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Including drivers etc.

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Use of algorithms where the output can change with the same input. Example - stochastic gradient descent

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If data is preprocessed it is essential to keep a record of how the data was preprocessed especially when the preprocessing is non-deterministic.

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It's essentially impossible for anybody to replicate GPT3/GPT4 models since the computational power needed is out-of-reach

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-Hardware equipments

-Resources (Money, Time etc.)

-Availability of datasets

-Environment and hyper-parameters

-Lack of implementation details

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Using different versions of libraries

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Only raw data is available and preprocessing is not documented.

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OpenAI purposefully did not reveal details about their newest GPT-4 model, citing competition and safety reasons

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It is much easier to get published with new results compared to results that confirm the status-quo. Hence, there is an implicit incentive to "manufacture" surprising results and often reproduction studies can't achieve the same results

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Dataset gets updated

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For example in Database research (where a lot of machine learning experimentation happens) you are not allowed to release benchmarks of proprietary database engines. Access to technologies may only be granted with NDAs

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Different versions across libraries => Use dockers!

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Missing software package  information (e.g. version)

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Missing software package  information (e.g. version)

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Even the of sequence in which software was installed can produce differences. Solution => Docker

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Use Github to have versioning and insight into the thought process.

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During experimentation, parameters such as hyperparameter values and batch sizes change. Without properly logging the changes in these parameters, it becomes difficult to understand and replicate the model.

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You can't reproduce an experiment in case you've spilled your beverage over your laptop.