tensorflow - 使用 RTX 2080 ti 的 Ubuntu 18.04 中的分段错误（核心转储）

Question

我最近购买了RTX 2080 ti，以便在本地运行一些深度学习项目。我曾多次尝试在 Ubuntu 18.04 中安装 tensorflow-gpu，唯一可行的指南如下：https ://www.pugetsystems.com/labs/hpc/Install-TensorFlow-with-GPU-Support- the-Easy-Way-on-Ubuntu-18-04-without-installing-CUDA-1170/#look-at-the-job-run-with-tensorboard

但是，当我开始运行脚本时，会出现以下错误：

Using TensorFlow backend.
Train on 60000 samples, validate on 10000 samples
2019-01-09 14:49:06.748318: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-01-09 14:49:07.730143: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-01-09 14:49:07.732970: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: 
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
pciBusID: 0000:01:00.0
totalMemory: 10.73GiB freeMemory: 10.23GiB
2019-01-09 14:49:07.733071: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-01-09 14:49:30.666591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-09 14:49:30.666636: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 
2019-01-09 14:49:30.666646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N 
2019-01-09 14:49:30.667094: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9875 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
Epoch 1/15
Segmentation fault (core dumped)

在此处输入图像描述

谁能给我一些关于如何使 tensorflow 在我的 GPU 上正常工作的反馈？

谢谢你。

标签： tensorflowdeep-learning

Answer 1

你可以在这里试试这个。

我在：RTX 2080，ubuntu 16.04

你需要安装：

cuda 10.0
cuDNN v7.4.1.5
libcudnn7-dev_7.4.1.5-1+cuda10.0_amd64
libcudnn7-doc_7.4.1.5-1+cuda10.0_amd64
libcudnn7_7.4.1.5-1+cuda10.0_amd64
nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64

英伟达-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.39       Driver Version: 418.39       CUDA Version: 10.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce RTX 2080    Off  | 00000000:02:00.0 Off |                  N/A |
| 22%   39C    P0    N/A /  N/A |      0MiB /  7951MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

一些原因 nvidia-smi 显示 10.1，但那是错误的

nvcc --版本：

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130

你可以在这里一步一步得到它：

1. NVIDIA-Linux driver: https://www.nvidia.com/Download/index.aspx?lang=en-us
2. cuda https://developer.nvidia.com/cuda-downloads
3. cudnn: https://developer.nvidia.com/rdp/cudnn-download
4. install: libcudnn7-dev, libcudnn7-doc, libcudnn7_7
5. install: nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb

下载 libcudnn 和 nvidia-machine-learning：

https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/

我正在使用：

tensorflow (1.13.1) tensorflow-gpu (1.13.1) tf-nightly-gpu (1.14.1.dev20190509)

内部代码，例如（我在 tensorflow 中的 LSTM 上进行 GPU 工作！）如果您的代码以以下开头：

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
keras.backend.set_session(sess)