04. Create the Project Environment

Create and activate an environment

All of these instructions are to be completed via a terminal/command line prompt.

1. Create and Activate an Environment

Git and version control

These instructions also assume you have git installed for working with Github from a terminal window, but if you do not, you can download that first from this Github installation page.

Now, you're ready to create your local environment!

  1. If you haven't already done so, clone the project repository, and navigate to the project folder. 
git clone https://github.com/udacity/DevOps_Microservices.git
cd DevOps_Microservices/project-ml-microservice-kubernetes
  1. Create (and activate) a new environment, named .devops with Python 3. If prompted to proceed with the install (Proceed [y]/n) type y.
python3 -m venv ~/.devops
source ~/.devops/bin/activate
At this point your command line should look something like: `(.devops) <User>:project-ml-microservice-kubernetes<user>$`. The `(.devops)` indicates that your environment has been activated, and you can proceed with further package installations.

  1. Installing dependencies via project Makefile. Many of the project dependencies are listed in the file requirements.txt; these can be installed using pip commands in the provided Makefile. While in your project directory, type the following command to install these dependencies.
make install

Now most of the .devops libraries are available to you. There are a couple of other libraries that we'll be using, which can be downloaded as specified, below.

Other Libraries

While you still have your .devops environment activated, you will still need to install:

  • Docker
  • Hadolint
  • Kubernetes (Minikube)

Docker

You will need to use Docker to build and upload a containerized application. If you already have this installed and created a docker account, you may skip this step.

  1. You’ll need to create a free docker account, where you’ll choose a unique username and link your email to a docker account. Your username is your unique docker ID.

  2. To install the latest version of docker, choose the Community Edition (CE) for your operating system, on docker’s installation site. It is also recommended that you install the latest, stable release:

  3. After installation, you can verify that you’ve successfully installed docker by printing its version in your terminal: docker --version

Run Lint Checks

This project also must pass two lint checks; hadolint checks the Dockerfile for errors and pylint checks the app.py source code for errors.

  1. Install hadolint following the instructions, on hadolint's page:

    For Mac:

    brew install hadolint

    For Windows:

    scoop install hadolint

  2. In your terminal, type: make lint to run lint checks on the project code. If you haven’t changed any code, all requirements should be satisfied, and you should see a printed statement that rates your code (and prints out any additional comments):

   ------------------------------------
   Your code has been rated at 10.00/10

Install Minikube

To run a Kubernetes cluster locally, for testing and project purposes, you need the Kubernetes package, Minikube. This operates in a virtual machine and so you'll need to download two things: A virtual machine (aka a hypervisor) then minikube. Thorough installation instructions can be found here. Here is how I installed minikube:

  1. Install VirtualBox:

    For Mac:

  brew cask install virtualbox

For Windows, I recommend using a Windows host.

  1. Install minikube:

    For Mac:

  brew cask install minikube

For Windows, I recommend using the Windows installer.

That's it!

Setting up an environment is an important part of development. Now you are ready to start working on the project files to containerize a machine learning application!

You should return to this page if you need to troubleshoot any dependency issues.

Troubleshooting

  • In general, you can verify installation by checking the version of a library, ex. kubectl version or docker --version. If there is no package found, you may need to install that library.

  • Mac issue: If you get an error error: invalid active developer path, that means you need to install some Xcode developer tools. You can do this on Mac by running this terminal command: xcode-select --install

Next Concept