any plan to have a native TF support for Metal API (or at least to Apple silicon acceleration)

I don’t know if the TFRT runtime will still give, at some point, other kind of HW integratoin paths in TF other then pluggable devices that what is currently supported with the tensorflow-metal wheel:

  

      github.com/tensorflow/community
  

  
    
  
    
  

  
    
      RFC: Adding Pluggable Device For TensorFlow
    

    
      tensorflow:master ← jzhoulon:master
    

    
      
        opened 
        
          
        
        Jun 24, 2020
      
      

      
        
          
          jzhoulon
        
      

      
        
          +348
          -0
        
      
    
  


  
    This RFC will be open for comment until Monday, July 20th, 2020.

# **Pluggabl…e device for TensorFlow**

| Status        | Accepted                                             |
:-------------- |:---------------------------------------------------- |
| **RFC #**     | [262](https://github.com/tensorflow/community/pull/262)|
| **Author(s)** | Zhoulong Jiang (zhoulong.jiang@intel.com), Yiqiang Li (yiqiang.li@intel.com),  Eric Lin (eric.lin@intel.com), Jianhui Li (jian.hui.li@intel.com) |
| **Sponsor**   | Anna Revinskaya (annarev@google.com)                 |
| **Updated**   | 2020-08-13                                           |

## **Objective**

Implement a pluggable device mechanism which allows to run existing TensorFlow programs on a new device without user changing the code.  Users only need to install a plugin in a specified directory, and the mechanism is able to discover and plug in the capabilities offered by the plugin.

This RFC is based on the Modular TensorFlow  [RFC](https://github.com/tensorflow/community/pull/77), which aims to extend the TensorFlow design to plugin capabilities like adding a new device support.  The modular device interface is based on StreamExecutor C API [RFC](https://github.com/tensorflow/community/pull/257).
  

  

  
    
    
  

  


/cc @penporn