Log information proposing to use cuDNN when appropriate

deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ConvolutionLayer.java

@@ -29,6 +29,7 @@
 import org.deeplearning4j.nn.layers.BaseLayer;
 import org.deeplearning4j.nn.params.ConvolutionParamInitializer;
 import org.deeplearning4j.util.ConvolutionUtils;
+import org.deeplearning4j.util.OneTimeLogger;
 import org.nd4j.linalg.activations.IActivation;
 import org.nd4j.linalg.api.memory.MemoryWorkspace;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -41,6 +42,7 @@
 
 import java.util.Arrays;
 import java.util.Map;
+import java.util.Properties;
 
 
 /**
@@ -80,6 +82,13 @@ void initializeHelper() {
         } catch (Throwable t) {
             if (!(t instanceof ClassNotFoundException)) {
                 log.warn("Could not initialize CudnnConvolutionHelper", t);
+            } else {
+                Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
+                if (p.getProperty("backend").equals("CUDA")) {
+                    OneTimeLogger.info(log, "cuDNN not found: "
+                                    + "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
+                                    + "For more information, please refer to: https://deeplearning4j.org/cudnn", t);
+                }
             }
         }
     }

deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/SubsamplingLayer.java

@@ -28,6 +28,7 @@
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.layers.AbstractLayer;
 import org.deeplearning4j.util.ConvolutionUtils;
+import org.deeplearning4j.util.OneTimeLogger;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.impl.transforms.IsMax;
 import org.nd4j.linalg.api.ops.impl.transforms.convolution.Pooling2D;
@@ -39,6 +40,7 @@
 import org.nd4j.linalg.util.ArrayUtil;
 
 import java.util.Arrays;
+import java.util.Properties;
 
 
 /**
@@ -77,6 +79,13 @@ void initializeHelper() {
         } catch (Throwable t) {
             if (!(t instanceof ClassNotFoundException)) {
                 log.warn("Could not initialize CudnnSubsamplingHelper", t);
+            } else {
+                Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
+                if (p.getProperty("backend").equals("CUDA")) {
+                    OneTimeLogger.info(log, "cuDNN not found: "
+                                    + "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
+                                    + "For more information, please refer to: https://deeplearning4j.org/cudnn", t);
+                }
             }
         }
     }

deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/BatchNormalization.java

@@ -9,6 +9,7 @@
 import org.deeplearning4j.nn.layers.BaseLayer;
 import org.deeplearning4j.nn.params.BatchNormalizationParamInitializer;
 import org.deeplearning4j.optimize.api.IterationListener;
+import org.deeplearning4j.util.OneTimeLogger;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastAddOp;
 import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastDivOp;
@@ -23,6 +24,7 @@
 import java.util.Arrays;
 import java.util.Collection;
 import java.util.List;
+import java.util.Properties;
 
 /**
  * Batch normalization layer.
@@ -61,6 +63,13 @@ void initializeHelper() {
         } catch (Throwable t) {
             if (!(t instanceof ClassNotFoundException)) {
                 log.warn("Could not initialize CudnnBatchNormalizationHelper", t);
+            } else {
+                Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
+                if (p.getProperty("backend").equals("CUDA")) {
+                    OneTimeLogger.info(log, "cuDNN not found: "
+                                    + "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
+                                    + "For more information, please refer to: https://deeplearning4j.org/cudnn", t);
+                }
             }
         }
     }

deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/LocalResponseNormalization.java

@@ -6,14 +6,18 @@
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.layers.AbstractLayer;
+import org.deeplearning4j.util.OneTimeLogger;
 import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.INDArrayIndex;
 import org.nd4j.linalg.indexing.NDArrayIndex;
 import org.nd4j.linalg.ops.transforms.Transforms;
 import org.nd4j.linalg.primitives.Pair;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
+import java.util.Properties;
+
 import static org.nd4j.linalg.indexing.NDArrayIndex.interval;
 
 /**
@@ -81,6 +85,13 @@ void initializeHelper() {
         } catch (Throwable t) {
             if (!(t instanceof ClassNotFoundException)) {
                 log.warn("Could not initialize CudnnLocalResponseNormalizationHelper", t);
+            } else {
+                Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
+                if (p.getProperty("backend").equals("CUDA")) {
+                    OneTimeLogger.info(log, "cuDNN not found: "
+                                    + "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
+                                    + "For more information, please refer to: https://deeplearning4j.org/cudnn", t);
+                }
             }
         }
     }

deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/LSTM.java

@@ -26,10 +26,13 @@
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.params.LSTMParamInitializer;
+import org.deeplearning4j.util.OneTimeLogger;
 import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.primitives.Pair;
 
 import java.util.Map;
+import java.util.Properties;
 
 /**
  * LSTM layer implementation.
@@ -69,6 +72,13 @@ void initializeHelper() {
         } catch (Throwable t) {
             if (!(t instanceof ClassNotFoundException)) {
                 log.warn("Could not initialize CudnnLSTMHelper", t);
+            } else {
+                Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
+                if (p.getProperty("backend").equals("CUDA")) {
+                    OneTimeLogger.info(log, "cuDNN not found: "
+                                    + "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
+                                    + "For more information, please refer to: https://deeplearning4j.org/cudnn", t);
+                }
             }
         }
     }