AWS HPC Blog
Scale Reinforcement Learning with AWS Batch Multi-Node Parallel Jobs
Autonomous robots are increasingly used across industries, from warehouses to space exploration. While developing these robots requires complex simulation and reinforcement learning (RL), setting up training environments can be challenging and time-consuming. AWS Batch multi-node parallel (MNP) infrastructure, combined with NVIDIA Isaac Lab, offers a solution by providing scalable, cost-effective robot training capabilities for sophisticated behaviors and complex tasks.
Adding functionality to your applications using multiple containers in AWS Batch
Discover how to coordinate multiple applications in separate containers within a single AWS Batch job definition. Learn the benefits of this approach and how to share resources between containers for more efficient, scalable deployments.
Enhancing Equity Strategy Backtesting with Synthetic Data: An Agent-Based Model Approach – part 2
Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 2 covers implementation details and results.
Enhancing Equity Strategy Backtesting with Synthetic Data: An Agent-Based Model Approach
Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 1 of 2 covers the theoretical foundations of the approach.
How to use rate-limited resources in AWS Batch jobs with resource aware scheduling
Struggling with bottlenecks in your batch processing? AWS Batch’s new resource aware scheduling capability could be the solution your business needs. This feature allows you to define and manage consumable resources, helping maximize the use of your compute power. Check out our blog to learn more.
Predict the unpredictable: Disrupting drug lead optimization using quantum mechanics simulation in the cloud
Quantum mechanics meets drug discovery: QSimulate’s latest advancements in QM-based FEP simulation are poised to transform the industry. Our blog post takes you on a journey through the groundbreaking science and innovative software that are redefining the future of drug design.
Harnessing the power of agent-based models for mitigating supply chain risks and managing costs
Discover how a new simulation-based approach using agent-based modeling, rich data, and advanced analytics can help you build resilient supply chains that withstand disruptions. Learn more in our latest post.
Using the Terraform AWS Cloud Control provider for managing AWS Batch resources
The Terraform AWS Cloud Control (AWSCC) provider now supports AWS Batch job definitions, enabling you to leverage recent and future enhancements to AWS Batch. Learn more in our latest blog post.
Adding configurable namespaces, persistent volume claims, and other features for AWS Batch on Amazon EKS
Exciting updates to AWS Batch on Amazon EKS! Configurable namespaces, persistent volume claims, and more. Check out our blog post to see how these features can help manage your complex containerized workloads.
Three recipes you don’t want to miss for AWS Parallel Computing Service
AWS Parallel Computing Service now supports AWS CloudFormation, enabling you to deploy and scale HPC workloads as code. Check out our open-source HPC Recipes Library for quick cluster deployments.