thundersvm | ThunderSVM : A Fast SVM Library on GPUs and CPUs | Machine Learning library

 by   Xtra-Computing C++ Version: 0.3.12 License: Apache-2.0

kandi X-RAY | thundersvm Summary

kandi X-RAY | thundersvm Summary

thundersvm is a C++ library typically used in Artificial Intelligence, Machine Learning applications. thundersvm has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

The mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. Key features of ThunderSVM are as follows. Why accelerate SVMs: A survey conducted by Kaggle in 2017 shows that 26% of the data mining and machine learning practitioners are users of SVMs. Documentation | Installation | API Reference (doxygen).

            kandi-support Support

              thundersvm has a medium active ecosystem.
              It has 1474 star(s) with 209 fork(s). There are 53 watchers for this library.
              It had no major release in the last 12 months.
              There are 64 open issues and 148 have been closed. On average issues are closed in 143 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of thundersvm is 0.3.12

            kandi-Quality Quality

              thundersvm has no bugs reported.

            kandi-Security Security

              thundersvm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              thundersvm is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              thundersvm releases are available to install and integrate.
              Installation instructions, examples and code snippets are available.

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            thundersvm Key Features

            No Key Features are available at this moment for thundersvm.

            thundersvm Examples and Code Snippets

            No Code Snippets are available at this moment for thundersvm.

            Community Discussions


            Installing CUDA Windows 10
            Asked 2021-Jan-29 at 12:06

            I am trying to install the CUDA toolkit in order to be able to use Thundersvm in my personal computer. However I keep getting the following message in the GUI installer: "You already have a newer version of the NVIDIA Frameview SDK installed"

            I read in the CUDA forums that this most probably results from having installed Geforce Experience (which I have installed). So I tried removing it from the Programs and Features windows panel. However I still got the error, so my guess is that the "Nvidia Corporation" folder was not removed.

            In the same question, they also suggested performing a custom install. However I could not find any information on how to do a custom install of the CUDA toolkit. I would really appreciate if someone could explain how to do this custom install or safely remove the previous drivers. I thought of using DDU but I read that sometimes it may actually lead to trouble.



            Answered 2021-Jan-29 at 12:06

            I had the same problem while I was trying to get TensorFlow to use my NVIDIA GTX1070 GPU for calculations. Here's what allowed me to perform the CUDA Toolkit installation on my Windows 10 machine.

            As the error message in the installer says - you already have a newer Frameview SDK installed. It was the case for me.

            1. Go to Settings/Uninstall or modify programs.
            2. Remove the NVIDIA Frameview program. It should be there with GeForce Experience, PhysX, etc.

            Uninstalling only this NVIDIA program didn't cause any driver problems for my machine and I was able to progress through the CUDA Toolkit instalator.



            How to use ThunderSVM (GPU mode) on Pycharm in Windows 10
            Asked 2019-Oct-15 at 05:23

            I followed the instructor on the official github of ThunderSVM and installed it for Windows. I can run ThuderSVM in git-bash CLI. The question here shows that we can add ThunderSVM into Keras, how about Pycharm? My question is how to run ThunderSVM on Pycharm.

            In the folder thundersvm/build/lib: I have thundersvm.exp, thundersvm.lib, thundersvm-train.exp, thundersvm-train.lib, thundersvm-predict.exp, and thundersvm-predict.lib files.

            I installed Pycharm on Windows 10 and interpreted it using python 3.6 through Anaconda. My GPU is RTX 2070.

            Thank you in advance and sorry for my bad English.



            Answered 2019-Oct-15 at 05:23

            I have an answer to my question.

            1. Cloning ThunderSVM and went to C:\Users\user-name\thundersvm\python\thundersvm
            2. Copying all files in here and post them in a new folder in Pycharmproject.
            3. Opening Pycharm and reconfigure Project Interpreter in File/Settings.

            I can run ThunderSVM in Pycharm right now. However, I realize that the built-in SVM of sklearn is faster than ThunderSVM in my case (many different small training data = many times to train SVM, and I have to write down the model in each time training). I tried SVM of tensorflow: tf.contrib.learn.SVM also and received the same result (tensorflow is faster than ThunderSVM but less accuracy).


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install thundersvm

            Download the Python wheel file (For Python3 or above). Install the Python wheel file.
            For Linux pip install thundersvm for CUDA 9.0 - linux_x86_64 CPU - linux_x86_64
            For Windows (64bit) CUDA 10.0 - win64 CPU - win64
            If you run into issues that can be traced back to your version of gcc, use cmake with a version flag to force gcc 6. That would look like this:. If make -j doesn't work, please simply use make. The number of CPU cores to use can be specified by the -o option (e.g., -o 10), and refer to Parameters for more information.
            You will see Accuracy = 0.98 after successful running.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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