CUDA 10.1和NVidia驱动程序v440安装在我的Ubuntu 18.04系统上。我不明白为什么nvidia-smi工具在安装的版本是10.1时报告CUDA版本10.2（请参阅下一节）。
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Quadro M1200 On | 00000000:01:00.0 On | N/A |
| N/A 45C P0 N/A / N/A | 962MiB / 4042MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1346 G /usr/lib/xorg/Xorg 107MiB |
| 0 1647 G /usr/bin/gnome-shell 57MiB |
| 0 2521 G /usr/lib/xorg/Xorg 414MiB |
| 0 2655 G /usr/bin/gnome-shell 206MiB |
| 0 3549 C python 26MiB |
| 0 4236 G ...quest-channel-token=1063048282371062146 139MiB |
+-----------------------------------------------------------------------------+
每当我尝试运行Tensorflow（Python）程序时，它似乎都能正确检测笔记本电脑上的GPU，但是在初始化过程中会产生许多错误，并且不会在GPU上运行仿真，这可以通过GPU的使用来证明如上所示。
2020-02-13 17:37:53.162545: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-02-13 17:37:53.167709: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2020-02-13 17:37:53.215323: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-13 17:37:53.215893: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x56196a0c1980 executing computations on platform CUDA. Devices:
2020-02-13 17:37:53.215913: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Quadro M1200, Compute Capability 5.0
2020-02-13 17:37:53.235780: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2904000000 Hz
2020-02-13 17:37:53.236381: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x56196c491c70 executing computations on platform Host. Devices:
2020-02-13 17:37:53.236413: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-02-13 17:37:53.236721: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-13 17:37:53.237160: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: Quadro M1200 major: 5 minor: 0 memoryClockRate(GHz): 1.148
pciBusID: 0000:01:00.0
2020-02-13 17:37:53.237367: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory
2020-02-13 17:37:53.237508: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory
2020-02-13 17:37:53.237645: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory
2020-02-13 17:37:53.237811: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory
2020-02-13 17:37:53.237948: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory
2020-02-13 17:37:53.238083: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory
2020-02-13 17:37:53.243683: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2020-02-13 17:37:53.243719: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1663] Cannot dlopen some GPU libraries. Skipping registering GPU devices...
2020-02-13 17:37:53.243745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-02-13 17:37:53.243760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0
2020-02-13 17:37:53.243772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N
2020-02-13 17:37:53.273148: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
WARNING:tensorflow:From /home/xxxxxxx/.local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
有关系统和已安装软件包的一些事实：
# lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 18.04.4 LTS
Release: 18.04
Codename: bionic
# dpkg --get-selections |grep -i cuda
cuda install
cuda-10-1 install
cuda-command-line-tools-10-1 install
cuda-compiler-10-1 install
cuda-cudart-10-1 install
cuda-cudart-dev-10-1 install
cuda-cufft-10-1 install
cuda-cufft-dev-10-1 install
cuda-cuobjdump-10-1 install
cuda-cupti-10-1 install
cuda-curand-10-1 install
cuda-curand-dev-10-1 install
cuda-cusolver-10-1 install
cuda-cusolver-dev-10-1 install
cuda-cusparse-10-1 install
cuda-cusparse-dev-10-1 install
cuda-demo-suite-10-1 install
cuda-documentation-10-1 install
cuda-driver-dev-10-1 install
cuda-drivers install
cuda-gdb-10-1 install
cuda-gpu-library-advisor-10-1 install
cuda-libraries-10-1 install
cuda-libraries-dev-10-1 install
cuda-license-10-1 install
cuda-license-10-2 install
cuda-memcheck-10-1 install
cuda-misc-headers-10-1 install
cuda-npp-10-1 install
cuda-npp-dev-10-1 install
cuda-nsight-10-1 install
cuda-nsight-compute-10-1 install
cuda-nsight-systems-10-1 install
cuda-nvcc-10-1 install
cuda-nvdisasm-10-1 install
cuda-nvgraph-10-1 install
cuda-nvgraph-dev-10-1 install
cuda-nvjpeg-10-1 install
cuda-nvjpeg-dev-10-1 install
cuda-nvml-dev-10-1 install
cuda-nvprof-10-1 install
cuda-nvprune-10-1 install
cuda-nvrtc-10-1 install
cuda-nvrtc-dev-10-1 install
cuda-nvtx-10-1 install
cuda-nvvp-10-1 install
cuda-repo-ubuntu1804 install
cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01 deinstall
cuda-runtime-10-1 install
cuda-samples-10-1 install
cuda-sanitizer-api-10-1 install
cuda-toolkit-10-1 install
cuda-tools-10-1 install
cuda-visual-tools-10-1 install
# dpkg --get-selections |grep -P 'nvidia-[^\s]+\s+install$'
libnvidia-cfg1-440:amd64 install
libnvidia-common-435 install
libnvidia-common-440 install
libnvidia-compute-440:amd64 install
libnvidia-decode-440:amd64 install
libnvidia-encode-440:amd64 install
libnvidia-fbc1-440:amd64 install
libnvidia-gl-440:amd64 install
libnvidia-ifr1-440:amd64 install
nvidia-compute-utils-440 install
nvidia-dkms-440 install
nvidia-driver-440 install
nvidia-kernel-common-440 install
nvidia-kernel-source-440 install
nvidia-machine-learning-repo-ubuntu1804 install
nvidia-modprobe install
nvidia-prime install
nvidia-settings install
nvidia-utils-440 install
xserver-xorg-video-nvidia-440 install
$ pip list|grep -i tensorflow
tensorflow-estimator (1.14.0)
tensorflow-gpu (1.14.0)
要在GPU上运行Python Tensorflow模拟，我还需要做其他事情吗？我该如何诊断？
答案 0 :(得分：1)
从Could not dlopen library 'libcudart.so.10.0';中可以得出，您的tensorflow软件包是根据CUDA 10.0构建的。您应该自己安装CUDA 10.0或从源代码构建（针对CUDA 10.1或10.2）。