Probably the most impressive thing about my code is it takes just 3,000 seconds 
to train on 1GB using CPU (1 core), If only train it and not call it for 
predictions like I was. In C++ this would be way faster, at least 4x, and so 
would be 10 mins per GB of text. 1 hour and 40 mins for 10GB. 100GB about 16 
hours.

https://arxiv.org/pdf/1906.08237.pdf

"After tokenization with SentencePiece [17], we
obtain 2.78B, 1.09B, 4.75B, 4.30B, and 19.97B subword pieces for Wikipedia, 
BooksCorpus, Giga5,
ClueWeb, and Common Crawl respectively, which are 32.89B in total"

So that is about 33 billion tokens that are each about 3 letters long I think, 
hence used ~100GB I think. Most probably at least 10-30GBs.

"Specifically, we train on 512 TPU v3 chips for 500K steps with an Adam weight 
decay
optimizer, linear learning rate decay, and a batch size of 8192, which takes 
about 5.5 days."

= $245,000 on google cloud
https://www.reddit.com/r/MachineLearning/comments/c59ikz/r_it_costs_245000_to_train_the_xlnet_model512_tpu/

This raises a new question I will seek to find out hard. If nncp takes 2 days 
for 1GB, is his actually incredibly fast, and if so, are we being (not you but 
the benchmark - what it suggests but doesn't show...) LIED TO --- what if nncp 
does bad at predictions when try completing user prompts --- unlike GPT-2 that 
used Lots of compute? This would suggest lots of compute is Needed, or at least 
until I finish my code and show them it is not needed. And would suggest the 
best AIs are not being benchmarked cuz need to be "downsized" and can't be 
downsized, cuz you know, they use lots of neurons and throw backprop through it 
carelessly, but they neeeed that stuffz.... Would be nice if someone can help 
me make nncp generate completions cuz I dooon't know where or how in his c++ to 
change it to do so.....if you good at it i need u
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