我真的将 GPU 用于 tensorflow 吗？

Question

我正在学习神经网络，并尝试使用 GPU。我正在使用：

• Python 3.8
• tensorflow-gpu 2.6.0
• PyCharm
• PyCharm 的 Jupiter 插件
• 显卡 NVIDIA 3080 TI - 12 Gb

我已经安装了 CUDA 11.4（以及其他几个）和 CudNN-v8.2.4.15

我的起始代码很平常：

import os
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'

import pandas as pd
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

from tensorflow import keras
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv1D
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator

from sklearn.preprocessing import MinMaxScaler, OneHotEncoder

问题是，当我开始学习神经网络时，我正在任务管理器中检查资源，它看起来很奇怪，就像它使用的是 CPU 而不是 GPU。有截图（抱歉俄语）：

所以当我启动进程时，CP 使用率从 5% 增长到 40%，而显卡内存从 2GB 增长到 6GB。

感觉像是在使用显卡内存，但使用 CPU 进行计算。有可能吗？

UPD：木星日志：

"E:\Program Files\JetBrains\PyCharm 2020.3\bin\runnerw.exe" C:\Users\levsh\AppData\Local\Programs\Python\Python38\python.exe -m jupyter notebook --no-browser --notebook-dir=C:/Users/levsh/PycharmProjects/ipynb
[W 2021-11-01 02:53:42.606 LabApp] 'notebook_dir' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.
[W 2021-11-01 02:53:42.607 LabApp] 'notebook_dir' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.
[W 2021-11-01 02:53:42.607 LabApp] 'notebook_dir' has moved from NotebookApp to ServerApp. This config will be passed to ServerApp. Be sure to update your config before our next release.
[I 2021-11-01 02:53:42.614 LabApp] JupyterLab extension loaded from c:\users\levsh\appdata\local\programs\python\python38\lib\site-packages\jupyterlab
[I 2021-11-01 02:53:42.614 LabApp] JupyterLab application directory is C:\Users\levsh\AppData\Local\Programs\Python\Python38\share\jupyter\lab
[I 02:53:42.620 NotebookApp] Serving notebooks from local directory: C:/Users/levsh/PycharmProjects/ipynb
[I 02:53:42.620 NotebookApp] Jupyter Notebook 6.4.3 is running at:
[I 02:53:42.620 NotebookApp] http://localhost:8888/?token=a34f13cb67f5cf89bff0a8b8242b69a8727197a98ddb298f
[I 02:53:42.620 NotebookApp]  or http://127.0.0.1:8888/?token=a34f13cb67f5cf89bff0a8b8242b69a8727197a98ddb298f
[I 02:53:42.620 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 02:53:42.625 NotebookApp] 
    
    To access the notebook, open this file in a browser:
        file:///C:/Users/levsh/AppData/Roaming/jupyter/runtime/nbserver-9860-open.html
    Or copy and paste one of these URLs:
        http://localhost:8888/?token=a34f13cb67f5cf89bff0a8b8242b69a8727197a98ddb298f
     or http://127.0.0.1:8888/?token=a34f13cb67f5cf89bff0a8b8242b69a8727197a98ddb298f
[I 02:53:42.626 NotebookApp] 302 GET /api/kernelspecs/ (127.0.0.1) 0.000000ms
[I 02:53:42.694 NotebookApp] Kernel started: 036ed1a8-7e7e-49bb-8c86-3efcfb4b993c, name: python3
[W 02:53:42.703 NotebookApp] No session ID specified
2021-11-01 02:53:49.739123: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-11-01 02:53:50.367361: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2021-11-01 02:53:50.367428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9440 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080 Ti, pci bus id: 0000:1f:00.0, compute capability: 8.6
2021-11-01 02:53:50.526516: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2021-11-01 02:53:52.226233: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8204
2021-11-01 02:53:55.133752: I tensorflow/stream_executor/cuda/cuda_blas.cc:1760] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.

UPD2：TYZ 评论的更新 - 不，不是。

tf.debugging.set_log_device_placement(True)

print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) 
# Num GPUs Available:  1

tf.config.list_physical_devices('GPU') 
# [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))
# Device mapping: 
# /job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: NVIDIA GeForce RTX 3080 Ti, pci bus id: 0000:1f:00.0, compute capability: 8.6

在日志中看起来也不错：

2021-11-01 04:34:49.606386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /device:GPU:0 with 9440 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080 Ti, pci bus id: 0000:1f:00.0, compute capability: 8.6
2021-11-01 04:36:58.513741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9440 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080 Ti, pci bus id: 0000:1f:00.0, compute capability: 8.6

Answer 1

在程序运行时检查您的 nvidia-smi（nvidia 系统管理界面），寻找 Volatile GPU-util。 任务管理器不能很好地指示 GPU 使用情况（也不能非常准确地使用其他资源，例如 RAM 和 temps imo...）。 3080 Ti 的 GPU 温度为 71 度这一事实表明肯定使用了 GPU（除非其他进程正在使用它）

例如，我现在正在使用 RTX 3090 进行训练，我的 smi 命令行输出看起来像（截断屏幕截图中的进程）：

但我的任务管理器看起来像（注意 gpu 使用情况）：

现在，如果您遇到某种 I/O 瓶颈，即从 CPU 加载张量的时间过长，导致 GPU 处于空闲状态，那么这是一个不同的问题，可以通过分析和其他工具来解决，以确保加载过程得到优化。