0

My system is Ubuntu 14.04 on EC2.:

nvidia-smi Sun Oct 2 13:35:28 2016 +------------------------------------------------------+ | 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 GRID K520 Off | 0000:00:03.0 Off | N/A | | N/A 37C P0 35W / 125W | 11MiB / 4095MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ ubuntu@ip-XXX-XX-XX-990:~$ nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2015 NVIDIA Corporation Built on Tue_Aug_11_14:27:32_CDT_2015 Cuda compilation tools, release 7.5, V7.5.17

I Installed CUDA 7.5 and CuDNN 5.1.

I have the proper files in /usr/local/local/lib64 and include folders.

Tensorflow line gives nothing:

 sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) >>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) Device mapping: no known devices. I tensorflow/core/common_runtime/direct_session.cc:252] Device mapping: >>>

Please help (Thanks a lot :)).

Improve this question
4
  • @talonmies existing solutions are not working, I have checked the path variables again and again. Commented Oct 2, 2016 at 16:03
  • You appear to have a working CUDA installation. Therefore this'd question is a tensorflow configuration issue and nothing to do with CUDA programming. That is why I recovered the tag. Commented Oct 2, 2016 at 16:05
  • @talonmies yes, thats what i thought, there must be some problem in linking or path...like that. could you suggest anything i could try? thanks. Commented Oct 2, 2016 at 16:06
  • I had same problem. I recommend you to check Tensorflow compatibility with CUDA/cuDNN/Python from this link Commented Aug 10, 2020 at 5:42

1 Answer 1

Reset to default
2

How did you build tensorflow?

If you did it with bazel did you add correctly --config=cuda?

If you installed it with pip did you took correctly the one with gpu enable?

EDIT:

You can see here how to install with pip: https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html#pip-installation

You need to take the one with binary compatible with gpu:

# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7 # Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below. $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp27-none-linux_x86_64.whl # Mac OS X, GPU enabled, Python 2.7: $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py2-none-any.whl # Ubuntu/Linux 64-bit, GPU enabled, Python 3.4 # Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below. $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp34-cp34m-linux_x86_64.whl # Ubuntu/Linux 64-bit, GPU enabled, Python 3.5 # Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below. $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp35-cp35m-linux_x86_64.whl # Mac OS X, GPU enabled, Python 3.4 or 3.5: $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py3-none-any.whl

then install tensorflow:

# Python 2 $ sudo pip install --upgrade $TF_BINARY_URL # Python 3 $ sudo pip3 install --upgrade $TF_BINARY_URL
Improve this answer
Sign up to request clarification or add additional context in comments.

2 Comments

what is the pip option for gpu? I didn't see any gpu command alonside pip. Please let me know if there is any command, I will mark it as answer. Thanks.
I edited my answer, you can check it. I hope it helps. No real option just need take theright binary.

Start asking to get answers

Find the answer to your question by asking.

Ask question

Explore related questions

See similar questions with these tags.