Version(s): 1.4.1, 1.5.0
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker
Dockerfile.cpu: CPU version based on TF Dockerfile for CPU (starts with ubuntu:16.04)
Dockerfile-tf{141|150}-nv384.81.gpu: GPU versions based on TF Dockerfile for GPU (CUDA{8|9} and CuDNN{6|7}). nvidia driver 384.81 is installed in the produced container(s). If a host machine has the same nvidia driver version (384.81), it allows to run containers on GPUs of such a host machine by means of e.g. singularity 2.2.1.
convnet-benchmarks: https://github.com/soumith/convnet-benchmarks/tree/master/tensorflow
MNIST:
https://www.tensorflow.org/versions/r1.2/get_started/mnist/pros
https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/examples/tutorials/mnist/mnist_deep.py
To build a corresponding docker image, one needs docker-ce (e.g. https://docs.docker.com/install/linux/docker-ce/ubuntu/):
1. link 'Dockerfile' to the proper file (.cpu or .gpu)
2. $> docker build -t tf-benchmarks .
https://hub.docker.com/r/vykozlov/tensorflow/tags/
To run CPU version, execute for example:
$> docker run -it vykozlov/tensorflow:tag
It is also possibe to use udocker (https://github.com/indigo-dc/udocker) (more advanced GPU support to come soon!):
$> udocker run vykozlov/tensorflow:tag
To run GPU version, one needs nvidia-docker (https://github.com/NVIDIA/nvidia-docker). Execute for example:
$> nvidia-docker run -it vykozlov/tf-benchmarks:latest-gpu