Metadata-Version: 2.1
Name: ec2-cluster
Version: 0.3.3rc2
Summary: A tool for launching and running commands on multiple EC2 instances
Home-page: https://github.com/armandmcqueen/ec2-cluster
Author: Armand McQueen
Author-email: armandmcqueen@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Requires-Dist: paramiko (==2.5.1)
Requires-Dist: fabric2
Requires-Dist: boto3
Requires-Dist: pyyaml
Requires-Dist: tabulate

# ec2-cluster

Simple library and CLI to work with clusters of EC2 instances. Multi-purpose, but created to make deep learning distributed training infrastructure easier. 

`ec2-cluster` is designed for simple distributed tasks where Kubernetes is overkill or where fast cluster spin up/down is crucial. It provides the ability to launch a cluster, to retrieve IP addresses for all nodes in the cluster, to delete the cluster and to execute commands on some or all of the instances. 

Other benefits:
- Resilient to EC2 capacity limits. If instances are not available, `ec2-cluster` will retry until the all nodes in the cluster are created or until the user-set timeout is reached.
- Easy to quickly launch duplicate clusters for parallel training runs.
- Can write orchestration logic that needs to be run when launching a cluster, e.g. enabling passwordless ssh between all instances for Horovod-based training

## Examples

### Library

### CLI

## Goals

- Provide the minimal set of features to run distributed deep learning training jobs on EC2 instances.
- Provide libraries, not a framework or platform.
- Make cluster environments reproducible to allow for parallelization of experiments
- Make cluster launches fast
- Be resilient to EC2 capacity limitations
- Encourage ephemeral infrastructure design
- Focus on iterative, not disruptive, improvements on the common methodology of manually launching EC2 instances, ssh-ing to them, configuring environments by hand and running scripts

### Overview

`ec2-cluster` can be consumed in two ways:

- A CLI for launching, describing and deleting clusters. 
- A python library for scripting.  

This library has three main components:L
- **infra**: creating cluster infrastructure
- **orch**: orchestrating simple runtime cluster configuration (e.g. generate a hostfile with runtime IPs)
- **control**: running commands on the cluster

### CLI Quick Start

### Library Quick Start







