# deepops

**Repository Path**: h-zzz/deepops

## Basic Information

- **Project Name**: deepops
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-15
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

DeepOps
===

GPU infrastructure and automation tools

## Overview

The DeepOps project encapsulates best practices in the deployment of GPU server clusters and sharing single powerful nodes (such as [NVIDIA DGX Systems](https://www.nvidia.com/en-us/data-center/dgx-systems/)). DeepOps can also be adapted or used in a modular fashion to match site-specific cluster needs. For example:

* An on-prem, air-gapped data center of NVIDIA DGX servers where DeepOps provides end-to-end capabilities to set up the entire cluster management stack
* An existing cluster running Kubernetes where DeepOps scripts are used to deploy Kubeflow and connect NFS storage
* An existing cluster that needs a resource manager / batch scheduler, where DeepOps is used to install Slurm, Kubernetes, or a hybrid of both
* A single machine where no scheduler is desired, only NVIDIA drivers, Docker, and the NVIDIA Container Runtime

Check out the [video tutorial](https://drive.google.com/file/d/1RNLQYlgJqE8JMv0np8SdEDqeCN2piavF/view) for how to use DeepOps to deploy Kubernetes and Kubeflow on a single DGX Station. This provides a good base test ground for larger deployments.

## Releases

Latest release: [DeepOps 20.06 Release](https://github.com/NVIDIA/deepops/releases/tag/20.06)

It is recommended to use the latest release branch for stable code (linked above). All development takes place on the master branch, which is generally functional but may change significantly between releases.

## Getting Started

For detailed help or guidance, read through our [Getting Started Guide](docs/getting-started.md) or pick one of the deployment options documented below.

## Deployment Options

### Supported distributions

DeepOps currently supports the following Linux distributions:

* NVIDIA DGX OS 4
* Ubuntu 18.04 LTS
* CentOS 7

### Kubernetes

Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications.

Consult the [DeepOps Kubernetes Deployment Guide](docs/kubernetes-cluster.md) for instructions on building a GPU-enabled Kubernetes cluster using DeepOps.

For more information on Kubernetes in general, refer to the [official Kubernetes docs](https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/).

### Slurm

Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters.

Consult the [DeepOps Slurm Deployment Guide](docs/slurm-cluster.md) for instructions on building a GPU-enabled Slurm cluster using DeepOps.

For more information on Slurm in general, refer to the [official Slurm docs](https://slurm.schedmd.com/overview.html).

### DGX POD Hybrid Cluster

A hybrid cluster with both Kubernetes and Slurm can also be deployed. This is recommended for [DGX POD](https://www.nvidia.com/en-us/data-center/dgx-pod-reference-architecture/) and other setups that wish to make maximal use of the cluster.

Consult the [DeepOps DGX POD Deployment Guide](docs/dgx-pod.md) for step-by-step instructions on building a GPU-enabled hybrid cluster using DeepOps.

### Virtual

To try DeepOps before deploying it on an actual cluster, a virtualized version of DeepOps may be deployed on a single node using Vagrant. This can be used for testing, adding new features, or configuring DeepOps to meet deployment-specific needs.

Consult the [Virtual DeepOps Deployment Guide](virtual/README.md) to build a GPU-enabled virtual cluster with DeepOps.

## Updating DeepOps

To update from a previous version of DeepOps to a newer release, please consult the [DeepOps Update Guide](docs/update-deepops.md).

## Copyright and License

This project is released under the [BSD 3-clause license](https://github.com/NVIDIA/deepops/blob/master/LICENSE).

## Issues

NVIDIA DGX customers should file an NVES ticket via [NVIDIA Enterprise Services](https://nvid.nvidia.com/enterpriselogin/).

Otherwise, bugs and feature requests can be made by [filing a GitHub Issue](https://github.com/NVIDIA/deepops/issues/new).

## Contributing

To contribute, please issue a [pull request](https://help.github.com/articles/using-pull-requests/) against the master branch from a local fork.

A signed copy of the [Contributor License Agreement](https://raw.githubusercontent.com/NVIDIA/deepops/master/CLA) needs to be provided to deepops [at] nvidia.com before any change can be accepted.