Tag some junit tests with @Tag("long-running") to skip them during normal build
Signed-off-by: brian <brian@brutex.de>master
parent
f7be1e324f
commit
0e4be5c4d2
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@ -16,6 +16,7 @@ import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.optimize.listeners.PerformanceListener;
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import org.deeplearning4j.optimize.listeners.PerformanceListener;
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import org.junit.jupiter.api.Tag;
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import org.junit.jupiter.api.Test;
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import org.junit.jupiter.api.Test;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.activations.impl.ActivationLReLU;
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import org.nd4j.linalg.activations.impl.ActivationLReLU;
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@ -122,7 +123,7 @@ public class App {
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return conf;
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return conf;
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}
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}
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@Test
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@Test @Tag("long-running")
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public void runTest() throws Exception {
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public void runTest() throws Exception {
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App.main(null);
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App.main(null);
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}
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}
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@ -44,6 +44,7 @@ import org.deeplearning4j.nn.conf.layers.*;
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import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
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import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.optimize.listeners.PerformanceListener;
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import org.deeplearning4j.optimize.listeners.PerformanceListener;
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import org.junit.jupiter.api.Tag;
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import org.junit.jupiter.api.Test;
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import org.junit.jupiter.api.Test;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.dataset.DataSet;
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@ -70,7 +71,7 @@ public class App2 {
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final static INDArray label_real = Nd4j.ones(BATCHSIZE, 1);
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final static INDArray label_real = Nd4j.ones(BATCHSIZE, 1);
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final static INDArray label_fake = Nd4j.zeros(BATCHSIZE, 1);
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final static INDArray label_fake = Nd4j.zeros(BATCHSIZE, 1);
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@Test
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@Test @Tag("long-running")
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void runTest() throws IOException {
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void runTest() throws IOException {
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Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
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Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
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@ -31,6 +31,7 @@ import org.deeplearning4j.nn.conf.layers.DropoutLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.deeplearning4j.nn.weights.WeightInit;
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import org.junit.jupiter.api.Tag;
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import org.junit.jupiter.api.Test;
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import org.junit.jupiter.api.Test;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.activations.impl.ActivationLReLU;
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import org.nd4j.linalg.activations.impl.ActivationLReLU;
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@ -100,7 +101,7 @@ public class MnistSimpleGAN {
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return new MultiLayerNetwork(discConf);
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return new MultiLayerNetwork(discConf);
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}
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}
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@Test
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@Test @Tag("long-running")
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public void runTest() throws Exception {
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public void runTest() throws Exception {
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main(null);
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main(null);
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}
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}
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