chore: bump version number

README.md

@@ -4,7 +4,7 @@
 
 [![Build Status](https://msazure.visualstudio.com/Cognitive%20Services/_apis/build/status/Azure.mmlspark?branchName=master)](https://msazure.visualstudio.com/Cognitive%20Services/_build/latest?definitionId=83120&branchName=master) [![codecov](https://codecov.io/gh/Azure/mmlspark/branch/master/graph/badge.svg)](https://codecov.io/gh/Azure/mmlspark) [![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/Azure/mmlspark/releases) [![Release Notes](https://img.shields.io/badge/version-0.17-blue)](https://github.com/Azure/mmlspark/releases) [![version](https://mmlspark.blob.core.windows.net/icons/badges/master_version3.svg)](#sbt) 
+[![Release Notes](https://img.shields.io/badge/release-notes-blue)](https://github.com/Azure/mmlspark/releases) [![Release Notes](https://img.shields.io/badge/version-0.18.0-blue)](https://github.com/Azure/mmlspark/releases) [![version](https://mmlspark.blob.core.windows.net/icons/badges/master_version3.svg)](#sbt) 
 
 
 MMLSpark is an ecosystem of tools aimed towards expanding the distributed computing framework
@@ -129,9 +129,9 @@ MMLSpark can be conveniently installed on existing Spark clusters via the
 `--packages` option, examples:
 
 ```bash
-spark-shell --packages Azure:mmlspark:0.17
-pyspark --packages Azure:mmlspark:0.17
-spark-submit --packages Azure:mmlspark:0.17 MyApp.jar
+spark-shell --packages com.microsoft.ml.spark:mmlspark_2.11:0.18.0
+pyspark --packages com.microsoft.ml.spark:mmlspark_2.11:0.18.0
+spark-submit --packages com.microsoft.ml.spark:mmlspark_2.11:0.18.0 MyApp.jar
 ```
 
 This can be used in other Spark contexts too. For example, you can use MMLSpark
@@ -146,14 +146,14 @@ 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: `Azure:mmlspark:0.17`.  Ensure this library is
+For the coordinates use: `com.microsoft.ml.spark:mmlspark_2.11:0.18.0`.  Ensure this library is
 attached to all clusters you create.
 
 Finally, ensure that your Spark cluster has at least Spark 2.1 and Scala 2.11.
 
 You can use MMLSpark 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/MMLSpark%20Examples%20v0.17.dbc`
+`https://mmlspark.blob.core.windows.net/dbcs/MMLSpark%20Examples%20v0.18.0.dbc`
 
 ### Docker
 
@@ -185,39 +185,20 @@ the above example, or from python:
 ```python
 import pyspark
 spark = pyspark.sql.SparkSession.builder.appName("MyApp") \
-            .config("spark.jars.packages", "Azure:mmlspark:0.17") \
+            .config("spark.jars.packages", "com.microsoft.ml.spark:mmlspark_2.11:0.18.0") \
             .getOrCreate()
 import mmlspark
 ```
 
 <img title="Script action submission" src="http://i.imgur.com/oQcS0R2.png" align="right" />
 
-### HDInsight
-
-To install MMLSpark on an existing [HDInsight Spark
-Cluster](https://docs.microsoft.com/en-us/azure/hdinsight/), you can execute a
-script action on the cluster head and worker nodes.  For instructions on
-running script actions, see [this
-guide](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-customize-cluster-linux#use-a-script-action-during-cluster-creation).
-
-The script action url is:
-<https://mmlspark.azureedge.net/buildartifacts/0.17/install-mmlspark.sh>.
-
-If you're using the Azure Portal to run the script action, go to `Script
-actions` → `Submit new` in the `Overview` section of your cluster blade.  In
-the `Bash script URI` field, input the script action URL provided above.  Mark
-the rest of the options as shown on the screenshot to the right.
-
-Submit, and the cluster should finish configuring within 10 minutes or so.
-
 ### SBT
 
