NVIDIA CUDA Toolkit – Installation on Ubuntu 18.04 with Coffee Lake

18.04cudanvidiaopencl

I love the install of 18.04 and I also work with blender3d a great deal. I need the CUDA toolkit in order to render with my GPU instead of my CPU.

I have read that it's critical that I get the right toolkit or could have some pretty bad issues. Just want to confirm that it is available for Ubuntu 18.04.

Also, where to get it and confirm its the right one?

Thank you

Best Answer

It looks as though the CUDA 9.1 is actually in the official 18.04 repositories now. Run the following from a terminal window:

sudo apt install nvidia-cuda-toolkit

After it is installed run nvcc -V to confirm. You should see something similar to this:

terrance@terrance-ubuntu:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Nov__3_21:07:56_CDT_2017
Cuda compilation tools, release 9.1, V9.1.85

The toolkit also installs the necessary drivers and support for OpenCL. Just install clinfo and run it to see:

sudo apt install clinfo

Then you should get something similar to the following:

terrance@terrance-ubuntu:~$ clinfo
Number of platforms                               1
  Platform Name                                   NVIDIA CUDA
  Platform Vendor                                 NVIDIA Corporation
  Platform Version                                OpenCL 1.2 CUDA 9.2.101
  Platform Profile                                FULL_PROFILE
  Platform Extensions                             cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_copy_opts cl_nv_create_buffer
  Platform Extensions function suffix             NV

  Platform Name                                   NVIDIA CUDA
Number of devices                                 1
  Device Name                                     GeForce GTX 760
  Device Vendor                                   NVIDIA Corporation
  Device Vendor ID                                0x10de
  Device Version                                  OpenCL 1.2 CUDA
  Driver Version                                  396.24
  Device OpenCL C Version                         OpenCL C 1.2 
  Device Type                                     GPU
  Device Topology (NV)                            PCI-E, 02:00.0
  Device Profile                                  FULL_PROFILE
  Device Available                                Yes
  Compiler Available                              Yes
  Linker Available                                Yes
  Max compute units                               6
  Max clock frequency                             1032MHz
  Compute Capability (NV)                         3.0
  Device Partition                                (core)
    Max number of sub-devices                     1
    Supported partition types                     None
  Max work item dimensions                        3
  Max work item sizes                             1024x1024x64
  Max work group size                             1024
  Preferred work group size multiple              32
  Warp size (NV)                                  32
  Preferred / native vector sizes                 
    char                                                 1 / 1       
    short                                                1 / 1       
    int                                                  1 / 1       
    long                                                 1 / 1       
    half                                                 0 / 0        (n/a)
    float                                                1 / 1       
    double                                               1 / 1        (cl_khr_fp64)
  Half-precision Floating-point support           (n/a)
  Single-precision Floating-point support         (core)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
    Correctly-rounded divide and sqrt operations  Yes
  Double-precision Floating-point support         (cl_khr_fp64)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
  Address bits                                    64, Little-Endian
  Global memory size                              2095710208 (1.952GiB)
  Error Correction support                        No
  Max memory allocation                           523927552 (499.7MiB)
  Unified memory for Host and Device              No
  Integrated memory (NV)                          No
  Minimum alignment for any data type             128 bytes
  Alignment of base address                       4096 bits (512 bytes)
  Global Memory cache type                        Read/Write
  Global Memory cache size                        98304 (96KiB)
  Global Memory cache line size                   128 bytes
  Image support                                   Yes
    Max number of samplers per kernel             32
    Max size for 1D images from buffer            134217728 pixels
    Max 1D or 2D image array size                 2048 images
    Max 2D image size                             16384x16384 pixels
    Max 3D image size                             4096x4096x4096 pixels
    Max number of read image args                 256
    Max number of write image args                16
  Local memory type                               Local
  Local memory size                               49152 (48KiB)
  Registers per block (NV)                        65536
  Max number of constant args                     9
  Max constant buffer size                        65536 (64KiB)
  Max size of kernel argument                     4352 (4.25KiB)
  Queue properties                                
    Out-of-order execution                        Yes
    Profiling                                     Yes
  Prefer user sync for interop                    No
  Profiling timer resolution                      1000ns
  Execution capabilities                          
    Run OpenCL kernels                            Yes
    Run native kernels                            No
    Kernel execution timeout (NV)                 Yes
  Concurrent copy and kernel execution (NV)       Yes
    Number of async copy engines                  1
  printf() buffer size                            1048576 (1024KiB)
  Built-in kernels                                
  Device Extensions                               cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_copy_opts cl_nv_create_buffer

NULL platform behavior
  clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...)  NVIDIA CUDA
  clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...)   Success [NV]
  clCreateContext(NULL, ...) [default]            Success [NV]
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT)  No platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU)  No devices found in platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU)  No platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR)  No devices found in platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM)  Invalid device type for platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL)  No platform

ICD loader properties
  ICD loader Name                                 OpenCL ICD Loader
  ICD loader Vendor                               OCL Icd free software
  ICD loader Version                              2.2.11
  ICD loader Profile                              OpenCL 2.1

To install the NVIDIA graphics drivers in 18.04LTS, follow the steps below:

In a terminal window, type in:

sudo apt-add-repository ppa:graphics-drivers/ppa

Then run the update:

sudo apt update

Then install the graphics driver:

sudo apt install nvidia-driver-396

After a reboot, you can run nvidia-smi to see if it is installed:

terrance@terrance-ubuntu:~$ nvidia-smi
Wed May  2 22:38:14 2018       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 396.24                 Driver Version: 396.24                    |
|-------------------------------+----------------------+----------------------+
| 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 GTX 760     Off  | 00000000:02:00.0 N/A |                  N/A |
| 49%   51C    P0    N/A /  N/A |    262MiB /  1998MiB |     N/A      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0                    Not Supported                                       |
+-----------------------------------------------------------------------------+

Hope this helps!

