Запуск тензорного потока на GPU кластере в virtualenv
Я установил версию tenorflow для GPU в virtualenv, следуя этиминструкции, Проблема в том, что при запуске сеанса возникает ошибка сегментации. То есть этот код:
import tensorflow as tf
sess = tf.InteractiveSession()
выходит со следующей ошибкой:
(tesnsorflowenv)user@machine$ python testtensorflow.py
I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcublas.so.7.0 locally
I tensorflow/stream_executor/dso_loader.cc:93] Couldn't open CUDA library libcudnn.so.6.5. LD_LIBRARY_PATH: :/vol/cuda/7.0.28/lib64
I tensorflow/stream_executor/cuda/cuda_dnn.cc:1382] Unable to load cuDNN DSO
I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcufft.so.7.0 locally
I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcuda.so locally
I tensorflow/stream_executor/dso_loader.cc:101] successfully opened CUDA library libcurand.so.7.0 locally
I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 40
Segmentation fault
Я пытался копать глубже, используя GDB, но получил только следующие дополнительные выводы:
[New Thread 0x7fffdf880700 (LWP 32641)]
[New Thread 0x7fffdf07f700 (LWP 32642)]
... lines omitted
[New Thread 0x7fffadffb700 (LWP 32681)]
[Thread 0x7fffadffb700 (LWP 32681) exited]
Program received signal SIGSEGV, Segmentation fault.
0x0000000000000000 in ?? ()
Есть идеи, что здесь происходит и как это исправить?
Вот вывод nvidia-smi:
+------------------------------------------------------+
| NVIDIA-SMI 352.63 Driver Version: 352.63 |
|-------------------------------+----------------------+----------------------+
| 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 K80 On | 0000:06:00.0 Off | 0 |
| N/A 65C P0 142W / 149W | 235MiB / 11519MiB | 81% E. Process |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K80 On | 0000:07:00.0 Off | 0 |
| N/A 25C P8 30W / 149W | 55MiB / 11519MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
| 2 Tesla K80 On | 0000:0D:00.0 Off | 0 |
| N/A 27C P8 26W / 149W | 55MiB / 11519MiB | 0% Prohibited |
+-------------------------------+----------------------+----------------------+
| 3 Tesla K80 On | 0000:0E:00.0 Off | 0 |
| N/A 25C P8 28W / 149W | 55MiB / 11519MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
| 4 Tesla K80 On | 0000:86:00.0 Off | 0 |
| N/A 46C P0 85W / 149W | 206MiB / 11519MiB | 97% E. Process |
+-------------------------------+----------------------+----------------------+
| 5 Tesla K80 On | 0000:87:00.0 Off | 0 |
| N/A 27C P8 29W / 149W | 55MiB / 11519MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
| 6 Tesla K80 On | 0000:8D:00.0 Off | 0 |
| N/A 28C P8 26W / 149W | 55MiB / 11519MiB | 0% Prohibited |
+-------------------------------+----------------------+----------------------+
| 7 Tesla K80 On | 0000:8E:00.0 Off | 0 |
| N/A 23C P8 30W / 149W | 55MiB / 11519MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
Спасибо за любую помощь в этом вопросе!