All threads must be able to stop together, exceptions must be caught and reported, and queues must be properly closed when stopping. However, it is not always easy to implement a Python program that drives threads as described above. The TensorFlow Session object is multithreaded, so multiple threads can easily use the same session and run ops in parallel. This architecture has many benefits, as highlighted in the Reading data how to, which also gives an overview of functions that simplify the construction of input pipelines.
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