Kubernetes#

Lithops with kubernetes as serverless compute backend.

Installation#

Install kubernetes backend dependencies:

python3 -m pip install lithops[kubernetes]

Configuration#

Edit your Lithops config and add the following keys:

  lithops:
      backend : k8s

Make sure you have a kubernetes cluster configuration file.
- Option 1: You have the config file in ~/.kube/config
- Option 2: You have the config file in another location, and you exported the KUBECONFIG variable:
```
export KUBECONFIG=<path-to-kube-config-file>
```
- Option 3: You have the config file in another location, and you set the kubecfg_path var in the Lithops config:
```
k8s:
    kubecfg_path: <path-to-kube-config-file>
```

Configure a private container registry for your runtime#

Configure Docker hub#

To configure Lithops to access a private repository in your docker hub account, you need to extend the kubernetes config and add the following keys:

k8s:
    ....
    docker_server    : docker.io
    docker_user      : <Docker hub Username>
    docker_password  : <DOcker hub access TOEKN>

Configure IBM Container Registry#

To configure Lithops to access to a private repository in your IBM Container Registry, you need to extend the kubernetes config and add the following keys:

k8s:
    ....
    docker_server    : us.icr.io
    docker_user      : iamapikey
    docker_password  : <IBM IAM API KEY>
    docker_namespace : <namespace>  # namespace name from https://cloud.ibm.com/registry/namespaces

Summary of configuration keys for kubernetes:#

Group	Key	Default	Mandatory	Additional info
k8s	kubecfg_path		no	Path to kubecfg file. Mandatory if config file not in `~/.kube/config` or KUBECONFIG env var not present
k8s	kubecfg_context		no	kubernetes context to use from your kubeconfig file. It will use the default active context if not provided
k8s	namespace	default	no	Kubernetes namespace to use for lithops execution
k8s	docker_server	docker.io	no	Container registry URL
k8s	docker_user		no	Container registry user name
k8s	docker_password		no	Container registry password/token. In case of Docker hub, login to your docker hub account and generate a new access token here
k8s	rabbitmq_executor	False	no	Alternative K8s backend accelerating parallel function execution (map) thanks to rabbitmq group calls and warm-state pods of higher granularity. For more information here.
k8s	max_workers	100	no	Max number of workers per `FunctionExecutor()`
k8s	worker_processes	1	no	Number of Lithops processes within a given worker. This can be used to parallelize function activations within a worker. It is recommendable to set this value to the same number of CPUs of the container.
k8s	runtime		no	Docker image name.
k8s	runtime_cpu	1	no	CPU limit. Default 1vCPU
k8s	runtime_memory	512	no	Memory limit in MB. Default 512MB
k8s	runtime_timeout	600	no	Runtime timeout in seconds. Default 600 seconds
k8s	master_timeout	600	no	Master pod timeout in seconds. Default 600 seconds

Test Lithops#

Once you have your compute and storage backends configured, you can run a hello world function with:

lithops hello -b k8s -s ibm_cos

Viewing the execution logs#

You can view the function executions logs in your local machine using the lithops client:

lithops logs poll

Kubernetes

Contents