Skip to content

About

Continuous monitoring tools for deployed clinical prediction models

Resources

Stars

3 stars

Watchers

1 watching

Forks

Latest commit

 

History

12 Commits

Folders and files

Repository files navigation

Deployr Dashboard

Table of Contents

Virtual Environment

Next, we will run the code virtually,

Step 1: Create a virtual environment

python -m venv <env_path>
source <env_path>/bin/activate
pip install -r requirements.txt

This will create the virtual environment, activate it, and install all the dependencies.

Now, set the two environment variable as such in your terminal.

export COSMOS_HOST=<COSMOS_HOST>
export COSMOS_READ_KEY=<COSMOS_READ_KEY>
export COSMOS_DB_ID=<COSMOS_DB_ID>

Step 2: Run the main.py file

streamlit run main.py

Docker Desktop

Now, let us run the code in docker. First, you will need to install docker desktop: Mac, Windows. Make sure to select the right software for your device.

Next, open the docker desktop and keep it running while you are working with following code:

docker image build -t <image_name> . 

docker run \
-p 8080:8080 \
-e COSMOS_HOST=<COSMOS_HOST> \
-e COSMOS_READ_KEY=<COSMOS_READ_KEY> \
-e COSMOS_DB_ID=<COSMOS_DB_ID> \
<image_name>

This will build the docker and run the docker. You can check Dockerfile to see exactly what is happening inside.

This is what the site should look like:

alt text

About

Continuous monitoring tools for deployed clinical prediction models

Resources

Stars

3 stars

Watchers

1 watching

Forks

Releases

Packages

Contributors

Languages