Ubuntu 16.04 Nvidia Docker setup

1. Tensorflow compatible libs

https://www.tensorflow.org/versions/r0.12/get_started/os_setup

https://www.tensorflow.org/install/install_linux

2. Pre-installation actions

https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker

https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu

http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#pre-installation-actions

3. Install the Nvidia driver

http://programmingmatrix.blogspot.com/2017/10/ubuntu-1604-nvidia-gpu-driver-cuda-and.html

http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu-installation

4. Install Nvidia Docker
 
https://github.com/NVIDIA/nvidia-docker

GPU support

Prior to installing TensorFlow with GPU support, ensure that your system meets all NVIDIA software requirements. To launch a Docker container with NVidia GPU support, enter a command of the following format:

$ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage

where:

• -p hostPort:containerPort is optional. If you plan to run TensorFlow programs from the shell, omit this option. If you plan to run TensorFlow programs as Jupyter notebooks, set both hostPort and containerPort to 8888.
• TensorFlowGPUImage specifies the Docker container. You must specify one of the following values:
  • gcr.io/tensorflow/tensorflow:latest-gpu, which is the latest TensorFlow GPU binary image.
  • gcr.io/tensorflow/tensorflow:latest-devel-gpu, which is the latest TensorFlow GPU Binary image plus source code.
  • gcr.io/tensorflow/tensorflow:version-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image.
  • gcr.io/tensorflow/tensorflow:version-devel-gpu, which is the specified version (for example, 0.12.1) of the TensorFlow GPU binary image plus source code.

We recommend installing one of the latest versions. For example, the following command launches the latest TensorFlow GPU binary image in a Docker container from which you can run TensorFlow programs in a shell:

$ nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash

The following command also launches the latest TensorFlow GPU binary image in a Docker container. In this Docker container, you can run TensorFlow programs in a Jupyter notebook:

$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu

The following command installs an older TensorFlow version (0.12.1):

$ nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:0.12.1-gpu

Docker will download the TensorFlow binary image the first time you launch it. For more details see the TensorFlow docker readme.

Next Steps

You should now validate your installation.

5. Test that tensorflow links with libcudnn

workon deep learning_3.5
python

>>> import tensorflow

6. Check the Tensorflow version

python -c "import tensorflow; print(tensorflow.__version__)"

7. Test that Tensorflow is using the GPU

python -c "import tensorflow as tf; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))"

8. Setup a Docker volume for persistence

https://github.com/anurag/fastai-course-1




9. How to use Nvidia Docker

https://medium.com/@ceshine/docker-nvidia-gpu-nvidia-docker-808b23e1657