Difficulty: Beginner
Estimated Time: 10 minutes

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

Sign up for the beta

If you liked what you saw and you think it will be useful in your work, please sign up for the beta at:

beta.dotscience.io

Please also join our Slack channel to chat to us and give us feedback!

slack.dotscience.io

Don’t stop now! The next scenario will only take about 10 minutes to complete.

Dotscience Workshop

Step 1 of 7

Launch 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 \ quay.io/dotmesh/dotscience-cli-installer:v8

Click the code above, and wait about a minute. You should see a result saying TOKEN. 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!