To solve the problem of too high I/O
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- performance
Research direction
The issue mentions an FFmpeg-to-Python raw YUV420 pipeline and a model/decoder speed mismatch, but names no repository files, tests, or entry points. Start by locating the stream-decoding and model-inference paths, then establish a maintainer-approved benchmark and memory limit; the issue does not yet define a concrete success condition.
Written by the indexing model from the issue text.
Description
In my experimental verification, I/O overhead accounts for approximately 70-80% of the total time. Given this significant I/O overhead, I've considered using FFmpeg to decode the stream into a raw YUV420 byte stream and pipe it into a Python script to reduce I/O overhead. However, this would cause memory overflows due to the mismatch between the model speed and FFmpeg parsing speed.
I'd like to apply this model to real-world scenarios. Could you offer some new insights into addressing I/O issues?
- Dominant language
- Python
- Stars
- 829
- Forks
- 138
- PR merge metrics
- No merged PRs in 30d
Getting set up
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.
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