Installing CUDA

Driver

I recommend to install the nvidia driver from Ubuntu's package manager. If you install Bumblebee, you don't need to worry about the driver. Otherwise, after installation, disable the nvidia libraries as described on https://askubuntu.com/a/107746/6969. If you do not do so, you'll loose 3D acceleraration and possibly get stuck on a low resolution.

Toolkit

Basically you have to download the installer, make it executable and run it. - Download the installer. As of this writing, 4.2.9 is the most recent driver. As I've a 64-bit OS, I use the 64-bit 11.04 Ubuntu package (though I'm running 12.04) - Make it executable and allow installation to /usr/local/cuda:

chmod +x cudatoolkit_4.2.9_linux_64_ubuntu11.04.run
sudo ./cudatoolkit_4.2.9_linux_64_ubuntu11.04.run

When the installation message occurs which asks you where to install CUDA, just press Enter to accept the default /usr/local/cuda:

......................................
Enter install path (default /usr/local/cuda, '/cuda' will be appended):

After installation, it'll print some messages that suggest to put the cuda library directory to your library search path:

========================================

* Please make sure your PATH includes /tmp/cuda/cuda/bin
* Please make sure your LD_LIBRARY_PATH
*   for 32-bit Linux distributions includes /tmp/cuda/cuda/lib
*   for 64-bit Linux distributions includes /tmp/cuda/cuda/lib64:/tmp/cuda/cuda/lib
* OR
*   for 32-bit Linux distributions add /tmp/cuda/cuda/lib
*   for 64-bit Linux distributions add /tmp/cuda/cuda/lib64 and /tmp/cuda/cuda/lib
* to /etc/ld.so.conf and run ldconfig as root

* Please read the release notes in /tmp/cuda/cuda/doc/

* To uninstall CUDA, remove the CUDA files in /tmp/cuda/cuda
* Installation Complete

You may skip this step if you want, but then you've to set LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH later when running a program.

SDK

If you want to be able to compile CUDA applications, you can install the SDK in a similar way as described above. Download, make it executable and run it (not as root, e.g. without sudo!). When compiling parts of it, I had to make some changes to NVIDIA_GPU_Computing_SDK/C/common/common.mk though:

after about line 189, OPENGLLIB := -lGL -lGLU -lX11 -lXi -lXmu, add:

OPENGLLIB += -L/usr/lib/nvidia-current -L/usr/lib32/nvidia-current

after about line 271, swap ${RENDERCHECKGLLIB} with ${OPENGLLIB} such that it looks like:
```
LIB += $(RENDERCHECKGLLIB) ${OPENGLLIB} $(PARAMGLLIB) ${LIB} -ldl -rdynamic
```
a few (5) lines further, do the same, but remove -lcuda too.
a few (7) lines further, do the same as you did in line 271.

Using CUDA

CUDA does not need a nvidia-driven X server to work. In that case you can run your random test program like:

LD_LIBRARY_PATH=/usr/lib/nvidia-current:/usr/lib32/nvidia-current:$LD_LIBRARY_PATH someComputallyIntensiveProgram

If you have not added CUDA to your library path, you will need:

LD_LIBRARY_PATH=/usr/lib/nvidia-current:/usr/lib32/nvidia-current:/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH someComputallyIntensiveProgram

(you can strip the 32-bit paths from it if your program is 64-bit).

If the CUDA program does have something to display using OpenGL, you have to use optirun:

 optirun blender

Or, if you did not have CUDA added to your default path:

LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH optirun blender

Ubuntu – CUDA sample code did not get installed through sudo apt-get install nvidia-cuda-toolkit; any solutions

I have exactely the same issue. I am trying to test my working setup with Ubuntu 16.04 and GTX1080 with CUDA 7.5 for parallel computing, and was hoping to easily get this done by using the packages provided in the repos. Unfortunately as noted above they are missing. It is possible to use the runfile install for installing only the samples. See e.g. for an older version:

https://stackoverflow.com/questions/27306724/ubuntu-14-04-how-to-install-cuda-6-5-without-installing-nvidia-driver

This works also for 7.5. Simply say "no" when prompted for driver and toolkit installation. You will also need to set the right toolkit libraries. This installs cuda samples into your home by default. However there seem to be an issue with libraries not being found as the package from the repo also puts them into a different location.

However some simple samples like matrixMul compile and run fine.

There might be a simple workaround by adding the right path, but probably installing at least cuda-toolkit together with samples from the runfile should make it work 'out-of-the-box' - have not tried it yet...

Best Answer

Related Solutions

Nvidia CUDA – How to Get Nvidia CUDA or OpenCL Working on Laptops

Installing CUDA

Driver

Toolkit

SDK

Using CUDA

Ubuntu – CUDA sample code did not get installed through sudo apt-get install nvidia-cuda-toolkit; any solutions

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