Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
# THIS LINUX SETUP SCRIPT HAS MORPHED INTO A WHOLE PROJECT: HTTPS://OMAKUB.ORG | |
# PLEASE CHECKOUT THAT PROJECT INSTEAD OF THIS OUTDATED SETUP SCRIPT. | |
# | |
# | |
# Libraries and infrastructure | |
sudo apt update -y | |
sudo apt install -y \ | |
docker.io docker-buildx \ | |
build-essential pkg-config autoconf bison rustc cargo clang \ |
ruby '2.7.1' | |
gem 'rails', github: 'rails/rails' | |
gem 'tzinfo-data', '>= 1.2016.7' # Don't rely on OSX/Linux timezone data | |
# Action Text | |
gem 'actiontext', github: 'basecamp/actiontext', ref: 'okra' | |
gem 'okra', github: 'basecamp/okra' | |
# Drivers |
This guide was written because I don't particularly enjoy deploying Phoenix (or Elixir for that matter) applications. It's not easy. Primarily, I don't have a lot of money to spend on a nice, fancy VPS so compiling my Phoenix apps on my VPS often isn't an option. For that, we have Distillery releases. However, that requires me to either have a separate server for staging to use as a build server, or to keep a particular version of Erlang installed on my VPS, neither of which sound like great options to me and they all have the possibilities of version mismatches with ERTS. In addition to all this, theres a whole lot of configuration which needs to be done to setup a Phoenix app for deployment, and it's hard to remember.
For that reason, I wanted to use Docker so that all of my deployments would be automated and reproducable. In addition, Docker would allow me to have reproducable builds for my releases. I could build my releases on any machine that I wanted in a contai
import asyncio | |
loop = asyncio.get_event_loop() | |
async def hello(): | |
await asyncio.sleep(3) | |
print('Hello!') | |
if __name__ == '__main__': | |
loop.run_until_complete(hello()) | |