lindong28 commented on code in PR #115: URL: https://github.com/apache/flink-ml/pull/115#discussion_r905821192
########## docs/content/docs/try-flink-ml/quick-start.md: ########## @@ -27,213 +27,64 @@ under the License. # Quick Start This document provides a quick introduction to using Flink ML. Readers of this -document will be guided to create a simple Flink job that trains a Machine +document will be guided to submit a simple Flink job that trains a Machine Learning Model and use it to provide prediction service. -## Maven Setup +## Prerequisite -In order to use Flink ML in a Maven project, add the following dependencies to -`pom.xml`. +Please make sure you have installed Flink 1.15.0 in your local environment and +configured `$FLINK_HOME`. For instructions of setting up Flink environment, +please refer to [Flink's +document](https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/try-flink/local_installation/). -{{< artifact flink-ml-core >}} +## Download & Compile Flink ML -{{< artifact flink-ml-iteration >}} +<!-- TODO: Modify this section after Flink ML's binary distribution is released --> -{{< artifact flink-ml-lib >}} +In order to follow guidelines in this document, you need to have the latest +binary distribution of Flink ML. You can acquire the distribution by building +Flink ML's source code locally with the following command. -The example code provided in this document requires additional dependencies on -the Flink Table API. In order to execute the example code successfully, please -make sure the following dependencies also exist in `pom.xml`. - -```xml -<dependency> - <groupId>org.apache.flink</groupId> - <artifactId>flink-clients</artifactId> - <version>1.15.0</version> -</dependency> -``` - -```xml -<dependency> - <groupId>org.apache.flink</groupId> - <artifactId>flink-table-planner-loader</artifactId> - <version>1.15.0</version> -</dependency> -``` - -## Flink ML Example - -Kmeans is a widely-used clustering algorithm and has been supported by Flink ML. -The example code below creates a Flink job with Flink ML that initializes and -trains a Kmeans model, and finally uses it to predict the cluster id of certain -data points. - -```java -import org.apache.flink.ml.clustering.kmeans.KMeans; -import org.apache.flink.ml.clustering.kmeans.KMeansModel; -import org.apache.flink.ml.linalg.DenseVector; -import org.apache.flink.ml.linalg.Vectors; -import org.apache.flink.streaming.api.datastream.DataStream; -import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; -import org.apache.flink.table.api.Table; -import org.apache.flink.table.api.bridge.java.StreamTableEnvironment; -import org.apache.flink.types.Row; -import org.apache.flink.util.CloseableIterator; - -public class QuickStart { - public static void main(String[] args) { - StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); - StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); - - String featuresCol = "features"; - String predictionCol = "prediction"; - - // Generate train data and predict data as DataStream. - DataStream<DenseVector> inputStream = env.fromElements( - Vectors.dense(0.0, 0.0), - Vectors.dense(0.0, 0.3), - Vectors.dense(0.3, 0.0), - Vectors.dense(9.0, 0.0), - Vectors.dense(9.0, 0.6), - Vectors.dense(9.6, 0.0) - ); - - // Convert data from DataStream to Table, as Flink ML uses Table API. - Table input = tEnv.fromDataStream(inputStream).as(featuresCol); - - // Creates a K-means object and initialize its parameters. - KMeans kmeans = new KMeans() - .setK(2) - .setSeed(1L) - .setFeaturesCol(featuresCol) - .setPredictionCol(predictionCol); - - // Trains the K-means Model. - KMeansModel model = kmeans.fit(input); - - // Use the K-means Model for predictions. - Table output = model.transform(input)[0]; - - // Extracts and displays prediction result. - for (CloseableIterator<Row> it = output.execute().collect(); it.hasNext(); ) { - Row row = it.next(); - DenseVector vector = (DenseVector) row.getField(featuresCol); - int clusterId = (Integer) row.getField(predictionCol); - System.out.println("Vector: " + vector + "\tCluster ID: " + clusterId); - } - } -} +```bash +cd ${path_to_flink_ml} +mvn clean package -DskipTests ``` -After placing the code above into your Maven project and executing it, -information like below will be printed out to your terminal window. +Then you will find Flink ML's binary distribution in +`./flink-ml-dist/target/flink-ml-*-bin/flink-ml*/`. Copy the generated jars to +your Flink environment to finish the setup. +```shell +cd ./flink-ml-dist/target/flink-ml-*-bin/flink-ml*/ +cp ./lib/*.jar $FLINK_HOME/lib/ ``` -Vector: [0.3, 0.0] Cluster ID: 1 -Vector: [9.6, 0.0] Cluster ID: 0 -Vector: [9.0, 0.6] Cluster ID: 0 -Vector: [0.0, 0.0] Cluster ID: 1 -Vector: [0.0, 0.3] Cluster ID: 1 -Vector: [9.0, 0.0] Cluster ID: 0 -``` - -## Breaking Down The Code - -### The Execution Environment -The first lines set up the `StreamExecutionEnvironment` to execute the Flink ML -job. You would have been familiar with this concept if you have experience using -Flink. For the example program in this document, a simple -`StreamExecutionEnvironment` without specific configurations would be enough. +## Starting Flink cluster and submitting Flink ML example job -Given that Flink ML uses Flink's Table API, a `StreamTableEnvironment` would -also be necessary for the following program. +Under `$FLINK_HOME` directory, you may follow these instructions to start a +Flink cluster and submit Flink ML examples to it. -```java -StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); -StreamTableEnvironment tEnv = StreamTableEnvironment.create(env); ``` - -### Creating Training & Inference Data Table - -Then the program creates the Table containing data for the training and -prediction process of the following Kmeans algorithm. Flink ML operators -search the names of the columns of the input table for input data, and produce -prediction results to designated column of the output Table. - -```java -DataStream<DenseVector> inputStream = env.fromElements( - Vectors.dense(0.0, 0.0), - Vectors.dense(0.0, 0.3), - Vectors.dense(0.3, 0.0), - Vectors.dense(9.0, 0.0), - Vectors.dense(9.0, 0.6), - Vectors.dense(9.6, 0.0) -); - -Table input = tEnv.fromDataStream(inputStream).as(featuresCol); +./bin/start-cluster.sh +./bin/flink run -c org.apache.flink.ml.examples.clustering.KMeansExample ./lib/flink-ml-examples*.jar ``` -### Creating, Configuring, Training & Using Kmeans - -Flink ML classes for Kmeans algorithm include `KMeans` and `KMeansModel`. -`KMeans` implements the training process of Kmeans algorithm based on the -provided training data, and finally generates a `KMeansModel`. -`KmeansModel.transform()` method encodes the Transformation logic of this -algorithm and is used for predictions. - -Both `KMeans` and `KMeansModel` provides getter/setter methods for Kmeans -algorithm's configuration parameters. The example program explicitly sets the -following parameters, and other configuration parameters will have their default -values used. - -- `K`, the number of clusters to create -- `seed`, the random seed to initialize cluster centers -- `featuresCol`, name of the column containing input feature vectors -- `predictionCol`, name of the column to output prediction results +The command above would submit and execute Flink ML's `KMeansExample` job. There +are also example jobs for other Flink ML algorithms, and you can find them in +`flink-ml-examples` module. -When the program invokes `KMeans.fit()` to generate a `KMeansModel`, the -`KMeansModel` will inherit the `KMeans` object's configuration parameters. Thus -it is supported to set `KMeansModel`'s parameters directly in `KMeans` object. - -```java -KMeans kmeans = new KMeans() - .setK(2) - .setSeed(1L) - .setFeaturesCol(featuresCol) - .setPredictionCol(predictionCol); - -KMeansModel model = kmeans.fit(input); - -Table output = model.transform(input)[0]; +A sample output in your terminal is as follows. ``` +Features: [9.0, 0.0] Cluster ID: 1 +Features: [0.3, 0.0] Cluster ID: 0 +Features: [0.0, 0.3] Cluster ID: 0 +Features: [9.6, 0.0] Cluster ID: 1 +Features: [0.0, 0.0] Cluster ID: 0 +Features: [9.0, 0.6] Cluster ID: 1 -### Collecting Prediction Result - -Like all other Flink programs, the codes described in the sections above only -configures the computation graph of a Flink job, and the program only evaluates -the computation logic and collects outputs after the `execute()` method is -invoked. Collected outputs from the output table would be `Row`s in which -`featuresCol` contains input feature vectors, and `predictionCol` contains -output prediction results, i.e., cluster IDs. - -```java -for (CloseableIterator<Row> it = output.execute().collect(); it.hasNext(); ) { - Row row = it.next(); - DenseVector vector = (DenseVector) row.getField(featuresCol); - int clusterId = (Integer) row.getField(predictionCol); - System.out.println("Vector: " + vector + "\tCluster ID: " + clusterId); -} ``` -``` -Vector: [0.3, 0.0] Cluster ID: 1 -Vector: [9.6, 0.0] Cluster ID: 0 -Vector: [9.0, 0.6] Cluster ID: 0 -Vector: [0.0, 0.0] Cluster ID: 1 -Vector: [0.0, 0.3] Cluster ID: 1 -Vector: [9.0, 0.0] Cluster ID: 0 -``` +You have successfully ran a Flink ML job. Now please feel free to explore other Review Comment: Is this paragraph copied and pasted from other places? It seems that there is no further section in this document. -- This is an automated message from the Apache Git Service. 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