Science on Google Cloud Platform (GCP)
The goal of this playbook it to aid researchers in developing healthcare systems and workflows on GCP. This playbook will provide GCP architecture and solutions examples for implementing genomics and secondary analysis, patient monitoring, variant analysis, radiological image extraction, and usage of the healthcare API.
The goal of this playbook is to assist researchers in designing and implementing High Performance Computing workflows on GCP. More specifically, this playbook will focus on architecture and solutions examples for building and storing singularity containers, hosting Jupyter notebooks in slurm clusters, setting up NFS file storage for Google Kubernetes Engine (GKE), and running TensorFlow inference workloads at scale.
The goal of this playbook is to aid researchers as they transition historically on-premise datasets, workloads and pipelines to GCP. This playbook will provide researchers with methods for managing data lifecycles, accessing public datasets, leveraging cloud APIs and API gateways, managing data costs, and sharing and visualizing data.
The goal of this playbook is to aid researchers in migrating existing research workloads and data onto GCP. This playbook will provide NIH-specific processes, sample GCP architectures, and examples for migrating existing research projects and data to GCP.