hands_on_tensorflow_high_level_apis's Introduction

Hands on tensorflow high level apis

Tutorial on TensorFlow High Level APIs

Extensions to add

The notebooks work with jupyter notebook extensions. Once you access jupyter, before starting the notebook, go on the Nbextensions tab and check the following extensions:

Exercice2
ExecuteTime
highlighter
Table of Contents (2)

You can then start the notebooks, and you're good to go!

Setup working environment -DOCKER-

Execute the following commands in the same directory as Dockerfile.

Build image

docker build -t tf2_devoxx19 .

Check image list

docker images

Run

docker run -d -p 8888:8888 -p 6006:6006 tf2_devoxx19

Check container id

docker ps

Next

Open in browser at http://127.0.0.1:8888

To retrieve the token for your notebooks type

docker logs <container_id>

At the end

docker stop <container_id>

Setup working environment -VIRTUALENV-

Make sure you have Python 3.5 ou 3.6 installed:

python3 --version

If you don’t have it you can download it here.

If you don’t have virtualenv installed type

pip3 install virtualenv

Go the the project root and create the virtual environment and activate it (change python version to 3.5 if you have python 3.5 installed):

virtualenv --python=python3.6 tf2_devoxx19
source ./tf2_devoxx19/bin/activate

Install requirements and launch notebooks:

pip3 install --upgrade -r requirements.txt
jupyter contrib nbextension install --user
jupyter nbextensions_configurator enable --user
jupyter notebook

To exit virtual environment: