[D] My experience with running PyTorch on the M1 GPU

• machine-learning-notes

  2 741 0.0 Jupyter Notebook

  Collection of useful machine learning codes and snippets (originally intended for my personal use) (by rasbt)

And a link to the code examples here on GitHub.

• Pytorch

  336 77,783 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• vision

  19 15,423 9.5 Python

  Datasets, Transforms and Models specific to Computer Vision

  

$ python vgg16-cifar10.py --device "cuda" torch 1.11.0+cu102 device cuda Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz 170499072it [00:46, 3628424.66it/s] Extracting data/cifar-10-python.tar.gz to data Downloading: "https://github.com/pytorch/vision/archive/v0.11.0.zip" to /home/md/.cache/torch/hub/v0.11.0.zip Epoch: 001/001 | Batch 0000/1406 | Loss: 2.6563 Epoch: 001/001 | Batch 0100/1406 | Loss: 2.4686 Epoch: 001/001 | Batch 0200/1406 | Loss: 2.1224 Epoch: 001/001 | Batch 0300/1406 | Loss: 2.1879 Epoch: 001/001 | Batch 0400/1406 | Loss: 2.1733 Epoch: 001/001 | Batch 0500/1406 | Loss: 2.2413 Epoch: 001/001 | Batch 0600/1406 | Loss: 2.0518 Epoch: 001/001 | Batch 0700/1406 | Loss: 2.1621 Epoch: 001/001 | Batch 0800/1406 | Loss: 1.9033 Epoch: 001/001 | Batch 0900/1406 | Loss: 1.8379 Epoch: 001/001 | Batch 1000/1406 | Loss: 1.9572 Epoch: 001/001 | Batch 1100/1406 | Loss: 1.8823 Epoch: 001/001 | Batch 1200/1406 | Loss: 1.7978 Epoch: 001/001 | Batch 1300/1406 | Loss: 2.0239 Epoch: 001/001 | Batch 1400/1406 | Loss: 1.8389 Time / epoch without evaluation: 6.75 min <------------------ Epoch: 001/001 | Train: 25.52% | Validation: 26.40% | Best Validation (Ep. 001): 26.40% Time elapsed: 9.03 min Total Training Time: 9.03 min Test accuracy 26.54% Total Time: 9.48 min

• apple_m1_pro_python

  1 165 4.7 Jupyter Notebook

  A collection of ML scripts to test the M1 Pro MacBook Pro

The code is [on GitHub](https://github.com/tcapelle/apple_m1_pro_python/tree/main/pytorch) and the blog post http://wandb.me/pytorch_m1

Suggest a related project