The Jetson Nano has a 40 pin header but the pins are configured as GPIO. How does one convert this to SPI? After following the instructions in the NVidia documentation I do not find spi* devices in /dev.
The instructions in Jetson hacks is out dated as well.
The instructions in Jetson hacks is for Jetpack 4.3 - I am currently on Jetpack 4.6.
To setup SPI, follow the instructions in the NVidia documentation. Then run sudo modprobe spidev. Now running ls /dev/spi* will show spidev0.0 etc.
Related
I have a working cuda-10.0 toolkit and 470 driver. I need to use new virtual memory management features that I found in 10.2 driver. And I can't install more than 10.x because my old video card has compute capability 3.0.
So after applying new toolkit with:
sudo sh ./cuda_10.2.1_linux.run --toolkit --silent --override
it is as I think successfully installed:
But now in folder with "cuda-10.2" there is almost nothing, "bin" folder only has uninstaller and no "nvcc" and others. And newly created link links to that "nothing". How to deal with it?
I tried official docs and googling but nothing was found.
The patch updates for CUDA 10.2 do not contain complete toolkits. The idea behind a "patch" is that it contains only the files necessary to address the items that the patch is focused on.
To get a full CUDA 10.2 CUDA tookit install, you must first install using a full CUDA 10.2 toolkit installer, and a typical filename for that would be cuda_10.2.89_440.33.01_linux.run (runfile installer to match your indicated runfile installer usage). After that, if you decide you need/want the items addressed by the patch, you must also install the desired patch.
Note the statement on the download page:
These patches require the base installer to be installed first.
We are using a GTX 1080
with Ubuntu 16.04,
we installed CUDA 7.5, cudnn v5.1
We used compute capabilities 6.1
The 16.04 installation of CUDA was
made using the 15.04 Ubuntu version
and some very minor changes suggested by
https://www.pugetsystems.com/labs/hpc/NVIDIA-CUDA-with-Ubuntu-16-04-beta-on-a-laptop-if-you-just-cannot-wait-775/
All this seems to have worked fine.
In trying to install tensorflow from sources, per Google's
instructions for anything other than the default configuration,
we have run into a problem.
We do not know if this is something wrong on your end or Google's.
If you cannot help us, can you refer us to someone who can?
Thank you.
Below is the relevant run from a script file, with embedded special characters edited out.
laefsky#main:~/anaconda2/envs/tensorflow/tensorflow file://main/home/laefsky/anaconda2/envs/tensorflow/tensorflow(tensorflow) laefsky#main: ~/anaconda2/envs/tensorflow/tensorflow laefsky#main ~/anaconda2/envs/tensorflow/tensorflow$ ./configure
~/anaconda2/envs/tensorflow/tensorflow ~/anaconda2/envs/tensorflow/tensorflow
Please specify the location of python. [Default is /home/laefsky/anaconda2/envs/tensorflow/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] N
No Google Cloud Platform support will be enabled for TensorFlow
Found possible Python library paths:
/home/laefsky/anaconda2/envs/tensorflow/lib/python2.7/site-packages
Please input the desired Python library path to use. Default is [/home/laefsky/anaconda2/envs/tensorflow/lib/python2.7/site-packages]
/home/laefsky/anaconda2/envs/tensorflow/lib/python2.7/site-packages
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:
Please specify the location where CUDA toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the Cudnn version you want to use. [Leave empty to use system default]:
Please specify the location where cuDNN library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]: "6.1"
INFO: Starting clean (this may take a while). Consider using --expunge_async if the clean takes more than several minutes.
......
ERROR: /home/laefsky/anaconda2/envs/tensorflow/tensorflow/third_party/gpus/cuda_configure.bzl:442:18: function 'repository_rule' does not exist.
ERROR: com.google.devtools.build.lib.packages.BuildFileContainsErrorsException: error loading package 'external': Extension file '#local_config_cuda//cuda:build_defs.bzl' may not be loaded from a WORKSPACE file since the extension file is located in an external repository.
Configuration finished
laefsky#main:~/anaconda2/envs/tensorflow/tensorflow[]7;file://main/home/laefsky/anaconda2/envs/tensorflow/tensorflow(tensorflow) []0;laefsky#main: ~/anaconda2/envs/tensorflow/tensorflow[[01;32mlaefsky#main[[00m:[[01;34m~/anaconda2/envs/tensorflow/tensorflow$ logout
I'm trying to use cuda to accelerate tensorflow. I'm running tensorflow using the docker image.
