Home Articles posted by Jaerock (Page 2)

CUDA and cuDNN inside a Conda Env

DO NOT INSTALL cuda through $ sudo apt install cuda since this will install the latest NVIDIA driver as well without asking. The newest NVIDIA driver might not work with a particular kernel version. Through my ordeals, I figured out that only some particular combinations work.

The safest way to install CUDA is to use a conda environment. First, install cuda and cudnn inside your conda environment. All the conda related libraries are located in ~/anaconda3/envs/<env-name>/lib. To let your environment know the location of the CUDA libraries LD_LIBRARY_PATH needs to be used.

Activate the environment first. Assuming the environment name is env-name, the command is like this.

conda activate env-name

Then run the following commands.

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export OLD_LD_LIBRARY_PATH=${LD_LIBRARY_PATH}' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

mkdir -p $CONDA_PREFIX/etc/conda/deactivate.d
echo 'export LD_LIBRARY_PATH=${OLD_LD_LIBRARY_PATH}' >  $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
echo 'unset OLD_LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh

Deactivate your environment and activate it again. Check if your TensorFlow properly works with GPUs.

python3 -c "import os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'; import tensorflow as tf; print('Num GPUs Available: ', len(tf.config.list_physical_devices('GPU')))"

You will sse Num GPUs Available: #

If # is other than 0, you are all set.

6
1

Ubuntu Installation on Alienware x15 R2

Last few days, I spent hours and hours making NVIDIA GPU (GeForce RTX 3070 Ti 8GB) work on my Alienware x15 R2.

I would like to share the lessons that I learned from the ordeal.

The most important point is that “NEVER EVER INSTALL cuda meta package via apt install.” If you do, it will replace your current NVIDIA driver with the latest release which does not work with your current kernel.

Here are combinations that worked and did not work for your reference.

TL;DR

  • Install Ubuntu 20.04 LTS.
  • Update software. Make sure your kernel is 5.15.0-53.
  • Upgrade the OS to Ubuntu 22.04 LTS. This upgrade got NVIDIA driver 515 installed by default.
  • Install a Ubuntu driver of Killer 1690/1675/1650 Wi-Fi.
    • $ sudo apt install backport-iwlwifi-dkms

Ubuntu 20.04 LTS

  • kernel version: 5.8.0-43
  • NVIDIA driver: 471

This fresh install works with GPU. But, WIFI, speaker, and microphone do not work. After Linux firmware installation from one of the recent versions at http://mirrors.edge.kernel.org/ubuntu/pool/main/l/linux-firmware/, WIFI and speaker work.

When I update the software, which includes kernel upgrade, the kernel version is as follows.

  • kernel version: 5.15.0-53

This kernel does not work with NVIDIA driver 471.

When I install the latest kernel, 6.0.9 as of 11/22/2022, WIFI and speaker work but no luck on GPU.

Ubuntu 22.04 LTS

Here is what I did to make WIFI, speaker, mic, and even suspend work.

  • Install Ubuntu 20.04 LTS. (I couldn’t install 22.04 LTS directly since, somehow, my Alienware didn’t allow me to install 22.04 LTS from a bootable thumb drive)
  • Update software. This got my kernel upgrade from 5.8.0-43 to 5.15.0-53.
  • Upgrade the OS to Ubuntu 22.04 LTS. (When I upgraded my Ubuntu 20.04, the kernel version was 5.15.0-53. I haven’t tried to upgrade to 22.04 LTS from the original kernel)
    • This upgrade got NVIDIA driver 515 installed by default.
  • This upgraded Ubuntu 22.04 LTS (kernel 5.15.0-53) with NVIDIA driver 515 makes GPU, speaker, microphone work except WIFI.
  • After checking Alienware x15 R2 specifications, I knew Killer WIFI AX1675 was used for the machine.
  • Install a Ubuntu driver of Killer 1690/1675/1650 Wi-Fi.
    • $ sudo apt install backport-iwlwifi-dkms

NSF Award

My proposal to acquire an autonomous plug-in hybrid vehicle has been awarded. The project title is “MRI: Acquisition of Autonomous Plug-In Hybrid Vehicle Platform for Multidisciplinary Research and Education at the University of Michigan-Dearborn“ (Award #2214830). The project duration is from Sep 1, 2022, to Aug 31, 2025. The total award amount is $244,610.

The project overview is as follows.

This proposal is to acquire an Autonomous Plug-In HYbrid Vehicle research platform (APIHYV) to advance fundamental science and engineering research and education activities by multidisciplinary faculty at the University of Michigan-Dearborn (UM-D). The proposed platform will be crucial research instrumentation to significantly enhance collaborative and interdisciplinary research and education at UM-D in several research activities, including embodied cognitive vehicle, in-vehicular network security, energy consumption, environmental perception, cybersecurity, and driver behavior analyses in electric and advanced mobilities. The instrument will also substantially improve undergraduate and graduate research training in various engineering programs such as electrical, computer, robotics, mechanical, and industrial engineering departments at UM-D located in the Metro Detroit area, in which the General Motors (GM), Ford Motor Company, and Chrysler are headquartered. The proposed instrumentation, the APIHYV consists of (i) a Chrysler Pacifica Plug-in Hybrid, (ii) Drive-By-Wire (DBW) systems, and (iii) a sensor suite (LIDARs, radar, RGB cameras, and Global Navigation Satellite System (GNSS)). The Drive-By-Wire kits with steer, brake, throttle, and shift-by-wire controller modules can programmatically and electronically control the vehicle’s steering, throttle, and brake without the addition of mechanical components. Much research has been done on automotive, robotics, cybersecurity, energy systems, human-vehicle interface at UM-D. Yet, they have not been able to conduct collaborative research in a realistic environment with a full-scale programmable vehicle. Each research group has to work in a simulated or simplified environment so that the proposed methods/algorithms were not be able to be fully validated. The project team proposes ten transformative research topics to be enabled by the request instrumentation. The autonomous plug-in hybrid vehicle research platform requested will substantially improve UM-D’s current capabilities with proposed research activities.