我最近购买了 RTX 2080 ti ，以便在本地运行一些深度学习项目。我曾尝试在Ubuntu 18.04中多次安装tensorflow-gpu，看来唯一有效的指南如下：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，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)
有人可以向我提供一些有关如何使Tensorflow与我的GPU正常工作的反馈吗？
谢谢。
答案 0 :(得分：0)
您可以在这里尝试。
我正在使用：RTX 2080，Ubuntu 16.04
您需要安装：
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
nvidia-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 |
+-----------------------------------------------------------------------------+
某些原因的nvidia-smi显示10.1，但这是错误的
nvcc --version：
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
您可以在这里逐步获取它：
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
要下载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）张量流gpu（1.13.1）tf-nightly-gpu （1.14.1.dev20190509）
如果您的代码以以下内容开头，则为内部代码，例如（我在tensorflow中使GPU在LSTM上工作！）
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
keras.backend.set_session(sess)