Firstly, when I launch the gpu image, it has a mismatch in the LT_LIBRARY_PATH environment variable:
~# echo $LD_LIBRARY_PATH
/usr/local/nvidia/lib:/usr/local/nvidia/lib64:
root#d578acbbc2cd:~# ls /usr/local/
bin cuda cuda-7.0 etc games include lib man sbin share src
There's no nvidia directory there. When I try to run the convolutional.py demo, it can't initialise the cuda support:
# python models/image/mnist/convolutional.py
Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
I tensorflow/core/common_runtime/local_device.cc:25] Local device intra op parallelism threads: 8
modprobe: ERROR: ../libkmod/libkmod.c:556 kmod_search_moddep() could not open moddep file '/lib/modules/4.2.0-23-generic/modules.dep.bin'
E tensorflow/stream_executor/cuda/cuda_driver.cc:466] failed call to cuInit: CUDA_ERROR_UNKNOWN
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:98] retrieving CUDA diagnostic information for host: d578acbbc2cd
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:106] hostname: d578acbbc2cd
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:131] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:242] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 352.68 Tue Dec 1 17:24:11 PST 2015
GCC version: gcc version 5.2.1 20151010 (Ubuntu 5.2.1-22ubuntu2)
"""
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:135] kernel reported version is: 352.68
I tensorflow/core/common_runtime/gpu/gpu_init.cc:112] DMA:
I tensorflow/core/common_runtime/local_session.cc:45] Local session inter op parallelism threads: 8
It then goes on to train using cpu only.
# find /usr -name libcuda.so
/usr/lib/x86_64-linux-gnu/libcuda.so
So in the docker image, there's only the gnu cpu cuda implementation. No NVIDIA stuff. In the host ubuntu 15.10 session, I have libcuda.so installed:
$ find /usr -name libcuda.so
/usr/lib/x86_64-linux-gnu/libcuda.so
/usr/lib/i386-linux-gnu/libcuda.so
/usr/local/cuda-7.5/targets/x86_64-linux/lib
/stubs/libcuda.so
So these seem to be stubs ... not sure why.
Is there some trick to getting this to work?
Try rebuilding the Docker image directly from the Tensorflow repository (i.e. don't rely on the image on the container registry) and use https://github.com/NVIDIA/nvidia-docker to run the container (the Docker command described in the Tensorflow documentation is not portable).
I had a similar problem, though not in docker. The libcuda.so in /usr/local/cuda/lib64/stubs was a broken sym link. When I searched for libcuda.so it only turned up a file in a lib32 folder.
It seems that the problem was how I originally installed the NVIDIA device driver. At some point in the driver install process you're given the option to install the lib32 drivers. I had thought this meant in addition to lib64 drivers so I selected it. Turns out it only installs lib32 and not lib64 drivers.
I reinstalled the NIVDIA device driver, this time not selecting the lib32 'option'. Now tensorflow finds libcuda.so.
I had the same problem with running tensorflow on a Ubuntu machine after I upgraded my driver to 352.63 and 352.93. (I remember it works with 346.* but when I try to install 346., it installs 352. automatically for some reason).
I finally figured out that it's caused by permission issue. (I can run it with root) So, I changed the permission of the libcuda.so.352-63 file to executable by anyone and it works well now.
Hope this will be helpful to those still struggling with this issue.
I didn't try the docker one, but I guess it's also caused by permission setting.
Try this command
sudo apt-get install nvidia-modprobe
As mentioned here:
https://github.com/tensorflow/tensorflow/issues/394
and
http://kkjkok.blogspot.in/2016_08_01_archive.html
After I updated NVIDIA driver to 378.09 on Ubuntu 14.10 I had the same error,
although all the right for lib files were set correctly.
Thanks to #PhoenixQ, I tried to run with sudo and it worked.
After that I tried to run without sudo one more time and error disappeared. I'm not sure what ecxactly happened, but maybe something was configured during call with sudo, which was not possible withous sudo.
So the solution:
Try to run the same thing with sudo.
After this. Tryu running without sudo. Worked for me.
I successfully installed caffe on my dual-boot laptop (GTX 860M, Windows 7 + Ubuntu 14.04.2). All the tests were successfully passed. When I restarted, however, the ubuntu got stuck on the opening screen (the one with ubuntu logo and five red dots). Don't know what to do with it.
Has anyone run into the same issue before? I reckon something is wrong with graphic card driver booting. I installed newest CUDA 7 Toolkit with nvidia drivers built inside. Since all tests were passed before I restarted, it seems that the driver would work once successfully booted.
the stuck screen is like this: http://i.stack.imgur.com/pRtEF.jpg
I had a similar issue when trying to install Caffe on my system. The steps below worked for me, but it has at least one known issue (documented below).
I'm not sure what precisely caused this problem, but it surely has something to do with the Nvidia Driver and Cuda Toolkit installation and is not caused by Caffe.
