run PyTorch training on the M1 GPU

Setup

You will need Python installed, the preferred way is using MiniForge

# The version of Anaconda may be different depending on when you are installing`
$ curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
$ sh Miniforge3-MacOSX-arm64.sh
# and follow the prompts. The defaults are generally good.

Then, create an environment to use Python and PyTorch:

$ conda create --name="pt" "python<3.11"


# activate the environment
$ conda activate pt

# with conda
conda install pytorch torchvision -c pytorch

# or with pip
$ pip install torch torchvision


# install dependencies of this training script 😎
$ pip install wandb tqdm transformers datasets

For more details on installing Python check this report.

The setup.sh script takes care of installing all the above

$ sh setup.sh

For Linux, replace the MiniForge binaries with the ones for Linux: https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh

Verify your Install

Run the following in python

import torch


torch.__version__
>>> '1.13.1'


torch.tensor([1,2,3], device="mps")

Running the Benchmark

In your terminal run:

$ python train_pets.py --gpu_name="M1Pro GPU 16 Cores" #replace with your GPU name
  • Pass the --gpu_name flag to group the runs, I am not able to detect this automatically on Apple.
  • To run on cpu pass --device="cpu" or for CUDA --device="cuda" (you need a linux PC with an Nvidia GPU)
  • You can also pass other params, and play with different batch_size and model_name.

Bert Benchmark

In your terminal run:

$ python train_bert.py --gpu_name="M1Pro GPU 16 Cores" #replace with your GPU name

Notes

You have many parameters available to override, call the script with the flag --help to see them all. To extract the maximum performance of you GPU you may need to adjust batch_size and maybe run for longer than 1 epochs.

(pt) thomascapelle@mac-mini pytorch % python train_pets.py --help
usage: train_pets.py [-h] [--entity ENTITY] [--batch_size BATCH_SIZE] [--epochs EPOCHS]
                     [--num_experiments NUM_EXPERIMENTS] [--learning_rate LEARNING_RATE]
                     [--image_size IMAGE_SIZE] [--model_name MODEL_NAME] [--dataset DATASET]
                     [--device DEVICE] [--gpu_name GPU_NAME] [--num_workers NUM_WORKERS]
                     [--mixed_precision] [--channels_last] [--optimizer OPTIMIZER]

options:
  -h, --help            show this help message and exit
  --entity ENTITY
  --batch_size BATCH_SIZE
  --epochs EPOCHS
  --num_experiments NUM_EXPERIMENTS
  --learning_rate LEARNING_RATE
  --image_size IMAGE_SIZE
  --model_name MODEL_NAME
  --dataset DATASET
  --device DEVICE
  --gpu_name GPU_NAME
  --num_workers NUM_WORKERS
  --mixed_precision
  --channels_last
  --optimizer OPTIMIZER