In addition to the common runtime issues that were discussed previously, Windows users may face cuDNN-specific errors while training a CNN. The actual root cause could be different and is tagged under UnsatisfiedLinkError:

o.d.n.l.c.ConvolutionLayer - Could not load CudnnConvolutionHelper
 java.lang.UnsatisfiedLinkError: no jnicudnn in java.library.path
 at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1867) ~[na:1.8.0_102]
 at java.lang.Runtime.loadLibrary0(Runtime.java:870) ~[na:1.8.0_102]
 at java.lang.System.loadLibrary(System.java:1122) ~[na:1.8.0_102]
 at org.bytedeco.javacpp.Loader.loadLibrary(Loader.java:945) ~[javacpp-1.3.1.jar:1.3.1]
 at org.bytedeco.javacpp.Loader.load(Loader.java:750) ~[javacpp-1.3.1.jar:1.3.1]
 Caused by: java.lang.UnsatisfiedLinkError: C:UsersJürgen.javacppcachecuda-7.5-1.3-windows-x86_64.jarorgytedecojavacppwindows-x86_64jnicudnn.dll: Can't find dependent libraries
 at java.lang.ClassLoader$NativeLibrary.load(Native Method) ~[na:1.8.0_102]

Perform the following steps to fix the issue:

1. Download the latest dependency walker here: https://github.com/lucasg/Dependencies/.
2. Add the following code to your DL4J main() method:

try {
 Loader.load(<module>.class);
 } catch (UnsatisfiedLinkError e) {
 String path = Loader.cacheResource(<module>.class, "windows-x86_64/jni<module>.dll").getPath();
 new ProcessBuilder("c:/path/to/DependenciesGui.exe", path).start().waitFor();
 }

3. Replace <module> with the name of the JavaCPP preset module that is experiencing the problem; for example, cudnn. For newer DL4J versions, the necessary CUDA libraries are bundled with DL4J. Hence, you should not face this issue.

If you feel like you might have found a bug or functional error with DL4J, then feel free to create an issue tracker at https://github.com/eclipse/deeplearning4j.

You're also welcome to initiate a discussion with the Deeplearning4j community here: https://gitter.im/deeplearning4j/deeplearning4j.