After completing the steps below, I've been able to successfully install Caffe on my system with the following tutorials and guides:
Official Install Guide
Github Install Guide
Update
Recently, I had the exact same problem trying to make Cuda 7.5 work on Ubuntu 14.04; this approach also solved that problem. Specs:
CPU: Intel Core i7-4700MQ (4x 2.40 GHz with Hyperthreading)
GPU: NVidia GT 940M
RAM: 8 GB
HDD: 52.7 GB (of which 6.7 GB used after installation)
INSTALL NVIDIA DRIVER AND CUDA ON UBUNTU 14.04
Source: ubuntuforums.org/showthread.php?t=2246526
!! Known Issues !!
After the system has been suspended (or hibernated, not confirmed), all applications using the Nvidia Driver and Cuda 6.5 Toolkit will freeze. When this happens, the command sudo shutdown -r now will print the reboot message but nothing will happen.
Executed and tested on a fresh 64-bit Ubuntu 14.04 install with the following hardware specifications:
CPU: Intel Core i5-2410m (2x 2.30 GHz with Hyperthreading)
GPU: NVidia GT 540M
RAM: 6 GB
HDD: 52.7 GB (of which 8.6 GB used after installation)
The following command was executed before installation:
sudo apt-get -y build-essential vim git llvm clang
The following steps resulted in a stable system with the latest Nvidia Driver and Cuda 6.5 Toolkit installed:
Remove all traces of previous/legacy Nvidia Drivers and Cuda Toolkits or perform a fresh Ubuntu 14.04 install.
Download the latest Nvidia Driver .run file for Ubuntu 14.04 and your system specifications to the ~/Downloads directory.
e.g.: NVIDIA-Linux-x86_64-346.35.run
Download the latest Cuda 6.5 Toolkit .run file for Ubuntu 14.04 and your system specifications to the ~/Downloads directory.
e.g.: cuda_6.5.14_linux_64.run
Blacklist the 'nouveau' Driver by appending the following lines to /etc/modprobe.d/blacklist.conf (nouveau is a free open-source driver for Nvidia cards, it is the default for Ubuntu 14.04):
blacklist nouveau
options nouveau modeset=0
Reboot the system, do NOT log in but drop to the terminal with CTRL+ALT+F1
Kill lightdm (replace 'lightdm' with your own Display Manager if you have changed it, lightdm is the default for Ubuntu 14.04):
sudo service lightdm stop
The next step is critical, make sure to check twice before continuing!
Run the Nvidia Driver installer with the --no-opengl-files option (the option prevents OpenGL files from being overwritten; without this option, Unity would not function properly and the screen would freeze after login):
sudo chmod +x ~/Downloads/NVIDIA-Linux-x68_64-346.35.run
sudo ~/Downloads/NVIDIA-Linux-x68_64-346.35.run --no-opengl-files
Accept the EULA and acknowledge all further warnings but deny to install anything extra.
Reboot and login to the desktop, verify with the 'Additional Drivers' (System Settings > Software & Updates > Additional Drivers) utility that the manually installed driver is in use.
Open a terminal and install the Cuda 6.5 Toolkit:
sudo chmod +x ~/Downloads/cuda_6.5.14_linux_64.run
sudo ~/Downloads/cuda_6.5.14_linux_64.run
Accept the EULA, do NOT install the driver, install the Toolkit and the Examples (if you want to), leave all default directories in place.
Add the Cuda 6.5 Toolkit environment variables by appending the following lines to ~/.bashrc:
# For 32-bit systems, append these:
export PATH=$PATH:/usr/local/cuda-6.5/bin
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib
# For 64-bit systems, append these:
export PATH=$PATH:/usr/local/cuda-6.5/bin
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64
The Nvidia Driver and Cuda 6.5 Toolkit should now be correctly installed.
Optional: confirm your Nvidia Driver and Cuda 6.5 Toolkit installation.
Confirm the Nvidia Driver installation by running the following command:
nvidia-smi
Confirm the Cuda Compiler installation by running the following command:
nvcc -V
Confirm everything works by building and running the optionally installed Cuda Examples: (build-essential is required to use 'make')
sudo apt-get install -y build-essential
cd ~/NVIDIA_CUDA-6.5_SAMPLES/1_Utilities/deviceQuery
make
./deviceQuery
cd ~/NVIDIA_CUDA-6.5_SAMPLES/1_Utilities/bandwidthTest
make
./bandwidthTest
This problem is not related to caffe.
The problem is that the nVidia driver that is installed from the ubuntu software center does not support your card.
Uninstall any nvidia package (sudo apt-get purge nvidia-*) and install the latest driver version from the nvidia website.
