Distributed Learning with TensorFlow2 is not working - tensorflow

Question

I'm trying to get distributed TF working in VS-Code with the Tensorflow version 2.0.0a (the CPU Version).
I'm using a Windows and a Linux System (two different computers) and both are working well alone.
For the distibuted TF I followed the tutorial at
https://www.tensorflow.org/alpha/guide/distribute_strategy .
I already tried different ports and turning off the firewalls. I also tried to switch the master system from Windows to Linux but now i think it might be a Problem with the code or maybe the TF-Version which is labeled as experimental.
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow_datasets as tfds
import tensorflow as tf
import json
import os
BUFFER_SIZE = 10000
BATCH_SIZE = 64
def scale(image, label):
image = tf.cast(image, tf.float32)
image /= 255
return image, label
datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)
train_datasets_unbatched = datasets['train'].map(scale).shuffle(BUFFER_SIZE)
train_datasets = train_datasets_unbatched.batch(BATCH_SIZE)
def build_and_compile_cnn_model():
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(
loss=tf.keras.losses.sparse_categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),
metrics=['accuracy'])
return model
#multiworker conf:
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["192.168.0.12:2468", "192.168.0.13:1357"]
},
'task': {'type': 'worker', 'index': 0}
})
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
NUM_WORKERS = 2
GLOBAL_BATCH_SIZE = 64 * NUM_WORKERS
#--------------------------------------------------------------------
#In the following line the error occurs
train_datasets = train_datasets_unbatched.batch(GLOBAL_BATCH_SIZE)
#--------------------------------------------------------------------
with strategy.scope():
multi_worker_model = build_and_compile_cnn_model()
multi_worker_model.fit(x=train_datasets, epochs=3)
I expect the worker to start the learning process but instead I get the error:
"F tensorflow/core/framework/device_base.cc:33] Device does not implement name()"

Answer 1

As far as i know, each worker should have a unique task index, for example:
on the first machine you should have:
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["192.168.0.12:2468", "192.168.0.13:1357"]
},
'task': {'type': 'worker', 'index': 0}
})
and on the second:
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["192.168.0.12:2468", "192.168.0.13:1357"]
},
'task': {'type': 'worker', 'index': 1}
})