AWS Open Source Blog

re:Cap part one – open source at re:Invent 2019

Andy Jassy giving the 2019 re:Invent keynote.

As the dust settles after another re:Invent closes, I wanted to put together a quick summary of all the open source-related announcements that happened in the run up to this year’s re:Invent and the week itself. If you are interested in open source in mobile web development, devops, containers, security, big data and data analytics, machine learning, databases, emerging technologies and more, you can read about all the announcements and catch up on sessions from re:Invent, and then take a look at the workshops in case want to try them out from the comfort of your own keyboard.

This is part one of three, in which we’ll be covering all things data, analytics and machine learning. Part two will cover mobile web development and DevOps and part three will cover compute and emerging technologies such as robotics and blockchain, as well as all other areas of open source including Java.

Big data, data analytics, and databases

Announcements

One of the key announcements that we made during re:Invent was the New Amazon managed Apache Cassandra service. Above is the announcement made during Andy’s keynote. You can also read Matt Assay’s post on how we are Contributing to Apache Cassandra community.

There were also some great announcements prior to re:Invent:

Sessions

Here’s a selection of related sessions:

Workshops

Machine Learning

Announcements

The AWS machine learning stack is the broadest and deepest toolkit you can provide your data scientists and web developers, and we made many announcements during re:Invent. Here I will cover the open source-related
announcements:

  • First up was Amazon Sagemaker operators for Kubernetes, allowing you to kick off machine learning workloads on Kubernetes, adding Amazon SageMaker as a custom resource. Read more, including some detailed examples, in Introducing Amazon Sagemaker operators for Kubernetes and then check out the code in the GitHub repo https://github.com/aws/amazon–sagemaker–operator–for–k8s
  • Netflix also announced the open sourcing of a new project, Metaflow, a human–centric framework (Python library) for data science that has been battle tested within Netflix against hundreds of data science projects. In the post, Netflix explain how they partnered with AWS to provide a seamless integration between Metaflow and various AWS services.
  • We announced Deep Java Library (DJL), an open source library to develop Deep Learning models in Java. The Deep Java Library home page includes links to the GitHub repo where you will find demo code and examples. This announcement was quickly followed by the Deep Graph Library (DGL) , a Python package built for easy implementation of graph neural network model families, on top of existing deep learning frameworks such as PyTorch, MXNet, Gluon, etc.
  • Finally, TensorFlow 1.15 is now supported on the Deep Learning AMIs, Deep Learning containers and Amazon SageMaker, and TensorFlow 2.0 is available on the Deep Learning AMIs (and watch this space for it coming to containers and Amazon SageMaker, too)

Sessions

From machine learning frameworks to running machine learning on containers and using open source tools, here are our picks:

Workshops

AIM403 – Deep learning with Apache MXNet

Keep up to date with open source at AWS

I hope this summary has been useful. I have looked for all the session videos that have been uploaded to date, but if I have missed anything, please get in touch and I will update this summary. Remember to check out the Open Source homepage to keep up to date with all our activity in open source by following us on Twitter @AWSOpen.

Ricardo Sueiras

Ricardo Sueiras

Cloud Evangelist at AWS. Enjoy most things where technology, innovation and culture collide into sometimes brilliant outcomes. Passionate about diversity and education and helping to inspire the next generation of builders and inventors with Open Source.