diff --git a/README.md b/README.md index 90a75b1472..3e4e09b3ee 100644 --- a/README.md +++ b/README.md @@ -155,9 +155,9 @@ MMLSpark can be conveniently installed on existing Spark clusters via the `--packages` option, examples: ```bash - spark-shell --packages Azure:mmlspark:0.14 - pyspark --packages Azure:mmlspark:0.14 - spark-submit --packages Azure:mmlspark:0.14 MyApp.jar + spark-shell --packages Azure:mmlspark:0.15 + pyspark --packages Azure:mmlspark:0.15 + spark-submit --packages Azure:mmlspark:0.15 MyApp.jar ``` This can be used in other Spark contexts too. For example, you can use MMLSpark @@ -172,14 +172,14 @@ 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:0.14`. Ensure this library is +For the coordinates use: `Azure:mmlspark:0.15`. 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%20v0.14.dbc``` +```https://mmlspark.blob.core.windows.net/dbcs/MMLSpark%20Examples%20v0.15.dbc``` ### Docker @@ -212,7 +212,7 @@ the above example, or from python: ```python import pyspark spark = pyspark.sql.SparkSession.builder.appName("MyApp") \ - .config("spark.jars.packages", "Azure:mmlspark:0.14") \ + .config("spark.jars.packages", "Azure:mmlspark:0.15") \ .getOrCreate() import mmlspark ``` @@ -228,7 +228,7 @@ 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: -. +. 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 @@ -244,7 +244,7 @@ your `build.sbt`: ```scala resolvers += "MMLSpark Repo" at "https://mmlspark.azureedge.net/maven" - libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.14" + libraryDependencies += "com.microsoft.ml.spark" %% "mmlspark" % "0.15" ``` ### Building from source diff --git a/docs/R-setup.md b/docs/R-setup.md index 1c1b47b40c..09996c4bb1 100644 --- a/docs/R-setup.md +++ b/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-0.14.zip") + devtools::install_url("https://mmlspark.azureedge.net/rrr/mmlspark-0.15.zip") ... ``` @@ -23,7 +23,7 @@ It will take some time to install all dependencies. Then, run: library(sparklyr) library(dplyr) config <- spark_config() - config$sparklyr.defaultPackages <- "Azure:mmlspark:0.14" + config$sparklyr.defaultPackages <- "Azure:mmlspark:0.15" 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-0.14.zip") + devtools::install_url("https://mmlspark.azureedge.net/rrr/mmlspark-0.15.zip") library(sparklyr) library(dplyr) sc <- spark_connect(method = "databricks") diff --git a/docs/docker.md b/docs/docker.md index 670216a3c1..8bd6bd285c 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:0.14` for the `0.14` tag. +`microsoft/mmlspark:0.15` for the `0.15` 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:0.14 + microsoft/mmlspark:0.15 ``` 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:0.14 + microsoft/mmlspark:0.15 ``` 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:0.14`** +* **`microsoft/mmlspark:0.15`** 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 57b67af1e5..4d7170aea4 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 default setup that includes an HDInsight cluster and 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: @@ -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%2F0.14%2Fdeploy-main-template.json) +template](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fmmlspark.azureedge.net%2Fbuildartifacts%2F0.15%2Fdeploy-main-template.json) in the Azure portal. (If needed, you click the **Edit template** button to view and edit the