Kubeflow Pipelines on Tekton reaches 1.0, Watson Studio Pipelines now obtainable in open beta – IBM Developer


Our final weblog submit asserting Kubeflow Pipelines on Tekton mentioned how Kubeflow Pipelines grew to become a main car to deal with the wants of each DevOps engineers and knowledge scientists. As a reminder, Kubeflow Pipelines on Tekton is a venture within the MLOps ecosystem, and gives the next advantages:

  • For DevOps of us, Kubeflow Pipelines faucets into the Kubernetes ecosystem, leveraging its scalability and containerization ideas.
  • For Knowledge scientists and MLOps practitioners, Kubeflow Pipelines gives a Python interface to outline and deploy Pipelines, enabling metadata assortment and lineage monitoring.
  • For DataOps of us, Kubeflow Pipelines brings in ETL bindings to take part extra totally in collaboration with friends by offering assist for a number of ETL elements and use instances.

The pipelines workforce has been busy the previous couple of months creating enhancements for Kubeflow Pipelines on Tekton to deal with extra MLOps and DataOps wants, and making a secure, production-ready deliverable. As a part of this, we’re excited to announce that the venture has reached 1.0 milestone. Moreover, IBM’s providing constructed on prime of this open supply venture, Watson Studio Pipelines, is now obtainable in open beta!

Kubeflow Pipelines on Tekton 1.0 launch

We’re excited to announce the 1.0 launch for Kubeflow Pipelines on Tekton (KFP-Tekton) venture. Many options akin to graph recursion, conditional loops, caching, any sequencer, dynamic parameters assist, and the like had been added to the venture within the technique of reaching this milestone. These new options weren’t supported within the Tekton venture natively, however they’re essential for working real-world machine studying workflows utilizing Kubeflow Pipelines.

This weblog highlights a few of these new functionalities we launched on this model, particularly that deal with knowledge flows.

These enhancements embody:

Pipeline loops

The present Tekton design doesn’t enable any loop or sub-pipeline contained in the pipeline definition. Lately, Tekton launched the idea of Tekton customized duties to permit customers to outline their very own workload definition by constructing their very own controller reconcile strategies. This opened the door for us to assist Kubeflow Pipeline loops and recursions that weren’t potential earlier than on Tekton. We’re bringing again these enhancements to the Tekton group.

The ParallelFor loop in Kubeflow Pipeline is a loop that runs duties on a set of parameters in parallel. For Tekton, the kfp-tekton workforce constructed a Tekton customized job controller that reconciles a number of Tekton sub-pipelines in parallel over a set of parameters (each static and dynamic), and helps parallelism to manage the variety of parallel working sub-pipelines.

This can be a enormous step ahead for what we will obtain on Tekton, and it permits Tekton to deal with pipelines which might be rather more complicated.

The diagram beneath describes the flows for 3 various kinds of parallel loops.

  • Typical loops are loops that traverse a listing of duties over one argument.
  • Multi-args loops are much like typical loops however with a number of arguments.
  • Situation loops are loops that may break or proceed based mostly on a sure situation.

pipeline loop image


Recursion permits the identical code block to execute and exit based mostly on dynamic circumstances. Present Tekton options don’t enable for recursion.

Nonetheless, with the brand new Tekton customized job controller that the KFP-Tekton constructed for loops and sub-pipelines, we will now run sub-pipelines with circumstances that may refer again to itself to create recursions, and it may be prolonged to cowl nested parallel loops inside recursions. This demonstrates how the KFP-Tekton workforce is main a number of the leading edge options for Tekton and bringing again to the Tekton group.

The next diagram exhibits that the recursive perform is outlined as a sub-pipeline and may refer again to itself to create recursions.

Recursion flow

Pluggable Tekton customized job

The KFP-Tekton workforce additionally labored on a brand new technique to allow customers to plug their very own Tekton customized job right into a Kubeflow Pipeline. For instance, a person would possibly wish to calculate an expression with out creating a brand new employee pod. On this case, the person can plug within the Frequent Expression Language (CEL) customized job from Tekton to calculate the expression inside a shared controller with out creating a brand new employee pod.

The pluggable Tekton customized job in Kubeflow Pipeline offers extra flexibility to customers that wish to optimize their pipelines additional and compose duties which might be presently not potential with the default Tekton job API. The KFP-Tekton workforce additionally contributes to Tekton to make the customized job API extra function full akin to supporting timeout, retry, and inlined customized job spec.

