Deploying MLflow on Kubernetes (K8s) using Ansible involves automating the process of creating the necessary Kubernetes resources and configurations for running MLflow services. Here's a brief overview of the steps involved:
Setting up Ansible: Make sure you have Ansible installed on your local machine or the server you're using for deployment.
Creating Ansible Playbooks: Create Ansible playbooks to define the deployment process. Ansible playbooks are YAML files that contain a series of tasks to be executed.
Creating Kubernetes Configurations: In your Ansible playbooks, you'll need to create Kubernetes resources such as deployments, services, and possibly ingress rules. You can use Ansible's k8s module to manage these resources.
Configuring MLflow: Your playbook should also include steps to configure MLflow. This may involve creating a configuration file for MLflow with the necessary settings, including database connections and storage configurations.
Applying Playbooks: Run the Ansible playbooks using the ansible-playbook command. Ansible will execute the tasks defined in the playbook and create the specified Kubernetes resources.
Verifying Deployment: After the deployment is complete, you can verify that MLflow services are up and running within your Kubernetes cluster. Check the status of pods, services, and any other relevant resources.
Open a terminal and navigate to the directory containing your Ansible playbook and Kubernetes configuration files. Run the following command:
ansible-playbook -i inventory.yml k8s_mlflow.yml
systemctl restart NetworkManager.service
sudo service docker restart