This is an automated email from the ASF dual-hosted git repository.
ofuks pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-dlab.git
The following commit(s) were added to refs/heads/master by this push:
new af995e9 Updated RELEASE_NOTES
af995e9 is described below
commit af995e98b3b3cf526fb9741a3e5117dd1e04f3aa
Author: Oleh Fuks <olegfuk...@gmail.com>
AuthorDate: Tue Dec 10 18:41:25 2019 +0200
Updated RELEASE_NOTES
---
RELEASE_NOTES.md | 82 --------------------------------------------------------
1 file changed, 82 deletions(-)
diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md
index c0b806e..cf4e3a3 100644
--- a/RELEASE_NOTES.md
+++ b/RELEASE_NOTES.md
@@ -1,5 +1,4 @@
# DLab is Self-service, Fail-safe Exploratory Environment for Collaborative
Data Science Workflow
-# DLab is Self-service, Fail-safe Exploratory Environment for Collaborative
Data Science Workflow
## New features in v2.2
**All Cloud platforms:**
@@ -59,84 +58,3 @@
- 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
-
-## New features in v2.1
-**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
-- 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
-
-**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
-
-**MS azure and AWS:**
-- implemented ability to manage total billing quota for DLab as well as
billing quota per user
-
-## Improvements in v2.1
-
-**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
-
-## Bug fixes in v2.1
-**AWS:**
-- fixed pricing retrieval logic to optimize RAM usage on SSN for small
instances
-
-## Known issues in v2.1
-**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)
-- DeepLearning creation fails
-
-**Microsoft Azure:**
-- creation of Zeppelin 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
-
-## Known issues caused by cloud provider limitations in v2.1
-
-**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
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscr...@dlab.apache.org
For additional commands, e-mail: commits-h...@dlab.apache.org
