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Posted to commits@dlab.apache.org by of...@apache.org on 2019/12/13 15:10:05 UTC
[incubator-dlab] branch develop updated: [DLAB-1383] Updated
RELEASE_NOTES.md
This is an automated email from the ASF dual-hosted git repository.
ofuks pushed a commit to branch develop
in repository https://gitbox.apache.org/repos/asf/incubator-dlab.git
The following commit(s) were added to refs/heads/develop by this push:
new 8ce3d97 [DLAB-1383] Updated RELEASE_NOTES.md
8ce3d97 is described below
commit 8ce3d97c2e22b4235ae10acc9f899b057595eb4e
Author: Oleh Fuks <ol...@gmail.com>
AuthorDate: Fri Dec 13 17:09:39 2019 +0200
[DLAB-1383] Updated RELEASE_NOTES.md
---
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
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