我打开了一个谷歌协作笔记本,在上面运行一个python包,打算用GPU处理它
接下来link我选择了GPU选项(在Runtime
选项中)并下载了所需的软件包,以便将GPU与Pytorch和Cuda一起使用。然而,出于某种原因,它表明存在一个CPU而不是GPU
安装软件包(需要使用conda)
!pip install -q condacolab
import condacolab
condacolab.install()
✨🍰✨ Everything looks OK!
!mamba install -c conda-forge scikit-learn
!mamba install -q openmm
!mamba install -q pytorch torchvision cudatoolkit=11.1 -c pytorch
!mamba install -c rdkit rdkit
!mamba install -q ipykernel
!mamba install -q matplotlib
连接到google drive,其中包含脚本的.py文件为:
from google.colab import drive
drive.mount('/content/drive/')
Mounted at /content/drive/
但在运行此脚本时:
torch.cuda.is_available()
False
它不承认
运行下面的脚本时,它也无法识别任何gpu:
def try_gpu(i=0):
"""Return gpu(i) if exists, otherwise return cpu()."""
if torch.cuda.device_count() >= i + 1:
return torch.device(f'cuda:{i}')
return torch.device('cpu')
def try_all_gpus():
"""Return all available GPUs, or [cpu(),] if no GPU exists."""
devices = [
torch.device(f'cuda:{i}') for i in range(torch.cuda.device_count())]
return devices if devices else [torch.device('cpu')]
try_gpu(), try_gpu(10), try_all_gpus()
输出均为“cpu”:
(device(type='cpu'), device(type='cpu'), [device(type='cpu')])
但是,当检查是否有带有Tenseflow的GPU时,我发现有:
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
Found GPU at: /device:GPU:0
我不确定是否相关,但也添加了以下内容:
!nvidia-smi
Mon Jun 7 15:02:40 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 465.27 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |
| N/A 77C P0 33W / 70W | 222MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
关于如何使Pytork在google colab笔记本上与GPU协同工作的任何想法/提示,我们将不胜感激
目前没有回答
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