python – nvidia-docker中的TensorFlow:对cuInit的调用失败:C
我一直致力于使用依赖于TensorFlow的应用程序作为具有nvidia-docker的docker容器.我已经在tensorflow / tensorflow:latest-gpu-py3图像上编译了我的应用程序.我用以下命令运行我的docker容器:
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: 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 容器看起来似乎正常启动,我的应用程序似乎确实在运行.当我向它发送请求以进行预测时,预测会正确返回 – 但是当我在CPU上运行推断时,我会期望以较慢的速度运行,所以我认为很明显GPU由于某种原因没有被使用.我也试过在同一个容器中运行nvidia-smi,以确保它看到我的GPU,这些是结果: +-----------------------------------------------------------------------------+ | 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 | |=============================================================================| +-----------------------------------------------------------------------------+ 我当然不是这方面的专家 – 但看起来GPU从容器内部可见.有关如何使用TensorFlow的任何想法? 解决方法
我在我的ubuntu16.04桌面上运行tensorflow.
我用GPU运行代码的工作很好.但是今天我找不到带有以下代码的gpu设备 导入张量流为tf 当我运行tf.Session()时,我意识到以下问题
我在系统详细信息中检查了我的Nvidia驱动程序,并检查了驱动程序,cuda和cudnn的nvcc -V,nvida-smi.一切似乎都很好. 然后我去了附加驱动程序检查驱动程序的详细信息,在那里我发现有很多版本的NVIDIA驱动程序和最新版本被选中.但是当我第一次安装驱动程序时,只有一个. 所以我选择旧版本,并应用change. 然后我运行tf.Session()问题也在这里.我想我应该重新启动计算机,重新启动后,这个问题就消失了. sess = tf.Session()2018-07-01 12:02:41.336648:I tensorflow / core / platform / cpu_feature_guard.cc:140]您的CPU支持未编译此TensorFlow二进制文件的指令:AVX2 FMA2018-07-01 12:02:41.464166:I tensorflow / stream_executor / cuda / cuda_gpu_executor.cc:898]从SysFS读取的成功NUMA节点具有负值(-1),但必须至少有一个NUMA节点,因此返回NUMA节点零2018-07-01 12:02:41.464482:I tensorflow / core / common_runtime / gpu / gpu_device.cc:1356]找到具有属性的设备0:名称:GeForce GTX 1070主要:6个未成年人:1个memoryClockRate(GHz):1.8225pciBusID:0000:01:00.0totalMemory:7.93GiB freeMemory:7.27GiB2018-07-01 12:02:41.464494:I tensorflow / core / common_runtime / gpu / gpu_device.cc:1435]添加可见的gpu设备:02018-07-01 12:02:42.308689:I tensorflow / core / common_runtime / gpu / gpu_device.cc:923]具有强度1边缘矩阵的设备互连StreamExecutor:2018-07-01 12:02:42.308721:I tensorflow / core / common_runtime / gpu / gpu_device.cc:929] 02018-07-01 12:02:42.308729:I tensorflow / core / common_runtime / gpu / gpu_device.cc:942] 0:N2018-07-01 12:02:42.309686:I tensorflow / core / common_runtime / gpu / gpu_device.cc:1053]创建TensorFlow设备(/ job:localhost / replica:0 / task:0 / device:GPU:0 with 7022 MB存储器) – >物理GPU(设备:0,名称:GeForce GTX 1070,pci总线ID:0000:01:00.0,计算能力: (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |