Tensorflow 没有检测到我的 GPU。我该怎么办（2021 年 5 月）？
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【中文标题】Tensorflow 没有检测到我的 GPU。我该怎么办（2021 年 5 月）？【英文标题】：Tensorflow is not detecting my GPUs. What shall I do (May 2021)? 【发布时间】：2021-07-29 21:15:07 【问题描述】：TF 版本：2.4.1 CUDA 版本：11.1
tf.test_is_gpu_available() -- 返回 --> FALSE tf.test.is_built_with_cuda() -- 返回 --> TRUE
我尝试将 TF 恢复到 2.4.0，但没有成功
我也试过了：
$ pip uninstall tensorflow
$ pip install tensorflow-gpu
但似乎没有任何效果，TF 只是没有检测到我的 GPU
编辑 1：
nvcc --version 的输出：
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Mon_Oct_12_20:09:46_PDT_2020
Cuda compilation tools, release 11.1, V11.1.105
Build cuda_11.1.TC455_06.29190527_0
nvidia-smi 的输出
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.73.01 Driver Version: 460.73.01 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 GeForce RTX 3090 Off | 00000000:01:00.0 Off | N/A |
| 30% 35C P8 23W / 300W | 23MiB / 24268MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 GeForce RTX 3090 Off | 00000000:43:00.0 Off | N/A |
| 30% 40C P8 27W / 300W | 5MiB / 24268MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 2 GeForce RTX 3090 Off | 00000000:81:00.0 Off | N/A |
| 64% 63C P2 179W / 300W | 24043MiB / 24268MiB | 59% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 2362 G /usr/lib/xorg/Xorg 9MiB |
| 0 N/A N/A 2564 G /usr/bin/gnome-shell 12MiB |
| 1 N/A N/A 2362 G /usr/lib/xorg/Xorg 4MiB |
| 2 N/A N/A 2362 G /usr/lib/xorg/Xorg 4MiB |
| 2 N/A N/A 14304 C python3 24035MiB |
+-----------------------------------------------------------------------------+
在运行 tf.test.is_gpu_avaliable() 时，我收到以下警告：
WARNING:tensorflow:From Spell_correction.py:35: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
2021-05-07 21:46:21.855460: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-05-07 21:46:21.856690: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:43:00.0 name: GeForce RTX 3090 computeCapability: 8.6
coreClock: 1.695GHz coreCount: 82 deviceMemorySize: 23.70GiB deviceMemoryBandwidth: 871.81GiB/s
2021-05-07 21:46:21.856716: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-05-07 21:46:21.856735: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-05-07 21:46:21.856747: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-05-07 21:46:21.856759: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-05-07 21:46:21.856771: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-05-07 21:46:21.856829: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.1/lib64
2021-05-07 21:46:21.856846: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-05-07 21:46:21.856856: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-05-07 21:46:21.856863: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-05-07 21:46:21.942589: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-05-07 21:46:21.942626: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2021-05-07 21:46:21.942633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
另一个观察：
Pytorch 正在检测 GPU，而 TF 没有。
torch.cuda.is_available() --> TRUE tf.test.is_gpu_available() --> FALSE
【问题讨论】：
(nvcc --version) 的输出是什么？ 请运行一些 tensorflow 代码并将输出包含在您的问题中，这包含关键信息，例如加载任何 CUDA 库和检测您的 GPU。任何其他信息都是无用的。 2.4.1 使用 CUDA 11.0。您不能使用 CUDA 11.1 作为 CUDA 11.0 的替代品。 @RobertCrovella，我检查过：TF: 2.4.1 CUDA: 11.0 cuDNN: 8 但还是和上面一样 你的意思是你仍然看到这样的输出：Could not load dynamic library 'libcusolver.so.10'; dlerror: libcusolver.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11.1/lib64 在这种情况下你没有正确设置你的LD_LIBRARY_PATH。
【参考方案1】：
如果你使用 ubuntu 20.04，我建议按照here 的步骤进行操作。我最近遇到了同样的问题。
你有
NVIDIA-SMI 460.73.01 Driver Version: 460.73.01 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 GeForce RTX 3090 Off | 00000000:01:00.0 Off | N/A |
| 30% 35C P8 23W / 300W | 23MiB / 24268MiB | 0% Default |
| | | N/A
尝试获取最新版本的 NVIDIA 465 和 Cuda 11.3。对于我的情况，nvidia-smi 如下：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 465.19.01 Driver Version: 465.19.01 CUDA Version: 11.3 |
|-------------------------------+----------------------+----------------------+
| 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. |
我做了什么；
(1) 我完全卸载了 NVIDIA 和 CUDA see here 小心点。
sudo apt-get remove --purge '^nvidia-.*'
sudo apt-get install ubuntu-desktop
sudo rm /etc/X11/xorg.conf
echo 'nouveau' | sudo tee -a /etc/modules
(2) 我下载了NVIDIA，下载.run 文件并简单地运行sudo bash NVIDIA*.run
(3) 我下载了cuDNN 并按照here 的描述执行以下操作
tar -xzvf cudnn-11.3-.*.tgz
sudo cp cuda/include/cudnn.h /usr/local/cuda/include
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
同时检查 .bashrc 文件以及描述的here:
cd ~
gedit .bashrc 或 nano .bashrc
#最后加上这个：
export PATH=/usr/local/cuda/bin$PATH:+:$PATH
export PATH=/usr/local/cuda-11.3/bin$PATH:+:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64\$LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/cuda11.3/targets/x86_64linux\$LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH
那么，pip install tensorflow-gpu==2.4.1
【讨论】：
这里是nvcc --version：nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2021 NVIDIA Corporation Built on Sun_Mar_21_19:15:46_PDT_2021 Cuda compilation tools, release 11.3, V11.3.58 Build cuda_11.3.r11.3/compiler.29745058_0以上是关于Tensorflow 没有检测到我的 GPU。我该怎么办（2021 年 5 月）？的主要内容，如果未能解决你的问题，请参考以下文章
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