The problem here is that the only operation in the Dataset.map() function is a tf.py_func() op. This op calls back into the local Python interpreter to run a function in the same process. Increasing num_parallel_calls will increase the number of TensorFlow threads that attempt to call back into Python concurrently. However, Python has something called the “Global Interpreter Lock” that prevents more than one thread from executing code at once. As a result, all but one of these multiple parallel calls will be blocked waiting to acquire the Global Interpreter Lock, and there will be almost no parallel speedup (and perhaps even a slight slowdown).
Your code example didn’t include the definition of the squarer() function, but it might be possible to replace tf.py_func() with pure TensorFlow ops, which are implemented in C++, and can execute in parallel. For example—and just guessing by the name—you could replace it with an invocation of tf.square(x), and you might then enjoy some parallel speedup.
Note however that if the amount of work in the function is small, like squaring a single integer, the speedup might not be very large. Parallel Dataset.map() is more useful for heavier operations, like parsing a TFRecord with tf.parse_single_example() or performing some image distortions as part of a data augmentation pipeline.