Why is it challenging to ensure reproducibility in neural networks

Why is it more challenging compared to ML?

1. Randomness
   • Neural networks often involve various sources of randomness, such as weight initialization, dropout, and data shuffling during training.
   • These random factors can lead to different results each time the model is run, even with the same code and data.
2. Parallelism
   • Deep learning frameworks like PyTorch and TensorFlow are designed to take advantage of parallel computing capabilities, such as utilizing multiple GPUs or distributed systems.
   • Parallelism introduces additional sources of non-determinism, as the order of operations across parallel workers may vary.
3. Platform and library dependencies
   • Deep learning models rely on various libraries, platforms, and hardware configurations.
   • Minor differences in the versions of these dependencies or the underlying hardware can lead to variations in the results.

Best practices

1. Set random seeds
   • Set random seeds for the random number generators used in the model, as well as the libraries and frameworks involved.
   • This helps ensure that random operations are reproducible across runs.
2. Control environment
   • Create a consistent software environment by specifying the versions of the libraries and frameworks used.
   • Use virtual environments or containerization tools like Docker to isolate the environment and ensure consistent dependencies.
3. Record hyperparameters
   • Keep a record of all the hyperparameters used in the model, including network architecture, optimizer settings, learning rate, batch size, etc.
   • This allows you to recreate the model with the exact same configuration.
4. Save and load models
   • Save the trained model parameters to disk after training.
   • This allows you to load the same model and evaluate it on new data or resume training from the same point in the future.
5. Checkpointing
   • Periodically save model checkpoints during training, so you can restore the model to a specific state and continue training if needed.
6. Validate data preprocessing
   • Ensure consistent data preprocessing and handling.
   • Any transformations or augmentations applied to the data should be properly documented and consistently applied during training and evaluation.
7. Document hardware and software configurations
   • Document the hardware specifications (e.g., CPU, GPU) and the versions of the libraries, frameworks, and dependencies used.
   • This helps reproduce the environment and configurations for future runs.

Reference

• OpenAI. (2023). ChatGPT [Large language model]. https://chat.openai.com/chat
• source of teaser: Unsplash의Wolfgang Hasselmann

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