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hh-mnist

Hyperparameter Hippies MNIST project

Tyrone and Dan's implementation

# on local machine
./send.sh
# on Nvidia machine
source ssh.sh
# encoder-only
uv run train_model.py --entity wandb-team --project wandb-project
# encoder-decoder
uv run --group cuda124 create_composite_images.py
uv run --group cuda124 train_complex_model.py --entity wandb-team --project wandb-project

Data will be placed in the data directory.

To run the webserver:

uv run --group inference -- streamlit run webserver.py

To run the slides

# must already have Node.js installed
npm install -g pnpm 
pnpm install
pnpm dev

Remote deploy

Build and deploy instructions

Any hosting service accepting Docker will do. You need enough RAM for the model.

Setup

  1. Install Docker Engine on your server. Docker should be runnable by someone other than root. Follow the instructions on the Docker website.
  2. Your server must have a static IP address.
  3. Create an entry in your ~/.ssh/config file, or use ssh-agent to alias a name to your server that incorporates its username and any private key needed to log into it.
  4. docker context create server-name --docker "ssh://server-name"

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