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

2020-09-17 13:44AnswerThe Python

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:

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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Ubuntu18中的pycharm不能調用tensorflow-gpu的問題

解決辦法:

1.修改~/.bashrc

將pycahrm的路徑加到環境中,示例如下:

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

刷新生效:

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source ~/.bashrc

2.修改pycharm中的環境變量

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

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

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內容請搜索服務器之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持服務器之家!

原文鏈接:https://www.cnblogs.com/answerThe/p/12261656.html

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