tensorflow（不是 tensorflow-gpu）：對 cuInit 的調用失敗：未知錯誤（303）

Question

我的測試

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()

我的錯誤

2019-12-27 10:51:17.887009: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/openmpi/lib:
2019-12-27 10:51:17.888489: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2019-12-27 10:51:17.888992: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (3e7d899714a9): /proc/driver/nvidia/version does not exist
2019-12-27 10:51:17.890608: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-12-27 10:51:17.915554: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2904000000 Hz
2019-12-27 10:51:17.918061: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x56101fca67e0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2019-12-27 10:51:17.918228: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

我的環境

烏布努圖 18

張量流 1.15.0/1.14.0

我的問題

我已經查看了類似的問題，例如TensorFlow：對 cuInit 的調用失敗：CUDA_ERROR_NO_DEVICE

關鍵的區別是我沒有安裝tensorflow-gpu包。 普通 TensorFlow 如何引發此錯誤？

Answer 1

您可以嘗試使用tensorflow-cpu包而不是tensorflow來在僅使用 cpu 的機器上解決此問題。

pip uninstall tensorflow
pip install tensorflow-cpu

Answer 2

安裝 nvidia-modprobe 可以解決這個問題。

sudo apt install nvidia-modprobe

您可以嘗試的其他解決方案是：

• 卸載並安裝 CUDA 和 cuDNN。
• 安裝 tensorflow-gpu。
• 卸載並安裝不同的 Nvidia 驅動程序版本。

問題也可能是在使用 sudo 運行 Python 之前只存在一些/dev/nvidia*文件，使用$ ls /dev/nvidia* ，在運行設備節點驗證腳本后， /dev/nvidia-uvm文件被添加。

Answer 3

我在 Windows 操作系統上遇到了同樣的問題。 我按照 tensorflow 關於安裝 CUDA、cudnn 等的說明進行操作，並嘗試了上述答案中的建議——但沒有成功。 解決我的問題的是更新我的 GPU 驅動程序。 您可以通過以下方式更新它們：

1. 按下 windows 按鈕 + r
2. 輸入devmgmt.msc
3. 右鍵單擊“顯示適配器”並單擊“屬性”選項
4. 轉到“驅動程序”選項卡並選擇“更新驅動程序”。
5. 最后，點擊“自動搜索更新的驅動程序軟件”
6. 重新啟動機器並再次運行以下檢查：

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()

Sample output:
2022-01-17 14:01:54.999571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce 940MX major: 5 minor: 0 memoryClockRate(GHz): 1.189
pciBusID: 0000:01:00.0
2022-01-17 14:01:54.999820: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2022-01-17 14:01:55.000888: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2022-01-17 14:01:55.006480: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce 940MX major: 5 minor: 0 memoryClockRate(GHz): 1.189
pciBusID: 0000:01:00.0
2022-01-17 14:01:55.006696: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2022-01-17 14:01:55.007744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2022-01-17 14:01:55.008511: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-01-17 14:01:55.009162: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2022-01-17 14:01:55.009876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2022-01-17 14:01:55.010857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1391 MB memory) -> physical GPU (device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0, compute capability: 5.0)