问题描述
我一直在努力获取一个依赖TensorFlow的应用程序，该应用程序可以与 nvidia-docker 一起用作Docker容器。我已经在 tensorflow / tensorflow：latest-gpu-py3 映像的顶部编译了我的应用程序。我使用以下命令运行Docker容器：
I have been working on getting an application that relies on TensorFlow to work as a docker container with nvidia-docker. I have compiled my application on top of the tensorflow/tensorflow:latest-gpu-py3 image. I run my docker container with the following command:
sudo nvidia-docker run -d -p 9090：9090 -v / src / weights： / weights myname / myrepo：mylabel
通过 portainer 查看日志时，我看到以下内容：
When looking at the logs through portainer I see the following:
2017-05-16 03:41:47.715682: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-16 03:41:47.715896: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-16 03:41:47.715948: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-05-16 03:41:47.715978: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-16 03:41:47.716002: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-05-16 03:41:47.718076: E tensorflow/stream_executor/cuda/cuda_driver.cc:405] failed call to cuInit: CUDA_ERROR_UNKNOWN
2017-05-16 03:41:47.718177: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:158] retrieving CUDA diagnostic information for host: 1e22bdaf82f1
2017-05-16 03:41:47.718216: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: 1e22bdaf82f1
2017-05-16 03:41:47.718298: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: 367.57.0
2017-05-16 03:41:47.718398: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:369] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 367.57 Mon Oct 3 20:37:01 PDT 2016
GCC version: gcc version 4.8.4 (Ubuntu 4.8.4-2ubuntu1~14.04.3)
"""
2017-05-16 03:41:47.718455: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: 367.57.0
2017-05-16 03:41:47.718484: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:300] kernel version seems to match DSO: 367.57.0
The容器似乎可以正常启动，并且我的应用程序确实正在运行。当我向其发送预测请求时，预测会正确返回-但是在CPU上进行推理时，我期望的速度很慢，因此，我认为很明显，由于某种原因未使用GPU。我还尝试过在同一容器中运行 nvidia-smi ，以确保它可以看到我的GPU，并且这些结果是这样的：
The container does seem to start properly, and my application does appear to be running. When I send requests to it for predictions the predictions are returned correctly - however at the slow speed I would expect when running inference on the CPU, so I think it's pretty clear that the GPU is not being used for some reason. I've also tried running nvidia-smi from within that same container to make sure it is seeing my GPU and these are the results for that:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.57 Driver Version: 367.57 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GRID K1 Off | 0000:00:07.0 Off | N/A |
| N/A 28C P8 7W / 31W | 25MiB / 4036MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
我当然不是专家-但确实可以从容器内部看到GPU。关于如何使用TensorFlow进行操作的任何想法？
I'm certainly no expert in this - but it does appear that the GPU is visible from inside the container. Any ideas on how to get this working with TensorFlow?
推荐答案
我尝试安装nvidia-modrpobe，但仍然存在相同的错误。
然后一个简单的系统重启对我有用
I tried installing nvidia-modrpobe, but still the same error.Then a simple system reboot worked for me
这篇关于nvidia-docker中的TensorFlow：对cuInit的调用失败：CUDA_ERROR_UNKNOWN的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！