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- #CONFIGURE WEKA JAR HOW TO#
- #CONFIGURE WEKA JAR INSTALL#
- #CONFIGURE WEKA JAR ZIP FILE#
- #CONFIGURE WEKA JAR ZIP#
- #CONFIGURE WEKA JAR MAC#
The experiment is configured to use Cross Validation with 10 folds. The experimenter configures the test options for you with sensible defaults. Design ExperimentĬlick the “New” button to create a new experiment configuration.
#CONFIGURE WEKA JAR HOW TO#
Take my free 14-day email course and discover how to use the platform step-by-step.Ĭlick to sign-up and also get a free PDF Ebook version of the course. Need more help with Weka for Machine Learning? The Weka Experimenter allows you to design your own experiments of running algorithms on datasets, run the experiments and analyze the results. The Weka GUI Chooser lets you choose one of the Explorer, Experimenter, KnowledgeExplorer and the Simple CLI (command line interface).Ĭlick the “Experimenter” button to launch the Weka Experimenter. This may involve finding it in program launcher or double clicking on the weka.jar file.
#CONFIGURE WEKA JAR INSTALL#
If you are interested in machine learning, then I know you can figure out how to download and install software into your own computer.
#CONFIGURE WEKA JAR MAC#
I’m on a Mac myself, and like everything else on Mac, Weka just works out of the box. You may already have Java installed and if not, there are versions of Weka listed on the download page (for Windows) that include Java and will install it for you. Visit the Weka Download page and locate a version of Weka suitable for your computer (Windows, Mac or Linux).
![configure weka jar configure weka jar](https://miro.medium.com/max/1400/1*2F0S0QMZQ9S8bqAPCZCXHw.png)
gradlew build -x test publishToMavenLocal -Dcuda= # Replace with either "10.0", "10.1", or "10.2" gradlew build -x test publishToMavenLocal As of now it is not provided in any maven repository, therefore you need to install this package to your local. It is also possible to include this package as maven project.
![configure weka jar configure weka jar](https://miro.medium.com/max/1838/1*hV5Zqp40kODCUatW7vdoqQ.png)
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.
![configure weka jar configure weka jar](https://miro.medium.com/max/1400/1*LR_q2ODYOJD_rDMu9cUh5g.png)
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
#CONFIGURE WEKA JAR ZIP#
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.
#CONFIGURE WEKA JAR ZIP FILE#
You need to unzip the Weka zip file to a directory of your choice.