Should you publish ML experiment results to GitHub?
A Reddit thread asks whether machine learning experiment outputs should be committed to a public repository. It touches on reproducibility and transparency, but the discussion lacks specific actionable guidance.
When running machine learning experiments, you often end up with files like model weights, logs, and evaluation metrics. Uploading these to a public repository like GitHub makes it easier for others to verify or reproduce your work, which is generally considered good practice in research.
However, large files can slow down a repository, and if any sensitive data is mixed in, publishing could cause problems. The thread appears to be a community question without a clear consensus or detailed advice, so its practical value is limited without reading the full discussion directly.
Key points
- Publishing results improves reproducibility and builds trust in your work
- Large files like model weights are better hosted on dedicated platforms (HuggingFace, S3) than GitHub
- Check for sensitive data before making anything public
- The original thread lacks detail, so visit it directly for community responses
Quick term guide
- machine learning
- A type of AI where computers learn patterns from data rather than following hand-written rules.
- repository
- The folder that holds all the code files for a software project, often called a 'repo'
- reproducibility
- The ability for someone else to run the same experiment and get the same results.
- model weights
- The internal numbers an AI learns during training — saving them lets you reuse or share the trained AI.
- weights
- The internal values an AI model learns during training; sharing them is what makes a model truly runnable by others
- valuation
- The amount investors think a company is worth.
- metrics
- Numbers and statistics used to measure how well a business is performing.
- responses
- An OpenAI API feature for creating and handling model answers.