Is the tensorflow-macos fork still the recommended way of installing Tensorflow?

The tensorflow-macos repo has been archived and the last commit redirects users to the plugin page; however, this page still instructs users to install the now-archived fork. Are these instructions still up to date? Also, what is the plan long-term for Metal/M1 acceleration in Tensorflow—will the necessary changes eventually be upstreamed, if they haven't already?

Up vote post of BigfootLives Down vote post of BigfootLives
662 views
Posted by
BigfootLives
Reply
Add a Comment

Replies

I'm working on a Metal/DirectX backend for Swift for TensorFlow while simultaneously resurrecting that archived project. It should be released in a few months, and will be more optimized than Apple's PluggableDevice implementation. It will also allow training on an iPhone or iPad, so you can experience fast GPU-accelerated machine learning on an Apple chip even if you have a low-end Intel Mac. Furthermore, it will support training on the integrated GPUs on Intel Macs, which tensorflow-macos doesn't.

@BigfootLives if this sounds like a solution to your question, I can provide links to GitHub repositories about it.

Posted by
philipturner
Up vote reply of philipturner Down vote reply of philipturner
Post marked as solved

  • @philipturner that sounds great, thank you for your heroic efforts! Looking forward to using it. I’m curious, does your implementation make use of the neural engine in Apple chips, or is it limited to the GPU?

    —
    ob366

  • The neural engine can't be used for training. It uses only 16-bit half precision, not 16-bit bfloat16. That means gradients can't propagate through it for ML, but ANE can be used for inference. Only if Apple did what everybody else is doing and added BFloat16 acceleration or GPU matrix cores! Kudos to them for AMX on the CPU at least.

    —
    philipturner

  • That sounds very interesting! I'd love to see the repo. Still curious about Apple's plans for Python, of course.

    —
    BigfootLives