livepeer / livepeer/verification-classifier
Experiment with optimizations for feature extraction
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- Dominant language
- Jupyter Notebook
- Stars
- 8
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
Feature extraction is currently the most expensive step in verification (as noted here). We can investigate if there are any optimizations possible here i.e. algorithmic, hardware based [1].
[1] GPU acceleration should help with many of the calculations. The argument against GPU acceleration is why would someone with a GPU be outsourcing transcoding if they already have access to GPUs? This is a fair point. But, if it is the case that verification on a GPU requires less resources than transcoding on a GPU (either due to lower GPU utilization or due to the fact that verification can scale with a single GPU when transcoding with multiple GPUs) then this might still make sense as an option for users that do have access to a GPU.
Contributor guide
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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
The issue identifies feature extraction as the expensive step in verification but names no files or tests. Start by locating that step and profiling it, then investigate algorithmic and GPU-based alternatives; completion criteria are not defined beyond finding possible optimizations.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- 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