chore: release synapseml 0.9.0 (#1206)

README.md

@@ -1,10 +1,10 @@
 ![SynapseML](https://mmlspark.azureedge.net/icons/mmlspark.svg)
 
-# Microsoft Machine Learning for Apache Spark
+# Synapse Machine Learning
 
 [![Build Status](https://msdata.visualstudio.com/A365/_apis/build/status/microsoft.SynapseML?branchName=master)](https://msdata.visualstudio.com/A365/_build/latest?definitionId=17563&branchName=master) [![codecov](https://codecov.io/gh/Microsoft/SynapseML/branch/master/graph/badge.svg)](https://codecov.io/gh/Microsoft/SynapseML) [![Gitter](https://badges.gitter.im/Microsoft/MMLSpark.svg)](https://gitter.im/Microsoft/MMLSpark?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) 
 
-[![Release Notes](https://img.shields.io/badge/release-notes-blue)](https://github.com/Microsoft/SynapseML/releases) [![Scala Docs](https://img.shields.io/static/v1?label=api%20docs&message=scala&color=blue&logo=scala)](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc4/scala/index.html#package) [![PySpark Docs](https://img.shields.io/static/v1?label=api%20docs&message=python&color=blue&logo=python)](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc4/pyspark/index.html) [![Academic Paper](https://img.shields.io/badge/academic-paper-7fdcf7)](https://arxiv.org/abs/1810.08744)
+[![Release Notes](https://img.shields.io/badge/release-notes-blue)](https://github.com/Microsoft/SynapseML/releases) [![Scala Docs](https://img.shields.io/static/v1?label=api%20docs&message=scala&color=blue&logo=scala)](https://mmlspark.blob.core.windows.net/docs/0.9.0/scala/index.html#package) [![PySpark Docs](https://img.shields.io/static/v1?label=api%20docs&message=python&color=blue&logo=python)](https://mmlspark.blob.core.windows.net/docs/0.9.0/pyspark/index.html) [![Academic Paper](https://img.shields.io/badge/academic-paper-7fdcf7)](https://arxiv.org/abs/1810.08744)
 
 [![Version](https://img.shields.io/badge/version-1.0.0--rc4-blue)](https://github.com/Microsoft/SynapseML/releases) [![Snapshot Version](https://mmlspark.blob.core.windows.net/icons/badges/master_version3.svg)](#sbt) 
 
@@ -24,8 +24,8 @@ sub-millisecond latency web services, backed by your Spark cluster.
 
 SynapseML requires Scala 2.12, Spark 3.0+, and Python 3.6+.
 See the API documentation [for
-Scala](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc4/scala/index.html#package) and [for
-PySpark](https://mmlspark.blob.core.windows.net/docs/1.0.0-rc4/pyspark/index.html).
+Scala](https://mmlspark.blob.core.windows.net/docs/0.9.0/scala/index.html#package) and [for
+PySpark](https://mmlspark.blob.core.windows.net/docs/0.9.0/pyspark/index.html).
 
 <details>
 <summary><strong><em>Table of Contents</em></strong></summary>
@@ -149,7 +149,7 @@ the above example, or from python:
 ```python
 import pyspark
 spark = pyspark.sql.SparkSession.builder.appName("MyApp") \
-            .config("spark.jars.packages", "com.microsoft.azure:synapseml:1.0.0-rc4") \
+            .config("spark.jars.packages", "com.microsoft.azure:synapseml:0.9.0") \
             .config("spark.jars.repositories", "https://mmlspark.azureedge.net/maven") \
             .getOrCreate()
 import synapse.ml
@@ -162,7 +162,7 @@ your `build.sbt`:
 
 ```scala
 resolvers += "SynapseML" at "https://mmlspark.azureedge.net/maven"
-libraryDependencies += "com.microsoft.azure" %% "synapseml" % "1.0.0-rc4"
+libraryDependencies += "com.microsoft.azure" %% "synapseml" % "0.9.0"
 
 ```
 
@@ -172,9 +172,9 @@ SynapseML can be conveniently installed on existing Spark clusters via the
 `--packages` option, examples:
 
