我很喜欢 18.04 的安装，而且我也经常使用 blender3d。我需要 CUDA 工具包才能使用 GPU 而不是 CPU 进行渲染。
我读到过，获得正确的工具包至关重要，否则可能会出现一些非常严重的问题。只是想确认它是否适用于 Ubuntu 18.04。
另外，在哪里可以得到它并确认它是正确的？
谢谢
答案1
看起来CUDA 9.1现在实际上在官方 18.04 存储库中。从终端窗口运行以下命令：
sudo apt install nvidia-cuda-toolkit
安装后运行nvcc -V确认。您应该看到类似这样的内容：
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
该工具包还会安装所需的驱动程序和支持OpenCL。只需安装clinfo并运行它即可看到：
sudo apt install clinfo
然后你应该得到类似下面的内容：
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
然后安装显卡驱动程序：
sudo apt install nvidia-driver-396
重新启动后，您可以运行nvidia-smi查看它是否已安装：
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！
答案2
我设法在笔记本电脑上安装了 CUDA，但一直卡住，直到遇到 gcc-6 问题。总结一下：
- 安装 nvidia 专有驱动程序；
- 从 Ubuntu 存储库安装 nvidia-settings、nvidia-prime 和 nvidia-cuda-toolkit。
- 使用“nvcc --version”和/或“nvidia-smi”命令检查终端中是否安装了 CUDA。
- 最后，如果你看不到 CUDA，你必须确保你使用的是 gcc-6，而不是 gcc-7 或更高版本。我在此主题并且它有效。
1) 安装 gcc-6、g++-6（CUDA 需要 gcc-6 ！）2) 以 root 身份在 /usr/bin 中，删除或重命名 gcc、gcc-ar、gcc-nm、gcc-ranlib 和 g++（如果存在），然后 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++