A step-by-step tutorial on pre-training BERT-base from scratch using Hugging Face Transformers, Optimum Habana, and AWS DL1 (Habana Gaudi) instances. Covers dataset preparation using Wikipedia and BookCorpus, training a custom BertTokenizerFast tokenizer, preprocessing and tokenizing the dataset, and running distributed masked-language modeling pre-training across 8 HPU cores via GaudiTrainer. Includes a cost comparison showing Habana Gaudi delivers ~25% cost savings (~$1,650 for full pre-training) versus NVIDIA V100-based GPU setups (~$2,075), and notes that a full 1M-step pre-training would take roughly 125 hours.
