PaddlePaddle / PaddlePaddle/FastDeploy
PPLiteSeg在GPU上要怎么使用INT8加速
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- Dominant language
- Python
- Stars
- 3.7k
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- Avg merge
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- Merged PRs (30d)
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Description
Environment
FastDeploy version: e.g 0.8.0 or the latest code in develop branch
OS Platform: e.g. Windows x64 (arm or intel)
Hardware: e.g. Nvidia GPU 3060 CUDA 11.2 CUDNN 8.3
Program Language: e.g. Python 3.8
Problem description
PPLiteSeg在GPU上要怎么使用INT8加速，只看到了PaddleLite支持int8，在CPU或者GPU上要怎么使用int8加速呢
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
No files, tests, or entry points are mentioned. Start by locating the FastDeploy PPLiteSeg integration and its GPU backend or quantization documentation, then verify whether INT8 is supported and how it is configured. Done means documenting a confirmed GPU INT8 workflow, or clearly stating the unsupported cases.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning, performance
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100