当 CUDA_VISIBLE_DEVICES 不等于 0 时，扭矩作业找不到 GPU
Torque jobs cannot find GPU when CUDA_VISIBLE_DEVICES not equal 0
我遇到了一个奇怪的 GPU 扭矩分配问题。
我 运行 在一台配备两块 NVIDIA GTX Titan X GPU 的机器上运行 Torque 6.1.0。我正在使用 pbs_sched 进行日程安排。 nvidia-smi 静态输出如下：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.39 Driver Version: 375.39 |
|-------------------------------+----------------------+----------------------+
| 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 GTX TIT... Off | 0000:03:00.0 On | N/A |
| 22% 40C P8 15W / 250W | 0MiB / 12204MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX TIT... Off | 0000:04:00.0 Off | N/A |
| 22% 33C P8 14W / 250W | 0MiB / 12207MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
我有一个简单的测试脚本来评估 GPU 分配，如下所示：
#PBS -S /bin/bash
#PBS -l nodes=1:ppn=1:gpus=1:reseterr:exclusive_process
echo "CUDA_VISIBLE_DEVICES: $CUDA_VISIBLE_DEVICES"
~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery
deviceQuery 是 CUDA 附带的实用程序。当我从命令行 运行 它时，它正确地找到了两个 GPU。当我像这样从命令行限制到一台设备时...
CUDA_VISIBLE_DEVICES=0 ~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery
#or
CUDA_VISIBLE_DEVICES=1 ~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery
...它也能正确找到一个或另一个 GPU。
当我使用 qsub 将 test.sh 提交到队列时，当没有其他作业 运行ning 时，它再次正常工作。这是输出：
CUDA_VISIBLE_DEVICES: 0
~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX TITAN X" CUDA Driver Version / Runtime Version 8.0 / 8.0 CUDA Capability Major/Minor version number: 5.2 Total amount of global memory: 12204 MBytes (12796887040 bytes) (24) Multiprocessors, (128) CUDA Cores/MP: 3072 CUDA Cores GPU Max Clock rate: 1076 MHz (1.08 GHz) Memory Clock rate: 3505 Mhz Memory Bus Width: 384-bit L2 Cache Size: 3145728 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device PCI Domain ID / Bus ID / location ID: 0 / 3 / 0 Compute Mode:
< Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GTX TITAN X Result = PASS
但是，如果作业已经在 gpu0 上 运行ning（即如果它被分配 CUDA_VISIBLE_DEVICES=1），则该作业找不到任何 GPU。输出：
CUDA_VISIBLE_DEVICES: 1
~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
cudaGetDeviceCount returned 38
-> no CUDA-capable device is detected
Result = FAIL
有人知道这是怎么回事吗？
我想我已经解决了我自己的问题，但不幸的是我同时尝试了两件事。我不想回去确认哪个解决了问题。它是以下之一：
在构建之前从 Torque 的配置脚本中删除 --enable-cgroups 选项。
运行 Torque 安装过程中的这些步骤：
打包
sh torque-package-server-linux-x86_64.sh --install
sh torque-package-mom-linux-x86_64.sh --install
sh torque-package-clients-linux-x86_64.sh --install
对于第二个选项，我知道这些步骤已正确记录在 Torque 安装说明中。但是，我有一个简单的设置，其中只有一个节点（计算节点和服务器是同一台机器）。我认为 'make install' 应该完成软件包安装为该单个节点所做的一切，但也许我错了。
我遇到了一个奇怪的 GPU 扭矩分配问题。
我 运行 在一台配备两块 NVIDIA GTX Titan X GPU 的机器上运行 Torque 6.1.0。我正在使用 pbs_sched 进行日程安排。 nvidia-smi 静态输出如下：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.39 Driver Version: 375.39 |
|-------------------------------+----------------------+----------------------+
| 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 GTX TIT... Off | 0000:03:00.0 On | N/A |
| 22% 40C P8 15W / 250W | 0MiB / 12204MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX TIT... Off | 0000:04:00.0 Off | N/A |
| 22% 33C P8 14W / 250W | 0MiB / 12207MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
我有一个简单的测试脚本来评估 GPU 分配，如下所示：
#PBS -S /bin/bash
#PBS -l nodes=1:ppn=1:gpus=1:reseterr:exclusive_process
echo "CUDA_VISIBLE_DEVICES: $CUDA_VISIBLE_DEVICES"
~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery
deviceQuery 是 CUDA 附带的实用程序。当我从命令行 运行 它时，它正确地找到了两个 GPU。当我像这样从命令行限制到一台设备时...
CUDA_VISIBLE_DEVICES=0 ~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery
#or
CUDA_VISIBLE_DEVICES=1 ~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery
...它也能正确找到一个或另一个 GPU。
当我使用 qsub 将 test.sh 提交到队列时，当没有其他作业 运行ning 时，它再次正常工作。这是输出：
CUDA_VISIBLE_DEVICES: 0
~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "GeForce GTX TITAN X" CUDA Driver Version / Runtime Version 8.0 / 8.0 CUDA Capability Major/Minor version number: 5.2 Total amount of global memory: 12204 MBytes (12796887040 bytes) (24) Multiprocessors, (128) CUDA Cores/MP: 3072 CUDA Cores GPU Max Clock rate: 1076 MHz (1.08 GHz) Memory Clock rate: 3505 Mhz Memory Bus Width: 384-bit L2 Cache Size: 3145728 bytes Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096) Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers Total amount of constant memory: 65536 bytes Total amount of shared memory per block: 49152 bytes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z): (1024, 1024, 64) Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535) Maximum memory pitch: 2147483647 bytes Texture alignment: 512 bytes Concurrent copy and kernel execution: Yes with 2 copy engine(s) Run time limit on kernels: No Integrated GPU sharing Host Memory: No Support host page-locked memory mapping: Yes Alignment requirement for Surfaces: Yes Device has ECC support: Disabled Device supports Unified Addressing (UVA): Yes Device PCI Domain ID / Bus ID / location ID: 0 / 3 / 0 Compute Mode:
< Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 8.0, CUDA Runtime Version = 8.0, NumDevs = 1, Device0 = GeForce GTX TITAN X Result = PASS
但是，如果作业已经在 gpu0 上 运行ning（即如果它被分配 CUDA_VISIBLE_DEVICES=1），则该作业找不到任何 GPU。输出：
CUDA_VISIBLE_DEVICES: 1
~/test/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
cudaGetDeviceCount returned 38
-> no CUDA-capable device is detected
Result = FAIL
有人知道这是怎么回事吗？
我想我已经解决了我自己的问题，但不幸的是我同时尝试了两件事。我不想回去确认哪个解决了问题。它是以下之一：
在构建之前从 Torque 的配置脚本中删除 --enable-cgroups 选项。
运行 Torque 安装过程中的这些步骤：
打包
sh torque-package-server-linux-x86_64.sh --install
sh torque-package-mom-linux-x86_64.sh --install
sh torque-package-clients-linux-x86_64.sh --install
对于第二个选项，我知道这些步骤已正确记录在 Torque 安装说明中。但是，我有一个简单的设置，其中只有一个节点（计算节点和服务器是同一台机器）。我认为 'make install' 应该完成软件包安装为该单个节点所做的一切，但也许我错了。