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

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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!