Project designed to showcase a wide range of DevOps skills. It integrates infrastructure automation, continuous integration (CI), and continuous deployment (CD) practices, demonstrating how modern DevOps tools and methodologies can streamline software development workflows.
Jenkins
Docker
Make
Prometheus
Grafana
Python
make docker-network- Run Jenkins instance:
make jenkins-master-
Access Jenkins at http://localhost:8080 in your web browser
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Get Jenkins password and use it for initial login:
JENKINS_CONTAINER_ID=$(docker ps -a --filter "ancestor=jenkins/jenkins:lts" --format "{{.ID}}")
docker exec -it <JENKINS_CONTAINER_ID> /bin/bash -c "cat /var/jenkins_home/secrets/initialAdminPassword"- Jenkins agent
- Configure jenkins agent node - https://hub.docker.com/r/jenkins/inbound-agent
- Build custom Docker container with Python installed:
make jenkins-agent-build- Run agent container (replace <secret-id> in Makefile with one displayed in status window)
make jenkins-agentRun Prometheus container:
make prometheusConfigure Prometheus for Jenkins:
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Go to Manage Jenkins > Manage Plugins > Available plugins
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Search for and install the Prometheus Metrics Plugin.
Configure the plugin:
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Go to Manage Jenkins > Configure System
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Enable the Prometheus metrics endpoint in Jenkins (e.g. http://localhost:8080/prometheus).
Verify Prometheus Scraping:
- Open Prometheus at http://localhost:9090
- Navigate to Status → Targets
You should see the Jenkins job metrics under jenkins-master:8080.
- Run Grafana in a Docker Container:
make grafana- Access Grafana in your local browser http://localhost:3000 (admin/admin).
- Add Prometheus as a Data Source, Settings > Data Sources > Add data source - select Prometheus.
- Configure the following -> URL: http://prometheus:9090 (replace prometheus with the container name or service name of Prometheus).