The picture beneath exhibits how the common duties A and D are working inside a brand new devoted pod, whereas the customized duties B and C are working inside a shared controller to save lots of pod provision time and cluster assets.

image showing Tekton tasks completion


AnySequencer is a dependent job that begins when any one of many job or situation dependencies full efficiently. The advantage of AnySequencer over the logical OR situation is that with AnySequencer, the order of execution of the dependencies doesn’t matter. The pipeline doesn’t anticipate all the duty dependencies to finish earlier than shifting to the following step. You possibly can apply circumstances to implement the duty dependencies completes as anticipated.

The next picture exhibits how the AnySequencer job can begin a brand new job whereas an authentic job is ready for a dependency.

AnySequencer image


Kubeflow Pipelines caching offers task-level output caching. In contrast to Argo, by design, Tekton doesn’t generate the duty template within the annotations to carry out caching. To assist caching on Tekton, we enhanced the KubeFlow Pipeline cache server to auto-generate the duty template for Tekton because the hash code which caches all of the similar workloads with the identical inputs.

By default, compiling a pipeline provides metadata annotations and labels in order that outcomes from duties inside a pipeline run could be reused if that job is reused in a brand new pipeline run. This protects the pipeline run from re-executing the duty when the outcomes are already identified.

The next diagram exhibits the caching mechanism for Kubeflow Pipeline on Tekton (KFP-Tekton). All job executions and outcomes are saved as hash code within the database to find out cached duties.

caching flow

Watson Studio Pipelines now obtainable in Open Beta!

We’re excited to announce that Watson Studio Pipelines is now obtainable in Open Beta! This new Watson Studio providing permits customers to create repeatable and scheduled flows that automate pocket book, knowledge refinery, and machine studying pipelines: from knowledge ingestion to mannequin coaching, testing, and deployment. With an intuitive person interface, Watson Studio Pipelines exposes all the state-of-the-art knowledge science instruments obtainable in Watson Studio and permits customers to mix them into automation flows, creating steady integration / steady growth pipelines for AI.

Watson Studio Pipelines is constructed off of Kubeflow Pipelines on the Tekton runtime and is totally built-in into the Watson Studio platform, permitting customers to mix instruments together with:

  • Notebooks
  • Knowledge refinery flows
  • AutoAI experiments
  • Internet service / on-line deployments
  • Batch deployments
  • Import and export of venture and house belongings

The brand new options, pushed by DataOps state of affairs and leveraging the brand new Tekton extensions, are coming quickly:

The next instance showcases tips on how to import datasets into Watson Studio utilizing DataStage stream, create and run AutoAI Experiments with hyperparameter optimization, and serve the perfect tuned mannequin as an online service. It sends notification in case of a failure and at last executes a customized person script.


To expertise this AI lifecycle automation for your self, please go the Watson Studio Pipelines beta web page

Be a part of us to construct cloud-native Knowledge and AI Pipelines with Kubeflow Pipelines and Tekton

Please be part of us on the Kubeflow Pipelines with Tekton GitHub repository, attempt it out, give suggestions, and lift points. Moreover you’ll be able to join with us by way of the next:

  • To contribute and construct an enterprise-grade, end-to-end machine studying platform on OpenShift and Kubernetes, please be part of the Kubeflow group and attain out with any questions, feedback, and suggestions!
  • To get entry to Watson AI Pipelines, join for beta entry checklist.
  • If you would like assist deploying and managing Kubeflow in your on-premises Kubernetes platform, OpenShift, or on IBM Cloud, please join with us.
  • To run Pocket book-based pipelines utilizing a drag-and-drop canvas, please try the Elyra venture in the neighborhood, which offers AI-centric extensions to JupyterLab.
  • Try the OpenDataHub if you’re concerned about open supply initiatives within the Knowledge and AI portfolio, specifically Kubeflow, Kafka, Hive, Hue, and Spark, and tips on how to convey them collectively in a cloud-native means.


This weblog submit launched you to a number of the new enhancements that we’ve been engaged on to make Kubeflow Pipelines on Tekton extra extensible for customers. Our hope is that you just’ll discover the brand new performance that will help you remedy your DataOps wants.

Due to our contributors

Due to many contributors of Kubeflow Pipelines with Tekton for contributing to the varied facets of the venture, each internally and externally. A couple of I wish to particularly name out embody:

  • Adam Massachi
  • Christian Kadner
  • Jun Feng Liu
  • Yi-Hong Wang
  • Prashant Sharma
  • Feng Li
  • Andrew Butler
  • Jin Chi He
  • Michalina Kotwica
  • Andrea Fritolli
  • Priti Desai
  • Gang Pu
  • Peng Li
  • Błażej Rutkowski

Moreover, because of to OpenShift Pipelines and Tekton groups from Pink Hat, and the Elyra workforce for suggestions. Final however not the least, because of the Kubeflow Pipelines workforce from Google for serving to and offering assist.


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