I recommend you to change the cuda 7.5 ubuntu 15.04 version. I try it on the ubuntu 14.04, it solves this problem. And when I install cuda 7.5 ubuntu 14.04 version on ubuntu 14.04 I countered the exactly problem.
I have been trying to set up an Ubuntu environment on my laptop for some time now for CUDA programming. I am currently dual booting Windows 8 and Ubuntu 12.04 and want to install CUDA 5 on Ubuntu.
The laptop has a GeForce GT 640M graphics card (See below for full specs). It is an Optimus card.
Originally I was dual booting Ubuntu 11.10 and have tried tutorials on both 11.10 and 12.04.
I have tried many tutorials of all shapes and sizes, including this tutorial. The installation process shows the device driver installing and the Toolkit installing, and the Samples failing, but when I go to test a simple Vector Add CUDA program in NSight, "No compatible CUDA Device" error is thrown.
Ubuntu Details also still shows "Unknown" for Graphics
Suggestions?
Laptop Specs:
Acer V3-771G
Intel Core i7 2670QM
nVidia GeForce GT 640M 2GB - Optimus
16GB DDR3-1600 RAM
120GB SSD + 500GB HDD + 32GB Cache SSD
Since it is an optimus device, there are some extra steps to be able to use the nvidia GPU. While it is not necessary, I suggest that you use the bumblebee wrapper program because it is the easiest solution.
After you have installed the bumblebee wrapper you can run your programs using optirun programname or start a shell with the nvidia card activated: optirun bash --login
An added bonus is that the bumblebee daemon will disable the GPU when it is not running and will save you some battery.
If you don't care about battery life and just want CUDA to be always enabled without wrapping commands you can load the nvidia kernel module and then create the necessary device nodes manually:
mknod /dev/nvidia0 c 195 0
mknod /dev/nvidiactl c 195 255
(This advanced method lets you run cuda programs from the console without starting Xorg, for example when SSH-ing to a machine without a running X server.)
See also https://askubuntu.com/questions/131506/how-can-i-get-nvidia-cuda-or-opencl-working-on-a-laptop-with-nvidia-discrete-car for a more detailed discussion.
Try the command sudo apt-get install mesa-utils.
See if the graphics is recognized and then try to install cuda
If does not recognized with the first command try:
sudo add-apt-repository ppa:ubuntu-x-swat/x-updates
sudo apt-get update
sudo apt-get install nvidia-current
First install the following libraries & Tools:
sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev
Next we will blacklist some modules(drivers), in terminal enter:
sudo gedit /etc/modprobe.d/blacklist.conf
Add the following to the end of the file(one per line like so):
blacklist amd76x_edac
blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist nvidiafb
blacklist rivatv
Save the file and close the editor.
Now we want to get rid of any nvidia risiduals, in terminal:
sudo apt-get remove --purge nvidia*
Next you need to restart your machine (sudo reboot).
0) Press Ctrl+Alt+F1 at login screen(you don't have to login, we'll have to restart later anyway), then log in.
1) sudo service lightdm stop
2) cd Downloads
3) chmod +x devdriver*.run (your driver filename)
4) sudo ./devdriver*.run
You might have to run the driver-installer once, reboot(it will remove nouveau drivers) and repeat the steps again. Follow the installer instructions and it will be fine, when it asks you;
yes, you do want the 32-bit libraries and you DO want it to change the xorg.conf file.
Once the installer completes, restart (sudo reboot). You're done :]
In Order to install SDK and Toolkit,
use the steps 3 and 4 with the downloaded files. (.run)
In theory, the drivers included with CUDA 5.5 should natively support Optimus (as well as single GPU debugging for non-Optimus laptops). I haven't tried it yet because I'm waiting for a compute 3.5 Optimus laptop so that it'll support kernel recursion and HyperQ. In theory the HP Envy 15t-j000 has the GK208 version of the GT 740m, but I'd really rather have an ultrabook form factor like the upcoming Acer S3-392 with GT 735m. The NVIDIA guys at GTC assured me that Optimus should be working with the CUDA 5.5 RC. I found this 'CUDA Getting Started Guide for Linux' released this month that provides some flags for getting Optimus drivers installed correctly:
http://www.google.com/url?q=http://developer.download.nvidia.com/compute/cuda/5_5/rc/docs/CUDA_Getting_Started_Linux.pdf
Also, more information about GK208 Chips and Compute 3.5 in laptops:
https://devtalk.nvidia.com/default/topic/546357/sounds-like-gk208-laptops-cards-will-support-most-sm_35-features/
Anyone have luck with CUDA 5.5 and Optimus laptops under linux?