I'm recently following the Chromium build instructions for Windows but fetch yields the following error:
$ fetch chromium
/c/src/depot_tools/fetch: line 8: exec: python: not found
NB: Python 3.6 is already installed on my PC.
The Windows build instructions fail to specify the required Python version, but the Linux ones are clear:
System requirements
A 64-bit Intel machine with at least 8GB of RAM. More than 16GB is highly recommended.
At least 100GB of free disk space.
You must have Git and Python v2 installed already.
The executable for Python 3.x is python3, and the one for Python 2.x is python, therefore you need to install Python 2.x.
Related
I installed Caffe and DIGITs on python 3 and it seems to work partially, however I still get this Protobuf error about version 2.6.1 versus 3.5.
So I reinstalled Protobuf and was then following through with reinstalling Caffe. I have an Anaconda python3.6 environment and I was recompiling Caffe in that environment. During the cmake .. step, I saw that the version of python that is detected is Python2.7 instead of Python3--even though I am in the conda python 3.6 environment. Even the version of numpy detected is for 2.7.:
Found PythonLibs: /usr/lib/x86_64-linux-gnu/libpython2.7.so (found suitable version "2.7.12", minimum required is "2.7")
-- Found NumPy: /home/krishnab/.local/lib/python2.7/site-packages/numpy/core/include (found suitable version "1.11.0", minimum required is "1.7.1")
-- NumPy ver. 1.11.0 found (include: /home/krishnab/.local/lib/python2.7/site-packages/numpy/core/include)
So my real question is whether DIGITS works with Caffe in python 3? I could not find any clear indication in the documentation except for an opaque reference to an issue:
https://github.com/NVIDIA/DIGITS/issues/511
Can anyone clarify?
I'm running Python27 x32 and getting this error:
Could not load "nvrtc64_75.dll": %1 is not a valid Win32 application.
I've also tried with cuda8.
As I realized, NVRTC docs list x64 as a requirement:
NVRTC requires the following system configuration:
Operating System: Linux x86_64, Linux ppc64le, Linux aarch64, Windows x86_64, or Mac OS X.
(nvrtc64_75.dll really does have 0x8664 in IMAGE_FILE_HEADER and 0x20b (pe32+) magic.)
I'm trying to use libgpuarray's pygpu with theano and I've previously built it with Win32 mingw.
My understanding now is that I'll need to install an x64 version of python and start from there. I know I could use conda instead and the docs in libgpuarray talk about msvc, btw. it worked with mingw so far.
Am I interpreting this right? Is NVRTC really have no working Win32 edition?
edit: got the same %1 is not a valid Win32 error with conda x32 and msvc (no real surprise here).
Just as the documentation you linked indicates, NVRTC requires a 64-bit environment.
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'm building a MySQL plugin for Qt 4.4.3 Open Source Edition (Qt documentation), and using command:
cd %QTDIR%\src\plugins\sqldrivers\mysql
qmake "INCLUDEPATH+=C:\MySQL\include" "LIBS+=C:\MYSQL\MySQL Server <version>\lib\opt\libmysql.lib" mysql.pro
make
I manage to build it to my 64-bit Qt just fine using 64-bit MySQL dev files (using nmake). However, 32-bit build (with mingw-make) fails with linking problems:
Creating library file: c:\Coding\Qt\4.4.3\plugins\sqldrivers\libqsqlmysqld4.a
tmp/obj/debug_shared/qsql_mysql.o(.text+0x10d): In function `Z5codecP8st_mysql':
...lots of same stuff...
The dev files installed by MySQL 5.1 32-bit and 64-bit library are also different: the 64-bit includes libmysql.dll and six .lib files, while 32-bit includes those plus six .pdb files. Relevant to this issue?
Is anyone able to build the 32-bit plugin with Qt 4.4.3/MinGW using MySQL 5.1? Suggestions?
use mysql-noinstall-5.1.14-beta-win32