gpt-2 | Language Models are Unsupervised Multitask Learners

You need to activate GPU runtime while hosting the notebook session in AWS SageMaker. The code will automatically take care of utilizing GPU resources.

Looking at the link which you shared - it doesn't have any custom configs to manually specify GPU resources.

If it's handled automatically by the framework which you're using to train the network, then in an active GPU session it will automatically allocate GPU resources while training.

I didn't find the concrete answer to this question, but a workaround. For anyone looking for examples on how to fine-tune the GPT models from HuggingFace, you may have a look into this repo. They listed a couple of examples on how to fine-tune different Transformer models, complemented by documented code examples. I used the run_clm.py script and it achieved what I wanted.

Your code does not throw any error for me - I would try re-installing the most recent version of transformers - if that is a viable solution for you.

The en_core_web_trf uses a specific Transformers model, but you can specify arbitrary ones using the TransformerModel wrapper class from spacy-transformers. See the docs for that. An example config:

Using the dict indexing by key, and the list indexing by index

You can definitely retrain GPT-2. Are you only looking to train it for language generation purposes or do you have a specific downstream task you would like to adapt the GPT-2?

Both these tasks are possible and not too difficult. If you want to train the model for language generation i.e have it generate text on a particular topic, you can train the model exactly as it was trained during the pre-training phase. This means training it on a next-token prediction task with a cross-entropy loss function. As long as you have a dataset, and decent compute power, this is not too hard to implement.

When you say, 'discuss' the content of the book, it seems to me that you are looking for a dialogue model/chatbot. Chatbots are trained in a different way and if you are indeed looking for a dialogue model, you can look at DialoGPT and other models. They can be trained to become task-oriented dialog agents.

Data written to the filesystem does not persist when the container instance is stopped.

Cloud Run lifetime is the time between an HTTP Request and the HTTP response. Overlapped requests extend this lifetime. Once the final HTTP response is sent your container can be stopped.

Cloud Run instances can run on different hardware (clusters). One instance will not have the same temporary data as another instance. Instances can be moved. Your strategy of downloading a large file and saving it to the in-memory file system will not work consistently.

Filesystem access

Also note that the file system is in-memory, which means you need to have additional memory to store files.

You have initialized a RobertaForSequenceClassification model that per default (in case of roberta-base and roberta-large which have no trained output layers for sequence classification) tries to classify if a sequence belongs to one class or another. I used the expression "belongs to one class or another" because these classes have no meaning yet. The output layer is untrained and it requires a finetuning to give these classes a meaning. Class 0 could be X and Class 1 could be Y or the other way around. For example, the tutorial for finetuning a sequence classification model for the IMDb review dataset defines negative reviews as Class 0 and positive reviews as Class 1 (link).

You can check the number of supported classes with: