# Building a Custom Container

## About

The Custom Containers feature lets you pull your own image from a container registry eg Docker Hub. This article will help you prepare a custom Docker container and show you how to bring that Container into Gradient by creating either a Notebook or an Experiment with your custom container. We recommend using Docker to get the container image from your system to Gradient. 

{% hint style="info" %}
**ProTip!** Using a Custom Container does not require building one from scratch.  See this [article](https://paperspace.gitbook.io/gradient/master/notebooks/create-a-notebook/notebook-containers) for using one of the many freely available and up-to-date containers hosted on various container registries (eg [Docker Hub](https://hub.docker.com/), [NGC](https://ngc.nvidia.com/catalog/landing) etc.).
{% endhint %}

## Build a Custom Container with Gradient

1. **Create a Dockerfile**\
   Host on GitHub or a local file. Example on GitHub\
   Example: <https://github.com/Paperspace/tf-jupyter-dockerfile/blob/master/Dockerfile><br>
2. **Run a Job to build the container from the Dockerfile and publish to a container registry**\
   Example:&#x20;

```bash
paperspace jobs create \
--apiKey XXXXXXXXXXXXXXXXXXx \
--workspace /path/to/repo \
--useDockerfile true \
--buildOnly true \
--registryTarget my-registry/name:tag \
--registryTargetUsername my-username \
--registryTargetPassword XXXXXXXXXXXXX
```

## Build a Custom Container Locally

#### 1. To get started, you’ll need:

* An Ubuntu computer with [DockerCE](https://github.com/docker/docker-ce), [NVIDIA-docker](https://github.com/NVIDIA/nvidia-docker), and NVIDIA Drivers installed (if you don’t have a Linux machine, use a Paperspace Linux VM!).
* From that machine, you'll need to be logged into your [Docker Hub](https://hub.docker.com/) account \
  `docker login -u <username> -p <password>`

#### 2. Add a Docker file to a working directory on your system

You can make your own file (see Requirements below) or use one like this example: <https://github.com/Paperspace/tensorflow-python>

#### 3. In the same directory:

* Run: `docker build -t <name of image>` For the example file above, you would enter: `docker build -t test-container`
* Tag the image so that it can be added to a repo with the image id, your Docker Hub username, and a name for the image :

`docker tag <image id> <dockerhub username>/test-container:latest`

#### 4. Push the image to Docker Hub with your username:

`docker push <username>/test-container:latest`

### &#x20;Requirements of Custom Notebooks

* Python
* Jupyter and all of Jupyter dependencies must be installed:

[        http://jupyter.org/install](http://jupyter.org/install)

`conda install -c conda-forge jupyterlab`

If you don't specify a user, your container user will be 'root'&#x20;

## Bringing your Custom Container to Gradient

After you've pushed your custom container to Docker or you found a public container that is already there, it's time to pull it into Gradient!

### Notebooks &#x20;

Click the advanced options toggle on the notebook create a notebook page.&#x20;

Follow the rest of the steps [here](https://paperspace.gitbook.io/gradient/master/notebooks/create-a-notebook/notebook-containers/..#custom-containers) to create your Notebook by selecting your machine type, naming your notebook, and clicking Create.&#x20;