add info regarding tf-metal

README.md

@@ -68,6 +68,14 @@ The hyperparameters values for `clip_factor` and `eps` presented here are the de
 #### Model format
 It is recommended to use the SavedModel format when using this implementation. That is because the HDF5 format is only compatible with `TF <= 2.6` when using the model wrapper. However, if you are using older TF versions, both formats work out-of-the-box. The SavedModel format works fine for all versions of TF 2.x
 
+#### macOS compatibility
+Note that GradientAccumulator is perfectly compatible with macOS, both with and without GPUs. In order to have GPU support on macOS, you will need to install the tensorflow-compiled version that is compatible with metal:
+```
+pip install tensorflow-metal
+```
+
+GradientAccumulator can be used as usually. However, note that there only exists one tf-metal version, which should be equivalent to TF==2.5.
+
 
 ## Disclaimer
 In theory, one should be able to get identical results for batch training and using gradient accumulation. However, in practice, one may observe a slight difference. One of the cause may be when operations are used (or layers/optimizer/etc) that update for each step, such as Batch Normalization. It is **not** recommended to use BN with GA, as BN would update too frequently. However, you could try to adjust the `momentum` of BN (see [here](https://keras.io/api/layers/normalization_layers/batch_normalization/)).