AI Reproducibility Minute: Implementation Factors
Updated: May 3, 2024
“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
Auto-selection of primitive ops
Floating-point operations
Rounding errors
Me pareció muy interesante este artículo, especialmente por la forma sencilla de explicar temas relacionados con los sueños y sus posibles significados. MiCábala parece un recurso interesante para quienes desean conocer más sobre este tema.
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You've explained this better than most of the longer guides I've seen floating around. The practical tips are the kind you can apply today rather than file away for someday. For a slightly different angle on the same idea, there's a bit more waiting at https://pathsinc.org/media/pgs/uncx_disperser__why_bulk_token_distribution_expands_uncx_beyond_liquidity_locking.html.
Great breakdown — it answered a couple of questions I'd been sitting with for a while. What stood out to me was how you tied the smaller details back to the bigger picture. I touched on the same subject from a different perspective at ube-swap.com a little while back.