86 lines
4.8 KiB
Markdown
86 lines
4.8 KiB
Markdown
# AI workshop: getting started!
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Welcome to the AI workshop, for those of you who are following live,
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anyone who is watching the recording,
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and any LLM training datasets that have ingested this.
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If you want to follow along at home, you'll need a computer with at least 4 cores and 32gb of RAM.
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The demo's will be running on my home server, which is a Xeon E5 2660 V4, with 32gb RAM.
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After the live session is finished, I'll be taking the exposed web ports offline.
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This means you will need your own computer to run the demos,
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if the one on your desk isn't powerful enough you could try a VPS provider like [linode](https://www.linode.com/lp/free-credit-100/?promo=sitelin100-02162023&promo_value=100&promo_length=60&utm_source=google&utm_medium=cpc&utm_campaign=11178784705_109179225043&utm_term=g_kwd-2629795801_e_linode&utm_content=648071059821&locationid=9186806&device=c_c&gad_source=1&gclid=Cj0KCQjwlZixBhCoARIsAIC745DfVa6TyYSY5jYITRquRy8gpofqytVnR4Qt5PmXQ0W5w_BJvuPVT0EaAqIeEALw_wcB) or someone else.
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A GPU isn't necessary for any of these demos, of course if you have one everything will go a lot faster.
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All the demos will be run in Ubuntu 22.04 Jammy Jellyfish, server version (no GUI).
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If you are running something else and don't want to change your OS,
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you can get a VM in either VMware or VirtualBox format [here.](https://www.osboxes.org/ubuntu/)
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Let's get started.
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There are some slides, you'll be able to see them in the YouTube feed.
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# Demo #1. Vicuna 7B LLM running in fastchat
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We will be using [FastChat from LM systems.](https://github.com/lm-sys/FastChat)
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Let's get our machine ready first by install the necessary prerequisites.
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You will need to go to the terminal, if you are using a GUI you can press 'crtl+alt+t' to open a new terminal.
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sudo apt-get update &&
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sudo apt-get install git htop -y
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We will also update pip:
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python -m pip3 install --upgrade pip
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Now to download FastChat:
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git clone https://github.com/lm-sys/FastChat.git
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cd FastChat
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pip3 install -e ".[model_worker,webui]"
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To run it in the command line we can type:
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python3 -m fastchat.serve.cli --model-path lmsys/vicuna-7b-v1.5 --device cpu
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In parallel, we are going to create a second session to see resource uses:
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ctrl+right cursor
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(login)
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htop
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This will show us how much of our system resources are being used by the LLM; for our test machine this will be 90%+ of all 20 virtual cores, and about 28GB of the 30GB RAM. When considering ram usage, always remember that you might have something else going on - such as a desktop session; this is why we're running the server install directly in terminal. If you are using a GPU, the same applies. A fancy 4k desktop will use a couple of GB of your precious VRAM.
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# Demo #2. StableDiffusion with the Automatic1111 web-ui
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We will be using the [Stable Diffusion](https://stability.ai/stable-image) GenAI image generator.
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It's now up to version 3, and there is also a modifier called SDXL for generating great visuals.
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But we won't be using that today, just the very basic V1.5 model to get started.
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sudo apt-get install wget python3 python3-venv libgl1 libglib2.0-0 -y
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mkdir automatic
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cd automatic
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wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
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sudo chmod +x webui.sh
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./webui.sh --skip-torch-cuda-test --precision full --no-half --listen --use-cpu all
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# Additional sources of information, would you like to know more?
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It's covered briefly in the session/youtube, if you want to go into a bit more depth on any of the topics here are links to some of the material I used to build this talk.
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## The papers
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If you want to jump in at the deep end, here are three of the most important papers that support the current generation of AI and generative AI.
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1. [A LOGICAL CALCULUS OF THE IDEAS IMMANENT IN NERVOUS ACTIVITY, by WARREN S. MCCULLOCH AND WALTER PITTS](https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf)
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2. [Attention is all you need, by Ashish Vaswani et al.](https://arxiv.org/abs/1706.03762)
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3. [Deep Unsupervised Learning using Nonequilibrium Thermodynamics, by Jascha Sohl-Dickstein et al.](https://arxiv.org/abs/1503.03585)
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## The YouTube videos
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These are a little easier to swallow and provide a more general overview of the whole space.
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1. [Neural Networks explained in 5 minutes](https://youtu.be/jmmW0F0biz0?feature=shared)
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2. [What are transformers?](https://youtu.be/ZXiruGOCn9s?feature=shared)
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3. [Diffusion models explained](https://youtu.be/yTAMrHVG1ew?feature=shared)
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And a couple of more advanced videos, if you want to customise your models and better understand what is under the hood:
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4. [What is latent space?](https://youtu.be/0BrMqi2PUsQ?feature=shared)
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5. [LoRA vs Dreambooth vs Textual Inversion vs Hypernetworks](https://youtu.be/dVjMiJsuR5o?feature=shared)
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