【发布时间】：2020-10-03 03:36:40
【问题描述】：
我正在尝试在我拥有的笔记本电脑上运行 Pytorch。这是一个较旧的型号，但它确实有一个 Nvidia 显卡。我意识到这可能不足以进行真正的机器学习，但我正在尝试这样做，以便了解安装 CUDA 的过程。
我已按照 Ubuntu 18.04 的 installation guide 上的步骤进行操作（我的特定发行版是 Xubuntu）。
我的显卡是GeForce 845M，通过lspci | grep nvidia验证：
01:00.0 3D controller: NVIDIA Corporation GM107M [GeForce 845M] (rev a2)
01:00.1 Audio device: NVIDIA Corporation Device 0fbc (rev a1)
我也安装了 gcc 7.5，由gcc --version 验证
gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
我安装了正确的标头，并通过尝试使用sudo apt-get install linux-headers-$(uname -r) 安装它们来验证：
Reading package lists... Done
Building dependency tree
Reading state information... Done
linux-headers-4.15.0-106-generic is already the newest version (4.15.0-106.107).
然后我按照安装说明使用 10.1 版的本地 .deb。
Npw，当我运行 nvidia-smi 时，我得到：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.87.00 Driver Version: 418.87.00 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| 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 845M On | 00000000:01:00.0 Off | N/A |
| N/A 40C P0 N/A / N/A | 88MiB / 2004MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 982 G /usr/lib/xorg/Xorg 87MiB |
+-----------------------------------------------------------------------------+
然后我运行nvcc -V 我得到：
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
然后我从section 6.1 执行了安装后指令，因此echo $PATH 看起来像这样：
/home/isaek/anaconda3/envs/stylegan2_pytorch/bin:/home/isaek/anaconda3/bin:/home/isaek/anaconda3/condabin:/usr/local/cuda-10.1/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
echo $LD_LIBRARY_PATH 看起来像这样：
/usr/local/cuda-10.1/lib64
我的/etc/udev/rules.d/40-vm-hotadd.rules 文件如下所示：
# On Hyper-V and Xen Virtual Machines we want to add memory and cpus as soon as they appear
ATTR{[dmi/id]sys_vendor}=="Microsoft Corporation", ATTR{[dmi/id]product_name}=="Virtual Machine", GOTO="vm_hotadd_apply"
ATTR{[dmi/id]sys_vendor}=="Xen", GOTO="vm_hotadd_apply"
GOTO="vm_hotadd_end"
LABEL="vm_hotadd_apply"
# Memory hotadd request
# CPU hotadd request
SUBSYSTEM=="cpu", ACTION=="add", DEVPATH=="/devices/system/cpu/cpu[0-9]*", TEST=="online", ATTR{online}="1"
LABEL="vm_hotadd_end"
在所有这些之后，我什至编译并运行了示例。 ./deviceQuery 返回：
./deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce 845M"
CUDA Driver Version / Runtime Version 10.1 / 10.1
CUDA Capability Major/Minor version number: 5.0
Total amount of global memory: 2004 MBytes (2101870592 bytes)
( 4) Multiprocessors, (128) CUDA Cores/MP: 512 CUDA Cores
GPU Max Clock rate: 863 MHz (0.86 GHz)
Memory Clock rate: 1001 Mhz
Memory Bus Width: 64-bit
L2 Cache Size: 1048576 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 supports Compute Preemption: No
Supports Cooperative Kernel Launch: No
Supports MultiDevice Co-op Kernel Launch: No
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 = 10.1, CUDA Runtime Version = 10.1, NumDevs = 1
Result = PASS
和./bandwidthTest 返回：
[CUDA Bandwidth Test] - Starting...
Running on...
Device 0: GeForce 845M
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(GB/s)
32000000 11.7
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(GB/s)
32000000 11.8
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(GB/s)
32000000 14.5
Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
但在这一切之后，这个 Python sn-p（在安装了所有依赖项的 conda 环境中）：
import torch
torch.cuda.is_available()
返回False
有人知道如何解决这个问题吗？我尝试像这样将/usr/local/cuda-10.1/bin 添加到etc/environment：
PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games"
PATH=$PATH:/usr/local/cuda-10.1/bin
然后重新启动终端，但这并没有解决问题。我真的不知道还能尝试什么。
编辑 - @kHarshit 的 collect_env 的结果
Collecting environment information...
PyTorch version: 1.5.0
Is debug build: No
CUDA used to build PyTorch: 10.2
OS: Ubuntu 18.04.4 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: Could not collect
Python version: 3.6
Is CUDA available: No
CUDA runtime version: 10.1.243
GPU models and configuration: GPU 0: GeForce 845M
Nvidia driver version: 418.87.00
cuDNN version: Could not collect
Versions of relevant libraries:
[pip] numpy==1.18.5
[pip] pytorch-ranger==0.1.1
[pip] stylegan2-pytorch==0.12.0
[pip] torch==1.5.0
[pip] torch-optimizer==0.0.1a12
[pip] torchvision==0.6.0
[pip] vector-quantize-pytorch==0.0.2
[conda] numpy 1.18.5 pypi_0 pypi
[conda] pytorch-ranger 0.1.1 pypi_0 pypi
[conda] stylegan2-pytorch 0.12.0 pypi_0 pypi
[conda] torch 1.5.0 pypi_0 pypi
[conda] torch-optimizer 0.0.1a12 pypi_0 pypi
[conda] torchvision 0.6.0 pypi_0 pypi
[conda] vector-quantize-pytorch 0.0.2 pypi_0 pypi
【问题讨论】：
-
你能发布collect_env脚本的结果吗？
-
Pytorch 在通过 pip 或 conda 安装时不使用系统 cuda，它带有自己的 cuda 运行时副本，只要显卡的计算能力 >= 5.0 并且图形驱动程序支持所需的 cuda 版本。您是否使用
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch安装？可能是您安装了 pytorch 的 CPU 版本。 -
@jodag 我认为这已经解决了！您介意在答案中详细说明这到底是做什么的吗？我会在发布后立即接受。
-
用户可能还会发现这个问题（和答案）很有用stackoverflow.com/questions/67122586/…
-
@jodag
nvidia-cuda-toolkit（由 Canonical PPA 提供）和 NVIDIA 都有效。诀窍是创建一个新环境或强制重新安装pytorch。
标签： linux pytorch ubuntu-18.04