 ```bash
-spark-shell --packages com.microsoft.azure:synapseml:1.0.0-rc4
-pyspark --packages com.microsoft.azure:synapseml:1.0.0-rc4
-spark-submit --packages com.microsoft.azure:synapseml:1.0.0-rc4 MyApp.jar
+spark-shell --packages com.microsoft.azure:synapseml:0.9.0
+pyspark --packages com.microsoft.azure:synapseml:0.9.0
+spark-submit --packages com.microsoft.azure:synapseml:0.9.0 MyApp.jar
 ```
 
 This can be used in other Spark contexts too. For example, you can use SynapseML
@@ -189,15 +189,15 @@ cloud](http://community.cloud.databricks.com), create a new [library from Maven
 coordinates](https://docs.databricks.com/user-guide/libraries.html#libraries-from-maven-pypi-or-spark-packages)
 in your workspace.
 
-For the coordinates use: `com.microsoft.azure:synapseml:1.0.0-rc4` 
+For the coordinates use: `com.microsoft.azure:synapseml:0.9.0` 
 with the resolver: `https://mmlspark.azureedge.net/maven`. Ensure this library is
 attached to your target cluster(s).
 
 Finally, ensure that your Spark cluster has at least Spark 3.12 and Scala 2.12.
 
 You can use SynapseML in both your Scala and PySpark notebooks. To get started with our example notebooks import the following databricks archive:
 
-`https://mmlspark.blob.core.windows.net/dbcs/SynapseMLExamplesv1.0.0-rc4.dbc`
+`https://mmlspark.blob.core.windows.net/dbcs/SynapseMLExamplesv0.9.0.dbc`
 
 ### Apache Livy and HDInsight
 
@@ -210,7 +210,7 @@ Excluding certain packages from the library may be necessary due to current issu
 {
     "name": "synapseml",
     "conf": {
-        "spark.jars.packages": "com.microsoft.azure:synapseml:1.0.0-rc4",
+        "spark.jars.packages": "com.microsoft.azure:synapseml:0.9.0",
         "spark.jars.repositories": "https://mmlspark.azureedge.net/maven",
         "spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.12,org.scalactic:scalactic_2.12,org.scalatest:scalatest_2.12"
     }
@@ -224,7 +224,7 @@ In Azure Synapse, "spark.yarn.user.classpath.first" should be set to "true" to o
 {
     "name": "synapseml",
     "conf": {
-        "spark.jars.packages": "com.microsoft.azure:synapseml:1.0.0-rc4",
+        "spark.jars.packages": "com.microsoft.azure:synapseml:0.9.0",
         "spark.jars.repositories": "https://mmlspark.azureedge.net/maven",
         "spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.12,org.scalactic:scalactic_2.12,org.scalatest:scalatest_2.12",
         "spark.yarn.user.classpath.first": "true"

docs/R-setup.md

@@ -10,7 +10,7 @@ To install the current SynapseML package for R use:
 
 ```R
 ...
-devtools::install_url("https://mmlspark.azureedge.net/rrr/synapseml-1.0.0-rc4.zip")
+devtools::install_url("https://mmlspark.azureedge.net/rrr/synapseml-0.9.0.zip")
 ...
 ```
 
@@ -23,7 +23,7 @@ It will take some time to install all dependencies.  Then, run:
 library(sparklyr)
 library(dplyr)
 config <- spark_config()
-config$sparklyr.defaultPackages <- "com.microsoft.azure:synapseml:1.0.0-rc4"
+config$sparklyr.defaultPackages <- "com.microsoft.azure:synapseml:0.9.0"
 sc <- spark_connect(master = "local", config = config)
 ...
 ```
@@ -83,7 +83,7 @@ and then use spark_connect with method = "databricks":
 
 ```R
 install.packages("devtools")
-devtools::install_url("https://mmlspark.azureedge.net/rrr/synapseml-1.0.0-rc4.zip")
+devtools::install_url("https://mmlspark.azureedge.net/rrr/synapseml-0.9.0.zip")
 library(sparklyr)
 library(dplyr)
 sc <- spark_connect(method = "databricks")