Skip to content

Latest commit

 

History

History
52 lines (38 loc) · 1.88 KB

README.md

File metadata and controls

52 lines (38 loc) · 1.88 KB
Raw

KnowledgeVIS server

To process prompts written in the KnowledgeVIS interface, we use the Hugging Face Transformers API to download transformer models and run a Python Flask server that recieves prompts from the interface, runs the models, and sends the processed data back to the interface.

This version of the server uses Python 3.9.

The first time you run main.py you will download NLTK packages and the Hugging Face Transformers models. This may take a while depending on your download speed.

  • The NLTK packages will take up around 35MB of space.
  • The Hugging Face transformers will take up around 6.5GB of space.
    • bert-large-uncased-whole-word-masking is ~2.5GB.
    • roberta-large is ~2.5GB.
    • distilbert-base-uncased is ~500MB.
    • microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext is ~500MB.
    • allenai/scibert_scivocab_uncased is ~500MB.

Setup

  1. Open a command-line shell (Windows) or Terminal (MacOS, Linux) in a new window
  2. Navigate to this folder (server/)

Windows:

  1. Run py -3.9 -m venv venv
  2. Run .\venv\Scripts\activate

MacOS / Linux:

  1. Run python3.9 -m venv venv
  2. Run source venv/bin/activate

Both:

  1. Run python -m pip install -r requirements.txt
  2. Install PyTorch v2.0.x (instructions)
    • PyTorch is installed separately because some systems may support CUDA, which requires a different installation process and can significantly speed up the tool.
    • First, check if your GPU can support CUDA (link)
    • Then, follow the instructions linked above to determine if your system can support CUDA for computation.
  3. Run python main.py

Packages

  • Flask v2.3.x
  • Flask-Cors v3.0.x
  • nltk v3.8.x
  • numpy v1.24.x
  • pandas v2.0.x
  • scikit-learn v1.2.x
  • scipy v1.10.x
  • torch v2.0.x
  • transformers v4.30.x
  • waitress v2.1.x