JupyterHub 单用户无法使用 systemdspawner 使用 tensorflow gpu 支持
JupyterHub singleuser not able to use tensorflow gpu support using systemdspawner
(这是对 , the jupyterhub issue tracker and the jupyterhub/systemdspawner issue tracker )
的交叉发布
我有一个使用 SystemdSpawner 的私有 JupyterHub 设置，我尝试 运行 具有 gpu 支持的 tensorflow。
我遵循了 tensorflow instructions，或者尝试了一个已经配置的 AWS AMI（深度学习基础 AMI（Ubuntu 18.04）版本 21.0）和 NDVIDIA，都在 AWS EC2 g4 实例上。
在这两种设置中，我都可以在 (i)python 3.6 shell
中使用支持 gpu 的 tensorflow
>>> import tensorflow as tf
>>> tf.config.list_physical_devices('GPU')
2020-02-12 10:57:13.670937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-02-12 10:57:13.698230: 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
2020-02-12 10:57:13.699066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:00:1e.0 name: Tesla T4 computeCapability: 7.5
coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s
2020-02-12 10:57:13.699286: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-02-12 10:57:13.700918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-02-12 10:57:13.702512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-02-12 10:57:13.702814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-02-12 10:57:13.704561: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-02-12 10:57:13.705586: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-02-12 10:57:13.709171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-02-12 10:57:13.709278: 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
2020-02-12 10:57:13.710120: 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
2020-02-12 10:57:13.710893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
（关于 NUMA 节点的一些警告，但找到了 gpu）
同时使用 nvidia-smi 和 deviceQuery 显示 gpu:
$ nvidia-smi
Wed Feb 12 10:39:44 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.87.01 Driver Version: 418.87.01 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 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 33C P8 9W / 70W | 0MiB / 15079MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
$ /usr/local/cuda/extras/demo_suite/deviceQuery
/usr/local/cuda/extras/demo_suite/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "Tesla T4"
CUDA Driver Version / Runtime Version 10.1 / 10.0
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 15080 MBytes (15812263936 bytes)
(40) Multiprocessors, ( 64) CUDA Cores/MP: 2560 CUDA Cores
GPU Max Clock rate: 1590 MHz (1.59 GHz)
Memory Clock rate: 5001 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 4194304 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 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: 1024
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 3 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: Enabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 30
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.1, CUDA Runtime Version = 10.0, NumDevs = 1, Device0 = Tesla T4
Result = PASS
现在我启动 JupyterHub，登录并打开一个终端，在那里我得到：
$ nvidia-smi
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
和
$ /usr/local/cuda/extras/demo_suite/deviceQuery
cuda/extras/demo_suite/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
cudaGetDeviceCount returned 38
-> no CUDA-capable device is detected
Result = FAIL
还有
我怀疑存在某种 "sandbox"、缺少 ENV 变量等原因，因为在单用户环境中找不到 gpu 驱动程序，随后 tensorflow gpu 支持不起作用。
对此有什么想法吗？可能是一个小的配置调整，或者是由于架构根本无法解决；）
在您的 jupyterhub_config.py 中设置 c.SystemdSpawner.isolate_devices = False。
以下是 the documentation 的摘录：
Setting this to true provides a separate, private /dev for each user. This prevents the user from directly accessing hardware devices, which could be a potential source of security issues. /dev/null, /dev/zero, /dev/random and the ttyp pseudo-devices will be mounted already, so most users should see no change when this is enabled.
c.SystemdSpawner.isolate_devices = True
This requires systemd version > 227. If you enable this in earlier versions, spawning will fail.
Nvidia 使用设备（即 /dev 中的文件）。请参考their documentation for more information。那里应该有名为 /dev/nvidia* 的文件。使用 SystemdSpawner 隔离设备将阻止访问这些 Nvidia 设备。
Is there a way to still isolate the devices and enable GPU support?
我不确定...但我可以提供文档的指针。设置 c.SystemdSpawner.isolate_devices = True 在最终的 systemd-run 调用中设置 PrivateDevices=yes（source). Refer to the systemd documentation 有关 PrivateDevices 选项的更多信息。
您可以保留 isolate_devices = True 然后显式安装 nvidia 设备。虽然我不知道该怎么做...
