In this post we describe the process to launch large, self-supervised training jobs using AWS ParallelCluster and Facebook’s Vision Self-Supervised Learning (VISSL) library. Read the Post on the AWS Blog Channel
Read More
Training Machine Learning models involves multiple steps, it gets more complex and time consuming when the size of the data set for training is in the range of 100s of GBs. Data Scientists run through large number of experiments and …
Read More
Stateful applications rely on data being persisted and retrieved to run properly. When running stateful applications using Kubernetes, state needs to be persisted regardless of container, pod, or node crashes or terminations. This requires …
Read More
Starting today, access to D-Wave products and services has fully transitioned to the AWS Marketplace, and customers can no longer access the D-Wave 2000Q and Advantage systems via Amazon Braket, the cloud computing service of AWS. In this …
Read More
Amazon Braket recently launched the Aquila quantum processing unit (QPU) based on Rydberg atoms by QuEra Computing. In a previous post, we explored how researchers can use Rydberg devices to study problems and quantum phenomena in …
Read More
Pay-as-you-go resources are a compelling but budget-limited researchers performing HPC workloads need help working within the bounds of their grants. In this post, we show how to build a real-time cost guardian for AWS Batch to help enforce …
Read More
Today we announced the AWS Impact Computing Project at the Harvard Data Science Initiative (HDSI) to identify potential solutions that can improve the lives of humans, other species, and natural ecosystems. Deb Goldfarb describes its goals …
Read More
Amazon SageMaker is a fully-managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. ML is realized in inference. SageMaker offers four …
Read More