Tensorflow CUDA_ERROR_UNKNOWN on Google Cloud Platform

Question

I deployed a virtual machine using Deep Learning VM with Tesla A100 GPU, TensorFlow Enterprise 2.5 and CUDA 11.0. But I have no access to GPU/CUDA and get the following error.

E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error

At the time of deployment, I received this warning:

tensorflow has resource level warnings. The resource 'projects/click-to-deploy-images/global/images/tf-2-5-cu110-v20210619-debian-10' is deprecated. A suggested replacement is 'projects/click-to-deploy-images/global/images/tf-2-5-cu110-v20210624-debian-10'.

It is a an already existing image generated by google and many people are using it, but why can't I access the GPU or CUDA using it?

import tensorflow as tf
2021-07-05 17:05:14.901743: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
tf.__version__
'2.5.0'
print(tf.config.list_physical_devices())
2021-07-05 17:05:44.757638: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-05 17:05:44.840142: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
2021-07-05 17:05:44.840245: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: deeplearning-1-vm
2021-07-05 17:05:44.840258: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: deeplearning-1-vm
2021-07-05 17:05:44.841760: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 450.80.2
2021-07-05 17:05:44.841820: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 450.80.2
2021-07-05 17:05:44.841833: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 450.80.2
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]

The following details can help to figure out the problem.

a_k@deeplearning-1-vm:~$ nvidia-smi
Mon Jul  5 17:03:43 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.80.02    Driver Version: 450.80.02    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  A100-SXM4-40GB      Off  | 00000000:00:04.0 Off |                    0 |
| N/A   42C    P0    56W / 400W |      0MiB / 40537MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
a_k@deeplearning-1-vm:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Thu_Jun_11_22:26:38_PDT_2020
Cuda compilation tools, release 11.0, V11.0.194
Build cuda_11.0_bu.TC445_37.28540450_0

a_k@deeplearning-1-vm:~$ cat /usr/local/cuda/version.txt
CUDA Version 11.0.207

Rincon

this may be of interest – Juvenility

I tried multiple pre-built deep learning VM instances offered by google cloud with tf2.5, tf2.4 and tf2.3. None of them work for me. Reinstalling tensorflow or cuda on these pre-built instances generate installation/dependencies/permission errors. This solution didn't work for me. – Rincon

Answer 1

The problem is that versions of nvidia driver, cuda and tensorflow on all the pre-built instances provided by google cloud platform are not compatible (tf2.5 requires cuda>=11.2). I solved this problem by reinstalling latest version of CUDA on pre-built instance (tensorflow enterprise 2.5, CUDA 11.0) and now its working even after restarting the instance. Google must update their pre-build vm instances to solve

This discussion helped me to find the solution. In order to reinstall the CUDA, I didn't uninstall anything, just followed exactly these 6 instructions (for debian 10). Although, I have Ubuntu 18.4 but still it worked. It also asks you if you want to uninstall the previous cuda version (yes!).

Now, I have the following

a_k@a100-tfe25-vm:~$ nvidia-smi
Tue Jul  6 09:56:04 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.42.01    Driver Version: 470.42.01    CUDA Version: 11.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |
| N/A   38C    P0    52W / 400W |      0MiB / 40536MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

a_k@a100-tfe25-vm:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Wed_Jun__2_19:15:15_PDT_2021
Cuda compilation tools, release 11.4, V11.4.48
Build cuda_11.4.r11.4/compiler.30033411_0

a_k@a100-tfe25-vm:~$ python3
Python 3.7.10 | packaged by conda-forge | (default, Feb 19 2021, 16:07:37) 
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
2021-07-06 09:57:08.277452: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
>>> tf.__version__
'2.5.0'
>>> tf.config.list_physical_devices()
2021-07-06 09:57:30.897584: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-06 09:57:31.689883: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: NVIDIA A100-SXM4-40GB computeCapability: 8.0
coreClock: 1.41GHz coreCount: 108 deviceMemorySize: 39.59GiB deviceMemoryBandwidth: 1.41TiB/s
2021-07-06 09:57:31.689997: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-06 09:57:31.696712: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2021-07-06 09:57:31.696809: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-06 09:57:31.699051: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10
2021-07-06 09:57:31.699981: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10
2021-07-06 09:57:31.734585: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.10
2021-07-06 09:57:31.735833: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11
2021-07-06 09:57:31.738230: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2021-07-06 09:57:31.743485: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Rincon

Answer 2

From the fix provided in this Google Cloud Platform public forum we can mitigate the issue by:

• Fix #1: Use the latest DLVM image (M74 or later) in a new VM instance: They have released a fix for the newest DLVM image in M74 so you will no longer be affected by this issue.
• Fix #2 Patch your existing instance running images older than M74:

Run the following via an SSH session on the affected instance:

gsutil cp gs://dl-platform-public-nvidia/b191551132/restart_patch.sh /tmp/restart_patch.sh

chmod +x /tmp/restart_patch.sh

sudo /tmp/restart_patch.sh

sudo service jupyter restart

This only needs to be done once, and does not need to be rerun each time the instance is rebooted.

Kandace