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RiseML v1.2.3 (2018/05/09)

This release changes behaviour for failing hyperparameter experiments and pins the minio version.

Release Notes

  • Hyperparameter experiments no longer fail when a (sub-) experiment fails. You can also kill single (sub-) experiments.
  • The minio version is now pinned to RELEASE.2018-04-19T22-54-58Z, it introduced a regression bug in the latest versions

RiseML v1.2.2 (2018/04/09)

This release adds support for private registries on GKE and improves scheduling capabilities.

Release Notes

  • support private registries on GKE, automatically detected, no configuration needed
  • nodeSelectors.training and tolerations.training to better control where training and build jobs can be run
  • fix for TensorBoard redirects being wrong in some cases
  • fix for Postgresql deployment failing when hidden directories in mount path present

Upgrade Notes:

  • The default value for postgresql.persistence.subPath changed from "" to postgresql-db. Please make sure to use the value you used during your installation, i.e. add postgresql.persistence.subPath: "" to your configuration if it was previously not set.

RiseML v1.2.1 (2018/03/27)

This fixes a bug when upgrading from a previous version.

Release Notes

  • fix for Kubernetes sync service which was still using nodePorts which are no longer needed

RiseML v1.2.0 (2018/03/22)

🎉 Our latest release delivers some initial data management commands for the CLI and also fixes some issues with NVidia's device plugin and GKE. Furthermore, you can now install RiseML only to a specific part of your Kubernetes cluster!

Release Notes

  • riseml ls, riseml cp, riseml rm for managing your data and the output generated by your experiments. See how they work in our docs.
  • nodeSelectors.riseml: A new Helm config variable to tell RiseML to only install and run on Kubernetes nodes with a specific label!
  • Improved support for nvidia-docker2 and Nvidia's device plugin
  • Fix for gpu pools on GKE that added a node taint automatically
  • Fix for trailing '' in your run commands

RiseML v1.1.0 (2018/02/21)

This release brings Horovod support, a framework for distributed training with TensorFlow, as well as support for NVidia's K8s device plugin and GKE.

Release Notes

  • Horovod support
  • Support for Nvidia's device plugin for Kubernetes - just install it and configure your RiseML installation to use nvidiaDriverDir: /home/kubernetes/bin/nvidia
  • Fixes when building images on GKE
  • Several UTF-8 fixes when building images and printing training logs

RiseML v1.0.4 (2018/02/01)

This release fixes Tensorboard when using Tensorflow >= 1.4.0. NOTE: At the same time, this means, that Tensorboard is not working correctly with TF < 1.4.

RiseML v1.0.3 (2018/01/29)

This release improves autoscaling on AWS using our installer.

RiseML v1.0.2 (2018/01/17)

This release brings persistence for the NFS provisioner. Together with the latest version of RiseML's AWS installer, you will get a persisted and auto-scaling RiseML installation on AWS!

Release Notes

  • Configurable persistence for NFS provisioner
  • Auto-scaling and persistence when using RiseML AWS installer (see website)

RiseML v1.0.1 (2017/12/20)

This release fixes a minor installation issue with Kubernetes Version 1.9

Release Notes

  • Support installation on Kubernetes 1.9
  • Minimum Kubernetes Version is now 1.8

RiseML v1.0.0 (2017/12/19)

This marks our 1.0.0 release 🎊🎉. We focused on making this release more stable and fixed many smaller bugs and messages compared to the previous version. There are two major improvements. First, RiseML now makes use of Kubernetes' persistent volume claims for its data and output volumes. This tighter integration allows using any of the storage types that are supported in Kubernetes. Second, we now provide a quick installer, which spins up a complete RiseML cluster (including GPU nodes) on AWS for you. All you need is an AWS account and you're good to go in 15 minutes!

Installation

To install RiseML, follow the installation guide. New in this release is a simple installer that spins up a complete RiseML cluster on AWS for you. Check out our quick setup instructions.

Release Notes

  • Reworked storage integration with Kubernetes
  • Quick installer for AWS
  • Improved error reporting
  • Enable support for different plans: basic, professional
  • Fixed riseml init
  • Fixed Tensorboard links with latest Tensorflow versions
  • Many minor UX improvements (messages, command flags, ...)

Upgrading from Version v0.8.0

Before upgrading:

  • backup your existing data volumes
  • make sure to prepare data and output Kubernetes volumes as described in our documentation
  • remove the options data and output from you installation configuration (usually riseml-config.yml)
  • verify the configuration options in riseml-config.yml are still valid since they will be applied again (e.g., if you changed the account key to a different one it will be set to the one in riseml-config.yml)

To update to the newest version:

helm update riseml
helm upgrade riseml riseml-charts/riseml -f riseml-config.yml

If you need support with upgrading, please contact us.

RiseML v0.8.0 (2017/11/13)

🎉 This is the first public release of RiseML!

Installation

To install RiseML, follow the installation guide.

Release Notes

  • Introducing new features:
    • TensorFlow and TensorBoard support
    • Run distributed training experiments with TensorFlow
    • Run hyperparameter experiments
    • User Management
    • Monitor experiments' CPU, RAM, and GPU stats
  • Improved riseml.yml syntax for addressing frameworks
  • Improved scalability of log streaming
  • Renamed dockerBuild installation parameter to imageBuilder

Upgrading from release candidates

There is no migration path from release candidates. To upgrade, you need to completely uninstall and re-install RiseML:

  1. uninstall via helm del --purge riseml
  2. delete existing files on your database, git and log storage (only if persistence was configured)
  3. change dockerBuild to imageBuilder in riseml-config.yml
  4. re-install according to installation instructions.