The Kubernetes on AWS Project template gets you up and running with a fully functional cluster. Logging and monitoring with Prometheus and Grafana are installed, and once you run terraform apply you are ready to install Helm charts!
This is a fully formed and ready for production project template. Included is extensive technical documentation, Dockerfiles for creating custom images with either Miniconda or the base R rocker/shiny image, a base helm chart with the NGINX ingress, and and a helm chart with the NGINX ingress and an AWS EFS (networked storage) example configuration.
Build custom docker images, bring up your stack with one command, add plugins and custom REST APIs, and deploy to Kubernetes.
Free DevOps for Data Scientists tutorials emailed to you each week on topics such as getting started with Docker, deploying RShiny on AWS, deploying with Kubernetes, data visualization infrastructure, and more.
This is a course that is designed to get you from A, knowing little to very little about application deployment with Docker, to Z, deploying and scaling your Python applications with Docker, Docker Swarm and Kubernetes (for the brave!). Once you have your foundation set up you can deploy your applications in house or on the cloud using AWS, GCP, or Azure!
A simple howto guide to configure Apache Airflow with a PostgreSQL or MySQL database backend and Celery Executor.
Install and manage bioinformatics software without losing your mind using Conda, Modules and EasyBuild. Using these tools covers all your bases, including common software like samtools, creating bundles such as for RNASeq, and do not distribute software such as CellRanger from 10X Genomics. The ebook is available as a PDF and as a zip file containing the original markdown files used to generate the PDF.
Bioinformatics analyses requires varied computational resources, and sometimes you need real time data visualization. You need to be able to create plots, filter data tables, along with your own in house tools and dashboards.
Bioinformatics analyses requires varied computational resources. During an alignment you may need 20 compute nodes, and during variant calling or QC only 5. If you want to optimize your analyses and cut down on costs you want to take advantage of elastic capabilities on AWS, meaning that resources scale up and down as needed.
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