Yesterday, Deep Learning For Java (DL4J) released their new beta version, DL4J 1.0.0-beta4. The main highlight of this version is the full multi-datatype support for ND4J and DL4J, unlike past releases. The previous version deeplearning4j-1.0.0-beta3 was released last year. This 1.0.0-beta4 version also includes the addition of MKL-DNN support, new attention layers, and many more along with optimizations and bug fixes.
In past releases, all N-Dimensional arrays in ND4J were limited to a single datatype, set globally. Now, arrays of all datatypes may be used simultaneously. The supported datatypes are Double, Float, Half, Long, Int, Short, Ubyte, Byte, Bool and UTF8.
CUDA 10.1 support has been added and CUDA 9.0 support has been dropped. DL4J 1.0.0-beta4 also supports CUDA versions 9.2, 10.0 and 10.1. Mac (OSX) CUDA binaries are no longer provided. However, support for Linux and Windows CUDA, and OSX CPU(x86_64) is still available.
In DL4J 1.0.0-beta4, the periodic garbage collection is disabled by default; instead, garbage collection (GC) will be called only when it is required to reclaim memory from arrays that are allocated outside of workspaces.
To know more about the release, check the detailed release notes.
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