TensorFlow

tf.function Retracing

• New input shape/dtype causes retrace — expensive, prints warning
• Use input_signature for fixed shapes — @tf.function(input_signature=[tf.TensorSpec(...)])
• Python values retrace — pass as tensors, not Python ints/floats
• Avoid Python side effects in tf.function — only runs once during tracing

GPU Memory

• TensorFlow grabs all GPU memory by default — set memory_growth=True before any ops
• tf.config.experimental.set_memory_growth(gpu, True) — must be called before GPU init
• OOM with large models — reduce batch size or use gradient checkpointing
• CUDA_VISIBLE_DEVICES="" to force CPU — for testing without GPU

Data Pipeline

• tf.data.Dataset without .prefetch() — CPU/GPU idle time between batches
• .cache() after expensive ops — but before random augmentation
• .batch() before .map() for vectorized ops — faster than per-element
• num_parallel_calls=tf.data.AUTOTUNE — parallel preprocessing
• Dataset iteration in eager mode is slow — use in tf.function or model.fit