app.py

"""
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, make_tabs
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 state-of-the-art vision and language models at home on your workstation.
The first convolutional neural net to beat ImageNet
({cite("AlexNet", "https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf")})
was trained for 5-6 days on two gamer-grade GPUs. In contrast, today's TOP-1 ImageNet model
({cite("CoAtNet", "https://arxiv.org/abs/2106.04803")})
takes 20,000 TPU-v3 days. And things are even worse in the NLP world: training
{cite("GPT‑3", "https://arxiv.org/abs/2005.14165")} on a top-tier server
with 8x A100 would take decades.""")

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)

draw_current_progress()

content_text(f"""
We're training a model similar to {cite("OpenAI DALL-E", "https://openai.com/blog/dall-e/")},
that is, a transformer "language model" that generates images from text description.
It is trained on {cite("LAION-400M", "https://laion.ai/laion-400-open-dataset/")},
the world's largest openly available image-text-pair dataset with 400 million samples. Our model is based on
the {cite("dalle&#8209;pytorch", "https://github.com/lucidrains/DALLE-pytorch")} implementation
by {cite("Phil Wang", "https://github.com/lucidrains")} with a few tweaks to make it communication-efficient.
""", vspace_after=8)


with st.expander("How to train efficiently over the internet?"):
    content_text(f"""
Modern distributed training algorithms are designed for HPC networks with 10-100 gigabit per second bandwidth.
In turn, a typical Internet connection runs at 10-100 megabits per second: that’s three orders of magnitude slower.
To make distributed training over the Internet efficient, you need to win back these three orders of magnitude.
""")
    content_text(f"""
This may seem daunting at first, but in reality, DL researchers have already made all the necessary pieces for solving this puzzle:
<table style="border: 0px;"><tbody style="border: 0px;">
<tr><td> Speed-up (AllReduce)<br> </td> <td>Existing technique</td></tr>
<tr><td class=centered><strong>4-16x</strong></td><td>
  <strong>Large-batch training:</strong> {cite("You et al. (2019)", "https://arxiv.org/abs/1904.00962")} proposed a way for training neural networks efficiently with larger batches, and hence, fewer communication rounds.
</td></tr>
<tr><td class=centered><strong>4-64x</strong></td><td>
  <strong>Gradient Compression:</strong> from simple {cite("8-bit quantization", "https://arxiv.org/abs/1511.04561")}
   to advanced techniques such as {cite("Deep Gradient Compression", "https://arxiv.org/abs/1712.01887")}, 
   {cite("PowerSGD", "https://arxiv.org/abs/1905.13727")}, {cite("1-bit Adam", "https://arxiv.org/abs/2102.02888")},
    and many others. As a rule of thumb, you can safely reduce communication by 16-64x. More extreme compression is often
    possible, but it may affect stability or final quality.
</td></tr>
<tr><td class=centered><strong>4-24x</strong></td><td>
   <strong>Parameter sharing:</strong> reusing parameters between model layers results in a model with fewer parameters,
    and hence, fewer gradients to communicate. {cite("Lan et al. (2019)", "https://arxiv.org/abs/1909.11942")} and 
    {cite("Xue et al. (2021)", "https://arxiv.org/pdf/2107.11817.pdf")} propose efficient parameter sharing techniques
    for NLP and vision.
</td></tr>
<tr><td class=centered><strong>1.5-2x</strong></td><td>
   <strong>Overlapping computation with communication:</strong> running network communication in background while
   computing the next portion of gradients. This is a {cite("long-standing trick from HPC", "https://ur.booksc.eu/book/1624068/2d0506")}
    that was recently adapted for DL training. {cite("Ren et al. (2021)", "https://arxiv.org/abs/2101.06840")} show that
     updating parameters in background while computing the next batch of gradients does not reduce convergence.
</td></tr>
</tbody></table>
""")


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 <b>TODO</b>.<br>

<ul style="text-align: left; list-style-position: inside; margin-top: 12px; margin-left: -24px;">
    <li style="margin-top: 4px;">
        Join our organization on Hugging Face here: <b>TODO</b>. </li>
    <li style="margin-top: 4px;">
        The simplest way to start is with colab <b>TODO</b>;</li>
    <li style="margin-top: 4px;">
        You can find other starter kits, evaluation and inference notebooks <b>TODO IN OUR ORGANIZATION</b>;</li>
    <li style="margin-top: 4px;">
        If you have any issues, <b>TODO DISCORD BADGE</b> </li>
</ul>

Please note that we currently limit the number of colab participants to <b>TODO</b> 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 <b>TODO</b>
""")

content_title("How does it work?")
content_text("<b> TODO </b> General Story That Weaves Together Three Tabs Below . Lorem ipsum dolor sit amet, "
             "consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim"
             " ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. "
             "Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. "
             "Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.")

make_tabs()

make_footer()