def create_strategy()

in 07_training/serverlessml/flowers/utils/util.py [0:0]

• 22 lines of code
• 6 McCabe index (conditional complexity)

def create_strategy(mode):
    """
    mode has be to be one of the following:
    * cpu
    * gpus_one_machine
    * gpus_multiple_machines
    * tpu_colab
    * tpu_caip
    * the actual name of the cloud_tpu
    If you are using TPUs, this method has to be the very first thing you do.
    """
    if mode == "cpu":
        print("Using CPU.")
        return tf.distribute.OneDeviceStrategy("/cpu:0")
    elif mode == "gpus_one_machine":
        print("Using {} GPUs".format(len(tf.config.experimental.list_physical_devices("GPU"))))
        return tf.distribute.MirroredStrategy()
    elif mode == "gpus_multiple_machines":
        print("Using TFCONFIG=", os.environ["TF_CONFIG"])
        return tf.distribute.experimental.MultiWorkerMirroredStrategy()
    
    # treat as tpu
    if mode == "tpu_colab":
        tpu_name = "grpc://" + os.environ["COLAB_TPU_ADDR"]
    elif mode == "tpu_caip":
        tpu_name = None
    else:
        tpu_name = mode
        print("Using TPU: ", tpu_name)
    resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=tpu_name)
    tf.config.experimental_connect_to_cluster(resolver)
    # TPUs wipe out memory, so this has to be at very start of program
    tf.tpu.experimental.initialize_tpu_system(resolver)
    print("All devices: ", tf.config.list_logical_devices("TPU"))
    return tf.distribute.TPUStrategy(resolver)