chamikaramj commented on code in PR #23619:
URL: https://github.com/apache/beam/pull/23619#discussion_r997210939
##########
examples/multi-language/src/main/java/org/apache/beam/examples/multilanguage/PythonDataframeWordCount.java:
##########
@@ -49,7 +49,6 @@
* ./gradlew :examples:multi-language:pythonDataframeWordCount --args=" \
* --runner=DataflowRunner \
* --output=gs://{$OUTPUT_BUCKET}/count \
- * --experiments=use_runner_v2 \
Review Comment:
Yeah, we don't need this anymore.
##########
examples/java/src/main/java/org/apache/beam/examples/multilanguage/SklearnMnistClassification.java:
##########
@@ -0,0 +1,148 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.beam.examples.multilanguage;
+
+import java.util.ArrayList;
+import java.util.List;
+import org.apache.beam.sdk.Pipeline;
+import org.apache.beam.sdk.coders.VarLongCoder;
+import org.apache.beam.sdk.extensions.python.transforms.RunInference;
+import org.apache.beam.sdk.io.TextIO;
+import org.apache.beam.sdk.options.Default;
+import org.apache.beam.sdk.options.Description;
+import org.apache.beam.sdk.options.PipelineOptions;
+import org.apache.beam.sdk.options.PipelineOptionsFactory;
+import org.apache.beam.sdk.options.Validation.Required;
+import org.apache.beam.sdk.schemas.Schema;
+import org.apache.beam.sdk.schemas.Schema.FieldType;
+import org.apache.beam.sdk.transforms.Filter;
+import org.apache.beam.sdk.transforms.MapElements;
+import org.apache.beam.sdk.transforms.SerializableFunction;
+import org.apache.beam.sdk.transforms.SimpleFunction;
+import org.apache.beam.sdk.values.KV;
+import org.apache.beam.sdk.values.PCollection;
+import org.apache.beam.sdk.values.Row;
+import org.apache.beam.vendor.guava.v26_0_jre.com.google.common.base.Splitter;
+
+/**
+ * An example Java MUlti-language pipeline that Performs image classification
on handwritten digits
+ * from the <a href="https://en.wikipedia.org/wiki/MNIST_database";>MNIST</a>
database.
+ *
+ * <p>For more details and instructions for running this please see <a
+ *
href="https://github.com/apache/beam/tree/master/examples/multi-language";>here</a>.
+ */
+public class SklearnMnistClassification {
+
+ private String getModelLoaderScript() {
+ String s = "from apache_beam.ml.inference.sklearn_inference import
SklearnModelHandlerNumpy\n";
+ s = s + "from apache_beam.ml.inference.base import KeyedModelHandler\n";
+ s = s + "def get_model_handler(model_uri):\n";
+ s = s + " return
KeyedModelHandler(SklearnModelHandlerNumpy(model_uri))\n";
+
+ return s;
+ }
+
+ static class FilterFn implements SerializableFunction<String, Boolean> {
+
+ @Override
+ public Boolean apply(String input) {
+ return !input.startsWith("label");
+ }
+ }
+
+ static class KVFn extends SimpleFunction<String, KV<Long, Iterable<Long>>> {
+
+ @Override
+ public KV<Long, Iterable<Long>> apply(String input) {
+ String[] data = Splitter.on(',').splitToList(input).toArray(new String[]
{});
+ Long label = Long.valueOf(data[0]);
+ List<Long> pixels = new ArrayList<Long>();
+ for (int i = 1; i < data.length; i++) {
+ pixels.add(Long.valueOf(data[i]));
+ }
+
+ return KV.of(label, pixels);
+ }
+ }
+
+ static class FormatOutput extends SimpleFunction<KV<Long, Row>, String> {
+
+ @Override
+ public String apply(KV<Long, Row> input) {
+ return input.getKey() + " was mapped to " +
input.getValue().getString("inference");
+ }
+ }
+
+ void runExample(SklearnMnistClassificationOptions options, String
expansionService) {
+ Schema schema =
+ Schema.of(
+ Schema.Field.of("example",
Schema.FieldType.array(Schema.FieldType.INT64)),
+ Schema.Field.of("inference", FieldType.STRING));
+
+ Pipeline pipeline = Pipeline.create(options);
+ PCollection<KV<Long, Iterable<Long>>> col =
+ pipeline
+ .apply(TextIO.read().from(options.getInput()))
+ .apply(Filter.by(new FilterFn()))
+ .apply(MapElements.via(new KVFn()));
+ col.apply(
+ RunInference.ofKVs(getModelLoaderScript(), schema,
VarLongCoder.of())
+ .withKwarg("model_uri", options.getModelPath())
+ .withExpansionService(expansionService))
+ .apply(MapElements.via(new FormatOutput()))
+ .apply(TextIO.write().to(options.getOutput()));
+
+ pipeline.run().waitUntilFinish();
+ }
+
+ public interface SklearnMnistClassificationOptions extends PipelineOptions {
+
+ @Description("Path to an input file that contains labels and pixels to
feed into the model")
+
@Default.String("gs://apache-beam-samples/multi-language/mnist/example_input.csv")
+ String getInput();
+
+ void setInput(String value);
+
+ @Description("Path for storing the output")
+ @Required
+ String getOutput();
+
+ void setOutput(String value);
+
+ @Description(
+ "Path to a model file that contains the pickled file of a scikit-learn
model trained on MNIST data")
+
@Default.String("gs://apache-beam-samples/multi-language/mnist/example_model")
+ String getModelPath();
+
+ void setModelPath(String value);
+
+ /** Set this option to specify Python expansion service URL. */
+ @Description("URL of Python expansion service")
+ @Default.String("")
Review Comment:
The default value should work for all runner after 2.42.0 is released.
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