How to manually install CUDA and all necessary packages on my Jetson Nano without SDK Manager?

I have a Jetson Nano 4gb by Seeed Studio. I don’t have enough of the original 16Gb on the eMMC, so I followed these instructions (J1010 Boot From SD Card | Seeed Studio Wiki) to activate the sd-card. I had errors with installing packages in the Nvidia SDK, so I manually flashed the board.

But I found a complete lack of CUDA, cuDNN, OpenCV and other packages.

I need the OpenCV library with CUDA support for computer vision, and I only found a few different unofficial instructions (Install OpenCV on Jetson Nano - Q-engineering, Install CUDA 11 on Jetson Nano and Xavier NX - Latest Open Tech From Seeed, https://www.youtube.com/watch?v=art0-99fFa8) that are meant to help install them. Installations and compilations ended up with various errors related to some dependencies or something else (I have little experience with this). I wasn’t sure what I was doing because the initial conditions were different.
My system was on an sd card already. Is it a problem? Maybe the L4T 32.7.1 version is not suitable? I understand that only CUDA 10.2.300 is officially supported, isn’t it? Is it possible to install OpenCV before CUDA, could this cause bugs later on?

So: is there a guaranteed way in my case (assuming I just have a clean Jetson after manually flashing reComputer J1010 | J101 | Seeed Studio Wiki)? Maybe I should to manually put some CUDA packages from here (https://developer.nvidia.com/embedded/linux-tegra-r3271) ? How can I do that? I’ll be glad for any information if it’s really relevant.

Hi,

You will need a CUDA package and a custom OpenCV building from the source.
Please try to install CUDA via OTA so no extra libraries are installed.

$ sudo apt-get update
$ sudo apt-get install cuda

Then please build OpenCV from the source.
Below is an automatical script for your reference:

Thanks.

I seem to be getting a ridiculous error, but I have no idea what to do next so I don’t accidentally make things worse.

What do you see from “apt search cuda”?

This:

jet3l@jet3l-desktop:~$ apt search cuda
Sorting... Done
Full Text Search... Done
caffe-cpu/bionic 1.0.0-6 arm64
  Fast, open framework for Deep Learning (Meta)

cuda-command-line-tools-10-2/stable 10.2.460-1 arm64
  CUDA command-line tools

cuda-compiler-10-2/stable 10.2.460-1 arm64
  CUDA compiler

cuda-cudart-10-2/stable 10.2.300-1 arm64
  CUDA Runtime native Libraries

cuda-cudart-dev-10-2/stable 10.2.300-1 arm64
  CUDA Runtime native dev links, headers

cuda-cuobjdump-10-2/stable 10.2.300-1 arm64
  CUDA cuobjdump

cuda-cupti-10-2/stable 10.2.300-1 arm64
  CUDA profiling tools runtime libs.

cuda-cupti-dev-10-2/stable 10.2.300-1 arm64
  CUDA profiling tools interface.

cuda-documentation-10-2/stable 10.2.300-1 arm64
  CUDA documentation

cuda-driver-dev-10-2/stable 10.2.300-1 arm64
  CUDA Driver native dev stub library

cuda-gdb-10-2/stable 10.2.300-1 arm64
  CUDA-GDB

cuda-gdb-src-10-2/stable 10.2.300-1 arm64
  Contains the source code for cuda-gdb

cuda-libraries-10-2/stable 10.2.460-1 arm64
  CUDA Libraries 10.2 meta-package

cuda-libraries-dev-10-2/stable 10.2.460-1 arm64
  CUDA Libraries 10.2 development meta-package

cuda-memcheck-10-2/stable 10.2.300-1 arm64
  CUDA-MEMCHECK

cuda-minimal-build-10-2/stable 10.2.460-1 arm64
  Minimal CUDA 10.2 toolkit build packages.

cuda-nvcc-10-2/stable 10.2.300-1 arm64
  CUDA nvcc

cuda-nvdisasm-10-2/stable 10.2.300-1 arm64
  CUDA disassembler

cuda-nvgraph-10-2/stable 10.2.300-1 arm64
  NVGRAPH native runtime libraries

cuda-nvgraph-dev-10-2/stable 10.2.300-1 arm64
  NVGRAPH native dev links, headers

cuda-nvml-dev-10-2/stable 10.2.300-1 arm64
  NVML native dev links, headers

cuda-nvprof-10-2/stable 10.2.300-1 arm64
  CUDA Profiler tools

cuda-nvprune-10-2/stable 10.2.300-1 arm64
  CUDA nvprune

cuda-nvrtc-10-2/stable 10.2.300-1 arm64
  NVRTC native runtime libraries

cuda-nvrtc-dev-10-2/stable 10.2.300-1 arm64
  NVRTC native dev links, headers

cuda-nvtx-10-2/stable 10.2.300-1 arm64
  NVIDIA Tools Extension

cuda-samples-10-2/stable 10.2.300-1 arm64
  CUDA example applications

cuda-toolkit-10-2/stable 10.2.460-1 arm64
  CUDA Toolkit 10.2 meta-package

cuda-tools-10-2/stable 10.2.460-1 arm64
  CUDA Tools meta-package

cuda-visual-tools-10-2/stable 10.2.460-1 arm64
  CUDA visual tools

forge-doc/bionic 0.9.2-2 all
  documentation for forge

libarrayfire-cpu-dev/bionic 3.3.2+dfsg1-4ubuntu1 arm64
  Development files for ArrayFire (CPU backend)

