This is an automated email from the ASF dual-hosted git repository. ofuks pushed a commit to branch v2.2-RC1 in repository https://gitbox.apache.org/repos/asf/incubator-dlab.git
The following commit(s) were added to refs/heads/v2.2-RC1 by this push: new 8a35334 Updated Release notes 8a35334 is described below commit 8a35334f4baa934713b6ec98269e040a78e6b665 Author: ofuks <54886119+of...@users.noreply.github.com> AuthorDate: Wed Nov 20 11:21:18 2019 +0200 Updated Release notes --- RELEASE_NOTES.md | 94 ++++++++++++++++++++------------------------------------ 1 file changed, 34 insertions(+), 60 deletions(-) diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index c4f0f9e..cf4e3a3 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,86 +1,60 @@ # DLab is Self-service, Fail-safe Exploratory Environment for Collaborative Data Science Workflow -## New features in v2.1 +## New features in v2.2 **All Cloud platforms:** -- implemented tuning Apache Spark standalone cluster and local spark configurations from WEB UI (except for Apache Zeppelin) -- added a reminder after user logged in notifying that corresponding resources are about to be stopped/terminated -- implemented SSN load monitor: CPU, Memory, HDD +- added concept of Projects into DLab. Now users can unite under Projects and collaborate +- for ease of use we've added web terminal for all DLab Notebooks - updated versions of installed software: - * Jupyter 5.7.4 - * RStudio 1.1.463 - * Apache Zeppelin 0.8.0 - * Apache Spark 2.3.2 for standalone cluster - * Scala 2.12.8 - * CNTK 2.3.1 - * Keras 2.1.6 (except for DeepLearning - 2.0.8) - * MXNET 1.3.1 - * Theano 1.0.3 - * ungit 1.4.36 + * angular 8.2.7 -**AWS:** -- implemented tuning Data Engine Service from WEB UI (except for Apache Zeppelin) -- added support of new version of Data Engine Service (AWS EMR) 5.19.0 +**GCP:** +- added billing report to monitor Cloud resources usage into DLab, including ability to manage billing quotas +- updated versions of installed software: + * Dataproc 1.3 + +## Improvements in v2.2 +**All Cloud platforms:** +- implemented login via KeyCloak to support integration with multiple SAML and OAUTH2 identity providers +- added DLab version into WebUI +- augmented ‘Environment management’ page +- added possibility to tag Notebook from UI +- added possibility to terminate computational resources via scheduler -**MS azure and AWS:** -- implemented ability to manage total billing quota for DLab as well as billing quota per user +**GCP:** +- added possibility to create Notebook/Data Engine from an AMI image -## Improvements in v2.1 +**AWS and GCP:** +- UnGit tool now allows working with remote repositories over ssh +- implemented possibility to view Data Engine Service version on UI after creation +## Bug fixes in v2.2 **All Cloud platforms:** -- added ability to configure instance size/shape (CPU, RAM) from DLab UI for different user groups -- added possibility to install Java dependencies from DLab UI -- added alternative way to access analytical notebooks just by clicking on notebook's direct URL. - * added LDAP authorization in Squid (user should provide his LDAP credentials when accessing notebooks/Data Engine/Data Engine Service via browser) -- improved error handling for various scenarios on UI side -- added support of installing DLab into two VPCs - -**MS Azure:** -- it is now possible to install DLab only with private IP’s +- fixed sparklyr library (r package) installation on RStudio, RStudio with TensorFlow notebooks -## Bug fixes in v2.1 -**AWS:** -- fixed pricing retrieval logic to optimize RAM usage on SSN for small instances **GCP:** +- fixed a bug when Data Engine creation fails for DeepLearning template +- fixed a bug when Jupyter does not start successfully after Data Engine Service creation (create Jupyter -> create Data Engine -> stop Jupyter -> Jupyter fails) - fixed a bug when DeepLearning creation was failing -- fixed a bug which caused shared bucket to be deleted in case Edge node creation failed for new users -## Known issues in v2.1 +## Known issues in v2.2 **All Cloud platforms:** -- remote kernel list for Data Engine is not updated after stop/start Data Engine -- following links can be opened via tunnel for Data Engine/Data Engine: service: worker/application ID, application detail UI, event timeline, logs for Data Engine -- if Apache Zeppelin is created from AMI with different instance shape, spark memory size is the same as in created AMI. -- sparklyr library (r package) can not be installed on RStudio, RStudio with TensorFlow notebooks -- Spark default configuration for Apache Zeppelin can not be changed from DLab UI. Currently it can be done directly through Apache Zeppelin interpreter menu. -For more details please refer for Apache Zeppelin official documentation: https://zeppelin.apache.org/docs/0.8.0/usage/interpreter/overview.html -- shell interpreter for Apache Zeppelin is missed for some instance shapes -- executor memory is not allocated depending on notebook instance shape for local spark - - -**AWS** -- can not open master application URL on resource manager page, issue known for Data Engine Service v.5.12.0 -- java library installation fails on DLab UI on Data Engine Service in case when it is installed together with libraries from other groups. - -**GCP:** -- storage permissions aren't differentiated by users via Dataproc permissions (all users have R/W access to other users buckets) -- Data Engine Service creation is failing after environment has been recreated -- It is temporarily not possible to run playbooks using remote kernel of Data Engine (dependencies issue) -- Data Engine creation fails for DeepLearning template -- Jupyter does not start successfully after Data Engine Service creation (create Jupyter -> create Data Engine -> stop Jupyter -> Jupyter fails) +- Notebook name should be unique per project for different users in another case it is impossible to operate Notebook with the same name after the first instance creation **Microsoft Azure:** -- creation of Zeppelin or RStudio from custom image fails on the step when cluster kernels are removing -- start Notebook by scheduler does not work when Data Lake is enabled -- playbook running on Apache Zeppelin fails due to impossible connection to blob via wasbs protocol +- DLab deployment is unavailable if Data Lake is enabled +- custom image creation from Notebook fails and deletes existed Notebook + +**Refer to the following link in order to view the other major/minor issues in v2.2:** -## Known issues caused by cloud provider limitations in v2.1 +[Apache DLab: known issues](https://issues.apache.org/jira/issues/?filter=12347602 "Apache DLab: known issues") +## Known issues caused by cloud provider limitations in v2.2 **Microsoft Azure:** - resource name length should not exceed 80 chars - TensorFlow templates are not supported for Red Hat Enterprise Linux - low priority Virtual Machines are not supported yet -- occasionally billing data is not available for Notebook secondary disk **GCP:** - resource name length should not exceed 64 chars - billing data is not available -- **NOTE:** DLab has not been tested on GCP for Red Hat Enterprise Linux \ No newline at end of file +- **NOTE:** DLab has not been tested on GCP for Red Hat Enterprise Linux --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@dlab.apache.org For additional commands, e-mail: commits-h...@dlab.apache.org