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Orchestrates a compute job

Travis (.com) branch GitHub contributors

Table of Contents


The Operator Engine is a backend agent implementing part of the Ocean Protocol Compute to the Data OEP-12, in charge of orchestrate the compute infrastructure using Kubernetes as backend. Typically the Operator Engine retrieve the Workflows created by the Operator Service, in Kubernetes and manage the infrastructure necessary to complete the execution of the compute workflows.

The Operator Engine is in charge of retrieving all the Workflows registered in a K8s cluster, allowing to:

  • Orchestrate the flow of the execution
  • Start the configuration pod in charge of download the workflow dependencies (datasets and algorithms)
  • Start the pod including the algorithm to execute
  • Start the publishing pod that uploads the results to a remote storage(ipfs or S3)

Getting Started

Running the Engine

The operator engine is in charge of gathering all the Worflow requests directly from the K8s infrastructure. To do that, the operator engine needs to be running inside the K8s cluster where the engine will read the Workflows registered.

There are multiple configurations and deployments of K8s possible, it's out of the scope of this documentation page to describe how to configure your K8s cluster.

Applying the Operator Engine deployment

First is necessary to apply the operator-engine YAML defining the K8s deployment:

$ kubectl create ns ocean-compute
$ kubectl config set-context --current --namespace ocean-compute
$ kubectl apply -f kubernetes/sa.yml
$ kubectl apply -f kubernetes/binding.yml
$ kubectl apply -f kubernetes/operator.yml

This will generate the ocean-compute-operator deployment in K8s. You can check the Deployment was created successfully using the following command:

$ kubectl  get deployment ocean-compute-operator -o yaml

By default we use the ocean-compute namespace in the K8s deployments.

After apply the Deployment you should be able to see the operator-engine pod with the prefix ocean-compute-operator:

$ kubectl  get pod ocean-compute-operator-7b5779c47b-2r4j8

NAME                                      READY   STATUS    RESTARTS   AGE
ocean-compute-operator-7b5779c47b-2r4j8   1/1     Running   0          12m

Customize your Operator Engine deployment

The following settings needs to be configured:

Variable Description
nCPU How many CPUs can be used
cpuType Type of CPU
nGPU How many GPUs can be used
gpuRam How much RAM per GPU
gpuType Type of GPU
ramGB How much RAM can be used
diskGB How much diskspace can be used (Expressed in GB)
priceMinute Price per minute
description Description of this enviroment
maxJobs Maximum simultaneous jobs for this enviroment
maxJobDuration Maximum job duration in seconds
STORAGE_EXPIRY How long is the output kept in storage. Expressed in hours. 0 means no expiry
OPERATOR_PRIVATE_KEY Private key of address used to sign notifications and consume algo/inputs
IPFS_TYPE IPFS library to use. 'CLUSTER' to use ipfs-cluster, 'CLIENT' to use ipfs-client (default)
IPFS_OUTPUT, IPFS_ADMINLOGS IPFS gateway to upload the output data (algorithm logs & algorithm output) and admin logs (logs from pod-configure & pod-publish)
IPFS_OUTPUT_PREFIX, IPFS_ADMINLOGS_PREFIX Prefix used for the results files (see below)
IPFS_API_KEY, IPFS_API_CLIENT IPFS API Key and Client ID for authentication purpose (optional)
AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION S3 credentials for the logs and output buckets.
AWS_BUCKET_OUTPUT Bucket that will hold the output data (algorithm logs & algorithm output).
AWS_BUCKET_ADMINLOGS Bucket that will hold the admin logs (logs from pod-configure & pod-publish).
STORAGE_CLASS Storage class to use (see next section).
NOTIFY_START_URL URL to call when a new job starts.
NOTIFY_STOP_URL URL to call when a new job ends.
SERVICE_ACCOUNT K8 service account to run pods. Defaults to 'default'
NODE_SELECTOR K8 node selector (if defined)
PULL_SECRET ImagesPullSecret (if defined) (see
PULL_POLICY imagePullPolicy (if defined) (see
FILTERING_CONTAINER Filtering pod image to use for filtering (if defined)
LOG_CFG, LOG_LEVEL Define the location of the log file and logging level, respectively

Only one method of uploading is going to be used. Priority is:

  • first IPFS vars are checked. If they exists, then IPFS will be used
  • 2nd, AWS vars are checked. If they exists, then AWS S3 will be used


This will allow you to have the following scenarios:

  1. Port 5001 will be used to call addFIle, but the result will look like

  2. Port 5001 will be used to call addFIle, but the result will look like "ipfs://HASH" (you will hide your ipfs deployment)

  3. IPFS_EXPIRY_TIME = the default expiry time. "0" = unlimited


Engine will JSON POST the following for each action: - algoDID: Algorithm DID (if any) - jobId: Job ID - secret: Secret value (exported to algo pod as secret env) - DID: Array of input DIDs

Storage Expiry

Op-engine will pass a ENV variable called STORAGE_EXPIRY to pod-publishing (the env is defined in op-service and passed through from there).


If defined, all pods are going to contain the following selectors in the specs:

                  - matchExpressions:
                     - key: scope
                        operator: In
                        - $NODE_SELECTOR

This allows you to run C2D pods on specific nodes

Storage class

For minikube, you can use 'standard' class.

For AWS , please make sure that your class allocates volumes in the same region and zone in which you are running your pods.

