The Custom Containers feature lets you pull your own image from a container registry e.g., Docker Hub. This section will help you prepare a custom Docker container and show you how to bring that Container into Gradient by creating either a Notebook, Workflow, or Deployment with your custom container.
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's 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 Hub, NGC, etc., 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.
Just specify the path of the container e.g.,
paperspace/gradient-sdk from within a Workflow step using
image. Learn more here.
On the Choose Container step, navigate to the custom container tab and fill out the form. Note: A username and password must be provided for private Docker images.