Skip to content
Switch branches/tags
Go to file
* Kubeflow Roadmap update - with 1.5 details

These proposed changes include: identifying that 1.4.1 has been delivered, provides themes for 1.5 and provides details of major features in 1.5 by working group.   This is an initial proposal that needs review by the working group leads.

* correct formatting in KFP features

Moved KFP features under KFP Control Flow doc

* updating KFP section

updating KFP references with updates from KFP team

* Updated the 1.5 release date to March

updated the 1.5 release date to March

* Update

change Hyperparameter leader election to Katib leader election

Co-authored-by: Andrey Velichkevich <>

* Update

improve description and details of feature for metrics collector

Co-authored-by: Andrey Velichkevich <>

* Update Katib description for Early stopping in 1.5

updating with Andrey's suggestion (but without the world proper).   * Validation for Early Stopping algorithm settings helps users to proper reduce model overfitting

Co-authored-by: Andrey Velichkevich <>
10 contributors

Users who have contributed to this file

@jbottum @jlewi @ashahba @shannonbradshaw @mkbhanda @brettkoonce @cspavlou @avdaredevil @andreyvelich @jessiezcc

Kubeflow Roadmap

Kubeflow 1.5 Release, Due: March 2022


  • Switching to Emissary executor enables Kubeflow Pipelines deployment on Kubernetes >= v1.20, which runs on containerd runtime instead of Docker runtime
  • Improve model accuracy and reduce overfitting, especially with hyper parameter tuning
  • Simplify operations and optimize utilization (including spot instance use cases for distributed training)
  • More consistent user experience - UI appearance, features and naming
  • Improved documentation, tutorials and examples
  • Stretch - Support for K8s 1.22 and associated dependencies (cert mgr, istio)

Major Features from each Working Group (note: Individual WG versions are independent of Kubeflow's)

Kubeflow Pipelines, v1.8

  • Switching to Emissary executor enables Kubeflow Pipelines deployment on Kubernetes >= v1.20, which runs on containerd runtime instead of Docker runtime.

Katib, v0.13

  • Katib controller leader election for HA operations and faster recovery
  • Validation for Early Stopping algorithm settings helps users to reduce model overfitting
  • Improve SDK, AWS CI, parameter settings across frameworks (goptuna, optuna, hyperopt)
  • Update namespace label for Metrics Collector injection (note - breaking change)

Training Operator, v1.3

  • Elastic Training for PyTorch, restarts pod rather than whole job, supports spot instances
  • MPI addition to Unified training operator
  • Python SDK supported on PyPI

Notebooks, Central Dashboard, Web Apps, Manifest, v1.5

  • Exposing notebook idleness parameters, reduces infra used on idle notebooks
  • UI consistency between web apps along with frontend pagination, and remove cards and user responsive tables
  • Support for dark mode
  • More options to define PVC specification

KServe, v0.7

  • KFServing is rebranded to KServe
  • ModelMesh joins KServe
  • Triton model serving runtime defaults to v21.09
  • (Alpha feature) Raw kubernetes deployment support, Istio/Knative dependency is now optional

Detailed features, bug fixes and enhancements are identified in the Working Group Roadmaps

Kubeflow 1.4.1 Release, Delivered: December 2021

  • The need for a patch release was triggered by #2082
  • 1.4.1 Tracking issue: 2084

Kubeflow 1.4 Release, Delivered: October 2021

Themes and Major Features

  • Maturation of Version 2 (V2) Protocols in KFServing and Kubeflow Pipelines
  • Increased use of metadata for pipeline orchestration and model performance monitoring
  • Kubeflow Pipelines - introduce emissary executor (Alpha) which no longer depends on docker container runtime.
  • KFServing User Interface with model details, logs, yaml
  • Reduce redundant code in training operators and Kubeflow’s web apps
  • Faster, better builds with more CI/CD - Katib, Training Operators, Notebooks
  • Audit relevant docs pages and update for 1.4 features
  • Ongoing improvement to user documentation on

Detailed features, bug fixes and enhancements are identified in the Working Group Roadmaps

Kubeflow 1.3 Features, Released: April 2021

The Kubeflow 1.3 roadmap includes many User Interface (UI) improvements and core Kubeflow component upgrades to improve installation, management, and authentication. It also includes support for the latest Istio versions.

