Status: CUDA driver version is insufficient for CUDA runtime version _impl.InternalError: cudaGetDevice() failed. "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/device_lib.py",įor s in pywrap_tensorflow.list_devices(session_config=session_config) Tensorflow/core/common_runtime/gpu/gpu_:1484] Adding visible gpu TotalMemory: 11.25GiB freeMemory: 11.16GiB Name: Tesla K80 major: 3 minor: 7 memor圜lockRate(GHz): 0.8235 Tensorflow/core/common_runtime/gpu/gpu_:1405] Found device 0 with Instructions that this TensorFlow binary was not compiled to use: AVX2 FMA Tensorflow/core/platform/cpu_feature_:141] Your CPU supports Type “help”, “copyright”, “credits” or “license” for more information.
Singularity tensorflow:1.10.0-devel-gpu-p圓:~> python3 $ sudo singularity build –sandbox /path/to/sandbox docker://tensorflow/tensorflow/1.10.0-devel-gpu-p圓 | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. Singularity tensorflow:1.10.0-devel-gpu-p圓:~> nvidia-smi GCC/Compiler version (if compiling from source): TensorFlow installed from (source or binary): OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
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