Using WekaDeeplearning4j in a Maven Project In your CLASSPATH, however, means that the IDE cannot type-check the arguments. This has the benefit of not needing to include the WekaDeeplearning4j. One way to use this package through the Java API is to use reflection. The output for an incorrectly setup GPU will look like. Simply invoke the tool from the commandline: $ java -cp weka.Run. and WekaDeeplearning4j will check your GPU's availability.
Once WekaDeeplearning4j is installed, you can find IsGPUAvailable in the Tools menu in the GUIChooser: If the tool returns false, your GPU is not available to WekaDeeplearning4j (e.g., caused by incorrect drivers) and will If the tool returns true, your GPU is setup correctly and ready to use! GPU is identified and available to WekaDeeplearning4j. install-cuda-libs.sh ~/Downloads/wekaDeeplearning4j-cuda-10.2-1.6.0-linux-x86_64.zipĮnsuring your GPU is setup correctly may be difficult so to help out we've provided IsGPUAvailable, a simple diagnostic tool to test whether your If you want to download the library zip yourself, choose the appropriate combination of your platform and CUDA version from the latest release and point the installation script to the file, e.g.
The install script automatically downloads the libraries and copies them into your wekaDeeplearning4j package installation. Make sure CUDA is installed on your system as explained here. To add GPU support, download and run the latest install-cuda-libs.sh for Linux/Macosx or install-cuda-libs.ps1 for Windows. Which results in Installed Repository Loaded Packageġ.5.6 - Yes : Weka wrappers for Deeplearning4j You can check whether the installation was successful with $ java -cp \ Where must be replaced by the path pointing to the Weka jar file, and is the wekaDeeplearning4j package zip file.
Weka packages can be easily installed either via the user interface as described here, or simply via the commandline: $ java -cp \ Nvidia provides some good installation instructions for all platforms: The GPU additions needs the CUDA Toolkit 10.0, 10.1, or 10.2 backend with the appropriate cuDNN library to be installed on your system. CPUįor the package no further requisites are necessary.
You need to unzip the Weka zip file to a directory of your choice.
Mac OSĬlick here to download a disk image for Mac OS that contains a Mac application including Azul's 64-bit OpenJDK Java VM 11 JVM (weka-3-9-4-azul-zulu-osx.dmg 143 MB)Īll old versions of Weka are available from the Sourceforge website. Launching via the Program Menu or shortcuts will automatically use the included JVM to run Weka. This executable will install Weka in your Program Menu. WindowsĬlick here to download a self-extracting executable for 64-bit Windows that includes Azul's 64-bit OpenJDK Java VM 11 (weka-3-9-4-azul-zulu-windows.exe 117 MB) It may receive new features that break backwards compatibility.
This is the main development trunk of Weka and continues from the stable Weka 3.8 code line. We choose Weka 3.9 the development version Developer version The package management system requires an internet connection in order to download and install packages. Weka 3.8 and 3.9 feature a package management system that makes it easy for the Weka community to add new functionality to Weka. The stable version receives only bug fixes and feature upgrades that do not break compatibility with its earlier releases, while the development version may receive new features that break compatibility with its earlier releases.
For the bleeding edge, it is also possible to download nightly snapshots of these two versions. New releases of these two versions are normally made once or twice a year. There are two versions of Weka: Weka 3.8 is the latest stable version and Weka 3.9 is the development version.