tensorflow / tensorflow/probability
Performance issue in discussion/turnkey_inference_candidate/window_tune_nuts_sampling.py
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Description
Hello! Our static bug checker has found a performance issue in discussion/turnkey_inference_candidate/window_tune_nuts_sampling.py: Python type argument num_steps is passed to tf.function decorated function slow_adaptation_interval instead of tensors. As there is a for loop, every time the value changes, a new graph will be generated, and that can trigger tf.function retracing warning.
Here is the tensorflow document to support it.
Looking forward to your reply.
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First steps
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Research direction
Start in discussion/turnkey_inference_candidate/window_tune_nuts_sampling.py at slow_adaptation_interval around line 183 and its call around line 272, then inspect the loop near line 263. Read the linked TensorFlow guidance on passing tensors instead of Python literals. Done means the changing num_steps value no longer causes repeated tf.function graph generation or retracing warnings.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100