Apple’s New M1 Chip is a Machine Learning Beast

• coremltools

  11 4,037 8.7 Python

  Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.

  

And did you even know that Apple provides a CoreML conversion toolkit for converting models from PyTorch, TF, and SKLearn to CoreML? You can comfortably train in your preferred tool and then convert the models to CoreML. (https://github.com/apple/coremltools)

• Pytorch

  334 77,544 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

I am willing to bet that the TF fork is just a pointer on how to use their APIs to add support. PyTorch doesn't seem to be outright denying support for M1, asking users to stay tuned

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• tensorflow_macos

  33 2,887 3.4 Shell

  Discontinued TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.

  

They have a release here: https://github.com/apple/tensorflow_macos (not open sourced yet btw)

• MMdnn

  3 5,782 0.0 Python

  MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

  

Yes. But that's missing the point. Almost everything in the space uses a framework other than Core ML. Therefore most people need good development support for PyTorch/Tensorflow etc., not Core ML. The fact that Apple has a tool to import/convert models is nice, but not relevant. Also, there is onnx as an exchange format between the frameworks, and tools like MMdnn and others.

Suggest a related project