README.md

metadata

title: RLHF
emoji: 🏢
colorFrom: red
colorTo: gray
sdk: gradio
sdk_version: 3.1
app_file: app.py
pinned: false

An RLHF interface for data collection with Amazon Mechanical Turk and Gradio.

Instructions for someone to use for their own project

Install dependencies

First, create a Python virtual environment and install the project's dependencies as follows:

python -m pip install -r requirements.txt

Setting up the Space

1. Clone this repo and deploy it on your own Hugging Face space.
2. Add the following secrets to your space:
   • HF_TOKEN: One of your Hugging Face tokens.
   • DATASET_REPO_URL: The url to an empty dataset that you created the hub. It can be a private or public dataset.
   • FORCE_PUSH: "yes" When you run this space on mturk and when people visit your space on huggingface.co, the app will use your token to automatically store new HITs in your dataset. Setting FORCE_PUSH to "yes" ensures that your repo will force push changes to the dataset during data collection. Otherwise, accidental manual changes to your dataset could result in your space getting merge conflicts as it automatically tries to push the dataset to the hub. For local development, add these three keys to a .env file, and consider setting FORCE_PUSH to "no".

To launch the Space locally, run:

python app.py

The app will then be available at a local address, such as http://127.0.0.1:7860

Running data collection*

1. On your local repo that you pulled, create a copy of config.py.example, just called config.py. Now, put keys from your AWS account in config.py. These keys should be for an AWS account that has the AmazonMechanicalTurkFullAccess permission. You also need to create an mturk requestor account associated with your AWS account.
2. Run python collect.py locally.

Profit

Now, you should be watching hits come into your Hugging Face dataset automatically!

Tips and tricks

• Use caution while doing local development of your space and simultaneously running it on mturk. Consider setting FORCE_PUSH to "no" in your local .env file.
• huggingface spaces have limited computational resources and memory. If you run too many HITs and/or assignments at once, then you could encounter issues. You could also encounter issues if you are trying to create a dataset that is very large. Check the log of your space for any errors that could be happening.