我在Ubuntu 18.04计算机上的conda环境中运行Python 3.6，并且tensorflow无法识别我的GPU。
lsb_release -a 的输出：
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 18.04.1 LTS
Release: 18.04
Codename: bionic
来自 nvidia-smi 的信息：
NVIDIA-SMI 390.77 Driver Version: 390.77 Tesla P40
来自 nvcc -V 的信息：
Cuda compilation tools, release 9.1, V9.1.85
nvidia-debugdump -l </ strong>的输出：
Found 1 NVIDIA devices
Device ID: 0
Device name: Tesla P40
GPU internal ID: 0322417022310
lspci -nnk的输出| grep -i nvidia ：
a5dd:00:00.0 3D controller [0302]: NVIDIA Corporation GP102GL [Tesla P40] [10de:1b38] (rev a1)
Subsystem: NVIDIA Corporation GP102GL [Tesla P40] [10de:11d9]
Kernel driver in use: nvidia
Kernel modules: nvidia_drm, nvidia
conda --version 的输出：
conda 4.5.12
echo $ PATH 的输出（不带空格）：
/home/***/anaconda3/envs/tf_gpu/bin: /usr/local/cuda/bin: /home/***/.local/bin: /home/***/anaconda3/bin: /usr/local/sbin: /usr/local/bin: /usr/sbin: /usr/bin: /sbin: /bin: /usr/games: /usr/local/games: /snap/bin
回显$ LD_LIBRARY_PATH 的输出（不带空格）：
/usr/local/cuda/lib64: /usr/local/cuda/extras/CUPTI/lib64: /usr/local/cuda/lib64: /usr/local/cuda/extras/CUPTI/lib64:
好，所以我要这样安装我的环境：
conda create -n tf_gpu python=3.6 tensorflow-gpu
conda activate tf_gpu
这将安装以下软件包：
_tflow_select: 2.1.0-gpu
absl-py: 0.6.1-py36_0
astor: 0.7.1-py36_0
blas: 1.0-mkl
c-ares: 1.15.0-h7b6447c_1
ca-certificates: 2018.03.07-0
certifi: 2018.11.29-py36_0
cudatoolkit: 9.2-0
cudnn: 7.2.1-cuda9.2_0
cupti: 9.2.148-0
gast: 0.2.0-py36_0
grpcio: 1.16.1-py36hf8bcb03_1
h5py: 2.8.0-py36h989c5e5_3
hdf5: 1.10.2-hba1933b_1
intel-openmp: 2019.1-144
keras-applications: 1.0.6-py36_0
keras-preprocessing: 1.0.5-py36_0
libedit: 3.1.20170329-h6b74fdf_2
libffi: 3.2.1-hd88cf55_4
libgcc-ng: 8.2.0-hdf63c60_1
libgfortran-ng: 7.3.0-hdf63c60_0
libprotobuf: 3.6.1-hd408876_0
libstdcxx-ng: 8.2.0-hdf63c60_1
markdown: 3.0.1-py36_0
mkl: 2019.1-144
mkl_fft: 1.0.6-py36hd81dba3_0
mkl_random: 1.0.2-py36hd81dba3_0
ncurses: 6.1-he6710b0_1
numpy: 1.15.4-py36h7e9f1db_0
numpy-base: 1.15.4-py36hde5b4d6_0
openssl: 1.1.1a-h7b6447c_0
pip: 18.1-py36_0
protobuf: 3.6.1-py36he6710b0_0
python: 3.6.7-h0371630_0
readline: 7.0-h7b6447c_5
scipy: 1.1.0-py36h7c811a0_2
setuptools: 40.6.3-py36_0
six: 1.12.0-py36_0
sqlite: 3.26.0-h7b6447c_0
tensorboard: 1.12.0-py36hf484d3e_0
tensorflow: 1.12.0-gpu_py36he74679b_0
tensorflow-base: 1.12.0-gpu_py36had579c0_0
tensorflow-gpu: 1.12.0-h0d30ee6_0
termcolor: 1.1.0-py36_1
tk: 8.6.8-hbc83047_0
werkzeug: 0.14.1-py36_0
wheel: 0.32.3-py36_0
xz: 5.2.4-h14c3975_4
zlib: 1.2.11-h7b6447c_3
然后我在python控制台中检查可用设备的tensorflow：
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
打印以下内容：
2018-12-18 10:44:12.135984: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 14921140553341499580
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 17804082860482987174
physical_device_desc: "device: XLA_CPU device"
]
如您所见，没有GPU。有趣的是，如果我安装PyTorch，它可以毫无问题地识别GPU。现在，我尝试了在其他帖子中看到的各种方法，例如从conda中删除protobuf软件包和tensorflow-gpu并使用pip重新安装，但这并没有改变。
我该怎么做才能使Tensorflow识别GPU？非常感谢您的帮助！
类似的问题无法解决我的问题：
对于CuDNN安装，我遵循了该指南（直到Bazel说明为止）：