ibm_cloud_code_engine

CodeFlare on IBM Cloud Code Engine

IBM Cloud Code Engine is a fully managed, serverless platform that runs your containerized workloads, including web apps, microservices, event-driven functions, and batch jobs with run-to-completion characteristics.

CodeFlare can use IBM Cloud Code Engine to seamlessly scale pipelines on a serverless backend.

The following steps describe how to deploy the examples in notebooks on a serverless backend with Code Engine (they expand the steps to run Ray on Code Engine here)

Install pre-requisities

Install the pre-requisites for Code Engine and Ray:

1. Get ready with IBM Code Engine:

• Set up your Code Engine CLI
• [optional] Create your first Code Engine Project using the CLI

2. Set up the Kubernetes CLI

3. Install Kubernetes:

pip install kubernetes

Step 1 - Define your Ray Cluster on Code Engine

If not already done in the previous step, create a Code Engine project:

ibmcloud ce project create -n <your project name>

Select the Code Engine project and switch the kubectl context to the project:

ibmcloud ce project select -n <your project name> -k

Extract the Kubernetes namespace assigned to your project. The namespace can be found in the NAME column in the output of the command:

kubectl get namespace

Export the namespace:

export NAMESPACE=<namespace from above>

A reference Ray cluster definition can be customized for your namespace with the following commands:

cd ./deploy/ibm_cloud_code_engine/
sed "s/NAMESPACE/$NAMESPACE/" ./example-cluster.yaml.template > ./example-cluster.yaml

This reference deployment file will create a Ray cluster with following characteristics:

• a cluster named example-cluster with up to 10 workers using the Kubernetes provider pointing to your Code Engine project
• A head node type with 1 vCPU and 2GB of memory using the CodeFlare image codeflare:latest
• A worker node type with 1 vCPU and 2GB memory using the ray image codeflare:latest
• The default startup commands to start Ray within the container and listen to the proper ports
• The autoscaler upscale speed is set to 2 for quick upscaling in this short and simple demo

Step 2 - Start Ray cluster

You can now start the Ray cluster by running:

ray up example-cluster.yaml

This command will create the Ray head node as Kubernetes Pod in your Code Engine project. When you create the Ray cluster for the first time, it can take few minutes until the Ray image is downloaded from the container registry.

Step 3 - Run sample Pipeline with Jupyter

A Jupyter server will be automatically running on the head node. To access the server from your local machine, execute the command:

kubectl -n $NAMESPACE port-forward <ray-cluster-name> 8888:8888

You can now access the Jupyter server by pointing your browser to the following url:

http://127.0.0.1:8888/lab

Once in the the Jupyer envrionment, examples are found in the codeflare/notebooks directory in the container image. Documentation for reference use cases can be found in Examples.

To execute any of the notebooks with the Ray cluster running on Code Engine, edit the ray.init() line with the following parameters:

ray.init(address='auto', _redis_password='5241590000000000')

This change will allow pipelines to be auto-scaled on the underlying Ray cluster running on Code Engine (up to 10 workers with the reference deployment). The number of workers and scaling parameters can be adjusted in the yaml file.