the SOTA NLP results from training larger models (e.g. OpenAI’s GPT-2-1.5B model )
the current high cost of training larger models (e.g. with GPT2-1.5B - Jeremy estimated it would cost $50k-$100k ‘in a hurry’, and around $20k to train in 10 months )
the new research and applications that fast.ai students could engage in, using transfer learning with such larger models
Jeremy and Sylvain’s existing research in training smaller, GPT-2-like transformer models 
the OpenAI’s GPT-2-1.5B dataset should be easy to replicate 
TensorFlow has an API to distribute training 
combined with Fast.ai’s newly announced usage of Swift for TensorFlow 
SenseTime’s recently announced distributed ImageNet/AlexNet Training in 1.5 Minutes on a cluster of 512 Volta GPUs (Arxiv paper dated 19 Feb 2019 )
I can’t help but wonder if it’s possible to train a large model, like GPT-2-1.5B, both quickly and cheaply, using distributed training that utilises the compute resources of fast.ai students and anyone else who wants to contribute GPU/TPU/IPU resource.
I’m not sure how amenable a model like GPT2 is to data-parallelism and model-parallelism and, even if it is, maybe it’s unworkable (e.g. latency issues) using widely distributed asynchronous training compared to, say, using Mesh-TensorFlow  on a ‘local’ supercomputer.
But despite this, I thought it worth mentioning anyway. I would love to know what’s stopping such distributed training, and whether those obstacles are insurmountable or not.
In terms of the ethics of creating, distributing and using large models like GPT2 - my personal view is currently that others are going to replicate GPT2-like results anyway, it’s just a matter of time, if not already done. I think the days of taking information at face value (including text, audio and video) are long over, and that digitally signing information, as Jeremy points out , makes sense to me.
As fast.ai students, we could adhere to a Code of Conduct in how we use such models, maybe digitally signing any products derived from such models, so it’s explicitly traceable and trust is in responsibly-managed certificate authorities, rather than in raw information.
 Language Models are Unsupervised Multitask Learners - https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
 Some thoughts on zero-day threats in AI, and OpenAI’s GP2 - https://www.fast.ai/2019/02/15/openai-gp2/
If you’re in a hurry and you want to get this done in a month, then you’re going to need 80 GPUs. You can grab a server with 8 GPUs from the AWS spot market for $7.34/hour. That’s around $5300 for a month. You’ll need ten of these servers, so that’s around $50k to train the model in a month. OpenAI have made their code available, and described how to create the necessary dataset, but in practice there’s still going to be plenty of trial and error, so in practice it might cost twice as much.
If you’re in less of a hurry, you could just buy 8 GPUs. With some careful memory handling (e.g. using Gradient checkpointing) you might be able to get away with buying RTX 2070 cards at $500 each, otherwise you’ll be wanting the RTX 2080 ti at $1300 each. So for 8 cards, that’s somewhere between $4k and $10k for the GPUs, plus probably another $10k or so for a box to put them in (with CPUs, HDDs, etc). So that’s around $20k to train the model in 10 months (again, you’ll need some extra time and money for the data collection, and some trial and error).
 Jeremy’s announcement - Swift for TensorFlow: The Next-Generation Machine Learning Framework (TF Dev Summit ’19) - https://youtu.be/s65BigoMV_I?t=1739
 Optimizing Network Performance for Distributed DNN Training on GPU Clusters - https://arxiv.org/pdf/1902.06855.pdf
 Mesh-TensorFlow: Model Parallelism for Supercomputers (TF Dev Summit ’19) - https://www.youtube.com/watch?v=HgGyWS40g-g