Hkube 101

HKube is a cloud-native open source framework to run distributed pipeline of algorithms built on Kubernetes.

HKube optimally utilizing pipeline's resources, based on user priorities and heuristics.

Features #

  • Distributed pipeline of algorithms

    • Receives DAG graph as input and automatically parallelizes your algorithms over the cluster.
    • Manages the complications of distributed processing, keep your code simple (even single threaded).
  • Language Agnostic - As a container based framework designed to facilitate the use of any language for your algorithm.

  • Batch Algorithms - Run algorithms as a batch - instances of the same algorithm in order to accelerate the running time.

  • Optimize Hardware Utilization

    • Containers automatically placed based on their resource requirements and other constraints, while not sacrificing availability.
    • Mixes critical and best-effort workloads in order to drive up utilization and save resources.
    • Efficient execution and clustering by heuristics which uses pipeline and algorithm metrics with combination of user requirements.
  • Build API - Just upload your code, you don't have to worry about building containers and integrating them with HKube API.

  • Cluster Debugging

    • Debug a part of a pipeline based on previous results.
    • Debug a single algorithm on your IDE, while the rest of the algorithms running in the cluster.
  • Jupyter Integration - Scale your jupyter running tasks Jupyter with hkube.

Installation #

Dependencies #

HKube runs on top of Kubernetes so in order to run HKube we have to install it's prerequisites.

Helm #

  1. Add the HKube Helm repository to helm:

    helm repo add hkube
  2. Configure a docker registry for builds
    Create a values.yaml file for custom helm values

  # pull secret is only needed if docker hub is not accessible
    registry: ''
    namespace: ''
    username: ''
    password: ''
  # enter your docker hub / other registry credentials
    registry: '' # can be left empty for docker hub
    namespace: '' # registry namespace - usually your username
    username: ''
    password: ''
  1. Install HKube chart

    helm install hkube/hkube  -f ./values.yaml --name my-release

This command installs HKube in a minimal configuration for development. Check production-deployment.

APIs #

There are three ways to communicate with HKube: Dashboard, REST API and CLI.

UI Dashboard #

Dashboard is a web-based HKube user interface. Dashboard supports every functionality HKube has to offer.



HKube exposes it's functionality with REST API.


hkubectl is HKube command line tool.

hkubectl [type] [command] [name]

# More information
hkubectl --help

Download hkubectl latest version.

# Check release page for latest version
curl -Lo hkubectl${latestVersion}/hkubectl-linux \
&& chmod +x hkubectl \
&& sudo mv hkubectl /usr/local/bin/

For mac replace with hkubectl-macos For Windows download hkubectl-win.exe

Config hkubectl with your running Kubernetes.

# Config
hkubectl config set endpoint ${KUBERNETES-MASTER-IP}

hkubectl config set rejectUnauthorized false

Make sure kubectl is configured to your cluster.

HKube requires that certain pods will run in privileged security permissions, consult your Kubernetes installation to see how it's done.

API Usage Example #

The Problem #

We want to solve the next problem with given input and a desired output:

  • Input: Two numbers N, k.
  • Desired Output: A number M so: sum

For example: N=3, k=5 will result:series

Solution #

We will solve the problem by running a distributed pipeline of three algorithms: Range, Multiply and Reduce.

Range Algorithm #

Creates an array of length N.

 N = 5
 5 -> [1,2,3,4,5]

Multiply Algorithm #

Multiples the received data from Range Algorithm by k.

k = 2
[1,2,3,4,5] * (2) -> [2,4,6,8,10]

Reduce Algorithm #

The algorithm will wait until all the instances of the Multiply Algorithm will finish then it will summarize the received data together .

[2,4,6,8,10] -> 30

Building a Pipeline #

We will implement the algorithms using various languages and construct a pipeline from them using HKube.


Pipeline Descriptor #

The pipeline descriptor is a JSON object which describes and defines the links between the nodes by defining the dependencies between them.

    "name": "numbers",
    "nodes": [
            "nodeName": "Range",
            "algorithmName": "range",
            "input": [""]
            "nodeName": "Multiply",
            "algorithmName": "multiply",
            "input": ["#@Range", "@flowInput.mul"]
            "nodeName": "Reduce",
            "algorithmName": "reduce",
            "input": ["@Multiply"]
    "flowInput": {
        "data": 5,
        "mul": 2

Note the flowInput: data = N = 5, mul = k = 2

Node dependencies #

HKube allows special signs in nodes input for defining the pipeline execution flow.

In our case we used:

(@)  —  References input parameters for the algorithm.

(#)  —  Execute nodes in parallel and reduce the results into single node.

(#@) — By combining # and @ we can create a batch processing on node results.


JSON Breakdown #

We created a pipeline name numbers.


The pipeline is defined by three nodes.


In HKube, the linkage between the nodes is done by defining the algorithm inputs. Multiply will be run after Range algorithm because of the input dependency between them.

Keep in mind that HKube will transport the results between the nodes automatically for doing it HKube currently support two different types of transportation layers object storage and files system.

Group 4 (3)

The flowInput is the place to define the Pipeline inputs:


In our case we used Numeric Type but it can be any JSON type (Object, String etc).

Advance Options #

There are more features that can be defined from the descriptor file.

