Getting an error "AttributeError: module 'tensorflow.python.distribute.input_lib' has no attribute 'DistributedDatasetInterface'"

After running the code

train(train_data, EPOCHS)

I'm getting error

"AttributeError: module 
'tensorflow.python.distribute.input_lib' has no attribute 'DistributedDatasetInterface'"

and after traching the error, it points at line 15--> of

def train(data, EPOCHS):
    # Loop through epochs
    for epoch in range(1, EPOCHS+1):
        print('\n Epoch {}/{}'.format(epoch, EPOCHS))
        progbar = tf.keras.utils.Progbar(len(data))

        # Creating a metric object
        r = Recall()
        p = Precision()

        # Loop through each batch
        for idx, batch in enumerate(data):
            # Run train step here
            loss = train_step(batch)
            yhat = siamese_model.predict(batch[:2])  #<--- line 15
            r.update_state(batch[2], yhat)
            p.update_state(batch[2], yhat)
            progbar.update(idx+1)
        print(loss.numpy(), r.result().numpy(), p.result().numpy())

        # Save checkpoints
        if epoch % 10 == 0:
            checkpoint.save(file_prefix=checkpoint_prefix)

Your error happens cuz there's mismatch between how you're trying to make prediction and what your dataset type allows. Tensorflow has changed some of its APIs across versions, please refer this this will help you to gain some knowledge about how actually perform distributed training Distributed training with TensorFlow

Try this maybe it can solve your problem

Try a direct model call

The simplest fix is to just call your model directly instead of using the predict method

# Change this line
yhat = siamese_model.predict(batch[:2])

# To this 
yhat = siamese_model(batch[:2], training=False)

This works because it bypasses the predict() machinery that's causing the error.

Also refer this github issue as well issue 61900