In the competitive world of Machine Learning (ML) research, publishing your code effectively can help bolster the reproducibility of your findings and make your work more valuable to the community. Based on a thorough analysis of over 200 ML research repositories, we’ve collated best practices to guide you in releasing your research code, which also align with the official guidelines from NeurIPS 2021. Let’s dive into the essentials.
Why Following Guidelines Matters
Having a clear structure and adhering to best practices in code release not only enhances reproducibility but also positively correlates with popularity. Repositories that align with the recommended practices tend to receive more GitHub stars, which can in turn amplify your work’s visibility in the academic community. For more detailed insights, visit our blog post.
Using the README.md Template
We provide a README.md template that serves as an excellent starting point for releasing ML research repositories. This template is informed by existing repositories that have excelled in the community. The sections in our template not only ease the process of your code’s reception but also maximize its impact.
The ML Code Completeness Checklist
The following checklist includes five critical components derived from the analysis of popular ML repositories. Checking off as many items as possible is strongly recommended for submitting to NeurIPS 2021:
- Specification of dependencies
- Training code
- Evaluation code
- Pre-trained models
- Comprehensive README file with results and commands
1. Specification of Dependencies
Think of dependencies as the ingredients required to prepare a delicious dish. Without the right ingredients, you won’t achieve the desired outcome. For Python projects, include files like requirements.txt, environment.yml, or setup.py to guide users on what they need. Clarity here prevents potential setup headaches for your users!
2. Training Code
Your training script acts like a recipe. It should outline the steps necessary to recreate the results discussed in your paper, including hyperparameters and techniques used. To optimize user experience, consider using train.py as an entry point for others to utilize your training script on their datasets.
3. Evaluation Code
Providing the evaluation metrics is as crucial as sharing the recipe for your dish; it allows others to verify your claims. Include a scripted evaluation process via eval.py so that users can understand how you arrived at your results.
4. Pre-trained Models
Imagine offering a ready-to-eat dish alongside your recipe; this enhances trust and usability. Providing a pre-trained model allows individuals to skip lengthy training times for immediate insights and experimentation based on your results.
5. README File with Results
Your README.md file should act as a welcoming guide and overview. Including a table of results, which can be visualized like a menu, lets users quickly grasp the potential of your code. Include clear instructions and links to relevant scripts to facilitate reproducibility, allowing others to reproduce your findings with ease.
Troubleshooting Tips
If you encounter issues during your code release, consider the following:
- Ensure all dependencies are listed and easily accessible.
- Double-check your training and evaluation scripts for clarity and accuracy.
- Make sure your README.md is comprehensive and user-friendly.
- If your repo isn’t garnering attention, consider enhancing its visibility with the checklist items above.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Additional Resources
To enrich your research code release, you can also utilize various platforms for hosting pre-trained model files and results leaderboards, such as:
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.