Bump the hard-wired version numbers to 0.11

README.md

@@ -123,9 +123,9 @@ MMLSpark can be conveniently installed on existing Spark clusters via the
 `--packages` option, examples:
 
    ```bash
-   spark-shell --packages Azure:mmlspark:0.10
-   pyspark --packages Azure:mmlspark:0.10
-   spark-submit --packages Azure:mmlspark:0.10 MyApp.jar
+   spark-shell --packages Azure:mmlspark:0.11
+   pyspark --packages Azure:mmlspark:0.11
+   spark-submit --packages Azure:mmlspark:0.11 MyApp.jar
    ```
 
 This can be used in other Spark contexts too, for example, you can use
@@ -141,7 +141,7 @@ Spark installed via pip with `pip install pyspark`.  You can then use
    ```
    import pyspark
    sp = pyspark.sql.SparkSession.builder.appName("MyApp") \
-               .config("spark.jars.packages", "Azure:mmlspark:0.10") \
+               .config("spark.jars.packages", "Azure:mmlspark:0.11") \
                .getOrCreate()
    import mmlspark
    ```
@@ -157,7 +157,7 @@ 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/0.10/install-mmlspark.sh>.
+<https://mmlspark.azureedge.net/buildartifacts/0.11/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
@@ -173,7 +173,7 @@ To install MMLSpark on the
 [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.ml.spark:mmlspark:0.10`.  Then, under
+For the coordinates use: `com.microsoft.ml.spark:mmlspark:0.11`.  Then, under
 Advanced Options, use `https://mmlspark.azureedge.net/maven` for the repository.
 Ensure this library is attached to all clusters you create.
 
@@ -188,7 +188,7 @@ your `build.sbt`:
 
    ```scala
    resolvers += "MMLSpark Repo" at "https://mmlspark.azureedge.net/maven"
-   libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.10"
+   libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.11"
    ```
 
 ### Building from source

docs/docker.md

@@ -29,7 +29,7 @@ You can now select one of the sample notebooks and run it, or create your own.
 In the above, `microsoft/mmlspark` 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
-`microsoft/mmlspark:0.10` for using the `0.10` tag.
+`microsoft/mmlspark:0.11` for the `0.11` tag.
 
 Leaving `microsoft/mmlspark` by itself has an implicit `latest` tag, so it is
 equivalent to `microsoft/mmlspark:latest`.  The `latest` tag is identical to the
@@ -47,7 +47,7 @@ that you will probably want to use can look as follows:
               -e ACCEPT_EULA=y \
               -p 127.0.0.1:80:8888 \
               -v ~/myfiles:/notebooks/myfiles \
-              microsoft/mmlspark:0.10
+              microsoft/mmlspark:0.11
    ```
 
 In this example, backslashes are used to break things up for readability; you
@@ -59,7 +59,7 @@ path and line breaks looks a little different:
               -e ACCEPT_EULA=y `
               -p 127.0.0.1:80:8888 `
               -v C:\myfiles:/notebooks/myfiles `
-              microsoft/mmlspark:0.10
+              microsoft/mmlspark:0.11
    ```
 
 Let's break this command and go over the meaning of each part:
@@ -143,7 +143,7 @@ Let's break this command and go over the meaning of each part:
      model.write().overwrite().save('myfiles/myTrainedModel.mml')
      ```
 
-* **`microsoft/mmlspark:0.10`**
+* **`microsoft/mmlspark:0.11`**
 
   Finally, this specifies an explicit version tag for the image that we want to
   run.

docs/gpu-setup.md

@@ -26,7 +26,7 @@ to check availability in your data center.
 MMLSpark provides an Azure Resource Manager (ARM) template to create a
 default setup that includes an HDInsight cluster and a GPU machine for
 training.  The template can be found here:
-<https://mmlspark.azureedge.net/buildartifacts/0.10/deploy-main-template.json>.
+<https://mmlspark.azureedge.net/buildartifacts/0.11/deploy-main-template.json>.
 
 It has the following parameters that configure the HDI Spark cluster and
 the associated GPU VM:
@@ -48,16 +48,16 @@ the associated GPU VM:
 - `gpuVirtualMachineSize`: The size of the GPU virtual machine to create
 
 There are actually two additional templates that are used from this main template:
-- [`spark-cluster-template.json`](https://mmlspark.azureedge.net/buildartifacts/0.10/spark-cluster-template.json):
+- [`spark-cluster-template.json`](https://mmlspark.azureedge.net/buildartifacts/0.11/spark-cluster-template.json):
   A template for creating an HDI Spark cluster within a VNet, including
   MMLSpark and its dependencies.  (This template installs MMLSpark using
   the HDI script action:
-  [`install-mmlspark.sh`](https://mmlspark.azureedge.net/buildartifacts/0.10/install-mmlspark.sh).)
-- [`gpu-vm-template.json`](https://mmlspark.azureedge.net/buildartifacts/0.10/gpu-vm-template.json):
+  [`install-mmlspark.sh`](https://mmlspark.azureedge.net/buildartifacts/0.11/install-mmlspark.sh).)
+- [`gpu-vm-template.json`](https://mmlspark.azureedge.net/buildartifacts/0.11/gpu-vm-template.json):
   A template for creating a GPU VM within an existing VNet, including
   CNTK and other dependencies that MMLSpark needs for GPU training.
   (This is done via a script action that runs
-  [`gpu-setup.sh`](https://mmlspark.azureedge.net/buildartifacts/0.10/gpu-setup.sh).)
+  [`gpu-setup.sh`](https://mmlspark.azureedge.net/buildartifacts/0.11/gpu-setup.sh).)
 
 Note that these child templates can also be deployed independently, if
 you don't need both parts of the installation.  Particularly, to scale
@@ -69,7 +69,7 @@ GPU VM setup template at experimentation time.
 ### 1. Deploy an ARM template within the [Azure Portal](https://ms.portal.azure.com/)
 
 [Click here to open the above main
-template](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fmmlspark.azureedge.net%2Fbuildartifacts%2F0.10%2Fdeploy-main-template.json)
+template](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fmmlspark.azureedge.net%2Fbuildartifacts%2F0.11%2Fdeploy-main-template.json)
 in the Azure portal.
 
 (If needed, you click the **Edit template** button to view and edit the
@@ -87,11 +87,11 @@ We also provide a convenient shell script to create a deployment on the
 command line:
 
 * Download the [shell
-  script](https://mmlspark.azureedge.net/buildartifacts/0.10/deploy-arm.sh)
+  script](https://mmlspark.azureedge.net/buildartifacts/0.11/deploy-arm.sh)
   and make a local copy of it
 
 * Create a JSON parameter file by downloading [this template
-  file](https://mmlspark.azureedge.net/buildartifacts/0.10/deploy-parameters.template)
+  file](https://mmlspark.azureedge.net/buildartifacts/0.11/deploy-parameters.template)
   and modify it according to your specification.
 
 You can now run the script — it takes the following arguments:
@@ -124,7 +124,7 @@ you for all needed values.
 ### 3. Deploy an ARM template with the MMLSpark Azure PowerShell
 
 MMLSpark also provides a [PowerShell
-script](https://mmlspark.azureedge.net/buildartifacts/0.10/deploy-arm.ps1)
+script](https://mmlspark.azureedge.net/buildartifacts/0.11/deploy-arm.ps1)
 to deploy ARM templates, similar to the above bash script.  Run it with
 `-?` to see the usage instructions (or use `get-help`).  If needed,
 install the Azure PowerShell cmdlets using the instructions in the