Displaying 1 to 10 from 10 results

kubeflow - Machine Learning Toolkit for Kubernetes

  •    Python

The Kubeflow project is dedicated to making machine learning on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to train, test, and deploy best-of-breed open-source predictive models to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run KubeFlow.This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs.

kubeflow - Machine Learning Toolkit for Kubernetes

  •    Go

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow is a platform for data scientists who want to build and experiment with ML pipelines. Kubeflow is also for ML engineers and operational teams who want to deploy ML systems to various environments for development, testing, and production-level serving.

vault-on-gke - Run @HashiCorp Vault on Google Kubernetes Engine (GKE) with Terraform

  •    HCL

This tutorial walks through provisioning a highly-available HashiCorp Vault cluster on Google Kubernetes Engine using HashiCorp Terraform as the provisioning tool. This tutorial is based on Kelsey Hightower's Vault on Google Kubernetes Engine, but focuses on codifying the steps in Terraform instead of teaching you them individually. If you would like to know how to provision HashiCorp Vault on Kuberenetes step-by-step (aka "the hard way"), please follow Kelsey's repository instead.

kubernetes-100days - Notes from 100 days with Kubernetes


Notes from the 100 days with Kubernetes — a container orchestration platform (for Apache Spark and Apache Kafka). I've long been thinking about what is my way of learning in a more effective way. I found taking notes in public (mainly on gitbook) very effective to keep the learning pace, and wanted to explore other tools.

CloudComputing - Sample programs for Cloud Computing course at UT Austin

  •    Python

This repository contains samples and examples demonstrating cloud resource provisioning and usage for Amazon AWS and Google Cloud Platform (GCP).

kubernetes-series - kubernetes series code

  •    Javascript

Here is a variety of folders for the various blog posts that they are attached to all around Kubernetes. Feel free to play and make comments. You can find the first post here.

terraforming-gke - Generate Terraform HCL files from existng GKE resources

  •    Ruby

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment. To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

caastle - Full-stack microservices deployment for Google Kubernetes Engine and Amazon Elastic Container Service

  •    Python

CaaStle is a full-stack microservices deployment tool for Google Kubernetes Engine (GKE) and Amazon ECS. Platform of containerized applications/microservices includes application/web servers, container orchestration engine clusters, and application’s external resource dependencies such as managed database servers.

k8s-gke-service-account-assigner - Provides different Google Service Accounts and Scopes for pods running on Kubernetes

  •    Go

Provides Google Service Account Tokens to containers running inside a kubernetes cluster based on annotations. Service accounts are attached to instances and are accessible by services through the transparent usage by the google-cloud-sdk of the Google instance metadata API. When using the google-cloud-sdk, a call is made to the Google instance metadata API which provides temporary credentials that are then used to make calls to the Google service.