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.
Create a Dockerfile Host on GitHub or a local file. Example on GitHub Example: https://github.com/Paperspace/tf-jupyter-dockerfile/blob/master/Dockerfile
Run a Job to build the container from the Dockerfile and publish to a container registry Example:
paperspace jobs create \--apiKey XXXXXXXXXXXXXXXXXXx \--workspace /path/to/repo \--useDockerfile true \--buildOnly true \--registryTarget my-registry/name:tag \--registryTargetUsername my-username \--registryTargetPassword XXXXXXXXXXXXX
From that machine, you'll need to be logged into your Docker Hub account
docker login -u <username> -p <password>
You can make your own file (see Requirements below) or use one like this example: https://github.com/Paperspace/tensorflow-python
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
docker push <username>/test-container:latest
Jupyter and all of Jupyter dependencies must be installed:
conda install -c conda-forge jupyterlab
If you don't specify a user, your container user will be 'root'
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!
Click the advanced options toggle on the notebook create a notebook page.
Follow the rest of the steps here to create your Notebook by selecting your machine type, naming your notebook, and clicking Create.