This contains the code samples and demos for the book “Cloud Computing for Science and Engineering”. Most of these samples are Jupyter Notebooks. If you do not have a copy of Jupyter you should download and install anaconda from Continuum Analytics https://www.continuum.io/downloads.
Another way to run Jupyter is to install Docker and run the jupyter/scipy-notebook container as follows
docker run -d -p 8888:8888 -e PASSWORD=”yourword” -e USE_HTTPS=yes jupyter/scipy-notebook
then go to https://localhost:8888 or, if you started this on a remote host use the ip address of the remote host.
This folder contains the files needed to do the simple document classification experiments using the science abstract from ArXiv catalog. these are used in aws-ml-container for examples in chapter 7.6. The notebook doc_analysisd7-physics.ipynb illustrates how to build the models for analyzing the physics documents.
This is a folder containing the files need to deploy an HPC cluster on AWS using CfnCluster. It is described in chapter 7.2.3 “Scale”
is the files needed to build the containers used in the AWS container service demo from chapter 7.6
contains the simple files used in the chapter 3, using cloud storage
is the source for the docker sample in section 6.4 in the containers chapter
celery gcloud kubernetes example again using the arxiv document predictor service.
this is the simple kinesis to spark example from 9.4 streaming data chapter.
this is the output of the movie created by the autoencoder.ipynb example discussed in the autoencocer supplement
This folder contains all the ipython notebooks
contains the lectures and exercises for the sc17 tutorial
is a folder containing some of the files for the singularity supplement