We created our own 'standard' class in AWS:

kubectl get storageclass standard -o yaml
- matchLabelExpressions:
    - key:
          - us-east-1a
kind: StorageClass
    fsType: ext4
    type: gp2
reclaimPolicy: Delete
volumeBindingMode: Immediate

Or we can use this for minikube:

kind: StorageClass
  name: standard
reclaimPolicy: Retain

For more information, please visit


After an algorithm job is done, you can run your own custom image that can do an analysis of the output folder. That image could detect data leaks and overwrite the output folder if needed Format is the usual docker image notation.

Customizing job templates

All pods(jobs) are started using the templates from operator_engine/templates/ folder. If you want to customize them (adding some apps customizations, labels, etc) then you can mount that folder using an external volume. Please make sure that you have all template and not only the custom ones.

Running in Development mode

If you run the operator-engine in development mode, it will allows to:

  • Get access to the operator-engine pod
  • Start and stop multiple times the operator-engine process, changing the code directly in the pod
  • Test with different configurations without re-generating docker images

Typically the main process of the operator-engine pod is the kopf process. You can get access to any operator-engine pod running the typical kubectl exec command, but if you want to stop kopf, modify the config and the code and try again, it's recommended to modify the starting command of the pod. You can do that un-comment the startup command in the Dockerfile file where you use tail instead of the kopf command. This will start the pod but not the kopf process inside the pod. Allowing to you to get access there and start/stop kopf as many times you want.

After changing the Dockerfile you can publish a new version of the operator-engine docker image. At this point, you can stop the ocean-compute-operator pod. Take into account the pod id in your deployment will be different:

$ kubectl delete pod ocean-compute-operator-7b5779c47b-2jrlp

This will force the pull of the latest version of the operator-engine to be downloaded and run in the K8s cluster. Having that you should be able to get access to the pod:

$ kubectl exec -it ocean-compute-operator-7b5779c47b-2jrlp bash

root@ocean-compute-operator-7b5779c47b-2jrlp:/operator_engine# ps aux

root         1  0.0  0.0   4080   736 ?        Ss   09:35   0:00 tail -f /dev/null
root         9  0.3  0.0   5752  3544 pts/0    Ss   09:45   0:00 bash
root        16  0.0  0.0   9392  3064 pts/0    R+   09:45   0:00 ps aux

Now inside the pod you can start kopf running the following command:

$ kopf run --standalone /operator_engine/

This should start the operator-engine subscribed to the Workflows registered in K8s.

Running in a not Develop mode

First time you create the operator setup, you need to initialize the operator deployment as we saw above using the command:

$ kubectl apply -f k8s_install/operator.yml

This should start automatically the ocean-compute-operator pod using by default the latest Docker image of the operator-engine. You can check everything is running:

$ kubectl get pod ocean-compute-operator-7b5779c47b-2r4j8  
NAME                                      READY   STATUS    RESTARTS   AGE
ocean-compute-operator-7b5779c47b-2r4j8   1/1     Running   0          114m

Preparation of your local environment

Once you have Kubectl able to connect you your K8s cluster, run the service is as simple as running the following commands:

virtualenv -p python3.7 venv
source venv/bin/activate
pip install -r requirements_dev.txt

Continuous Integration & Delivery

You can find the Travis compilation here:

And the Docker images here:


Automatic tests are set up via Travis, executing tox. Our tests use the pytest framework.

Testing in the K8s cluster

You can register a Workflow in K8s to check how the operator-engine orchestrate the compute execution using one of the test examples included in the project. You can register it running the following command:

$ kubectl apply -f k8s_install/workflow-1.yaml created

In the operator-engine pod you should see in the logs how the engine is doing some job:

[2019-09-17 12:27:03,730] ocean-operator       [INFO    ] Stage 0 with stageType Filtering
[2019-09-17 12:27:03,731] ocean-operator       [INFO    ] Running container openjdk:14-jdk
[2019-09-17 12:27:03,757] ocean-operator       [INFO    ] ConfigMap workflow-1 created
[2019-09-17 12:27:03,771] ocean-operator       [INFO    ] PersistentVolumeClaim workflow-1 created
[2019-09-17 12:27:03,790] ocean-operator       [INFO    ] Job workflow-1-configure-job created
[2019-09-17 12:27:03,803] ocean-operator       [INFO    ] Waiting configure pod to finish
[2019-09-17 12:27:13,826] ocean-operator       [INFO    ] Waiting configure pod to finish
[2019-09-17 12:27:23,853] ocean-operator       [INFO    ] Waiting configure pod to finish
[2019-09-17 12:27:33,892] ocean-operator       [INFO    ] Job workflow-1-algorithm-job created
[2019-09-17 12:27:33,901] ocean-operator       [INFO    ] Waiting algorithm pod to finish
[2019-09-17 12:27:43,942] ocean-operator       [INFO    ] Job workflow-1-publish-job created
[2019-09-17 12:27:43,951] ocean-operator       [INFO    ] Waiting publish pod to finish
[2019-09-17 12:27:53,978] ocean-operator       [INFO    ] Waiting publish pod to finish
[2019-09-17 12:28:04,003] ocean-operator       [INFO    ] Waiting publish pod to finish

You can check the individual logs of the compute pods using the standard K8s log command:

$ kubectl logs
ocean-compute-operator-7b5779c47b-2r4j8  workflow-1-configure-job-qk4pv           
workflow-1-algorithm-job-c9m4t           workflow-1-publish-job-dcfjc             
$ kubectl logs ocean-compute-operator-7b5779c47b-2r4j8

New Version

The script helps bump the project version. You can execute the script using {major|minor|patch} as first argument, to bump the version accordingly.


Copyright 2022 Ocean Protocol Foundation Ltd.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


Python library allowing to interact with the Kubernetes infrastructure



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