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AI Reproducibility Minute: Implementation Factors

researcher and practitioner survey[s] shows that 83.8% of participants are unaware of or unsure about any implementation-level variance.”



Even if you use the same dataset and software, machine learning (ML) results can vary when run on different hardware and software versions. In order to ensure your ML results can be reproduced by others, consider documenting the following factors:

  • Initialization seeds - note the seeds used

  • Parallel execution - note the number of threads used

  • Processing unit - note which processors were used

  • Software - include the exact version of the operating system and the complete software stack used.

Even better, include a link to the container. Other factors to consider:

Compiler settings

  1. Auto-selection of primitive ops

  2. Floating-point operations

  3. Rounding errors

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