Skip to content

Latest commit

 

History

History
 
 

bert

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
templates
templates
 
 
Dockerfile
Dockerfile
 
 
README.md
README.md
 
 
app.py
app.py
 
 
bert.png
bert.png
 
 
config.json
config.json
 
 
deploy.yaml
deploy.yaml
 
 
requirements.txt
requirements.txt
 
 

README.md

BERT on Akash

This repository contains the necessary files to deploy a Flask application that uses the BERT language model on the Akash network. BERT is a powerful language model that can understand and generate text in English.

Link to original Google Research Repo: https://github.com/google-research/bert

Files

  • Dockerfile: This file is used to build the Docker image for the application. It sets up an environment with Python and all the necessary libraries to run the application.
  • requirements.txt: This file lists the Python packages that need to be installed in the Docker image. This includes Flask for the web application and the Transformers library for the BERT model.
  • app.py: This is the main application file. It creates a Flask web application that uses the BERT model to predict the masked words in a sentence.
  • deploy.yaml: This file defines the Akash deployment configuration for the application. It specifies the resources needed to run the application and the Docker image to use.

Deployment

To deploy the application on the Akash network, you need to build and push the Docker image, and then deploy the application using the deploy.yaml file.

  1. Build the Docker image: docker build -t your-dockerhub-username/bert-base-uncased:0.0.1 .
  2. Push the Docker image: docker push your-dockerhub-username/bert-base-uncased:0.0.1
  3. Deploy the application on Akash: akash deploy create deploy.yaml --from $AKASH_KEY_NAME

Replace your-dockerhub-username with your Docker Hub username and $AKASH_KEY_NAME with the name of your Akash key.

Usage

The application listens on port 80 and accepts POST requests to the /predict endpoint. The POST request should contain a JSON object with a single attribute 'text' that contains the sentence with a word replaced by '[MASK]'. The application will return the sentence with the '[MASK]' replaced by the predicted word.

For example, you can use curl to send a POST request:

curl -X POST -H "Content-Type: application/json" -d '{"text":"This [MASK] model can understand and generate text in multiple languages."}' http://your-akash-deployment-url/predict

image

Video Demo

bert.demo.mov

Replace your-akash-deployment-url with the URL of your Akash deployment.