Running Tensorflow in a Docker Container in Ubuntu

I found that the easiest and hassle-free way of running the GPU version of Tensorflow is with the help of docker as you don’t have to worry about the CUDA versions and Tensorflow versions compatibility. This is a tutorial to use Tensorflow in a docker container in Ubuntu. If you were to use Tensorflow in a docker container in Mac or Windows, it will only be simpler than this tutorial because you just need to install Docker for Desktop application for Mac or Windows as opposed to a Docker Engine.

Install Docker CE in Ubuntu

Uninstall old versions

sudo apt-get remove docker docker-engine containerd runc

Set up the repository

sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg-agent \

Add Docker’s official GPG key

curl -fsSL | sudo apt-key add -

Verify the fingerprint

sudo apt-key fingerprint 0EBFCD88

Add the repository

sudo add-apt-repository "deb [arch=amd64] $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli

Verify the Docker CE installation

sudo docker run hello-world

Install nvidia-docker2 for GPU support (Only necessary if you’re planning on running Tensorflow in GPU)

If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers docker volume

ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f sudo apt-get purge -y nvidia-docker 

Add the package repositories

curl -s -L|sudo apt-key add - 

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) 

curl -s -L$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update 

Install nvidia-docker2 and reload the Docker daemon configuration

sudo apt-get install -y nvidia-docker2 

sudo pkill -SIGHUP dockerd 

Test nvidia-smi with the latest official CUDA image

docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi

Get Tensorflow docker image from docker hub

# run one of the following commands based on your need. Tag variants are
-gpu, -py3 and -jupyter

sudo docker pull tensorflow/tensorflow:latest-gpu-py3 #pulls the latest tensorflow docker image with GPU support and python3

sudo docker pull tensorflow/tensorflow:latest #pulls the latest tensorflow docker image with CPU only support and python2

Instantiate your container from your tensorflow docker image

# for tensorflow CPU image with python 2
sudo docker run -it --name=mytfcontainer tensorflow/tensorflow:latest bash

# for tensorflow GPU image with python 3
sudo docker run -it --runtime=nvidia --name=diwanshu1 tensorflow/tensorflow:latest-gpu-py3 bash

There you have it. Enjoy using tensorflow in docker


ER Diagram and their Relational Schema

ER Diagram is a conceptual design of a database. This is the first step to designing a database where we identity all the entities and the relationships in a database. The entities are represented by rectangles and the relationships are represented by diamonds. Below is an example of an ER Diagram for a library:

The above conceptual design of a database can be further materialized into something called Relational Schema where we convert all entities and relationships into tables. Entities can be easily converted into tables, but how do we convert relationships into tables? This is done by identifying primary keys in entities. To refresh your memory, primary keys are unique in a table( or entity). Once the primary keys are identified, the relationships can now be converted into tables by simply using the primary keys of the two entities the relationship connects to. For clarity, the Relational Schema of the above ER Diagram is shown below (Note: the primary keys that are starred in ER Diagram are bold faced in the below Relational Diagram).

When creating the table for relation entities, the keys identified in the above diagram will be used as foreign keys referencing the table where they are primary represented as primary keys