AWS Open Source Blog
Category: Technical How-to
Configuring Grafana Cloud Agent for Amazon Managed Service for Prometheus
This post was written by Robert Fratto, Imaya Kumar Jagannathan, and Alolita Sharma. The Grafana Cloud Agent is a lightweight alternative to running a full Prometheus server. It keeps the necessary parts for discovering and scraping Prometheus exporters and sending metrics to the backend, which in this case is the Amazon Managed Service for Prometheus […]
Continuous deployment of Cloud Custodian to AWS Control Tower
Cloud Custodian is an open source, cloud security, governance, and management tool that enables users to keep their Amazon Web Services (AWS) environment secure and well managed by defining policies in a YAML domain specific language (DSL). Cloud Custodian works by defining policies in a YAML file and running the defined policies against AWS accounts. […]
Setting up Grafana on EC2 to query metrics from Amazon Managed Service for Prometheus
The recently launched Amazon Managed Service for Prometheus (AMP) service provides a highly available and secure environment to ingest, query, and store Prometheus metrics. We can query the metrics from the AMP environment using Amazon Managed Grafana, a self-hosted Grafana server, or using the HTTP APIs. In this article, we will look at how to […]
How the Bottlerocket build system works
Bottlerocket is an open source, special-purpose operating system designed for hosting Linux containers, which was launched in 2020. As I delved into the Bottlerocket build system for a deeper understanding, I found it helpful to describe the system in detail (a form of rubber-duck debugging). This article is the result of my exploration and will […]
Leverage deep learning in Scala with GPU on Spark 3.0
This post was contributed by Qing Lan, Carol McDonald, and Kong Zhao. With the growing interest in deep learning (DL), more users are using DL in their production environments. Because DL requires intensive computational power, developers are leveraging GPUs to do their training and inference jobs. As part of a major Apache Spark initiative to […]
How Netflix uses Deep Java Library (DJL) for distributed deep learning inference in real-time
This post was written by Stanislav Kirdey, Lan Qing, Lai Wei, and Lu Huang. Netflix is one of the world’s largest entertainment services with over 260 million members in more than 190 countries. One of the ways Netflix is able to sustain a high-quality customer experience is by employing deep learning models in the observability […]
Improving zlib-cloudflare and comparing performance with other zlib forks
We worked with the maintainers of the Cloudflare fork of zlib (zlib-cloudflare) to improve the decompression performance on Arm and x86. With the changes, at level 6: On Arm: Compression performance: ~90 percent faster than zlib-madler (original zlib). Decompression performance: ~52 percent faster than zlib-madler. On x86: Compression performance: ~113 percent faster than zlib-madler. Decompression […]
Testing AWS Lambda functions written in Java
Testing is an essential task when building software. Testing helps improve software quality by finding bugs before they reach production. The sooner we know there is a defect in code, the easier and cheaper it is to correct. Automated tests are a central piece in reducing this feedback loop. In association with a continuous integration […]
Remote visualization in HPC using NICE DCV with ParallelCluster
NICE DCV is an AWS-owned high performance remote display protocol, which specializes in 2D/3D interactive streaming over the internet or a local network (e.g., WiFi). With the power of NICE DCV we can seamlessly connect to our remote session running either in the cloud or data center via internet from a local laptop. We can […]
Deploy fast.ai-trained PyTorch model in TorchServe and host in Amazon SageMaker inference endpoint
Over the past few years, fast.ai has become one of the most cutting-edge, open source, deep learning frameworks and the go-to choice for many machine learning use cases based on PyTorch. It has not only democratized deep learning and made it approachable to general audiences, but fast.ai has also become a role model on how […]









