""" This specific file was bodged together by ham-handed hedgehogs. If something looks wrong, it's because it is. If you're not a hedgehog, you shouldn't reuse this code. Use this instead: https://docs.streamlit.io/library/get-started """ import streamlit as st from st_helpers import make_header, content_text, content_title, cite, make_footer from charts import draw_current_progress st.set_page_config(page_title="Training Transformers Together", layout="centered") st.markdown("## Full demo content will be posted here on December 7th!") make_header() content_text(f""" There was a time when you could comfortably train SoTA vision and language models at home on your workstation. The first ConvNet to beat ImageNet took in 5-6 days on two gamer-grade GPUs{cite("alexnet")}. Today's top-1 imagenet model took 20,000 TPU-v3 days{cite("coatnet")}. And things are even worse in the NLP world: training GPT-3 on a top-tier server with 8 A100 would still take decades{cite("gpt-3")}.""") content_text(f""" So, can individual researchers and small labs still train state-of-the-art? Yes we can! All it takes is for a bunch of us to come together. In fact, we're doing it right now and you're invited to join! """, vspace_before=12) draw_current_progress() content_text(f""" The model we're training is called DALLE: a transformer "language model" that generates images from text description. We're training this model on LAION - the world's largest openly available image-text-pair dataset with 400 million samples. Our model is based on dalle-pytorch with several tweaks for memory-efficient training.""") content_title("How do I join?") content_text(""" That's easy. First, make sure you're logged in at Hugging Face. If you don't have an account, create one TODO.
Please note that we currently limit the number of colab participants to TODO to make sure we do not interfere with other users. If there are too many active peers, take a look at alternative starter kits here TODO """) content_text(" TODO UPDATE") make_footer()