From Keras to Production

Daten

https://www.kaggle.com/moltean/fruits

Notebooks

https://github.com/codecentric/from-keras-to-production-workshop.git

Images pullen

• docker pull codecentric/from-keras-to-production-baseimage
• docker pull codecentric/tensorflow-serving-baseimage
• docker pull codecentric/jenkins-python-image

Jupyterlab starten

docker run -p 8888:8888 --mount type=bind,source=$(pwd)/notebooks,target=/keras2production/notebooks codecentric/from-keras-to-production-baseimage

# Für Windows
docker run -p 8888:8888 --mount type=bind,source=%cd%/notebooks,target=/keras2production/notebooks codecentric/from-keras-to-production-baseimage

Jenkins starten

docker cp <jupyter-container>:keras2production/fruits fruits
docker run -p 8080:8080 -p 50000:50000 --mount type=bind,source=$(pwd)/fruits,target=/fruits --name jenkins_solution codecentric/jenkins-python-image

TensorFlow Serving starten

docker run -p 8501:8501 -p 8500:8500 --mount type=bind,source=$(pwd)/notebooks/6-models/fruits/,target=/models/fruits -e MODEL_NAME=fruits -t tensorflow/serving:1.12.0

Run slides

pip install -r requirements.txt
cd slides
jupyter nbconvert end2end_ds.ipynb --to slides --post serve --reveal-prefix=reveal.js

References and further information

IPython and Jupyterlab

https://ipython.readthedocs.io/en/stable/interactive/python-ipython-diff.html

Reinforcement Learning

https://www.youtube.com/watch?v=FCyZplb0ul4

Free Notebooks from Deep Learning with Python Book

https://github.com/fchollet/deep-learning-with-python-notebooks