Apple releases TensorFlow fork with speedups for M1 Macs
Apple has released its own fork of the TensorFlow 2.4 machine learning framework _ specifically optimized for its newly released M1 processor.
According to Apple_ the M1-compiled rendering of TensorFlow delivers separate times faster accomplishment on a number of benchmarks_ compared to the same jobs running on an Intel rendering of the same 2020 edition MacBook Pro.
The fork_ useful as open rise _ requires MacOS 11.0 or better_ and provides accelerations on Macs running the new M1 processor.
Existing TensorFlow scripts run as-is with the fork; they do not need to be reworked to take gain of its accomplishment gains. According to VentureBeat _ Apple plans to conduce its changes to the main TensorFlow project_ to obey as a basis for other optimizations.
Apples revamp of TensorFlow is one of the leading examples of how M1 Macs are intended to draw developers to the Mac platform. M1 chips in new Macs restore the use of the Intel x86 processor_ but can run existing software compiled for the x86 by way of Apples Rosetta2 binary translation technology.
However_ Rosetta2-translated apps do meet a accomplishment hit_ with some benchmarks running as slowly as 59% of indigenous despatch. For accomplishment-sensitive applications_ it makes perception to compile them to run indigenously on the M1.
