0

I run this code:

tf.test.is_gpu_available( cuda_only=False, min_cuda_compute_capability=None )

I get the following error:

2019-10-25 18:25:20.855191: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 2019-10-25 18:25:20.879831: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll 2019-10-25 18:25:21.461924: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce MX130 major: 5 minor: 0 memoryClockRate(GHz): 1.189 pciBusID: 0000:01:00.0 2019-10-25 18:25:21.470775: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check. 2019-10-25 18:25:21.503654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 --------------------------------------------------------------------------- InternalError Traceback (most recent call last) in ----> 1 tf.test.is_gpu_available( cuda_only=False, min_cuda_compute_capability=None )

~\Anaconda3\envs\deep_env\lib\site-packages\tensorflow_core\python\framework\test_util.py in is_gpu_available(cuda_only, min_cuda_compute_capability) 1430
1431 try: -> 1432 for local_device in device_lib.list_local_devices(): 1433 if local_device.device_type == "GPU": 1434 if (min_cuda_compute_capability is None or

~\Anaconda3\envs\deep_env\lib\site-packages\tensorflow_core\python\client\device_lib.py in list_local_devices(session_config) 39 return [ 40 _convert(s) ---> 41 for s in pywrap_tensorflow.list_devices(session_config=session_config) 42 ]

~\Anaconda3\envs\deep_env\lib\site-packages\tensorflow_core\python\pywrap_tensorflow_internal.py in list_devices(session_config) 2247 return ListDevicesWithSessionConfig(session_config.SerializeToString())
2248 else: -> 2249 return ListDevices() 2250 2251

InternalError: cudaGetDevice() failed. Status: cudaGetErrorString symbol not found.

After crearing the following venv:

conda create -n Deep_learning_env python=3.6

pip install -U numpy matplotlib pandas ipython
git clone https://github.com/scipy.git scipy
pip install https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp36-cp36m-win_amd64.whl
pip install https://cntk.ai/PythonWheel/GPU/cntk_gpu-2.7.post1-cp36-cp36m-win_amd64.whl
pip install tensorflow
pip install tensorflow-gpu
pip install gensim
pip install keras
pip install  --upgrade --no-deps cntk
pip install --upgrade --no-deps cntk-gpu

conda install theano pygpu
conda install -c peterjc123 pytorch
conda install -c anaconda cudatoolkit
conda install -c anaconda cudnn

conda list cudnn
# Name   Version  Build      Channel
cudnn    7.6.0    cuda10.1_0    anaconda
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:12:52_Pacific_Daylight_Time_2019
Cuda compilation tools, release 10.1, V10.1.243
python --version

Python 3.6.7
which nvcc
/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.1/bin/nvcc

NVIDIA GeForce MX130

more info: cntk 2.7 cntk-gpu 2.7 tensorflow 2.0.0 tensorflow-estimator 2.0.1 tensorflow-gpu 2.0.0 ipython 7.8.0

also when I import tensorflow I get the following warning:

tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_100.dll'; dlerror: cudart64_100.dll not found

1 Answer 1

0

You should checkout this: Github tensorflow Currently Cuda 10.1 is compatible with TF

2
  • 1
    On superuser, we add links only as reference, you should always provide at least a short explanation. Nov 6, 2019 at 18:00
  • i think my GPU NVIDIA GeForce MX130 does not support any tensor flow coding that why it did not work because i tried it with cuda 9 and the above is with cuda 10.1 correct me if i am wrong ... this is because i checked the website for the gpu and they did not list it with the supported once !! Nov 6, 2019 at 21:51

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