解決Ubuntu18中的pycharm不能調用tensorflow-gpu的問題

更新時間：2020年09月17日 10:41:47 作者：AnswerThe

這篇文章主要介紹了解決Ubuntu18中的pycharm不能調用tensorflow-gpu的問題，文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值，需要的朋友們下面隨著小編來一起學習學習吧

問題描述：我通過控制臺使用tensorflow-gpu沒問題，但是通過pycharm使用卻不可以，如下所示：

通過控制臺：

answer@answer-desktop:/$ python
Python 3.7.0 (default, Jun 28 2018, 13:15:42) 
[GCC 7.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2020-02-04 21:37:12.964610: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.1/lib64:/usr/local/cuda-10.1/lib64
2020-02-04 21:37:12.964749: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.1/lib64:/usr/local/cuda-10.1/lib64
2020-02-04 21:37:12.964777: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
>>> print(tf.test.is_gpu_available())
WARNING:tensorflow:From <stdin>:1: 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.
2020-02-04 21:37:37.267421: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 1795795000 Hz
2020-02-04 21:37:37.268461: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55913b67a840 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-02-04 21:37:37.268516: I tensorflow/compiler/xla/service/service.cc:176]  StreamExecutor device (0): Host, Default Version
2020-02-04 21:37:37.272139: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-02-04 21:37:37.481038: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-04 21:37:37.481712: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55913b6eb960 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-02-04 21:37:37.481755: I tensorflow/compiler/xla/service/service.cc:176]  StreamExecutor device (0): GeForce GTX 1060 3GB, Compute Capability 6.1
2020-02-04 21:37:37.482022: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-04 21:37:37.482528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties: 
pciBusID: 0000:03:00.0 name: GeForce GTX 1060 3GB computeCapability: 6.1
coreClock: 1.7085GHz coreCount: 9 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s
2020-02-04 21:37:37.482953: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-02-04 21:37:37.485492: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-02-04 21:37:37.487486: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-02-04 21:37:37.487927: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-02-04 21:37:37.490469: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-02-04 21:37:37.491950: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-02-04 21:37:37.499031: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-02-04 21:37:37.499301: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-04 21:37:37.500387: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-04 21:37:37.500847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-02-04 21:37:37.500941: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-02-04 21:37:37.502172: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-02-04 21:37:37.502212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]   0 
2020-02-04 21:37:37.502229: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:  N 
2020-02-04 21:37:37.502436: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-04 21:37:37.503003: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-02-04 21:37:37.503593: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/device:GPU:0 with 2934 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 3GB, pci bus id: 0000:03:00.0, compute capability: 6.1)
True
>>>

返回的True，說明可以

通過pycharm卻不行，如下圖，返回False

解決辦法：

1.修改~/.bashrc

將pycahrm的路徑加到環(huán)境中，示例如下：

alias pycharm="bash /home/answer/文檔/pycharm-professional-2019.3.2/pycharm-2019.3.2/bin/pycharm.sh"

刷新生效：

source ~/.bashrc

2.修改pycharm中的環(huán)境變量

選擇pycharm 菜單欄Run ——> Run-Edit Configurations ——> Environment variables——> 將cuda的路徑加進去例如：LD_LIBRARY_PATH=/usr/local/cuda-10.1/lib64

在運行就可以了

到此這篇關于解決Ubuntu18中的pycharm不能調用tensorflow-gpu的問題的文章就介紹到這了,更多相關pycharm不能調用tensorflow-gpu內容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關文章希望大家以后多多支持腳本之家！

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