(这是对
我有一个使用 SystemdSpawner 的私有 JupyterHub 设置，我尝试 运行 具有 gpu 支持的 tensorflow。
我遵循了 tensorflow instructions，或者尝试了一个已经配置的 AWS AMI（深度学习基础 AMI（Ubuntu 18.04）版本 21.0）和 NDVIDIA，都在 AWS EC2 g4 实例上。
在这两种设置中，我都可以在 (i)python 3.6 shell
中使用支持 gpu 的 tensorflow>>> import tensorflow as tf
>>> tf.config.list_physical_devices('GPU')
2020-02-12 10:57:13.670937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-02-12 10:57:13.698230: 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
2020-02-12 10:57:13.699066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:00:1e.0 name: Tesla T4 computeCapability: 7.5
coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.73GiB deviceMemoryBandwidth: 298.08GiB/s
2020-02-12 10:57:13.699286: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-02-12 10:57:13.700918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-02-12 10:57:13.702512: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-02-12 10:57:13.702814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-02-12 10:57:13.704561: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-02-12 10:57:13.705586: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-02-12 10:57:13.709171: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-02-12 10:57:13.709278: 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
2020-02-12 10:57:13.710120: 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
2020-02-12 10:57:13.710893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
（关于 NUMA 节点的一些警告，但找到了 gpu）
同时使用 nvidia-smi 和 deviceQuery 显示 gpu:
$ nvidia-smi
Wed Feb 12 10:39:44 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.87.01 Driver Version: 418.87.01 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 Tesla T4 On | 00000000:00:1E.0 Off | 0 |
| N/A 33C P8 9W / 70W | 0MiB / 15079MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
$ /usr/local/cuda/extras/demo_suite/deviceQuery
/usr/local/cuda/extras/demo_suite/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: "Tesla T4"
CUDA Driver Version / Runtime Version 10.1 / 10.0
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 15080 MBytes (15812263936 bytes)
(40) Multiprocessors, ( 64) CUDA Cores/MP: 2560 CUDA Cores
GPU Max Clock rate: 1590 MHz (1.59 GHz)
Memory Clock rate: 5001 Mhz
Memory Bus Width: 256-bit
L2 Cache Size: 4194304 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 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: 1024
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 3 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: Enabled
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 30
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.1, CUDA Runtime Version = 10.0, NumDevs = 1, Device0 = Tesla T4
Result = PASS
现在我启动 JupyterHub，登录并打开一个终端，在那里我得到：
$ nvidia-smi
NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.
和
$ /usr/local/cuda/extras/demo_suite/deviceQuery
cuda/extras/demo_suite/deviceQuery Starting...
CUDA Device Query (Runtime API) version (CUDART static linking)
cudaGetDeviceCount returned 38
-> no CUDA-capable device is detected
Result = FAIL
还有
我怀疑存在某种 "sandbox"、缺少 ENV 变量等原因，因为在单用户环境中找不到 gpu 驱动程序，随后 tensorflow gpu 支持不起作用。
对此有什么想法吗？可能是一个小的配置调整，或者是由于架构根本无法解决；）
在您的 jupyterhub_config.py 中设置 c.SystemdSpawner.isolate_devices = False。
以下是 the documentation 的摘录：
Setting this to true provides a separate, private /dev for each user. This prevents the user from directly accessing hardware devices, which could be a potential source of security issues. /dev/null, /dev/zero, /dev/random and the ttyp pseudo-devices will be mounted already, so most users should see no change when this is enabled.
c.SystemdSpawner.isolate_devices = TrueThis requires systemd version > 227. If you enable this in earlier versions, spawning will fail.
Nvidia 使用设备（即 /dev 中的文件）。请参考their documentation for more information。那里应该有名为 /dev/nvidia* 的文件。使用 SystemdSpawner 隔离设备将阻止访问这些 Nvidia 设备。
Is there a way to still isolate the devices and enable GPU support?
我不确定...但我可以提供文档的指针。设置 c.SystemdSpawner.isolate_devices = True 在最终的 systemd-run 调用中设置 PrivateDevices=yes（source). Refer to the systemd documentation 有关 PrivateDevices 选项的更多信息。
您可以保留 isolate_devices = True 然后显式安装 nvidia 设备。虽然我不知道该怎么做...