tensorflow / tensorflow/model-optimization
Pruning does not reduce inference time.
Nobody has claimed this yet.
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- Python
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Description
System information
- TensorFlow version (you are using): 2.3.0
- Are you willing to contribute it (Yes/No): No
Motivation
Currently pruning in tensorflow_model_optimization does not result in a reduction in inference time. Even though the pruned model is sparser than the original, the inference time remains the same. (This was tested on a Resnet model.)
Describe the feature Pruning sets the weights to zero, but does not prune the networks edges. Update the pruning feature such that the new sparse weights result in a corresponding increase in speed.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the pruning feature in tensorflow_model_optimization and reproduce the reported behavior with a ResNet model. Investigate how sparse weights are represented and whether they affect inference execution. Done means pruning produces a measurable inference-time reduction rather than only zero-valued weights.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100