运行时错误:GPU:0 上的 CUDA 运行时隐式初始化失败.状态:所有支持 CUDA 的设备都忙或不可用 [英] RuntimeError: CUDA runtime implicit initialization on GPU:0 failed. Status: all CUDA-capable devices are busy or unavailable

查看：212 发布时间：2021/9/5 20:04:46 python tensorflow gpu nvidia

本文介绍了运行时错误:GPU:0 上的 CUDA 运行时隐式初始化失败.状态:所有支持 CUDA 的设备都忙或不可用的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

问题:当我运行以下命令时

Problem: when I run the following command

python -c "import tensorflow as tf; tf.test.is_gpu_available(); print('version :' + tf.__version__)"

错误:

RuntimeError: CUDA runtime implicit initialization on GPU:0 failed. Status: all CUDA-capable devices are busy or unavailable

详情:

WARNING:tensorflow:From :1: is_gpu_available(来自 tensorflow.python.framework.test_util)已弃用，将在未来版本中删除.更新说明:请改用 tf.config.list_physical_devices('GPU').2021-04-18 21:02:51.839069: I tensorflow/core/platform/cpu_feature_guard.cc:143] 您的 CPU 支持该 TensorFlow 二进制文件未编译使用的指令:AVX2 AVX512F FMA2021-04-18 21:02:51.846775: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU 频率:2500000000 Hz2021-04-18 21:02:51.847076: I tensorflow/compiler/xla/service/service.cc:168] XLA 服务 0x7fc3bc000b20 为平台主机初始化(这不保证会使用 XLA).设备:2021-04-18 21:02:51.847104:I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor 设备(0):主机，默认版本2021-04-18 21:02:51.849876:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcuda.so.12021-04-18 21:02:51.911161:W tensorflow/compiler/xla/service/platform_util.cc:210] 无法为 CUDA 创建 StreamExecutor:0:无法为 CUDA 设备初始化 StreamExecutor 序号 0:内部:调用 cuDevicexRexRetainCt 失败:CUDA_ERROR_UNKNOWN:未知错误2021-04-18 21:02:51.911285:我 tensorflow/compiler/jit/xla_gpu_device.cc:161] 忽略可见的 XLA_GPU_JIT 设备.设备编号为 0，原因:内部:未找到平台 CUDA 支持的设备2021-04-18 21:02:51.911546: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] 成功从 SysFS 读取的 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，因此返回NUMA 节点零2021-04-18 21:02:51.912210:我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] 发现设备 0 具有以下属性:pciBusID:0000:00:07.0 名称:GRID T4-4Q 计算能力:7.5coreClock:1.59GHz coreCount:40 deviceMemorySize:3.97GiB deviceMemoryBandwidth:298.08GiB/s2021-04-18 21:02:51.912446:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudart.so.10.12021-04-18 21:02:51.914362:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcublas.so.102021-04-18 21:02:51.916358:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcufft.so.102021-04-18 21:02:51.916679:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcurand.so.102021-04-18 21:02:51.918787:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcusolver.so.102021-04-18 21:02:51.919993:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcusparse.so.102021-04-18 21:02:51.924652:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudnn.so.72021-04-18 21:02:51.924792: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] 成功从 SysFS 读取的 NUMA 节点具有负值 (-1)，但必须至少有一个 NUMA 节点，因此返回NUMA 节点零2021-04-18 21:02:51.925488: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] 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 节点零2021-04-18 21:02:51.926100:我 tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] 添加可见的 gpu 设备:02021-04-18 21:02:51.926146:我 tensorflow/stream_executor/platform/default/dso_loader.cc:44] 成功打开动态库 libcudart.so.10.1回溯(最近一次调用最后一次):文件"，第 1 行，在文件/home/miniconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py"，第324行，在new_func返回 func(*args, **kwargs)文件/home/miniconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/test_util.py"，第1496行，在is_gpu_available中对于 device_lib.list_local_devices() 中的 local_device:文件/home/miniconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/device_lib.py"，第43行，在list_local_devices_convert(s) for s in _pywrap_device_lib.list_devices(serialized_config)运行时错误:GPU:0 上的 CUDA 运行时隐式初始化失败.状态:所有支持 CUDA 的设备都忙或不可用

