Update docs links to new website URLs [WIP] (#452)

* Update docs links to new website URLs

Signed-off-by: Alex Black <blacka101@gmail.com>

* One more link

Signed-off-by: Alex Black <blacka101@gmail.com>

deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/exceptions/InvalidKerasConfigurationException.java

@@ -21,7 +21,7 @@ package org.deeplearning4j.nn.modelimport.keras.exceptions;
  * Indicates that user is attempting to import a Keras model configuration that
  * is malformed or invalid in some other way.
  *
- * See <a href="https://deeplearning4j.org/docs/latest/keras-import-overview">https://deeplearning4j.org/docs/latest/keras-import-overview</a> for more information.
+ * See <a href="https://deeplearning4j.konduit.ai/keras-import/overview">https://deeplearning4j.konduit.ai/keras-import/overview</a> for more information.
  *
  * @author dave@skymind.io
  */
@@ -40,6 +40,6 @@ public class InvalidKerasConfigurationException extends Exception {
     }
 
     private static String appendDocumentationURL(String message) {
-        return message + ". For more information, see http://deeplearning4j.org/docs/latest/keras-import-overview";
+        return message + ". For more information, see https://deeplearning4j.konduit.ai/keras-import/overview";
     }
 }

deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/exceptions/UnsupportedKerasConfigurationException.java

@@ -21,7 +21,7 @@ package org.deeplearning4j.nn.modelimport.keras.exceptions;
  * Indicates that user is attempting to import a Keras model configuration that
  * is not currently supported.
  *
- * See <a href="https://deeplearning4j.org/docs/latest/keras-import-overview">https://deeplearning4j.org/docs/latest/keras-import-overview</a>
+ * See <a href="https://deeplearning4j.konduit.ai/keras-import/overview">https://deeplearning4j.konduit.ai/keras-import/overview</a>
  * for more information and file an issue at <a href="https://github.com/eclipse/deeplearning4j/issues">https://github.com/eclipse/deeplearning4j/issues</a>.
  *
  * @author dave@skymind.io

deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbedding.java

@@ -103,7 +103,7 @@ public class KerasEmbedding extends KerasLayer {
                     "on Embedding layers. Zero Masking for the Embedding layer only works with unidirectional LSTM for now."
                     + " If you want to have this behaviour for your imported model " +
                     "in DL4J, apply masking as a pre-processing step to your input." +
-                    "See http://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent#masking for more on this.");
+                    "See https://deeplearning4j.konduit.ai/models/recurrent#masking-one-to-many-many-to-one-and-sequence-classification for more on this.");
 
         IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_EMBEDDING_INIT(),
                 enforceTrainingConfig, conf, kerasMajorVersion);

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/WorkspaceMode.java

@@ -17,10 +17,10 @@
 package org.deeplearning4j.nn.conf;
 
 /**
- * Workspace mode to use. See <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces">https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces</a><br>
+ * Workspace mode to use. See <a href="https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces">https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces</a><br>
  * <br>
  * NONE: No workspaces will be used for the network. Highest memory use, least performance.<br>
- * ENABLED: Use workspaces.<br>
+ * ENABLED: Use workspaces. This is the default and should almost always be used<br>
  * SINGLE: Deprecated. Now equivalent to ENABLED, which should be used instead.<br>
  * SEPARATE: Deprecated. Now equivalent to ENABLED, which sohuld be used instead.<br>
  *

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

@@ -38,7 +38,7 @@ import java.util.Map;
 
 /**
  * LSTM recurrent neural network layer without peephole connections. Supports CuDNN acceleration - see <a
- * href="https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn">https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn</a> for details
+ * href="https://deeplearning4j.konduit.ai/config/backends/config-cudnn">https://deeplearning4j.konduit.ai/config/backends/config-cudnn</a> for details
  *
  * @author Alex Black
  * @see GravesLSTM GravesLSTM class for an alternative LSTM (with peephole connections)

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java

@@ -1540,8 +1540,8 @@ public class ComputationGraph implements Serializable, Model, NeuralNetwork {
      * (not) clearing the layer input arrays.<br>
      * Note: this method should NOT be used with clearInputs = true, unless you know what you are doing. Specifically:
      * when using clearInputs=false, in combination with workspaces, the layer input fields may leak outside of the
-     * workspaces in which they were defined - potentially causing a crash. See <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces">
+     * workspaces in which they were defined - potentially causing a crash. See <a href="https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces">
-     *     https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces</a>
+     *     https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces</a>
      * for more details
      *
      * @param input An array of ComputationGraph inputs

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

@@ -86,7 +86,7 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
                 } else {
                     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/docs/latest/deeplearning4j-config-cudnn", t);
+                            + "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
                 }
             }
         } else if("CPU".equalsIgnoreCase(backend)){

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

@@ -78,7 +78,7 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
                 } else {
                     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/docs/latest/deeplearning4j-config-cudnn", t);
+                            + "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
                 }
             }
         } else if("CPU".equalsIgnoreCase(backend) ){

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

@@ -86,7 +86,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
                 } else {
                     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/docs/latest/deeplearning4j-config-cudnn", t);
+                            + "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
                 }
             }
         } else if("CPU".equalsIgnoreCase(backend)){

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

@@ -96,7 +96,7 @@ public class LocalResponseNormalization
                 } else {
                     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/docs/latest/deeplearning4j-config-cudnn", t);
+                            + "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
                 }
             }
         }

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

@@ -32,7 +32,7 @@ import java.util.Map;
 
 /**
  *
- * RNN tutorial: https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent
+ * RNN tutorial: https://deeplearning4j.konduit.ai/models/recurrent
  * READ THIS FIRST
  *
  * Bdirectional LSTM layer implementation.

