AWS re:Invent 2019 — AI/ML recap — Part 3: Frameworks & Infrastructure
Tags: aws, machinelearning, deeplearning, amazonwebservices
Published: 2019-12-18 21:52:09

---
title: AWS re:Invent 2019 — AI/ML recap — Part 3: Frameworks & Infrastructure
published: true
date: 2019-12-11 20:05:05 UTC
tags: aws, machinelearning, deeplearning, amazonwebservices
canonical_url: https://medium.com/@julsimon/aws-re-invent-2019-ai-ml-recap-part-3-frameworks-infrastructure-6cbdd049a0a7
---

### AWS re:Invent 2019 — AI/ML recap — Part 3: Frameworks & Infrastructure

In the last two previous posts, I introduced you to our new [high-level services](https://dev.to/juliensimon/aws-re-invent-2019-ai-ml-recap-part-1-ai-services-lai), and to the new [capabilities](https://dev.to/juliensimon/aws-re-invent-2019-ai-ml-recap-part-2-amazon-sagemaker-53no-temp-slug-7211741) we added to [Amazon SageMaker](https://aws.amazon.com/sagemaker). Thought I was done, didn’t ya? Nope, there’s more. There’s ALWAYS more.

In this final post, let’s talk about frameworks and infrastructure.

As always, happy to answer questions here or on [Twitter](https://twitter.com/julsimon).

### Amazon DeepComposer

Not sure when this one fits! [AWS DeepComposer](https://aws.amazon.com/deepcomposer/) is the world’s first musical keyboard combined with a generative AI service.

Blog: [https://aws.amazon.com/blogs/aws/aws-deepcomposer-compose-music-with-generative-machine-learning-models/](https://aws.amazon.com/blogs/aws/aws-deepcomposer-compose-music-with-generative-machine-learning-models/)

Documentation: [https://docs.aws.amazon.com/deepcomposer/latest/devguide/what-it-is.html](https://docs.aws.amazon.com/deepcomposer/latest/devguide/what-it-is.html)

{% youtube DfUdgvzbKII %}

{% youtube 0rO1s5Fpcss %}

### Amazon EC2 Inferentia (Inf1)

Inf1 instances are powered by [AWS Inferentia](https://aws.amazon.com/machine-learning/inferentia/) chips, and are designed to provide you with fast, low-latency inferencing.

Using the [Neuron SDK](https://github.com/aws/aws-neuron-sdk), which is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch, you can compile and load your models on Inf1.

![](https://cdn-images-1.medium.com/max/1024/1*l9tPPzu3eU8qyJ4VqXxRCw.png)

Blog: [https://aws.amazon.com/blogs/aws/amazon-ec2-update-inf1-instances-with-aws-inferentia-chips-for-high-performance-cost-effective-inferencing/](https://aws.amazon.com/blogs/aws/amazon-ec2-update-inf1-instances-with-aws-inferentia-chips-for-high-performance-cost-effective-inferencing/)

Neuron SDK: [https://github.com/aws/aws-neuron-sdk](https://github.com/aws/aws-neuron-sdk)

### TensorFlow support

TensorFlow 1.15 is now supported in [Deep Learning AMIs](https://docs.aws.amazon.com/dlami/latest/devguide/what-is-dlami.html), [Deep Learning containers](https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers-images.html) and [SageMaker](https://sagemaker.readthedocs.io/en/stable/sagemaker.tensorflow.html).

TensorFlow 2.0 is also available in the Deep Learning AMIs. I hear that it’s coming soon to Deep Learning containers and SageMaker as well, but shhhh.

![](https://cdn-images-1.medium.com/max/1024/1*OVZcL4TbSzMgYA4Y3J1jNg.png)

By the way, did you know that **85%** of all TensorFlow workloads in the cloud run on AWS? Check out the report for more details ([PDF](http://d1.awsstatic.com/whitepapers/Deep_Learning_on_AWS.pdf)).

### Deep Graph Library

The [Deep Graph Library](https://www.dgl.ai/) is an open source library built for easy implementation of graph neural networks, and it is now available on [Amazon SageMaker](https://aws.amazon.com/sagemaker/). Weird and fascinating stuff!

![](https://cdn-images-1.medium.com/max/874/1*HUJjbAloZ-W2AlPpvp4zdQ.png)

Blog: [https://aws.amazon.com/blogs/aws/now-available-on-amazon-sagemaker-the-deep-graph-library](https://aws.amazon.com/fr/blogs/aws/now-available-on-amazon-sagemaker-the-deep-graph-library/)

Documentation and examples: [https://www.dgl.ai/](https://www.dgl.ai/)

### Deep Java Library

The [Deep Java Library](https://djl.ai) is an open source library to develop Deep Learning models in Java. Does this mean that Java developers don’t have to learn Python anymore? ;)

![](https://cdn-images-1.medium.com/max/1024/1*JpUUISARGeVWlCuntRNbdQ.png)

Documentation and examples: [https://www.djl.ai/](https://www.dgl.ai/)