Material for Applied MAchine Learning Dyas (AMLD) 2020 workshop "Bayesian Inference: embracing uncertainty":

https://appliedmldays.org/workshops/hands-on-bayesian-machine-learning-embracing-uncertainty

R+Stan and Python+PyMC3 versions of the code will be provided in this repository. The main tutorial, however, will take place in Julia+Turing. For Julia, use the Docker image as explained below.

Warning !! Make sure that you have at least 30GB of free space !!

1. Install Docker from https://docs.docker.com/install/

• Desktop version for Mac and Windows (Requires creating docker hub account)
• Server version for Linux: Follow instructions under Install Docker Engine - Community

2. Verify installation by running the following in a terminal (Mac, Linux) or PowerShell (Windows):

docker run hello-world

This should output the following:

Hello from Docker!
This message shows that your installation appears to be working correctly.
...

3. Download Docker image from DockerHub.

docker pull semenovae/julia-workshop

1. Run the Julia environment

docker run -p 8888:8888 semenovae/julia-workshop

2. Create a new Jupyter notebook

3. At the end of the workshop, make sure to download your Jupyter notebook before ending the Docker session and deleting the Docker image

Ctrl+C

docker ps // To obtain container ID

docker rm container-id -f

1. Create docker image from Dockerfile and push it to Docker Hub

docker build -t your_dockerID/your_image_name:1 .
docker tag your_dockerID/your_image_name:1 dockerID/your_image_name:latest
docker push your_dockerID/your_image_name:1
docker push your_dockerID/your_image_name:latest

2. List Docker images

docker images

3. Remove all docker images

docker system prune -a