Our organization still has several legacy models running on TensorFlow 1.15. We’ve been hesitant to migrate because of the massive architectural changes in 2.x, like the removal of sessions and placeholders. Is the performance gain and the new Keras integration worth the technical debt of a full migration, or should we just keep the old systems as they are?
3 answers
The migration is absolutely worth it, but you don't have to do it all at once. The tf.compat.v1 module allows you to run your old code while gradually updating components. The transition to Eager Execution makes debugging ten times easier because you can use standard Python debuggers and print statements instead of dealing with abstract session graphs. Plus, the latest versions of TF 2.x have much better support for distributed training and modern hardware. Keeping 1.x code around is a security risk and prevents you from using the latest pre-trained models from the Model Garden.
Did you run into any significant performance regressions when you first made the switch, especially with high-throughput data pipelines?
The removal of tf.Session() makes the code so much cleaner. It feels like writing actual Python now instead of some strange DSL.
That’s the biggest win for me too, Lisa. The "Pythonic" feel of TF 2.x makes onboarding new developers much faster.
Paul, we actually saw a slight dip initially because we weren't using @tf.function correctly. Once we wrapped our training loops in the autograph decorator, the performance matched or exceeded our old 1.x sessions. It’s all about making sure you’re not accidentally running everything in pure eager mode during the heavy computation phases.