> For the complete documentation index, see [llms.txt](https://machine-learning.paperspace.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://machine-learning.paperspace.com/wiki/containers.md).

# Containers

![](/files/-LvOG4k3uFKDVoAjohIO)

Containers are lightweight, secure, and portable environments designed for developing, testing, and hosting software applications.  Containers run on top of VMs or bare-metal servers and were pioneered by  a company called [Docker](https://www.docker.com/resources/what-container).  They have recently become popular in the ML industry.

Container **orchestration** is dominated by Kubernetes, an open source system for automating and scaling application deployment. 

Container **registries** are where containers are hosted.  Examples include DockerHub and Amazon ECR.

A Docker file is a set of instructions (software packages to include) in a Docker image.  Deploying applications using Docker containers is straightforward.

* Build: Create an image from a Docker file
* Push: Save image in a registry
* Run: Application can be run anywhere
* Pull: Download containers from a registry

Docker tags specify a version e.g. `username/image_name:tag_name` 

## Containers + Gradient

Gradient can run any Docker container (for Notebooks, Experiments, Jobs, and Deployments) hosted on a public or private container registry.

Gradient maintains a list of [containers](https://docs.paperspace.com/gradient/notebooks/notebook-containers) pre-loaded with various up-to-date ML frameworks, libraries, drivers, and more.

Gradient also includes a way to build containers from a Dockerfile without any expertise in Docker itself. 

### Related Material

{% embed url="<https://docs.paperspace.com/gradient/notebooks/notebook-containers/building-a-custom-container>" %}