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Setup

Section overview

The first section of this tutorial covers setting up our project. This includes the following tasks:

  • Creating an account and repo in DAGsHub
  • Installing DVC as well as the optional installation needed to use remote caches for a project (we assume you have Git installed and are familiar with it to some extent).
  • Project initialization in Git and DVC
  • Creating a virtualenv and installing the needed requirements for the first part of the project

If you are familiar with these steps, you can skip to the next part which covers pipeline creation.

Joining DAGsHub...

...is really easy. Just sign up. Then, after logging in, create a new repo, simply by clicking on the plus sign and create repository in the nav bar.

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Repo creation button

This opens up a dialog, which should be somewhat familiar, in which you can set the repository name, description and other option.

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Repo creation dialog

For this tutorial, fill in the Name and description, and leave everything else in the default settings.

Info

Note the Local DVC cache URL setting, which is unique to DAGsHub. We will get back to it later in the tutorial.

Done with repo creation. On to project initialization.

Installing DVC

We need to make sure we have Git and DVC installed on our computer.

We won't cover Git installation here, but if you need it, here is a link to an installation guide.

This tutorial assumes a Python 3 installation.
Make sure you aren't using Python 2 by mistake, using Python --version.

For full documentation and all options for installation of DVC, see the official docs.

The fastest way to install DVC is using pip. Just open a terminal and type:

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pip install dvc==0.50.1
and voila. Done.

Warning

This tutorial was last updated to DVC version 0.50.1 . If you are using an older version, please update. If you are using a newer version, be aware that the behavior of some commands may change.

Setting up our project

Create a directory name "dagshub_mnist" for the project somewhere on your computer. Open a terminal and input the following:

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cd path/to/folder/dagshub_mnist

git init
dvc init

The last command initializes the project as a DVC project, which sets up the following directory structure:

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.dvc/
.dvc/.gitignore
.dvc/cache/
.dvc/config

This is very similar to the .git folder contained in every git repo, except some of its contents will be tracked using git.

  • .dvc/config is similar to .git/config. By default it's empty. More on this later on.
  • .dvc/.gitignore makes sure git ignores everything in the .dvc folder except the config file.
  • .dvc/cache is where DVC will keep the different versions of our data files. It's very similar in principle to .git/objects.
  • The other files & directories under .dvc are used internally by DVC, and shouldn't interest the average user.

Now, we will set the remote to our repo on dagshub.com. This can be done using the following command:

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git remote add origin https://dagshub.com/<username>/<repo-name>.git
Finally, let's create 3 folders, for each of our main project components.
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mkdir code data metrics
This will add a clear structure for the project.

Creating a virtual environment and installing requirements

To create and activate our virtual environment type the following commands into your terminal (still in the project folder):

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virtualenv .env
echo .env/ >> .gitignore
echo __pycache__/ >> .gitignore

source .env/bin/activate
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virtualenv .env
echo .env/ >> .gitignore
echo __pycache__/ >> .gitignore

.env\Scripts\activate.bat

The first command makes sure the virtual environment packages are not tracked by Git.
The second command activates our virtual python environment, which ensures that any python packages we use don't contaminate our global python installation.
The rest of this tutorial should be executed in the same shell session.
If you exit the shell session, or want to create another, make sure to activate the virtual environment in that shell session first.

Installing requirements

To install the requirements for the first part of this project, simply download the requirements.txt into your project folder.

Tip

Alternatively, you can create a file called requirements.txt and copy the following into it:

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numpy==1.15.4
pandas==0.23.4
python-dateutil==2.7.5
pytz==2018.7
scikit-learn==0.20.2
scipy==1.2.0
six==1.12.0
sklearn==0.0

Now, to install them type:

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pip install -r requirements.txt

Committing progress to Git

Let's check the Git status of our project:

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$ git status -s
A  .dvc/.gitignore
A  .dvc/config
?? .gitignore
?? requirements.txt

Now let's commit this to Git using the command line:

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git add .
git commit -m "Initialized project"

git push -u origin master
The last command might request your DAGsHub.com username and password.

What changed?

Let's open up our repo on DAGsHub. It should look something like this:

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Repo view after first commit

The top part should look familiar to anyone that has used a Git server before. However, notice the bottom window. This is a new addition in DAGsHub, and will, later on in this tutorial, show our data pipeline.

In the next section, we will use dvc to define our pipeline. That is where the magic happens.