"Last year, I upgraded to an M2 Max laptop, expecting that tensorflow-metal would facilitate effective local prototyping utilizing the Apple Silicon's capabilities.
It has been quite some time since tensorflow-metal was last updated, and there appear to be several unresolved issues noted by the community here. I've personally observed the following behavior with my setup:
Without tensorflow-metal:
import tensorflow as tf
for _ in range(10):
print(tf.random.normal((3,)).numpy())
[-1.4213976 0.08230731 -1.1260201 ]
[ 1.2913705 -0.47693467 -1.2886043 ]
[ 0.09144169 -1.0892165 0.9313669 ]
[ 1.1081179 0.9865657 -1.0298151]
[ 0.03328908 -0.00655857 -0.02662632]
[-1.002391 -1.1873596 -1.1168724]
[-1.2135247 -1.2823236 -1.0396363]
[-0.03492929 -0.9228362 0.19147137]
[-0.59353966 0.502279 0.80000925]
[-0.82247525 -0.13076428 0.99579334]
With tensorflow-metal:
import tensorflow as tf
for _ in range(10):
print(tf.random.normal((3,)).numpy())
[ 1.0031303 0.8095635 -0.0610961]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
[-1.3544159 0.7045493 0.03666191]
Given these observations, it seems there may be an issue with the randomness of tf.random.normal when using tensorflow-metal.
My current setup includes MacOS 14.5, tensorflow 2.14.1, and tensorflow-macos 2.14.1. I am interested in understanding if there are known solutions or workarounds for this behavior.
Furthermore, could anyone provide an update on whether tensorflow-metal is still being actively developed, or if alternative approaches are recommended for utilizing the GPU capabilities of this hardware?