In this workshop you’ll follow a set of hands-on exercises with support from our facilitators.
You will leave being able to use tools to ensure reproducibility & provenance of:
- environment - Docker
- code - Git
- data, code, environment, parameters & summary stats - Dotscience
Get Dotscience running on a cloud VM
You are now connected to a Linux VM in the cloud. The first thing we're going to do is to start Jupyter & Dotscience on this VM.
Normally, if you were running Dotscience on your Windows PC or Mac, we would provide a GUI installer. But on Linux, we can run this installer instead:
docker run --rm --net=host --name=dotscience-cli-installer \
-v /var/run/docker.sock:/var/run/docker.sock \
Click the code above, and wait about a minute.
You should see a result saying
Copy the token (highlight it, right click and copy or cmd+c on a Mac), then click the following link:
Jupyter should prompt you for a token. Paste the token into the first text field ("Password or token").
Inside Jupyter, you'll see a workspace pre-configured with empty dataset folders for this workshop.
Now also load up the Dotscience GUI.
It's useful to put the Jupyter window and the Dotscience window side-by-side so you can see how Dotscience tracks your work.
You should now have Jupyter loaded in one window, and Dotscience in another.
Now it's time to do load in a data science project!