Distributed log interleaving in asynchronous TensorFlow training

In distributed TensorFlow environments, diagnosing deployment failures often requires correlating C++ backend events with Python-level execution traces. While setting TF_CPP_MIN_LOG_LEVEL=0 ensures that critical messages are not suppressed, the asynchronous nature of distributed workers creates significant challenges for log reconstruction.

The primary difficulty lies in the non-deterministic ordering of log entries generated across multiple nodes. When a ResourceExhausted or InvalidArgument error occurs during graph execution, the interleaved log stream can make it impossible to identify the specific sequence of operations that led to the race condition or resource contention.

While tf.debugging.set_log_device offers fine-grained details, its performance overhead makes it impractical for production-grade diagnostics where these failures are most likely to occur.

Is there a native mechanism in TensorFlow TensorFlow to timestamp or synchronize log entries across workers to ensure a causal order during analysis? How can developers capture the state of specific tensors at the exact moment of a runtime failure without implementing custom checkpoint logic?