I ended up spending a non-trivial amount of time installing CUDA 10.2 on Ubuntu 18.04 on with intel 10thGen CPU and NVIDIA GEFORCE RTX 2060 GPU. Here is the recipe that worked for me. I hope this helps you if you have a similar configuration.
# Create a file at /etc/modprobe.d/blacklist-nouveau.conf with the following contents:
$ blacklist nouveau options nouveau modeset=0# Regenerate the kernel initramfs:
$ sudo update-initramfs -u
Step 3: Install CUDA using local runfile.
Installing CuDNN should be straight forward from here. This also fixed an issue with connecting to my external monitor using HDMI. Kudos to all the people posting their solutions on blogs, forums, Github, and medium pages.
One major enhancement of the recently released PyTorch 1.5 is a stable C++ frontend API parity with Python¹. C++ frontend API works well with Low Latency Systems, Highly Multi-threaded Environments, Existing C++ code bases, you can check the motivation and use cases of C++ frontend here³. I want get a taste of the PyTorch C++ frontend API by creating a small example. So I took a simple two layer neural network example from Learning PyTorch with Examples². The rest of this post details the steps to convert the two layer neural network using Python frontend API example to work with…
As soon as the COVID-19 started spreading globally, quite a few simulations were showing the expansion of COVID-19 all around the web. Then, I wanted to teach my kids about this pandemic that shook the world. Instead of showing them the Big-O complexity Chart, which does not provide an intuitive sense for the kids. I remembered the great story that was described in the Algorithms textbook Dasgupta et.al.
The story of Sissa According to the legend, the game of chess was invented by the Brahmin Sissa to amuse and teach his king. Asked by the grateful monarch what he wanted…
Machine Learning, Hardware, and High-Performance infrastructure enthusiast. Holds MS in Machine Learning GeorgiaTech and MTech in Electronics Design from IISc.