If you want to know which version of Keras came with your TensorFlow, use the following command:
import tensorflow as tf
print(tf.keras.__version__)
At the time of writing, this produced the following (from the alpha build of TensorFlow 2):
2.2.4-tf
Other features of Keras include built-in support for multi-GPU data parallelism, and also the fact that Keras models can be turned into TensorFlow Estimators and trained on clusters of GPUs on Google Cloud.
Keras is, perhaps, unusual in that it is has a reference implementation maintained as an independent open source project, located at www.keras.io.
It's maintained independently of TensorFlow, although TensorFlow does have a full implementation of Keras in the tf.keras module. The implementation has TensorFlow-specific augmentations, including support for eager execution, by default.
Eager execution means...
