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// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.vespa.model.ml;
import ai.vespa.models.evaluation.Model;
import ai.vespa.models.evaluation.ModelsEvaluator;
import ai.vespa.models.evaluation.RankProfilesConfigImporter;
import com.yahoo.component.ComponentId;
import com.yahoo.config.FileReference;
import com.yahoo.config.application.api.ApplicationPackage;
import com.yahoo.filedistribution.fileacquirer.FileAcquirer;
import com.yahoo.filedistribution.fileacquirer.MockFileAcquirer;
import com.yahoo.io.IOUtils;
import com.yahoo.path.Path;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.vespa.config.search.RankProfilesConfig;
import com.yahoo.vespa.config.search.core.RankingConstantsConfig;
import com.yahoo.vespa.model.VespaModel;
import com.yahoo.vespa.model.container.ContainerCluster;
import org.junit.Test;
import java.io.IOException;
import java.util.Set;
import java.util.stream.Collectors;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
/**
* @author bratseth
*/
public class ModelEvaluationTest {
@Test
public void testMl_serving() throws IOException {
Path appDir = Path.fromString("src/test/cfg/application/ml_serving");
Path storedAppDir = appDir.append("copy");
try {
ImportedModelTester tester = new ImportedModelTester("ml_serving", appDir);
assertHasMlModels(tester.createVespaModel());
// At this point the expression is stored - copy application to another location which do not have a models dir
storedAppDir.toFile().mkdirs();
IOUtils.copy(appDir.append("services.xml").toString(), storedAppDir.append("services.xml").toString());
IOUtils.copyDirectory(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile(),
storedAppDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
ImportedModelTester storedTester = new ImportedModelTester("ml_serving", storedAppDir);
assertHasMlModels(storedTester.createVespaModel());
}
finally {
IOUtils.recursiveDeleteDir(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
IOUtils.recursiveDeleteDir(storedAppDir.toFile());
}
}
/** Tests that we do not load models (which will waste memory) when not requested */
@Test
public void testMl_serving_not_activated() throws IOException {
Path appDir = Path.fromString("src/test/cfg/application/ml_serving_not_activated");
try {
ImportedModelTester tester = new ImportedModelTester("ml_serving", appDir);
VespaModel model = tester.createVespaModel();
ContainerCluster cluster = model.getContainerClusters().get("container");
assertNull(cluster.getComponentsMap().get(new ComponentId(ModelsEvaluator.class.getName())));
RankProfilesConfig.Builder b = new RankProfilesConfig.Builder();
cluster.getConfig(b);
RankProfilesConfig config = new RankProfilesConfig(b);
assertEquals(0, config.rankprofile().size());
}
finally {
IOUtils.recursiveDeleteDir(appDir.append(ApplicationPackage.MODELS_GENERATED_DIR).toFile());
}
}
private void assertHasMlModels(VespaModel model) {
ContainerCluster cluster = model.getContainerClusters().get("container");
assertNotNull(cluster.getComponentsMap().get(new ComponentId(ModelsEvaluator.class.getName())));
RankProfilesConfig.Builder b = new RankProfilesConfig.Builder();
cluster.getConfig(b);
RankProfilesConfig config = new RankProfilesConfig(b);
RankingConstantsConfig.Builder cb = new RankingConstantsConfig.Builder();
cluster.getConfig(cb);
RankingConstantsConfig constantsConfig = new RankingConstantsConfig(cb);
assertEquals(4, config.rankprofile().size());
Set<String> modelNames = config.rankprofile().stream().map(v -> v.name()).collect(Collectors.toSet());
assertTrue(modelNames.contains("xgboost_2_2"));
assertTrue(modelNames.contains("mnist_saved"));
assertTrue(modelNames.contains("mnist_softmax"));
assertTrue(modelNames.contains("mnist_softmax_saved"));
ModelsEvaluator evaluator = new ModelsEvaluator(new ToleratingMissingConstantFilesRankProfilesConfigImporter(MockFileAcquirer.returnFile(null))
.importFrom(config, constantsConfig));
assertEquals(4, evaluator.models().size());
Model xgboost = evaluator.models().get("xgboost_2_2");
assertNotNull(xgboost);
assertNotNull(xgboost.evaluatorOf());
assertNotNull(xgboost.evaluatorOf("xgboost_2_2"));
Model tensorflow_mnist = evaluator.models().get("mnist_saved");
assertNotNull(tensorflow_mnist);
assertNotNull(tensorflow_mnist.evaluatorOf("serving_default"));
assertNotNull(tensorflow_mnist.evaluatorOf("serving_default", "y"));
assertNotNull(tensorflow_mnist.evaluatorOf("serving_default.y"));
assertNotNull(evaluator.evaluatorOf("mnist_saved", "serving_default.y"));
assertNotNull(evaluator.evaluatorOf("mnist_saved", "serving_default", "y"));
Model onnx_mnist_softmax = evaluator.models().get("mnist_softmax");
assertNotNull(onnx_mnist_softmax);
assertNotNull(onnx_mnist_softmax.evaluatorOf());
assertNotNull(onnx_mnist_softmax.evaluatorOf("default"));
assertNotNull(onnx_mnist_softmax.evaluatorOf("default", "add"));
assertNotNull(onnx_mnist_softmax.evaluatorOf("default.add"));
assertNotNull(evaluator.evaluatorOf("mnist_softmax", "default.add"));
assertNotNull(evaluator.evaluatorOf("mnist_softmax", "default", "add"));
Model tensorflow_mnist_softmax = evaluator.models().get("mnist_softmax_saved");
assertNotNull(tensorflow_mnist_softmax);
assertNotNull(tensorflow_mnist_softmax.evaluatorOf());
assertNotNull(tensorflow_mnist_softmax.evaluatorOf("serving_default"));
assertNotNull(tensorflow_mnist_softmax.evaluatorOf("serving_default", "y"));
}
// We don't have function file distribution so just return empty tensor constants
private static class ToleratingMissingConstantFilesRankProfilesConfigImporter extends RankProfilesConfigImporter {
public ToleratingMissingConstantFilesRankProfilesConfigImporter(FileAcquirer fileAcquirer) {
super(fileAcquirer);
}
protected Tensor readTensorFromFile(String name, TensorType type, FileReference fileReference) {
return Tensor.from(type, "{}");
}
}
}
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