deepspeedai / deepspeedai/DeepSpeed
Add logging support for better observability
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 43.1k
- Forks
- 5k
- Avg merge
- 4d 15h
- Merged PRs (30d)
- 112
Description
Feature Request
Summary
The project would benefit from structured logging to make it easier to debug issues in production.
Motivation
Currently it can be difficult to understand what the application is doing at runtime without adding print statements. Proper logging would:
- Allow users to control verbosity with log levels (DEBUG, INFO, WARNING, ERROR)
- Make it easier to integrate with log aggregation systems
- Help diagnose issues in production without code changes
Proposed Implementation
import logging
log = logging.getLogger(__name__)
def my_function():
log.debug("Starting my_function")
# ...
log.info("my_function completed successfully")
Impact
Low risk change with high value for users running this in production environments.
Contributor guide
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 project files, tests, or entry points are identified in the issue. Start by surveying the Python runtime modules and current output paths to define where structured logging and configurable levels would apply; done requires a scoped implementation with integration guidance and tests for the intended logging behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- observability
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100