Instruct users to keep images small by using dedicated project directories.
Nobody has claimed this yet.
Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 35/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- gcp, python, tensorflow
- Domain
- cloud, documentation
Research direction
Start by locating the documentation for calling run() and review how users are told to prepare project files. Document the recommendation to use a dedicated project subdirectory containing only essential files, and ensure the guidance explains that virtual environments and large dependencies can cause builds to time out or take hours.
Written by the indexing model from the issue text.
Description
Calling run() on a directory which includes a virtual environment or other large files will either time-out or take hours to build. Any virtual environment used for Cloud will contain tensorflow, so we should by default recommend that users create a dedicated subdirectory for each project, containing only the essentials.
- Dominant language
- Python
- Stars
- 383
- Forks
- 93
- Avg merge
- 1d 3h
- Merged PRs (30d)
- 1
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.
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