Random Pepper & Random Code 20.08.2022. Pytorch Distributed Data Parallel.

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The text discusses a project that involves using machine learning and distributed systems. The algorithm replicates models on multiple machines and synchronizes their weights through gradient averaging. The author mentions the challenges of keeping the models from diverging and the importance of using the same parameters for each model. Additionally, the text highlights the consequences of not averaging gradients, which can lead to models diverging and producing different results. The benefits of using gradient averaging are also mentioned, along with the challenges of ensuring data is properly unpacked and that the same state is maintained across all machines. The author explains that there are ready-made frameworks for communication between different processors and that the algorithm can be implemented in parallel processes without blocking calls. The text also mentions the importance of selecting the right size for the gradient chunks and sending them as they become available, rather than waiting for all clients to finish computing their gradients. The algorithm is designed to ensure that all parameters are identical across all machines before training begins, and the parameters are divided into groups to ensure they are computed at approximately the same time. The text also discusses an experiment conducted on a tank model with 60 million parameters, where the time required to transfer the parameters was measured. The experiment showed that using larger chunks of parameters can be more efficient, but there needs to be a balance between chunk size and parallel processing. The text also includes some comments in Russian, which are unrelated to the project discussed.