From e2f5666a0047885219c163df4acba09ff918ffad Mon Sep 17 00:00:00 2001 From: Ce Gao Date: Tue, 7 Apr 2020 15:45:43 +0800 Subject: [PATCH] feat: Add 2020 roadmap (#1121) * feat: Add 2020 roadmap Signed-off-by: Ce Gao * fix: Address comments in #1121 Signed-off-by: Ce Gao * fix: Address comments Signed-off-by: Ce Gao --- ROADMAP.md | 48 ++++++++++++++++++------------------------------ 1 file changed, 18 insertions(+), 30 deletions(-) diff --git a/ROADMAP.md b/ROADMAP.md index bd318809ec7..f2bb5fc5ea6 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -1,43 +1,31 @@ # Katib 2020 Roadmap -This document provides a high level view of where Katib will grow in 2020. +## New Features -The original Katib design document can be found [here](https://docs.google.com/document/d/1ZEKhou4z1utFTOgjzhSsnvysJFNEJmygllgDCBnYvm8/edit#heading=h.7fzqir88ovr). +### Hyperparameter Tuning -# Katib 1.0 Readiness +- Support Early Stopping [#692](https://github.com/kubeflow/katib/issues/692) -* Stabilize APIs for Experiments - * Reconsider the design of Trial Template [#906](https://github.com/kubeflow/katib/issues/906) - * Early Stopping [#692](https://github.com/kubeflow/katib/issues/692) - * Resuming Experiment [#1061](https://github.com/kubeflow/katib/issues/1061), [#1062](https://github.com/kubeflow/katib/issues/1062) -* Fully integrate Katib with existing E2E examples: - * Xgboost - * Mnist - * GitHub issue summarization -* Publish API documentation, best practices, tutorials -* [Issues list](https://github.com/kubeflow/katib/issues) +### Neural Architecture Search -# Enhance HP Tuning Experience +- Support Advanced NAS Algorithms like DARTs, ProxylessNAS [#461](https://github.com/kubeflow/katib/issues/461) -The objectives here are organized around the three stages defined in the CUJ: +### Other Features -## 1. Defining Model and Parameters +- Support Auto Model Compression [#460](https://github.com/kubeflow/katib/issues/460) +- Support Auto Feature Engineering [#475](https://github.com/kubeflow/katib/issues/475) -Integration with KF distributed training components -* TFJob -* PyTorch -* Allow Katib to support other operator types generically [#341](https://github.com/kubeflow/katib/issues/341) +## Enhancements -## 2. Configuring a Experiment -* Supporting additional suggestion algorithms [#15](https://github.com/kubeflow/katib/issues/15) +### Hyperparameter Tuning -## 3. Tracking Model Performance -* UI enhancements: allowing data scientists to visualize results easier -* Support for persistent model and metadata storage - * Ideally users should be able to export and reuse trained models from a common storage +- Delete Suggestion deployment after Experiment is finished [#1061](https://github.com/kubeflow/katib/issues/1061) +- Save Suggestion state after deployment is deleted [#1062](https://github.com/kubeflow/katib/issues/1062) +- Reconsider the design of Trial Template [#906](https://github.com/kubeflow/katib/issues/906) +- Add validation for algortihms (a.k.a suggestions) [#1126](https://github.com/kubeflow/katib/issues/1126) -# Test and Release Infrastructure +### Neural Architecture Search -* Improve e2e test coverage -* Improve test harness -* Enhance release process; adding automation (see https://bit.ly/2F7o4gM) +- Refactor structure for NAS algorithms [#1125](https://github.com/kubeflow/katib/issues/1125) +- Refactor the design for NAS model constructor[#1127](https://github.com/kubeflow/katib/issues/1127) +- Katib UI fixes and enhancements \ No newline at end of file