madcore 1.9.11

Madcore Core CLI - Deep Learning & Machine Intelligence Infrastructure Controller

What is Madcore?

Madcore is a CLI tool for deployment and auto-configuration of data mining and analytics microservices. It is a Kubernetes-based unmodified KOPS/Minikube installation manager. However, single point of truth is preserved as a unified yaml file called “clusterfile”. Clusterfile controls generic aspects of provisioning, deployment, scale and configuration. All KOPS and Kubernetes templates are then populated from input clusterfile.

Install

Mac & Linux install form terminal.

pip install madcore

Minikube Environment Prerequisites

• Virtual Box

• Minikube 1.9

• Local PC 16GB of Ram (minikube is set at 8GB by default)

AWS Environment Prerequisites

• VPC in AWS (you will need id)

• Internet Gateway attached to VPC

• S3 Storage bucket for KOPS settings

Provision Locally on Minikube or in AWS Cloud

Currently Madcore is tested on Mac and Linux only. We are working on exposing clusterfiles and templates in a better way. By default they install with the python project files location similar to this lib/python2.7/site-packages/madcore

pip install madcore

Data Mining & Deep Learning Ecosystem

Functionality is grouped into instance groups (physically) and into namespaces (logically). Each software deployed here belongs to their respective owners. We do not interfere in containers but make sure that we find best containers for deployment in Kubernetes.

Goal of Madcore is to abstract deployment and configuration of data processing elements and have it available in working state out-of-the-box. This way anyone can start work on their actual problem and not spend time on deployment and configuration of common toolsets.

Deploy Core

Installation of core elements is a single command. Filenames in range of 100-200. You can comment out any of those installs. By commenting corresponding lines in your aws clusterfile. Registry and metrics elements are optional. You probably want to leave dashboard and ingress setup as everything else maps to it.

madcore --install-core

Core Stack	Description
dashboard	Kubernetes Dashboard
nfs	NFS 4.1 for utilized for Kubernetes persistent volume claims (StatefulSets)
registry2	(optional) docker registry v2
influxdb	InfluxDB for Heapster data
heapster	Kubernetes metrics collector
grafana	Grafana Dashboard pointed at InfluxDB for kube metrics
haproxy-ingress	HAProxy ingress (route external traffic and map to kube services)
ingress-default	default container reporting 404 when hitting anything but mapped endpoints
ingress echo	echo container to test ingress alive

Deploy neo4j

Neo4j and Dashboard is in the template file space of 9220-9229. Deploy using command below. Few second later you will have a working dashboard and single pod engine configuration ready to start your tests. Thi deployment is installed onto standard nodes instancegroup. This deployment lives its own neo4j namespace. It’s easy to remove it when you don’t require it anymore. It using standard neo4j:3.1.4-enterprise containers from docker hub maintainer by neo4j team. It is exposed through ingress and mapped through its own subodmain neo4j.<yourdomain.com>

madcore --install-neo4j

Neo4J Stack	Description
engine	Enterprise: neo4j:3.1.4-enterprise (subject to EULA)
ui	Dashboard

Deploy kafka

Kafka and Dashboard is in the template file space of 9240-9249. Deploy using command below. Few second later you will have a working dashboard and single pod engine configuration ready to start your tests. Thi deployment is installed onto standard nodes instancegroup. This deployment lives its own kafka namespace. It’s easy to remove it when you don’t require it anymore. It is exposed through ingress and mapped through its own subodmain kafka.<yourdomain.com> for Yahoo kafka dashboard and kafka.<yourdomain.com>/rest for Mailgun Pixy rest ui (grpc is listening internally but not exposed outside)

madcore --install-kafka

Kafka Stack	Containers
zookeeper	solsson/kafka:1.0.1
kafka	solsson/kafka:1.0.1
kafka-manager	solsson/kafka-manager
kafka-pixy	mailgun/kafka-pixy

Deploy Elasticsearch / FluentD / Kibana

Famous trio optimized for Kubernetes. Elasticsearch exposed through ingress as well as Kibana. Internally FluentD DaemonSets are deployed to ALL nodes and collect all logs from pods stdout along with kubernetes logs and aggregate in ElasticSearch. Deploy this when you have a need. There is a dedicated instance group for ELK so it doesn’t collide with any of your other applications.

madcore --install-elk

Kafka Stack	Containers
elasticsearch	docker.elastic.co/elasticsearch/elasticsearch-oss:6.0.0
fluentd	fluent/fluentd-kubernetes-daemonset:v0.12.33-elasticsearch
kibana	docker.elastic.co/kibana/kibana-oss:6.0.0

Chat with us on Gitter

If you want to try Madcore, make sure you join us on Gitter. We are now focused on building Machine Learning and Ai plugins as well as building Ingress listeners for social media and queueing mechanisms in Spark and Kafka. All based on Kubernetes. Chat with us now: https://gitter.im/madcore-ai/core

Mailing List

Visit https://madcore.ai to sign up for weekly newsletter on Machine Learning and AI simulations that are now possible with Madcore

Credits

We will be adding a formal Credits file into this project. For now just want to make clear that all registered brands/products remain property of their respective owners.

License

Madcore Project is distributed on MIT License (c) 2016-2017 Madcore Ltd (London, UK) https://madcore.ai