Allow external access to Containers

Introduction¶

Researchers who work with containers sometimes need to expose ports to access the container from remote. Some examples:

Using a Jupyter notebook that runs within the container
Using PyCharm to run python commands remotely.
Using TensorBoard to view machine learning visualizations

When using docker, the way Researchers expose ports is by declaring them when starting the container. Run:AI has similar syntax.

Run:AI is based on Kubernetes. Kubernetes offers an abstraction of the container's location. This complicates the exposure of ports. Kubernetes offers a number of alternative ways to expose ports. With Run:AI you can use all of these options (see the Alternatives section below), however, Run:AI comes built-in with ingress.

Ingress¶

Ingress allows access to Kubernetes services from outside the Kubernetes cluster. You configure access by creating a collection of rules that define which inbound connections reach which services. More information about ingress can be found here.

Setup¶

Before installing ingress, you must obtain an IP Address or an IP address range which is external to the cluster.

A Run:AI cluster is installed by:

Accessing the Administrator User Interface Clusters area at https://app.run.ai/clusters
Downloading a YAML file runai-operator.yaml and then
Applying it to Kubernetes. You must edit the YAML file. Search for localLoadBalancer:

localLoadBalancer
    enabled: true
    ipRangeFrom: 10.0.2.1
    ipRangeTo: 10.0.2.2

Set enabled to true and set the IP range appropriately.

To add or change a load balancer after the system has been installed run:

kubectl edit runaiconfig runai -n runai

Search for localLoadBalancer and edit the above. Then run:

kubectl rollout restart deployment runai-metallb-controller -n runai

To apply the changes

Usage¶

The Researcher uses the Run:AI CLI to set the method type and the ports when submitting the Workload. Example:

runai submit test-ingress -i jupyter/base-notebook -g 1  --interactive --service-type=ingress \
   --port 8888:8888 --command -- start-notebook.sh --NotebookApp.base_url=test-ingress

After submitting a Job through the Run:AI CLI, run:

runai list jobs

You will see the service URL with which to access the Jupyter notebook

The URL will be composed of the ingress end-point, the Job name and the port (e.g. https://10.255.174.13/test-ingress-8888.

For further details see CLI command runai submit and Launch an Interactive Workload Quickstart .

Alternatives¶

Run:AI is based on Kubernetes. Kubernetes offers an abstraction of the container's location. This complicates the exposure of ports. Kubernetes offers a number of alternative ways to expose ports:

NodePort - Exposes the Service on each Node’s IP at a static port (the NodePort). You’ll be able to contact the NodePort service from outside the cluster by requesting <NODE-IP>:<NODE-PORT> regardless of which node the container actually resides in.
LoadBalancer - Useful for cloud environments. Exposes the Service externally using a cloud provider’s load balancer.
Ingress (see example in link below) - Allows access to Kubernetes services from outside the Kubernetes cluster. You configure access by creating a collection of rules that define which inbound connections reach which services. More information about ingress can be found here.
Port Forwarding (see example in link below) - Simple port forwarding allows access to the container via localhost:<Port>.

See https://kubernetes.io/docs/concepts/services-networking/service for further details.