This step must only be finished if you want to deploy the code on GCP VMs and build the CI/CD with GitHub Actions.

Note that this step might result in a few costs on GCP. It won't be much. While developing this course, I spent only ~20$, which will probably be less for you.

Also, you can get some free credits if you have a new GCP account (I had 300$ free credits). Just be sure to delete the resources after you finish the course.

Access Lesson 7 on Medium for more detailed step-by-step instructions..

Before setting up the code, you must go to your GCP project and create a few additional resources. After, you can SSH to your machines and deploy your code.

We need a new GCP service account with admin rights & IAP access when working with GCP VMs. You have to create a new service account and assign to the new service account the following roles:

• Compute Instance Admin (v1)
• IAP-secured Tunnel User
• Service Account Token Creator
• Service Account User

IAP stands for Identity-Aware Proxy. It is a way to create tunnels that route TCP traffic. For your knowledge, you can read more about this topic using the following docs (you don't have to fully understand it to proceed to the next steps):

• Using IAP for TCP forwarding
• Overview of TCP forwarding

Create a firewall rule that exposes the following TCP ports: 8501, 8502, 8001.

Also, add a target tag called energy-forecasting-expose-ports.

Here is how my firewall rule looks like:

Here are 2 docs that helped me create and configure the ports for the firewall rule:

• Doc 1
• Doc 2

Now we will create a firewall rule allowing IAP for TCP Tunneling on all the VMs connected to the default network.

Docs on how to create the firewall rule.

Here is how my firewall rule looks like:

Go to your GCP project -> VM Instances -> Create Instance

Choose e2-standard-2: 2 vCPU cores - 8 GB RAM as your VM instance type.

Call it: ml-pipeline

Change the disk to 20 GB Storage

Pick region europe-west3 (Frankfurt) and zone europe-west3-c

Network: default

Also, check the HTTP and HTTPS boxes and add the energy-forecasting-expose-ports custom firewall rule we did a few steps back.

Here are 2 docs that helped me create and configure the ports for the firewall rule:

• Doc 1
• Doc 2

Go to your GCP project -> VM Instances -> Create Instance

Choose: e2-micro: 0.25 2 vCPU - 1 GB memory as your VM instance type.

Call it: app

Change the disk to: 15 GB standard persisted disk

Pick region europe-west3 (Frankfurt) and zone europe-west3-c

Network: default

Also, check the HTTP and HTTPS boxes and add the energy-forecasting-expose-ports custom firewall rule we created a few steps back.

Here are 2 docs that helped me create and configure the ports for the firewall rule:

• Doc 1
• Doc 2

If we want the external IP for our web app to be static (aka not to change), we have to attach a static address to our web app VM.

More precisely, we suggest adding it only to the app VM we created a few steps ahead.

That is perfectly fine if you want to also add a static external IP to the ml-pipeline VM.

Docs on reserving a static external IP address.

We will use your service account configured with admin rights for VMs and IAP access to SSH from your local machine to the GCP VMs.

First, we must tell the gcloud GCP CLI to use that service account.

To do so, you have to create a key for your service account and download it as a JSON file (same as you did for the buckets service accounts - here are some docs to refresh your mind).

After you download the file, you just have to run the following gcloud command in your terminal:

gcloud auth activate-service-account SERVICE_ACCOUNT@DOMAIN.COM --key-file=/path/key.json --project=PROJECT_ID

Check out this doc for more details about the gcloud auth command.

Now whenever you run commands with gcloud, it will use this service account to authenticate.

Let's connect through SSH to the ml-pipeline GCP VM you created a few steps ahead:

gcloud compute ssh ml-pipeline --zone europe-west3-c --quiet --tunnel-through-iap --project <your-project-id>

NOTE 1: Change the zone if you haven't created a VM within the same zone as us.
NOTE 2: Your project-id is NOT your project name. Go to your GCP projects list and find the project id.

Starting this point, if you configured the firewalls and service account correctly, as everything is Dockerized, all the steps will be 99% similar to the ones from the Set Up Additional Tools and Usage sections.

You can follow the same steps while your terminal has an SSH connection with the GCP machine.

Note that the GCP machine is a Linux machine. Thus, you can directly copy & paste the commands from the README regardless of the OS you use on your local machine.

Now you must repeat all the steps you've done setting The Pipeline locally, using this SSH connection.

Clone the code in the home directory of the VM:

Just SHH to the VM and run:

git clone https://github.com/iusztinpaul/energy-forecasting.git
cd energy-forecasting

Install Docker using the following commands:

Install Docker:

sudo apt update
sudo apt install --yes apt-transport-https ca-certificates curl gnupg2 software-properties-common
curl -fsSL https://download.docker.com/linux/debian/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/debian $(lsb_release -cs) stable"
sudo apt update
sudo apt install --yes docker-ce

Add sudo access to Docker:

sudo usermod -aG docker $USER
logout 

Login again to your machine:

gcloud compute ssh ml-pipeline --zone europe-west3-c --quiet --tunnel-through-iap --project <your-project-id>

Check out these docs for the full instructions.

Replace all cp commands with gcloud compute scp:

This command will help you to copy files from your local machine to the VM.

For example, instead of running:

cp -r /path/to/admin/gcs/credentials/admin-buckets.json credentials/gcp/energy_consumption

Run in a different terminal (not the one connected with SSH to your VM):

gcloud compute scp --recurse --zone europe-west3-c --quiet --tunnel-through-iap --project <your-project-id> /local/path/to/admin-buckets.json ml-pipeline:~/energy-forecasting/airflow/dags/credentials/gcp/energy_consumption/

This command will copy your local admin-buckets.json file to the ml-pipeline VM.

!!! This is all you need to know. All the other steps are the same as the ones run locally. Only Docker has a slightly different installation, and you need a different way to copy files from your local machine to the VM.

Now to access the Airflow application, go to your VM view from GCP and go to the Network tags section. You will find the External IP address column, as shown in the image below. Copy that IP and attach port 8080 to it.

For example, based on the External IP address from the image below, I accessed Airflow using this address: 35.207.134.188:8080.

Congrats! You connected to your own self-hosted Airflow application.

If it doesn't connect, give it a few seconds to load properly.

Let's connect through SSH to the app GCP VM you created a few steps ahead:

gcloud compute ssh app --zone europe-west3-c --quiet --tunnel-through-iap --project <your-project-id>

NOTE 1: Change the zone if you haven't created a VM within the same zone as us.
NOTE 2: Your project-id is NOT your project name. Go to your GCP projects list and find the project id.

Here the process is similar to deploying the ML Pipeline.

You can deploy the web app following the exact same steps described in Lesson 6 or in the Set Up Additional Tools & Usage sections of the GitHub repository.

But don't forget to keep in mind the same edge cases described in the Deploy - The Pipeline section.

👀 As you can see, here you have done everything manually. If you want to know how to create a simple CI/CD using GitHub Actions check out this section.