如何在带有Coffee Lake的18.04上安装NVIDIA CUDA工具包-是否受支持？

10

我喜欢18.04的安装，并且我还大量使用blender3d。我需要CUDA工具包才能使用我的GPU而不是CPU进行渲染。

我已经读到，拥有正确的工具包或可能会遇到一些非常糟糕的问题至关重要。只想确认它可用于Ubuntu 18.04。

另外，在哪里得到它并确认它是正确的？

谢谢

nvidia  18.04  cuda  opencl 
— 伟大的小说
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Answers:

11

看起来好像CUDA 9.1现在实际上已在官方18.04存储库中。从终端窗口运行以下命令：

sudo apt install nvidia-cuda-toolkit

安装完成后，运行nvcc -V进行确认。您应该看到类似以下内容：

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

该工具包还将安装必要的驱动程序和支持OpenCL。只需安装clinfo并运行它即可查看：

sudo apt install clinfo

然后，您应该获得类似于以下内容的信息：

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

要在18.04LTS中安装NVIDIA图形驱动程序，请按照以下步骤操作：

在终端窗口中，输入：

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

然后运行更新：

sudo apt update

然后安装图形驱动程序：

sudo apt install nvidia-driver-396

重新启动后，您可以运行nvidia-smi以查看是否已安装：

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                                       |
+-----------------------------------------------------------------------------+

希望这可以帮助！

— 特伦斯
source
在安装cuda工具包后在搅拌器中看到gpu仍然不走运-cuda的工具包确实按照您上面所说的那样安装了，并且能够通过nvcc进行确认-VI对于ubuntu来说是很新的，所以我不知道如果我正在使用1080ti图形驱动程序，那么我的机器是否可能正在使用通用视频驱动程序？我如何查找该信息，以及是否使用通用驱动程序，那么如何安装最新的1080ti vid驱动程序？非常感谢您
— greatfiction
@greatfiction如果要查看安装了哪些NVIDIA驱动程序，请nvidia-smi从终端窗口运行。
— Terrance '18
谢谢-我就是这样做的，这就是结果。找不到命令'nvidia-smi'，但可以使用以下命令安装：sudo apt install nvidia-340 sudo apt install nvidia-utils-390
— greatfiction
@greatfiction其实，让我更新我的回答对您从安装graphics-driversPPA
— 特伦斯
@greatfiction编辑了我安装图形驱动程序的答案。
— Terrance
1

我设法在笔记本电脑上安装了CUDA，但由于遇到问题，直到我遇到gcc-6问题时，它还是被卡住了。因此，总结一下：

  1. 安装nvidia专有驱动程序；
  2. 从Ubuntu存储库安装nvidia-settings，nvidia-prime和nvidia-cuda-toolkit。
  3. 使用“ nvcc --version”和/或“ nvidia-smi”命令检查终端中是否已安装CUDA。
  4. 最后，如果看不到CUDA，则必须确保使用的是gcc-6而不是gcc-7或更高版本。我在此线程中找到了解决方案，并且可以正常工作。

1）安装gcc-6，g ++-6（CUDA需要gcc-6！）2）在/ usr / bin中，以根用户身份删除或重命名gcc，gcc-ar，gcc-nm，gcc-ranlib和g ++（如果已安装）存在），则ln -s gcc-6 gcc; ln -s gcc-ar-6 gcc-ar; ln -s gcc-nm-6 gcc-nm; ln -s gcc-ranlib-6 gcc-ranlib; 和ln -s g ++-6 g ++

— 克里斯蒂安·奥布里
source
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