The 1.3 release plan includes the following features:

User Interface (UI) & Working Group enhancements to improve user experience and simplify workflows & operations

  • Completely new UIs for KFServing, Katib, Tensorboard & Volumes Manager
  • Notebooks
    • Important backend updates to Notebooks (i.e. to improve interop with Tensorboard)
    • Addition of R-Studio and Code-Server (VS-Code) support
  • Kubeflow Pipelines (KFP)
    • UI reorganization for better User Experience
    • Simplified view of dependency graphs
    • Multi-user feature enhancements in Kubeflow Pipelines
  • KFServing v0.5
    • Multi-model Serving
    • Ability to specify container fields on ML Framework spec such as env variable, liveness/readiness probes etc.
    • Ability to specify pod template fields on component spec such as NodeAffinity etc.
    • gRPC support Tensorflow Serving.
    • Triton Inference server V2 inference REST/gRPC protocol support
    • TorchServe predict integration
    • PyTorch Captum explain integration
    • SKLearn/XGBoost V2 inference REST/gRPC protocol support with MLServer
    • PMMLServer support
    • LightGBM support
    • Allow specifying timeouts on component spec
    • Simplified canary rollout, traffic split at knative revisions level instead of services level
    • Transformer to predictor call is now made async

Core improvements to Kubeflow Installation, Management, Authentication, and Istio

  • Support for latest Istio versions across Kubeflow applications:
    • KFP, Profile-Controller and KFAM will support the new AuthorizationPolicy API
  • Manifests refactor:
    • Easy installation of Kubeflow applications and common services
    • Easy creation of Kubeflow distributions
    • Moving manifest development to upstream application repositories
      • This allows separation of responsibilities between Application Owners and Distribution Owners.
      • These will be sync'ed on a regular basis.
      • This will result in a reduction of tech debt from old or duplicate manifests.

Kubeflow 1.2 Features, Release Date: November 2020

Kubeflow 1.2 provides valuable enhancements to HyperParameter Tuning, Pipelines, KFServing, Notebooks and the Training Operators, which improve Kubeflow operations and data scientist productivity.

1.2 includes the following features:

  • Katib 0.10 with the new v1beta1 API
  • Katib support for early stopping
  • Katib support for custom CRD in the new Trial template
  • Katib support to resume experiments
  • Katib support for multiple ways to extract metrics
  • KFServing support to add batcher module as sidecar
  • KFServing for the Alibi explainer upgrade to 0.4.0
  • KFServing for Triton inference server rename and integrations
  • Pipelines support for a Tekton backend option.
  • Kubeflow Pipelines 1.0.4, Changelog includes ~20 fixes and ~5 minor features.
  • Notebooks support for Affinity/Toleration configs
  • Update mxnet-operator manifest to v1
  • Correct XGBoostJob CRD group name and add singular name
  • Fix XGBoost Operator manifest issue
  • Move Pytorch operator e2e tests to AWS Prow
  • Support BytePS in MXNet Operator
  • Fix error when conditions is empty in tf-operator
  • Fix success Policy logic in MXNet Operator

For more details please see this post:

Kubeflow 1.1 Features, Release Date: Late June 2020

Kubeflow 1.1 will continue to enhance enterprise grade functionality for secure operations and upgrades. 1.1 will also simplify ML workflows to improve data scientist productivity.

The following features were delivered in Kubeflow 1.1:

  • Additional security use cases for GCP users (including support for private GKE & Anthos Service Mesh),design doc; #1705

  • A CVE scanning report and mitigation process, 4590

  • Improved workflow automation tools (fairing and kale) to simplify and mature the Core and EcoSystem supported CUJs

  • Establishment of Kubeflow Policy / Guidelines on how to implement authorization in web applications. Propose SubjectAccessReview in order to use K8s RBAC as the source of truth for Authz. 4899

  • Guidelines on how cluster admins can interact with Kubeflow's authorization. There are already some difficulties with the self-serve model, process of adding contributors to a namespace and the way KFAM is using magic annotations on RoleBindings (#4574 #4889 #4936 #4924 #4938). Document current workarounds. #4960

  • Decide when the CentralDashboard should show a namespace. Right now, this is done with KFAM in an error-prone way (magic annotations on RoleBindings). Design doc exploring different options (change KFAM to use SubjectAccessReview, use a model like GCP Console checking read permission on namespace, etc.) This is also related to item above. #4960

  • Ability to turn off the self-serve mode, as in many environments there are mechanisms other than the Kubeflow Dashboard that provision/share an environment for/with the user. (#4942)

  • Multi-User Authorization: Add support for K8s RBAC via SubjectAccessReview #3513

The 1.1 features are tracked in this Kanban board

Kubeflow 1.0

Kubeflow 1.0 was released on March 2, 2020. The 1.0 release consists of the following key pieces

  • A core set of applications targeting the critical user journey of build-train-deploy
  • Scaffolding to securely deploy and manage multi-user Kubeflow environments on-prem and in the cloud.
  • A process to graduate Kubeflow components to a stable version based upon an Application Requirements definition that has been defined and validated by the Community’s testing process.

