Posts related to installing software
Environment: Ubuntu 18.04 LTS 1. Navigate to the jetpack_download folder that has all the downloaded components for the JetPack. 2. Open a terminal that is in the jetpack_download directory. 3. Drag the NVIDIA_Nsight_Graphics_2018.6.L4T.25128383.run file into the terminal. 4. Press Enter […]
A detailed video walkthrough of how to install and run DeepStream SDK 3.0 on your Jetson Xavier machine. Get started using object detection and video analytics in your video stream/graphics pipeline today! A great way to learn the ins and […]
In order to get Nsight Eclipse launched without any errors there are two things that need to happen: Make sure the java-1_8_0-openjdk package is installed on your system https://www.digitalocean.com/community/tutorials/how-to-install-java-with-apt-on-ubuntu-18-04 Launch nsight with the following command: nsight -vm /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java Here […]
DO NOT USE A VIRTUAL MACHINE! I am having problems running code written on the host machine (VM Ubuntu 18.04 LTS) on the Jetson Xavier. I keep getting a “Exec Format Error” whenever I run remotely on the Jetson Xavier. […]
Use your NVIDIA GPU to make your computer vision project run faster! Below is a link to a detailed video walkthrough of adding the CUDA module to OpenCV on a Windows-based machine. The video walkthrough is 1 hour and 30 […]
A video walkthrough of how to go about installing TensorFlow + TensorBoard + Keras + Anaconda Python on a Windows based machine. The video walkthrough is 19+ minutes long.
An overview of how to install CUDA Python on your system. It will enable you to run Python code, but take advantage of your Cuda-enabled GPU to speed up your application. A marriage made in heaven?
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#code courtesy of NVIDIA #CUDA EDUCATION #Website: cudaeducation.com #Twitter: @cudaeducation #Email: cudaeducation@gmail.com #YouTube: Cuda Education | Please subscribe #Donate: Visit cudaeducation.com to donate to the cause import numpy as np import time from numba import vectorize, cuda @vectorize(['float32(float32, float32)'], target='cuda') def VectorAdd(a, b): return a + b def main(): N = 32000000 A = np.ones(N, dtype=np.float32) B = np.ones(N, dtype=np.float32) start = time.time() C = VectorAdd(A, B) vector_add_time = time.time() - start print ("C[:5] = " + str(C[:5])) print ("C[-5:] = " + str(C[-5:])) print ("VectorAdd took for % s seconds" % vector_add_time) if __name__=='__main__': main() |
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#CUDA EDUCATION #Website: cudaeducation.com #Twitter: @cudaeducation #Email: cudaeducation@gmail.com #YouTube: Cuda Education | Please subscribe #Donate: Visit cudaeducation.com to donate to the cause from numba import cuda print(cuda.gpus) |
Next: Install Tensorflow […]