Adam Cecile created MESOS-7730:
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Summary: CUDA not working anymore on 1.3.0
Key: MESOS-7730
URL: https://issues.apache.org/jira/browse/MESOS-7730
Project: Mesos
Issue Type: Bug
Components: containerization
Affects Versions: 1.3.0
Reporter: Adam Cecile
Fix For: 1.2.1
Hello,
My docker container using CUDA do not detect it anymore.
Here the tensorflow output with 1.2.1:
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I0628 12:39:45.505900 16309 exec.cpp:162] Version: 1.2.1
I0628 12:39:45.508358 16301 exec.cpp:237] Executor registered on agent
84c99d0b-8551-4f30-a9bc-6c1edbf7c18c-S1
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library
wasn't compiled to use SSE3 instructions, but these are available on your
machine and could speed up CPU computations.
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.
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.
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.
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.
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.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with
properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:82:00.0
Total memory: 7.92GiB
Free memory: 7.81GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow
device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:82:00.0)
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And with 1.3.0
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I0628 12:40:30.833947 16854 exec.cpp:162] Version: 1.3.0
I0628 12:40:30.836612 16845 exec.cpp:237] Executor registered on agent
84c99d0b-8551-4f30-a9bc-6c1edbf7c18c-S1
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:126] Couldn't open CUDA library
libcuda.so.1. LD_LIBRARY_PATH:
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname:
zelda.service.earthlab.lu
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported
version is: Not found: was unable to find libcuda.so DSO loaded into this
program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:363] driver version file
contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.66 Mon May 1
15:29:16 PDT 2017
GCC version: gcc version 4.9.2 (Debian 4.9.2-10)
"""
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported
version is: 375.66.0
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1065] LD_LIBRARY_PATH:
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1066] failed to find
libcuda.so on this system: Failed precondition: could not dlopen DSO:
libcuda.so.1; dlerror: libcuda.so.1: cannot open shared object file: No such
file or directory
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA
library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library
wasn't compiled to use SSE3 instructions, but these are available on your
machine and could speed up CPU computations.
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.
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.
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.
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.
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.
E tensorflow/stream_executor/cuda/cuda_driver.cc:509] failed call to cuInit:
CUDA_ERROR_NO_DEVICE
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:158] retrieving CUDA
diagnostic information for host: zelda.service.earthlab.lu
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname:
zelda.service.earthlab.lu
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported
version is: Not found: was unable to find libcuda.so DSO loaded into this
program
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:363] driver version file
contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module 375.66 Mon May 1
15:29:16 PDT 2017
GCC version: gcc version 4.9.2 (Debian 4.9.2-10)
"""
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported
version is: 375.66.0
{noformat}
All i did is upgrading/downgrading mesos package and restarted the container. I
did the test several time and it's 100% reproductible.
Regards, Adam.
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