"webhooks": {
    "progress": "http://my-url-to-progress",
      "result": "http://my-url-to-result"
  "priority": 3,
      "batchTolerance": 80,
      "concurrentPipelines": 2,
      "ttl": 3600,
  • webhooks - There are two types of webhooks, progress and result.

    You can also fetch the same data from the REST API.

    • progress:{jobId}/api/v1/exec/status
    • result: {jobId}/api/v1/exec/results
  • priority - HKube support five level of priorities, five is the highest. Those priorities with the metrics that HKube gathered helps to decide which algorithms should be run first.

  • triggers - There are two types of triggers that HKube currently support cron and pipeline.

    • cron - HKube can schedule your stored pipelines based on cron pattern.

      Check cron editor in order to construct your cron.

    • pipeline - You can set your pipelines to run each time other pipeline/s has been finished successfully .
  • options - There are other more options that can be configured:

    • Batch Tolerance - The Batch Tolerance is a threshold setting that allow you to control in which percent from the batch processing the entire pipeline should be fail.
    • Concurrency - Pipeline Concurrency define the number of pipelines that are allowed to be running at the same time.
    • TTL - Time to live (TTL) limits the lifetime of pipeline in the cluster. stop will be sent if pipeline running for more than ttl (in seconds).
    • Verbosity Level - The Verbosity Level is a setting that allows to control what type of progress events the client will notified about. The severity levels are ascending from least important to most important: trace debug info warn error critical.

Algorithm #

The pipeline is built from algorithms which containerized with docker.

There are two ways to integrate your algorithm into HKube:

  • Seamless Integration - As written above HKube can build automatically your docker with the HKube's websocket wrapper.
  • Code writing - In order to add algorithm manually to HKube you need to wrap your algorithm with HKube. HKube already has a wrappers for python,javaScript, java and .NET core.

Implementing the Algorithms #

We will create the algorithms to solve the problem, HKube currently support two languages for auto build Python and JavaScript.

Important notes:

  • Installing dependencies During the container build, HKube will search for the requirement.txt file and will try to install the packages from the pip package manager.
  • Advanced Operations HKube can build the algorithm only by implementing start function but for advanced operation such as one time initiation and gracefully stopping you have to implement two other functions init and stop.
Range (Python) #
def start(args):
    print('algorithm: range start')
    input = args['input'][0]
    array = list(range(input))
    return array

The start method calls with the args parameter, the inputs to the algorithm will appear in the input property.

The input property is an array, so you would like to take the first argument ("input":[""] as you can see we placed data as the first argument)

Multiply (Python) #
def start(args):
    print('algorithm: multiply start')
    input = args['input'][0]
    mul = args['input'][1]
    return input * mul

We sent two parameters "input":["#@Range","@flowInput.mul"], the first one is the output from Range that sent an array of numbers, but because we using batch sign (#) each multiply algorithm will get one item from the array, the second parameter we passing is the mul parameter from the flowInput object.

Reduce (Javascript) #
module.exports.start = args => {
    console.log('algorithm: reduce start');
    const input = args.input[0];
    return input.reduce((acc, cur) => acc + cur);

We placed ["@Multiply"] in the input parameter, HKube will collect all the data from the multiply algorithm and will sent it as an array in the first input parameter.

Integrate Algorithms #

After we created the algorithms, we will integrate them with the CLI.

Can be done also through the Dashboard.

Create a yaml (or JSON) that defines the algorithm:

# range.yml
name: range
env: python # can be python or javascript
  cpu: 0.5
  gpu: 1 # if not needed just remove it from the file
  mem: 512Mi

  path: /path-to-algorithm/range.tar.gz

Add it with the CLI:

hkubectl algorithm apply --f range.yml

Keep in mind we have to do it for each one of the algorithms.

Integrate Pipeline #

Create a yaml (or JSON) that defines the pipeline:

# number.yml
name: numbers
  - nodeName: Range
    algorithmName: range
      - ''
  - nodeName: Multiply
    algorithmName: multiply
      - '#@Range'
      - '@flowInput.mul'
  - nodeName: Reduce
    algorithmName: reduce
      - '@Multiply'
  data: 5
  mul: 2

Raw - Ad-hoc pipeline running #

For running our pipeline as raw-data:

hkubectl exec raw --f numbers.yml

Stored - Storing the pipeline descriptor for next running #

First we store the pipeline:

hkubectl pipeline store --f numbers.yml

Then you can execute it (if flowInput available)

# flowInput stored
hkubectl exec stored numbers

For executing the pipeline with other input, create yaml (or JSON) file with flowInput key:

# otherFlowInput.yml
  data: 500
  mul: 200

Then you can executed it by pipeline name:

# Executes pipeline "numbers" with data=500, mul=200
hkubectl exec stored numbers --f otherFlowInput.yml

Monitor Pipeline Results #

As a result of executing pipeline, HKube will return a jobId.

# Job ID returned after execution.
  jobId: numbers:a56c97cb-5d62-4990-817c-04a8b0448b7c.numbers

This is a unique identifier helps to query this specific pipeline execution.

  • Stop pipeline execution: hkubectl exec stop <jobId> [reason]

  • Track pipeline status: hkubectl exec status <jobId>

  • Track pipeline result: hkubectl exec result <jobId>

Next →Install Hkube