 If you are building a Spark application in Scala, add the following lines to
 your `build.sbt`:
 
 ```scala
-resolvers += "MMLSpark Repo" at "https://mmlspark.azureedge.net/maven"
-libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.17"
+libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.18.0"
 ```
 
 ### Building from source

docs/R-setup.md

@@ -10,7 +10,7 @@ To install the current MMLSpark package for R use:
 
 ```R
 ...
-devtools::install_url("https://mmlspark.azureedge.net/rrr/mmlspark-0.17.zip")
+devtools::install_url("https://mmlspark.azureedge.net/rrr/mmlspark-0.18.0.zip")
 ...
 ```
 
@@ -23,7 +23,7 @@ It will take some time to install all dependencies.  Then, run:
 library(sparklyr)
 library(dplyr)
 config <- spark_config()
-config$sparklyr.defaultPackages <- "Azure:mmlspark:0.17"
+config$sparklyr.defaultPackages <- "com.microsoft.ml.spark:mmlspark_2.11:0.18.0"
 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/mmlspark-0.17.zip")
+devtools::install_url("https://mmlspark.azureedge.net/rrr/mmlspark-0.18.0.zip")
 library(sparklyr)
 library(dplyr)
 sc <- spark_connect(method = "databricks")

docs/docker.md

@@ -28,7 +28,7 @@ You can now select one of the sample notebooks and run it, or create your own.
 In the above, `mcr.microsoft.com/mmlspark/release` specifies the project and image name that you
 want to run.  There is another component implicit here which is the _tag_ (=
 version) that you want to use — specifying it explicitly looks like
-`mcr.microsoft.com/mmlspark/release:0.17` for the `0.17` tag.
+`mcr.microsoft.com/mmlspark/release:0.18.0` for the `0.18.0` tag.
 
 Leaving `mcr.microsoft.com/mmlspark/release` by itself has an implicit `latest` tag, so it is
 equivalent to `mcr.microsoft.com/mmlspark/release:latest`.  The `latest` tag is identical to the
@@ -42,21 +42,19 @@ that you will probably want to use can look as follows:
 
 ```bash
 docker run -it --rm \
-           -e ACCEPT_EULA=y \
            -p 127.0.0.1:80:8888 \
            -v ~/myfiles:/notebooks/myfiles \
-           mcr.microsoft.com/mmlspark/release:0.17
+           mcr.microsoft.com/mmlspark/release:0.18.0
 ```
 
 In this example, backslashes are used to break things up for readability; you
 can enter it as one long like.  Note that in powershell, the `myfiles` local
 path and line breaks looks a little different:
 
     docker run -it --rm `
-               -e ACCEPT_EULA=y `
                -p 127.0.0.1:80:8888 `
                -v C:\myfiles:/notebooks/myfiles `
-               mcr.microsoft.com/mmlspark/release:0.17
+               mcr.microsoft.com/mmlspark/release:0.18.0
 
 Let's break this command and go over the meaning of each part:
 
@@ -139,7 +137,7 @@ Let's break this command and go over the meaning of each part:
     model.write().overwrite().save('myfiles/myTrainedModel.mml')
     ```
 
--   **`mcr.microsoft.com/mmlspark/release:0.17`**
+-   **`mcr.microsoft.com/mmlspark/release:0.18.0`**
 
     Finally, this specifies an explicit version tag for the image that we want to
     run.

src/main/scala/com/microsoft/ml/spark/cognitive/CognitiveServiceBase.scala

@@ -5,6 +5,7 @@ package com.microsoft.ml.spark.cognitive
 
 import java.net.URI
 
+import com.microsoft.ml.spark.build.BuildInfo
 import com.microsoft.ml.spark.core.contracts.HasOutputCol
 import com.microsoft.ml.spark.core.schema.DatasetExtensions
 import com.microsoft.ml.spark.io.http._
@@ -179,7 +180,7 @@ object URLEncodingUtils {
 object CognitiveServiceUtils {
 
   def setUA(req: HttpRequestBase): Unit = {
-    req.setHeader("User-Agent", "mmlspark/0.17")
+    req.setHeader("User-Agent", s"mmlspark/${BuildInfo.version}")
   }
 }