libarrayfire-cpu3/bionic 3.3.2+dfsg1-4ubuntu1 arm64
  High performance library for parallel computing (CPU backend)

libarrayfire-dev/bionic 3.3.2+dfsg1-4ubuntu1 arm64
  Common development files for ArrayFire

libarrayfire-doc/bionic 3.3.2+dfsg1-4ubuntu1 all
  Common documentation and examples for ArrayFire

libarrayfire-opencl-dev/bionic 3.3.2+dfsg1-4ubuntu1 arm64
  Development files for ArrayFire (OpenCL backend)

libarrayfire-opencl3/bionic 3.3.2+dfsg1-4ubuntu1 arm64
  High performance library for parallel computing (OpenCL backend)

libarrayfire-unified-dev/bionic 3.3.2+dfsg1-4ubuntu1 arm64
  Development files for ArrayFire (unified backend)

libarrayfire-unified3/bionic 3.3.2+dfsg1-4ubuntu1 arm64
  High performance library for parallel computing (unified backend)

libcupti-doc/bionic 9.1.85-3ubuntu1 all
  NVIDIA CUDA Profiler Tools Interface documentation

libcusolver-10-2/stable 10.3.0.300-1 arm64
  CUDA solver native runtime libraries

libcusolver-dev-10-2/stable 10.3.0.300-1 arm64
  CUDA solver native dev links, headers

libforge-dev/bionic 0.9.2-2 arm64
  development files for forge

libforge0/bionic 0.9.2-2 arm64
  high-performance OpenGL visualization

librandom123-dev/bionic 1.09+dfsg-1 all
  parallel random numbers library

librandom123-doc/bionic 1.09+dfsg-1 all
  documentation and examples of parallel random numbers library

libsuperlu-dist-dev/bionic 5.3.0+dfsg1-1 arm64
  Highly distributed solution of sparse linear equations

libsuperlu-dist5/bionic 5.3.0+dfsg1-1 arm64
  Highly distributed solution of sparse linear equations

libthrust-dev/bionic 1.9.1~9.1.85-3ubuntu1 all
  Thrust - Parallel Algorithms Library

libtrilinos-kokkos-dev/bionic 12.12.1-5 arm64
  Trilinos Kokkos programming model - development files

libtrilinos-kokkos12/bionic 12.12.1-5 arm64
  Trilinos Kokkos programming model - runtime files

numba-doc/bionic 0.34.0-3 all
  native machine code compiler for Python (docs)

nvidia-container-csv-cuda/stable 10.2.460-1 arm64
  Jetpack CUDA CSV file

nvidia-cuda/stable 4.6.4-b39 arm64
  NVIDIA CUDA Meta Package

nvidia-cuda-doc/bionic 9.1.85-3ubuntu1 all
  NVIDIA CUDA and OpenCL documentation

nvidia-l4t-cuda/stable 32.7.4-20230608212426 arm64 [upgradable from: 32.7.1-20220219090432]
  NVIDIA CUDA Package

pyrit/bionic 0.4.0-7.1build2 arm64
  GPGPU-driven WPA/WPA2-PSK key cracker

pyrit-opencl/bionic 0.4.0-1build1 arm64
  OpenCL extension module for Pyrit

python-arrayfire/bionic 3.3.20160624-2 all
  ArrayFire bindings for Python 2

python-arrayfire-doc/bionic 3.3.20160624-2 all
  documentation for the ArrayFire Python bindings

python-numba/bionic 0.34.0-3 arm64
  native machine code compiler for Python 2

python-pycuda-doc/bionic 2017.1.1-2 all
  module to access Nvidia‘s CUDA computation API (documentation)

python-pytools/bionic 2017.6-1 all
  big bag of things supplementing Python standard library

python3-arrayfire/bionic 3.3.20160624-2 all
  ArrayFire bindings for Python 3

python3-numba/bionic 0.34.0-3 arm64
  native machine code compiler for Python 3

python3-pytools/bionic 2017.6-1 all
  big bag of things supplementing Python 3 standard library

suricata/bionic 3.2-2ubuntu3 arm64
  Next Generation Intrusion Detection and Prevention Tool

vc-dev/bionic 1.3.3-3 arm64
  Library to ease explicit vectorization of C++ code

vim-syntastic/bionic 3.8.0-1 all
  Syntax checking hacks for vim

Should I download and install something additional?

It looks like the package name is not “cuda”. Instead, from that list, what happens with this, and is your requirement fulfilled via:
sudo apt-get install cuda-toolkit-10-2
(note that this would be runtime; if you need development headers, then you might also want a “-dev” version, and other tools)

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Yes indeed, it worked for me with this command. And then I installed opencv as shown in the reply from @AastaLLL. Thank you very much for your help!

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