Issue in reproducibility
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 42/100
- Issue type
- Bug
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- python, pytorch
- Domain
- machine-learning, testing-qa
Research direction
Clone the main branch of mateomorin/ttc-training, uncomment the last test in test_reproducibility, and run uv run pytest with the provided seeding setup. Compare the reproducibility test results and warnings; done should mean the test no longer demonstrates differing results after training on the synthetic data.
Written by the indexing model from the issue text.
Description
I am unable to get the exact result after training a ttc model on my data (synthetic data), even after setting every possible seed:
torch.manual_seed(42)
random.seed(42)
np.random.seed(42)
torch.use_deterministic_algorithms(True)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
pytorch_lightning.seed_everything(42)
For full reproducibility:
git clone -b main https://github.com/mateomorin/ttc-training.git
# uncomment last test of test_reproducibility
# potentialy change your data path if you do not have access to bucket projet-ape
uv run pytest # should have 2 passed, 1 failed and potential warnings
- Dominant language
- Python
- Stars
- 23
- Forks
- 7
- PR merge metrics
- No merged PRs in 30d
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.
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