Why does TensorFlow training become slower after several epochs?

Training slowdown can result from dataset caching issues, memory fragmentation, or dynamic graph overhead.

Over time, training may slow due to increasing memory pressure, inefficient caching strategies, or graph retracing in tf.function. If input pipelines are not optimized, IO becomes bottleneck. Additionally, dynamic shapes can force recompilation of computation graphs.