Revision 24f5148a7ae3e175a873f0210e56e2b70861a155 authored by dependabot[bot] on 23 March 2020, 16:12:45 UTC, committed by GitHub on 23 March 2020, 16:12:45 UTC
Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 1.15.0 to 1.15.2. - [Release notes](https://github.com/tensorflow/tensorflow/releases) - [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md) - [Commits](https://github.com/tensorflow/tensorflow/compare/v1.15.0...v1.15.2) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
1 parent 932648f
ROADMAP.md
# Katib 2020 Roadmap
This document provides a high level view of where Katib will grow in 2020.
The original Katib design document can be found [here](https://docs.google.com/document/d/1ZEKhou4z1utFTOgjzhSsnvysJFNEJmygllgDCBnYvm8/edit#heading=h.7fzqir88ovr).
# Katib 1.0 Readiness
* 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)
# Enhance HP Tuning Experience
The objectives here are organized around the three stages defined in the CUJ:
## 1. Defining Model and Parameters
Integration with KF distributed training components
* TFJob
* PyTorch
* Allow Katib to support other operator types generically [#341](https://github.com/kubeflow/katib/issues/341)
## 2. Configuring a Experiment
* Supporting additional suggestion algorithms [#15](https://github.com/kubeflow/katib/issues/15)
## 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
# Test and Release Infrastructure
* Improve e2e test coverage
* Improve test harness
* Enhance release process; adding automation (see https://bit.ly/2F7o4gM)
![swh spinner](/static/img/swh-spinner.gif)
Computing file changes ...