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Start with a small, eager-mode reproduction
TensorFlow 2 eager execution makes it easier to inspect operations step by step. Reduce the failure to a small, reproducible input and run the relevant model call or training step eagerly. Inspect input shapes and dtypes, labels, model outputs, loss, and gradients. TensorFlow’s Effective TensorFlow 2 guide and tf.function guide recommend getting code to run in eager mode before applying graph execution where needed.
If the issue appears only in a function decorated with @tf.function, temporarily enable eager execution for functions with tf.config.run_functions_eagerly(True). Use this as a diagnostic mode, then turn it off to reproduce the graph behavior that fails or performs slowly.
Distinguish tracing from runtime
Python code inside tf.function does not necessarily run each time the graph executes. A regular Python print runs during tracing, so it can help reveal when TensorFlow is tracing or retracing a function. Use tf.print when you need tensor values emitted during graph execution. This distinction prevents treating a one-time trace message as evidence about every training step.
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Find the first NaN or infinity
If a loss, activation, or weight becomes non-finite, identify the first operation that produces the invalid value rather than inspecting only the final loss. Enable numerical checks with tf.debugging.enable_check_numerics(); TensorFlow can then stop when an operation produces NaN or infinity, helping narrow the failure to its origin.
For a few known tensors at a known location, tf.print may be sufficient. When the origin is unclear or you need a broader view across eager and graph execution, use TensorBoard Debugger V2. It can provide execution history, tensor summaries and values, graph structure, source locations, and stack traces. The guide advises adding tf.debugging.experimental.enable_dump_debug_info() early enough to capture the activity you need to investigate.
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Correct the operation that creates the invalid value
Debugger V2’s tutorial traces a negative infinity to a logarithm applied to zero-valued probabilities. For that specific case, clipping values before taking the logarithm or using tf.keras.losses.CategoricalCrossentropy are possible remedies. They are not universal fixes: first establish which operation receives the invalid input, then choose a correction that matches the model and intended mathematics.
Debugger instrumentation adds overhead, and its impact depends on the debug mode, hardware, and workload. Use it to diagnose the problem rather than to judge normal training speed.
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Profile slow steps before changing the GPU setup
A slow training step does not by itself show that the GPU is underutilized or that more hardware is needed. TensorFlow’s Profiler guide describes profiling as a way to understand the time and memory consumed by TensorFlow operations and resolve performance bottlenecks. Use TensorBoard’s overview and trace tools to see whether time is spent on device computation, host-side work, host-to-device activity, or waiting for input.
For GPU workloads, follow TensorFlow’s GPU performance analysis guidance: diagnose the bottleneck on a single GPU before investigating multi-GPU behavior. Scaling out before locating the constraint can add complexity without addressing the source of the delay.
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When input delivery is the bottleneck
Use the tf.data performance analysis guide and Profiler input-pipeline analyzer to check whether input work is blocking model execution. If the pipeline is input-bound, inspect its stages and consider placing prefetch at the end of the pipeline so input preparation can overlap with model computation.
When changing data loading or preprocessing, benchmark the input pipeline independently as well as in the full training run. This helps distinguish a faster loader from a change in model or backpropagation time.
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Compare TensorFlow 1.x and 2.x training behavior
When investigating a migration, compare the training process over time rather than checking only final accuracy. TensorFlow’s migration debugging guide identifies these quantities for comparison:
- Learning rate
- Model weights
- Gradient scale
- Training and validation metrics
- Intermediate outputs
Look for the first meaningful divergence in these values. Pinpointing when behavior changes gives you a more useful lead than knowing only that the final results differ.
Quick Recap
Choose the diagnostic by symptom
| Question | Use | What it helps reveal |
|---|---|---|
| Does the code or training step fail in a way that is hard to inspect? | Eager execution | Step-by-step behavior and intermediate values. |
| Does the failure occur only on the compiled path? | @tf.function and graph execution |
The behavior that needs to be reproduced after the eager baseline. |
| Is a Python message appearing only once, or do you need runtime tensor values? | Python print for tracing; tf.print for execution |
Whether code is tracing or graph operations are emitting values. |
| Where does a NaN or infinity first appear? | tf.debugging.enable_check_numerics() |
The operation that produces a non-finite value. |
| Is the bad value’s origin obscure or spread across execution? | TensorBoard Debugger V2 | Broader tensor, graph, and source context. |
| Why is a training step slow? | TensorFlow Profiler overview and trace | Host, device, transfer, and input-pipeline timing. |
| Is the GPU waiting on data? | Profiler input-pipeline analyzer | Whether input work is blocking the device. |
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