The following applications graduated to stable versions in Kubeflow 1.0.

  • kfctl for deployment and upgrades
  • TFJob and PyTorch for distributed training
  • Jupyter notebook controller and web app
  • Profile controller and UI for multiuser management

The following applications are considered in a beta version in Kubeflow 1.0.

  • Katib for hyper-parameter tuning
  • fairing SDK to facilitate use of notebooks for build-train-deploy
  • Kale which extends jupyter notebooks to create, run, and explore KF pipelines Metadata SDK, UI, and backend
  • KFServing for model deployment and inference
  • Pipelines

Here is a preliminary list of limitations and requirements that will be part of our 1.0 release

  • ISTIO will be required as a service mesh and for AuthN and AuthZ support
  • We will only support a single shared Kubeflow deployment per Kubernetes cluster
  • Users can consume Kubeflow in their own, isolated namespace
  • Upgrades will require downtime

Kubeflow 0.7

Following Kubeflow's quarterly relese schedule Kubeflow 0.7 will be released in October 2019.


  • Notebook infrastructure to beta quality in 0.7
    • 1.0 in the following release
  • The notebook infrastructure has 2 primary pieces; the jupyter notebook controller and web application for managing notebooks
  • Kanban board


  • kfctl to beta quality in 0.7
    • 1.0 in the following release
  • v1beta1 for KfDef
    • Create a more consistent and clean API for describing Kubeflow deployments
    • Handle plugins and platforms consistently
  • Clean up kfctl flags and semantics
  • Upgradability between minor and major releases (issue)
  • Kanban board


  • Kubernetes' applications provide a unified view of the deployed Kubeflow applications and their state


  • Generic logger to auto log K8s resources to metadata store
  • Kanban board


  • Local execution of pipelines for easy development
  • Enhanced metadata integration

Enterprise support

  • Restrict kubeflow access to subset of namespaces (kubeflow/kubeflow#3623)
  • Profile controller to beta
  • Kubeflow identity management API and UI to beta

Onpremise support

  • E2E CI for setup and multi-user support in on premise environments
  • Kanban board

Data management

  • Persistent volume management via a central dashboard UI


  • Remove ksonnet from E2E testing
  • CI for applications in scope for 1.0 (notebooks, job operators, central dashboard etc..)
  • Kanban board

Support for hardware accelerated training and inference

  • Automatic injection of device configuration needed to use hardware accelerators for training and inference

Kubeflow 0.6

0.6 was released in mid-july; blog post


  • kustomize replaced ksonnet for application configuration
  • kfctl can now take a single YAML file specifying a KFDef describing Kubeflow (example)
  • ISTIO used as the service mesh


  • Introduced support for artifact tracking via a UI and python SDK
  • Predefined schemas for models, datasets, and evaluation metrics

Multi-user support

  • ISTIO and RBAC for AuthZ
  • Dex provides a reference implementation for AuthN
  • Integration with GCP and AWS AuthN services
  • Profile resource to manage per-team namespaces
  • Kubeflow identity management API to manage multiple users/teams

Training Operators

  • TFJob and PyTorch to 1.0


  • UI improvements:
    • 10x Perf improvement (e.g. load list of runs)
  • API performance optimization
  • streamlined run creation
  • stackdriver integration for logs (better debugging)
  • better visualization of metadata outputs
  • Tensorboard CRD
  • versioning for data volumes

Kubeflow 2019 roadmap

The roadmap provides a high level overview of key areas that will likely span multiple releases.

The roadmap provides links to critical user journeys(CUJs) that we want to deliver. A CUJ shows how a user would accomplish some critical task (for example build, train, and deploy a model).

Kubeflow does a major release at the end of every quarter. Minor releases occur as needed to fix important bugs.

For detailed information about what will be in a release look for the issues tagged "area/X.Y.Z".

If you are a member of the Kubeflow org you can use these search queries