From 1394ebbfd9da7c8eed37ddb32d9cef21b5974eee Mon Sep 17 00:00:00 2001 From: Josh Bottum Date: Wed, 3 Mar 2021 23:48:48 -0600 Subject: [PATCH] Update ROADMAP.md (#5603) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update ROADMAP.md I updated Kubeflow 1.1, added Kubeflow 1.2 and Kubelfow 1.3 roadmap items. * Update ROADMAP.md Improved wording of features to simplify understanding * Update ROADMAP.md Added details on KFServing 0.5 enhancements * Update ROADMAP.md updated the notebooks section in Kubeflow 1.3 with these modificiations, * Notebooks * Important backend updates to Notebooks (i.e. to improve interop with Tensorboard) * New and expanded Jupyter Notebook stack along with easy to customize common base images * Addition of R-Studio and Code-Server (VS-Code) support * Update ROADMAP.md Reorganized Working Group updates into 1st section. added that customizing jupyter base image is a stretch feature * Update ROADMAP.md Per Yuan, I deleted - * Process and tools for upgrades from Release N-1 to N i.e. 1.0.x to 1.1, [#304](https://github.com/kubeflow/kfctl/issues/304) Per James, I added - * Manage recurring Runs via new “Jobs” page (exact name on UI is TBD) * Update ROADMAP.md Added Multi-Model Serving, https://github.com/yuzliu/kfserving/blob/master/docs/MULTIMODELSERVING_GUIDE.md to KFServing 0.5 roadmap items --- ROADMAP.md | 83 +++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 79 insertions(+), 4 deletions(-) diff --git a/ROADMAP.md b/ROADMAP.md index 4f8cac0a7ed..e6efbc8f54c 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -141,13 +141,12 @@ Here is a preliminary list of limitations and requirements that will be part of * Users can consume Kubeflow in their own, isolated namespace * Upgrades will require downtime -## Kubeflow 1.1 Features, Target release: Late June 2020 +## 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 are under design review: +The following features were delivered in Kubeflow 1.1: -* Process and tools for upgrades from Release N-1 to N i.e. 1.0.x to 1.1, [#304](https://github.com/kubeflow/kfctl/issues/304) * Additional security use cases for GCP users (including support for private GKE & Anthos Service Mesh),[design doc](https://cloud.google.com/service-mesh/docs); [#1705](https://github.com/kubeflow/website/issues/1705) * A CVE scanning report and mitigation process, [4590](https://github.com/kubeflow/kubeflow/issues/4590) * Improved workflow automation tools (fairing and kale) to simplify and mature the Core and EcoSystem supported CUJs @@ -158,4 +157,80 @@ The following features are under design review: * 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](https://github.com/kubeflow/pipelines/issues/3513) -The 1.1 features are tracked in this [Kanban board](https://github.com/orgs/kubeflow/projects/36) \ No newline at end of file +The 1.1 features are tracked in this [Kanban board](https://github.com/orgs/kubeflow/projects/36) + +## 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: https://blog.kubeflow.org/release/official/2020/11/18/kubeflow-1.2-blog-post.html + +## Kubeflow 1.3 Features, Target release: March 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 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) + * New and expanded Jupyter Notebook stack along with easy to customize common base images - this is a stretch feature for 1.3 + * Addition of R-Studio and Code-Server (VS-Code) support +* Kubeflow Pipelines (KFP) + * UI reorganization for better User Experience + * Manage recurring Runs via new “Jobs” page (exact name on UI is TBD) + * Simplified view of dependency graphs + * Multi-user feature enhancements in Kubeflow Pipelines +* KFServing v0.5 + * [Multi-model Serving](https://github.com/yuzliu/kfserving/blob/master/docs/MULTIMODELSERVING_GUIDE.md) + * 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. + + + +