All-in-one Docker image for Deep Learning

Here are Dockerfiles to get you up and running with a fully functional deep learning machine. It contains all the popular deep learning frameworks with CPU and GPU support (CUDA and cuDNN included). The CPU version should work on Linux, Windows and OS X. The GPU version will, however, only work on Linux machines. See OS support for details

If you are not familiar with Docker, but would still like an all-in-one solution, start here: What is Docker?. If you know what Docker is, but are wondering why we need one for deep learning, see this

Specs

For Ubuntu 18.04 Docker

This is what you get out of the box when you create a container with the provided image/Dockerfile:

• Ubuntu 18.04
• CUDA 9.2
• cuDNN 7
• Tensorflow-gpu 1.12
• Keras
• iPython/Jupyter Notebook for python 2 and 3
• Numpy, SciPy, Pandas, Scikit Learn, Matplotlib, Seaborn, Xgboost, Statmodels, requests, imutils
• gunicorn web server
• OpenCV 3.4.2 + Opencv contribt
• Flask
• Mysql-connector

For Ubuntu 16.04 Docker

This is what you get out of the box when you create a container with the provided image/Dockerfile:

• Ubuntu 16.04
• CUDA 9
• cuDNN 7
• Tensorflow-gpu 1.12
• Keras
• iPython/Jupyter Notebook for python 2 and 3
• Numpy, SciPy, Pandas, Scikit Learn, Matplotlib, Seaborn, Xgboost, Statmodels, requests, imutils
• gunicorn web server
• OpenCV 3.4.2 + Opencv Contribt
• Flask
• Mysql-connector

Gogle Cloud K80 GPU Note

• You should install nvidia driver

curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb  
sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb  
sudo apt-get update  

• Sincronize the driver

sudo nvidia-smi -pm 1  

• Check the Driver

nvidia-smi

• If these steps don't work, try to install automatically by this:

sudo apt install ubuntu-drivers-common

• Or this:

sudo ubuntu-drivers autoinstall

• The last, BUILD YOUR DOCKER FILE

CUDA and CUDNN Note For Tensorflow

Tensorflow 1.4.x: CUDNN 6.0 and CUDA 8.0
Tensorflow >= 1.5: CUDNN 7.0 and CUDA 9.0