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

@@ -71,7 +71,7 @@ public class LSTM extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.layers.L
                 } else {
                     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/docs/latest/deeplearning4j-config-cudnn", t);
+                            + "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
                 }
             }
         }

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

@@ -52,8 +52,8 @@ import static org.nd4j.linalg.indexing.NDArrayIndex.*;
 
 /**
  *
- * RNN tutorial: <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent">https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent</a>
+ * RNN tutorial: <a href="https://deeplearning4j.konduit.ai/models/recurrent">https://deeplearning4j.konduit.ai/models/recurrent</a>
- * READ THIS FIRST if you want to understand what the heck is happening here.
+ * READ THIS FIRST if you want to understand this code.
  *
  * Shared code for the standard "forwards" LSTM RNN and the bidirectional LSTM RNN
  * This was extracted from GravesLSTM and refactored into static helper functions.  The general reasoning for this was

deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelWrapper.java

@@ -826,7 +826,7 @@ public class ParallelWrapper implements AutoCloseable {
         /**
          *  This method allows you to specify training mode for this instance of PW.<br>
          *  1) AVERAGING - stands for parameters averaging. Each X epochs weights and updaters state will be averaged across all models<br>
-         *  2) SHARED_GRADIENTS - stands for gradients sharing - more details available here: <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-intro">https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-intro</a><br>
+         *  2) SHARED_GRADIENTS - stands for gradients sharing - more details available here: <a href="https://deeplearning4j.konduit.ai/distributed-deep-learning/intro">https://deeplearning4j.konduit.ai/distributed-deep-learning/intro</a><br>
          *  3) CUSTOM - this method allows you to specify custom gradients accumulator, this giving you better control of configuration params for training.<br>
          *
          * @param mode

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/main/java/org/deeplearning4j/spark/util/SparkUtils.java

@@ -71,7 +71,7 @@ public class SparkUtils {
                     + "for ND4J INDArrays.\nWhen using Kryo, An appropriate Kryo registrator must be used to avoid"
                     + " serialization issues (NullPointerException) with off-heap data in INDArrays.\n"
                     + "Use nd4j-kryo_2.10 or _2.11 artifact, with sparkConf.set(\"spark.kryo.registrator\", \"org.nd4j.kryo.Nd4jRegistrator\");\n"
-                    + "See https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-howto#kryo for more details";
+                    + "See https://deeplearning4j.konduit.ai/distributed-deep-learning/howto#how-to-use-kryo-serialization-with-dl-4-j-and-nd-4-j for more details";
 
     private static String sparkExecutorId;
 

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/multilayer/TestSparkDl4jMultiLayer.java

@@ -108,7 +108,7 @@ public class TestSparkDl4jMultiLayer extends BaseSparkTest {
                             .activation(Activation.SOFTMAX).nIn(100).nOut(10).build())
                     .build();
 
-            //Configuration for Spark training: see https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-howto for explanation of these configuration options
+            //Configuration for Spark training: see https://deeplearning4j.konduit.ai/distributed-deep-learning/howto for explanation of these configuration options
 
             TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(batchSizePerWorker)
                     .averagingFrequency(2)

deeplearning4j/dl4j-integration-tests/src/test/java/org/deeplearning4j/integration/testcases/dl4j/misc/CharacterIterator.java

@@ -195,7 +195,7 @@ public class CharacterIterator implements DataSetIterator {
         // dimension 0 = number of examples in minibatch
         // dimension 1 = size of each vector (i.e., number of characters)
         // dimension 2 = length of each time series/example
-        //Why 'f' order here? See https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent data section "Alternative: Implementing a custom DataSetIterator"
+        //Why 'f' order here? See https://deeplearning4j.konduit.ai/models/recurrent data section "Alternative: Implementing a custom DataSetIterator"
         INDArray input = Nd4j.create(new int[]{currMinibatchSize, validCharacters.length, exampleLength}, 'f');
         INDArray labels = Nd4j.create(new int[]{currMinibatchSize, validCharacters.length, exampleLength}, 'f');
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/executioner/DefaultOpExecutioner.java

@@ -61,7 +61,7 @@ import java.util.*;
 @Slf4j
 public abstract class DefaultOpExecutioner implements OpExecutioner {
 
-    private static final String SCOPE_PANIC_MSG = "For more details, see the ND4J User Guide: deeplearning4j.org/docs/latest/nd4j-overview#workspaces-panic";
+    private static final String SCOPE_PANIC_MSG = "For more details, see the ND4J User Guide: https://deeplearning4j.konduit.ai/nd4j/overview#workspaces-scope-panic";
 
     protected ProfilingMode profilingMode = ProfilingMode.SCOPE_PANIC;