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| """ | |
| 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, cite | |
| 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 <b>you're invited to join!</b> | |
| """, vspace_before=12, vspace_after=16) | |
| draw_current_progress() | |