sassoftware / sassoftware/python-sasctl
Use PZMM to Save Tensorflow Model
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- Dominant language
- Python
- Stars
- 52
- Forks
- 45
- Avg merge
- 23h 38m
- Merged PRs (30d)
- 2
Description
I was working with a customer who was trying to use the pzmm.PickleModel.pickleTrainedModel() function on a TensorFlow model, but they were getting a 'TypeError: can't pickle weakref objects'. My ideal solution would be to for the PickleTrainedModel() function to have the ability to ingest Tensorflow models or have a similar function available for Tensorflow models. For now, I think the work-around would be to use Tensorflow's save() function to save the model, make that model available to Model Manager, and then edit the score code to use Tensorflow's load_model() function instead of un-pickling the model, but I am open to other work-around ideas.
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 reproducing the TypeError from pzmm.PickleModel.pickleTrainedModel() with a TensorFlow model, then compare the requested behavior with TensorFlow's save() and load_model() workflow. Done should mean an agreed, tested way for Model Manager to handle TensorFlow models, or a documented alternative if direct pickling is not supported.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100