Run-everything pipeline
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
Research direction
Start by checking which of the blocking issues #29, #33, #38 and #40 are already merged, since the pipeline calls their steps in order. The entry point is a new scripts/run_all.py (or a Makefile), which should chain the data, optimizer comparison, training/eval and final comparison steps, add a --quick flag and skip steps whose outputs exist unless --force is given. Done means python scripts/run_all.py --quick completes end-to-end in a few minutes.
Written by the indexing model from the issue text.
Description
Reproduce every result with one command.
Files: scripts/run_all.py (or Makefile)
Tasks
- Call data -> optimizer comparison -> custom training/eval -> fine-tuning/eval -> final comparison in order
-
--quickflag (2 epochs, small subset) for a fast smoke test - Skip steps whose outputs already exist unless
--force
Done when
python scripts/run_all.py --quick finishes end-to-end in a few minutes.
Depends on
#29 #33 #38 #40
Close with a commit or PR message containing Closes #<this issue>.
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 0
- Avg merge
- 1m
- Merged PRs (30d)
- 3
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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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