Sdkmanager not installed in jetson xavier nx


mic-710aix@ubuntu:~/Downloads$ sudo apt install ./sdkmanager_1.4.1-7402_amd641.deb
[sudo] password for mic-710aix:
Reading package lists… Done
Building dependency tree
Reading state information… Done
Note, selecting ‘sdkmanager:amd64’ instead of ‘./sdkmanager_1.4.1-7402_amd641.deb’
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:

The following packages have unmet dependencies:
sdkmanager:amd64 : Depends: libgconf-2-4:amd64 but it is not installable
Depends: libcanberra-gtk-module:amd64 but it is not installable
Depends: locales:amd64 but it is not installable
E: Unable to correct problems, you have held broken packages.

SDK Manager can install into AMD64 Computer, not ARM64 (like L4T devices)

Hi cosartificialintelligence

The SDK manager can only run on Ubuntu Desktop 16.04 and 18.04 on x64 system, please check the system requirement:
System Requirements :: NVIDIA SDK Manager Documentation

how can i install cuda,cudnn,tensor RT,pip,open cv etc in jetson xavier nx module,without sdkmanager.

how can i install cuda,cudnn,tensor RT,pip,open cv etc in jetson xavier nx module,without sdkmanager.

From: How to Install JetPack :: NVIDIA JetPack Documentation

Assuming your Jetson developer kit has been flashed with and is running L4T 32.3.1 or higher, the following commands will install all other JetPack components that correspond to your version of L4T:

sudo apt update

sudo apt install nvidia-jetpack

To view individual Debian packages which are part of nvidia-jetpack metapackage, enter the command:

sudo apt show nvidia-jetpack

E: Unable to locate package nvidia-jetpack

E: Unable to locate package nvidia-jetpack

How to download Jetpack 4.4 in jetson xavier module nx.
Thanks .

In this guide,

Unfortunately, I have not found the JetPack SDK packages via apt yet. I remember, these should be nvidia-l4t-<package_name> but I’m not sure about that.
Best wishes

You might try this on the host, then run the install again:

sudo apt update
sudo apt --fix-broken install
sudo apt-get install libgconf-2-4 libcanberra-gtk-module

please solve my request ,installing any packages this error occurs

Did you try this command as your screenhot’s last line mentioned:

sudo apt --fix-broken install

after installation of sudo apt --fix-broken install.
error occurs ,lib file are not installed. please solve my issues

Can you remove error processed archive which is /var/cache/apt/archives/libegl-mesa0_20.0.8-0ubuntu1~18.04.1_armhf.deb
I hope that it causes only broken package.
After removing .deb file, can you try installing again

it is not worked @mehmetdeniz ,can you please share another solution for that issue !
error11|690x387

The " trying to overwrite " error implies that you have conflicting packages in your system.You can try overwriting the package (can be a bit risky)

sudo dpkg -i --force-overwrite /var/cache/apt/archives/libegl-mesa0_20.0.8-0ubuntu1~18.04.1_armhf.deb

I found the same issue about another packages from here:

Hope it helps

You could add package “libegl-mesa0” to the install command. First try just installing that:
sudo apt-get install libegl-mesa0

If that fails, try adding it on the same command for installing the nano editor:
sudo apt-get install libegl-mesa0 nano

If it still says to use the fix broken part, then do that again after any of the failures.

Using external memory like (pendrive or memory card),In the external memory we download packages like opencv,cuda,cudnn,pytroch,tensorflow etc , pip install all packages and run the code on the external memory without help of internal memory. Is it possible ?

I think you installed via

pip install --target=<directory_path> package_name

or

pip install --install-option="–prefix=$PREFIX_PATH" package_name

There is also a video tutorial

To use it you can run

PYTHONPATH=/path/to/project python fileHash.py

source: https://stackoverflow.com/a/33724745

Alternatively, you can use virtualenv