diff --git a/README.md b/README.md index 86058697a5..d205a15229 100644 --- a/README.md +++ b/README.md @@ -103,9 +103,9 @@ MMLSpark can be conveniently installed on existing Spark clusters via the `--packages` option, examples: ```bash - spark-shell --packages Azure:mmlspark:0.9 - pyspark --packages Azure:mmlspark:0.9 - spark-submit --packages Azure:mmlspark:0.9 MyApp.jar + spark-shell --packages Azure:mmlspark:0.10 + pyspark --packages Azure:mmlspark:0.10 + spark-submit --packages Azure:mmlspark:0.10 MyApp.jar ``` @@ -119,7 +119,7 @@ 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: -. +. 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 @@ -135,7 +135,7 @@ 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:0.9`. Then, under +For the coordinates use: `com.microsoft.ml.spark:mmlspark:0.10`. Then, under Advanced Options, use `https://mmlspark.azureedge.net/maven` for the repository. Ensure this library is attached to all clusters you create. @@ -150,7 +150,7 @@ your `build.sbt`: ```scala resolvers += "MMLSpark Repo" at "https://mmlspark.azureedge.net/maven" - libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.9" + libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.10" ``` ### Building from source diff --git a/docs/docker.md b/docs/docker.md index 17fd10d015..279095b859 100644 --- a/docs/docker.md +++ b/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:0.9` for using the `0.9` tag. +`microsoft/mmlspark:0.10` for using the `0.10` 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:0.9 + microsoft/mmlspark:0.10 ``` 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:0.9 + microsoft/mmlspark:0.10 ``` 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:0.9`** +* **`microsoft/mmlspark:0.10`** Finally, this specifies an explicit version tag for the image that we want to run. diff --git a/docs/gpu-setup.md b/docs/gpu-setup.md index a0aa612f34..d8255cf859 100644 --- a/docs/gpu-setup.md +++ b/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 setup that includes an HDInsight cluster and/or a GPU machine for training. The template can be found here: -. +. 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/0.9/spark-cluster-template.json): +- [`spark-cluster-template.json`](https://mmlspark.azureedge.net/buildartifacts/0.10/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/0.9/install-mmlspark.sh).) -- [`gpu-vm-template.json`](https://mmlspark.azureedge.net/buildartifacts/0.9/gpu-vm-template.json): + [`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): 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/0.9/gpu-setup.sh).) + [`gpu-setup.sh`](https://mmlspark.azureedge.net/buildartifacts/0.10/gpu-setup.sh).) Note that these child templates can also be deployed independently, if you don't need both parts of the installation. @@ -66,7 +66,7 @@ you don't need both parts of the installation. ### 1. Deploy an ARM template within the [Azure Portal](https://ms.portal.azure.com/) [Click here to open the above -template](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fmmlspark.azureedge.net%2Fbuildartifacts%2F0.9%2Fdeploy-main-template.json) +template](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fmmlspark.azureedge.net%2Fbuildartifacts%2F0.10%2Fdeploy-main-template.json) in the Azure portal. (If needed, you click the **Edit template** button to view and edit the @@ -84,11 +84,11 @@ We also provide a convenient shell script to create a deployment on the command line: * Download the [shell - script](https://mmlspark.azureedge.net/buildartifacts/0.9/deploy-arm.sh) + script](https://mmlspark.azureedge.net/buildartifacts/0.10/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/0.9/deploy-parameters.template) + file](https://mmlspark.azureedge.net/buildartifacts/0.10/deploy-parameters.template) and modify it according to your specification. You can now run the script — it takes the following arguments: @@ -121,7 +121,7 @@ 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/0.9/deploy-arm.ps1) +script](https://mmlspark.azureedge.net/buildartifacts/0.10/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