Add no_automatic_dependency_tracking utility.
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This wraps the private equivalent decorators in Keras and TensorFlow.
abheesht17
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May 14, 2025
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| if keras.backend.backend() == "tensorflow": | ||
| import tensorflow as tf | ||
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| fn = tf.__internal__.tracking.no_automatic_dependency_tracking(fn) |
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Quick question, mainly for my understanding: why do we not disable this in core Keras itself?
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That is a good question. Apparently, we didn't need it.
Normally, this is about not tracking Layers, Metrics and Variables. The reason I need it is not because of that, it's because dicts, lists, etc. get wrapped into TrackedDict, TrackedList etc. and the Tensorflow equivalents. I need configuration structures that are nested, but then, when I pass the nested configs to some APIs, that API complains about TrackedDict, TrackedList, etc. All I want is to preserve the original structures.
abheesht17
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May 14, 2025
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This wraps the private equivalent decorators in Keras and TensorFlow.