WARNING:tensorflow:From :1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version. Instructions for updating: Use tf.config.list_physical_devices('GPU') instead. 2021-04-18 21:02:51.839069: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA 2021-04-18 21:02:51.846775: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2500000000 Hz 2021-04-18 21:02:51.847076: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fc3bc000b20 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2021-04-18 21:02:51.847104: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2021-04-18 21:02:51.849876: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2021-04-18 21:02:51.911161: W tensorflow/compiler/xla/service/platform_util.cc:210] unable to create StreamExecutor for CUDA:0: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_UNKNOWN: unknown error 2021-04-18 21:02:51.911285: I tensorflow/compiler/jit/xla_gpu_device.cc:161] Ignoring visible XLA_GPU_JIT device. Device number is 0, reason: Internal: no supported devices found for platform CUDA 2021-04-18 21:02:51.911546: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-04-18 21:02:51.912210: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:00:07.0 name: GRID T4-4Q computeCapability: 7.5 coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 3.97GiB deviceMemoryBandwidth: 298.08GiB/s 2021-04-18 21:02:51.912446: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2021-04-18 21:02:51.914362: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2021-04-18 21:02:51.916358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2021-04-18 21:02:51.916679: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2021-04-18 21:02:51.918787: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2021-04-18 21:02:51.919993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2021-04-18 21:02:51.924652: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2021-04-18 21:02:51.924792: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-04-18 21:02:51.925488: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-04-18 21:02:51.926100: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2021-04-18 21:02:51.926146: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 Traceback (most recent call last): File "", line 1, in File "/home/miniconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func return func(*args, **kwargs) File "/home/miniconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/test_util.py", line 1496, in is_gpu_available for local_device in device_lib.list_local_devices(): File "/home/miniconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/device_lib.py", line 43, in list_local_devices _convert(s) for s in _pywrap_device_lib.list_devices(serialized_config) RuntimeError: CUDA runtime implicit initialization on GPU:0 failed. Status: all CUDA-capable devices are busy or unavailable

操作系统平台和发行版(例如，Linux Ubuntu 16.04):ubuntu 18.04移动设备(例如 iPhone 8、Pixel 2、Samsung Galaxy)如果问题发生在移动设备上:云服务器从(源或二进制)安装的 TensorFlow:源
TensorFlow 版本:2.2.0.Python版本:3.7.7
使用virtualenv安装?点?conda?: pip &康达.
Bazel 版本(如果从源代码编译):2..0.0
GCC/编译器版本(如果从源代码编译):7.5
CUDA/cuDNN 版本:CUDA 10.1 &cuDNN 7.6.5
GPU型号和内存:
00:07.0 VGA 兼容控制器:NVIDIA Corporation 设备 1eb8 (rev a1)(prog-if 00 [VGA 控制器]).
子系统:NVIDIA Corporation Device 130e.
物理插槽:7标志:总线主控，快速devsel，延迟0，IRQ 37fc000000 处的内存(32 位，不可预取)[大小=16M]e0000000 处的内存(64 位，可预取)[大小=256M]fa000000 处的内存(64 位，不可预取)[size=32M]c500 处的 I/O 端口 [大小=128]功能:[68] MSI:Enable+ Count=1/1 Maskable- 64bit+使用的内核驱动程序:nvidia内核模块:nvidiafb、nouveau、nvidia_drm、nvidia

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): ubuntu 18.04 Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: cloud server TensorFlow installed from (source or binary): source
TensorFlow version: 2.2.0. Python version: 3.7.7
Installed using virtualenv? pip? conda?: pip & conda.
Bazel version (if compiling from source): 2..0.0
GCC/Compiler version (if compiling from source): 7.5
CUDA/cuDNN version: CUDA 10.1 & cuDNN 7.6.5
GPU model and memory:
00:07.0 VGA compatible controller: NVIDIA Corporation Device 1eb8 (rev a1) (prog-if 00 [VGA controller]).
Subsystem: NVIDIA Corporation Device 130e.
Physical Slot: 7 Flags: bus master, fast devsel, latency 0, IRQ 37 Memory at fc000000 (32-bit, non-prefetchable) [size=16M] Memory at e0000000 (64-bit, prefetchable) [size=256M] Memory at fa000000 (64-bit, non-prefetchable) [size=32M] I/O ports at c500 [size=128] Capabilities: [68] MSI: Enable+ Count=1/1 Maskable- 64bit+ Kernel driver in use: nvidia Kernel modules: nvidiafb, nouveau, nvidia_drm, nvidia