Do momentum and gradients required for predictions?
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 5.1k
- Forks
- 724
- PR merge metrics
- No merged PRs in 30d
Description
Method `_check_initialized()` called on each prediction methods call. It checks that embeddings, biases, gradients and momentum are set while only embeddings and biases are used for predictions. Do we really require gradients and momentum for predictions?
https://github.com/lyst/lightfm/blob/master/lightfm/lightfm.py#L262-L273
https://github.com/lyst/lightfm/blob/master/lightfm/lightfm.py#L259
IMO we can clean up momentums and gradients to minimize memory usage to use the model only for inference.
Contributor guide
No contributing guide indexed for this repository
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 in lightfm/lightfm.py at _check_initialized() and the prediction methods around lines 259-273. Trace which model attributes prediction actually uses, then define and verify the inference state so gradients and momentum can be cleaned up without preventing predictions or increasing required memory.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100