pytorch/serve

Handle invalid input intelligently ！

Open

#2,459 opened on Jul 12, 2023

View on GitHub
 (7 comments) (0 reactions) (0 assignees)Java (3,844 stars) (790 forks)batch import
debugginggood first issuetriaged

Description

🚀 The feature

Given a custom service that expects JSON as input, If the input is invalid JSON such as { "image": "I missed a quote} it will return { "code": 500, "type": "InternalServerException", "message": "Worker died." } and reload model. Is something can do to verify the accuracy of input to avoid such direct returns and model reload when it cannot be modified input?

Motivation, pitch

Is something can do to verify the accuracy of input to avoid such direct returns and model reload when it cannot be modified input?

Alternatives

No response

Additional context

No response

Contributor guide

Tech stack
pythonjavarest api
Domain
backendapimachine learning
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
mostly clear
Prerequisites
Familiarity with PyTorch ServeBasic understanding of JSON parsing
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
65
Research direction
Investigate the input validation pipeline in PyTorch Serve. Look at the request handler code in `frontend/server/src/main/java/org/pytorch/serve/http/InferenceRequestHandler.java` and the worker process in `backend/python/worker.py`. The issue suggests adding pre validation of JSON input before passing to the model worker. Consider implementing a JSON schema validation step in the frontend to catch invalid JSON early and return a 400 error instead of a 500. Check if there are existing input validation utilities in the codebase that can be leveraged or extended.
Handle invalid input intelligently ！ · pytorch/serve#2459 | Good First Issue