[GNU/Linux][Ubuntu 17.10 64bit] Unable to run/build FleXDemo/UnrealEngine+FleX

Hey there.

  1. I've been clonned NVIDIA FleX from official github repo: [code=bash]$ git clone https://github.com/NVIDIAGameWorks/FleX.git[/code] and tried to run the demo. the output was this error: [code=bash]$ ./bin/linux64/NvFlexDemoDebugCUDA_x64 Reshaping Error creating CUDA context.[/code] it seems CUDA installed successfully: [code=bash]$ apt list --installed | grep cuda (click here for output)[/code] any same case? solution? workaround?!
  2. After that, i've been clonned UnrealEngine+FleX:
    $ git clone -b FleX-4.17.1 https://github.com/NvPhysX/UnrealEngine.git
    

    and tried to build using these commands:

    $ ./Setup.sh
    $ ./GenerateProjectFiles.sh
    $ make
    

    … and theres so many errors/warnings in make output.
    anyone tried to build UnrealEngine+FleX on gnu/linux machine? please guide me.

  3. Off-Topic: After some googling to find a fine documention/tutorial on FleX, i've been found nothing. please give me some links if you know anything!

Execuse me for my bad english
Thanks.

Any answer?

Hi,

I’ve got the same problem:

$ ./bin/linux64/NvFlexDemoDebugCUDA_x64 Reshaping Error creating CUDA context.

I’ve wasted too much time on this !

I’ve built things according to requirements in the README.md file on Ubuntu 14.04. I am using a GT-640 graphics card on a Dell XPS computer.

For example, upon checking nvidia-smi, it shows a correct installation of Nvidia-396.54:

$nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 396.54                 Driver Version: 396.54                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GT 640      Off  | 00000000:01:00.0 N/A |                  N/A |
| 16%   25C    P8    N/A /  N/A |    215MiB /   973MiB |     N/A      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0                    Not Supported                                       |
+-----------------------------------------------------------------------------+

When I check the cuda toolkit with nvcc -V, it shows correct installation of version 8.0.44 as specified:

$nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Sun_Sep__4_22:14:01_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44

I’ve got g++ version 4.8.4, so when I type g++ -v I get:

g++ -v
Using built-in specs.
COLLECT_GCC=g++
COLLECT_LTO_WRAPPER=/usr/lib/gcc/x86_64-linux-gnu/4.8/lto-wrapper
Target: x86_64-linux-gnu
Configured with: ../src/configure -v --with-pkgversion='Ubuntu 4.8.4-2ubuntu1~14.04.4' --with-bugurl=file:///usr/share/doc/gcc-4.8/README.Bugs --enable-languages=c,c++,java,go,d,fortran,objc,obj-c++ --prefix=/usr --program-suffix=-4.8 --enable-shared --enable-linker-build-id --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --with-gxx-include-dir=/usr/include/c++/4.8 --libdir=/usr/lib --enable-nls --with-sysroot=/ --enable-clocale=gnu --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-gnu-unique-object --disable-libmudflap --enable-plugin --with-system-zlib --disable-browser-plugin --enable-java-awt=gtk --enable-gtk-cairo --with-java-home=/usr/lib/jvm/java-1.5.0-gcj-4.8-amd64/jre --enable-java-home --with-jvm-root-dir=/usr/lib/jvm/java-1.5.0-gcj-4.8-amd64 --with-jvm-jar-dir=/usr/lib/jvm-exports/java-1.5.0-gcj-4.8-amd64 --with-arch-directory=amd64 --with-ecj-jar=/usr/share/java/eclipse-ecj.jar --enable-objc-gc --enable-multiarch --disable-werror --with-arch-32=i686 --with-abi=m64 --with-multilib-list=m32,m64,mx32 --with-tune=generic --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu
Thread model: posix
gcc version 4.8.4 (Ubuntu 4.8.4-2ubuntu1~14.04.4) 

When I perform your check, I get the following:

$ apt list --installed | grep cuda
WARNING: apt does not have a stable CLI interface yet. Use with caution in scripts.

libcuda1-396/trusty,now 396.54-0ubuntu0~gpu14.04.1 amd64 [installed,automatic]

I also don’t find much support on the web. I’m excited to get Flex working, so I hope some folks can help with this.

–Henry C.

Hi all,

2 weeks later, I’m still stumped on this problem. I’ve now gone through the following in attempt to fix the issue:

https://devtalk.nvidia.com/default/topic/1029505/driveworks/dw_cuda_error-sdk-cannot-acquire-cuda-context-unknown-error/

First, I made sure that my paths were exported properly in both /etc/environment and in my bashrc.
Added the following to /etc/environment:

PATH=/usr/local/cuda-8.0/bin:/usr/local/cuda-8.0/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin
LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib:/usr/local/cuda-8.0/lib64:/usr/lib:/usr/local/cuda/targets/aarch64-linux/lib:/usr/local/cuda-8.0/lib:/usr/local/cuda-8.0/lib64:/usr/lib:/usr/local/cuda/targets/aarch64-linux/lib

in my bashrc, I’ve got this:

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64

I used

make

to make the CUDA 8.0 samples and check if things were working. Following the instructions with this link, I was able to run both deviceQuery and bandwidthTest and both passed. Here is the output of deviceQuery:

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GT 640"
  CUDA Driver Version / Runtime Version          9.2 / 8.0
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 972 MBytes (1019543552 bytes)
  ( 2) Multiprocessors, (192) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            954 MHz (0.95 GHz)
  Memory Clock rate:                             2500 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 262144 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 9.2, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GT 640
Result = PASS

I also ran the bandwidthTest:

[CUDA Bandwidth Test] - Starting...
Running on...

 Device 0: GeForce GT 640
 Quick Mode

 Host to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)	Bandwidth(MB/s)
   33554432			10516.1

 Device to Host Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)	Bandwidth(MB/s)
   33554432			10586.4

 Device to Device Bandwidth, 1 Device(s)
 PINNED Memory Transfers
   Transfer Size (Bytes)	Bandwidth(MB/s)
   33554432			55071.5

Result = PASS

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

At this point, I’d likely pay someone if they could get this going… please help!

–Henry C.