How to get Keras to use multiple GPUs with Tensorflow 2.0.0 =<

keras_multiple_gpu.py

from mymodel import create_model
from data import x, y

if __name__ == "__main__":
  from tensorflow import distribute as D
  from tensorflow import config
  # Get a list of physical devices, specifically GPUs
  devices = [device for device in config.list_physical_devices() if "GPU" == device.device_type]
  devices = ["/gpu:{}".format(i) for i, device in enumerate(devices)]
  # Setup a single machine multi-gpu strategy that does not use NCCL
  strat = D.MirroredStrategy(devices=devices, cross_device_ops=D.HierarchicalCopyAllReduce())
  with strat.scope():
     model = create_model(**kwargs)
  # Keras / TensorFlow is now using a mirrored learning strategy on all GPUs available
  model.fit(x, y)