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A runtime in Gradient Notebooks is the combination of a workspace or repo and a container. Runtimes are used to create environments that are exactly reproducible by others.

Introduction to runtimes in notebooks

In Gradient Notebooks, a runtime is defined by its container and workspace.

A workspace is the set of files managed by the Gradient Notebooks IDE while a container is the DockerHub or NVIDIA Container Registry image installed by Gradient.

A runtime does not specify a particular machine or instance type. One benefit of Gradient Notebooks is that runtimes can be swapped across a wide variety of instances.

Gradient Notebooks offers a list of Recommended runtimes. We can also select the All option to view a more expansive list of runtimes that work out of the box.

When creating a new notebook, Gradient offers a list of runtimes to select sorted by Recommended and All. We can use these runtime tiles or we can create our own runtimes.

How to use a runtime provided by Gradient

To explore the details of a particular runtime provided by Gradient, expand the Advanced Options section of the Create a notebook flow.

In this example, we will explore the PyTorch 1.10 runtime tile:

A runtime combines a specific workspace with a specific container. We can adjust either property in the Advanced options tab when creating a new notebook.

The PyTorch 1.10 tile consists of the following:

  • Workspace URL -
  • Container Name -

The Workspace URL parameter tells Gradient which files to load into the notebook file manager.

The Container Name parameter tells Gradient where to fetch the container image that will be used to build the notebook.

When we select Start Notebook, Gradient imports the file(s) located in the Workspace URL directory into the file manager and then imports and runs the container image specified defined in the Container Name field.

In this example, our new PyTorch 1.10 notebook contains the files from running on the container from

The top box highlights the files in the file manager while the bottom box highlights the container.

We can confirm this fact by noticing that our file manager is populated with the contents from the GitHub repository (in this case a single file) and that the bottom bar is displaying the name of the container image as expected.

List of base runtimes

Paperspace maintains a number of runtimes that make it easy to get started with preloaded notebook files and dependencies. These runtimes are provided as tiles when creating a new notebook in Gradient.

A number of runtime tiles are provided when creating a new notebook. Tiles are sortable by Recommended or All using the blue sorting button.

The following is a list of recommended runtimes that Paperspace maintains:

NameDescriptionContainer NameContainer RegistryWorkspace URL
PyTorch 1.12Latest PyTorch release (1.12) with GPU support.paperspace/gradient-base:pt112-tf29-jax0314-py39-20220803DockerHub
TensorFlow 2.9.1TensorFlow 2 with GPU support.paperspace/gradient-base:pt112-tf29-jax0314-py39-20220803DockerHub
Start from ScratchInclude libraries from machine learning from the pytorch, tensorflow and jax communities.paperspace/gradient-base:tf29-pt112-py39-2022-06-29DockerHubN/A
Paperspace + Paperspace's template is built for getting up and running with the enormously popular online MOOC.paperspace/fastai:2.0-fastbook-2022-06-29DockerHub
NVIDIA RAPIDSNVIDIA's library to execute end-to-end data science and analytics pipelines on GPU.rapidsai/rapidsai:22.06-cuda11.0-runtime-ubuntu18.04-py3.8NVIDIA
ClipIt-PixelDrawA creative library for generating pixel art from simple text prompts.paperspace/clip-pixeldraw:jupyterDockerHub
Hugging Face Transformers + NLPA state-of-the-art NLP library from Hugging Facepaperspace/gradient-base:pt112-tf29-jax0314-py39-20220803DockerHub
DALL-E MiniAn impressive image generation model that runs on JAX.paperspace/gradient-base:pt112-tf29-jax0314-py39-20220803DockerHub
Hugging Face Optimum on IPURun through various Hugging Face tutorials optimized for Graphcore IPUs.graphcore/pytorch-jupyter:2.6.0-ubuntu-20.04-20220804DockerHub
PyTorch on IPURun through various PyTorch tutorials optimized for Graphcore IPUs.graphcore/pytorch-jupyter:2.6.0-ubuntu-20.04-20220804DockerHub
Tensorflow 2 on IPURun through several Tensorflow 2 tutorials optimized for Graphcore IPUs.graphcore/tensorflow-jupyter:2-amd-2.6.0-ubuntu-20.04-20220804DockerHub

Previous runtime versions

These are a sampling of previous runtime verions which you can run by copying the desired runtime's values for the Container Name and Workspace URL into the Advanced Options menu when creating a Notebook. Runtimes are added and updated frequently.

NameDescriptionContainer NameContainer RegistryWorkspace URL
Python 3.9Python 3.9 with GPU support.paperspace/python-runtime:py39-2022-07-06DockerHubN/A
PyTorch 1.11Latest PyTorch release (1.8) with GPU support.paperspace/nb-pytorch:22.02-py3DockerHub
TensorFlow 2.7.0TensorFlow 2 with GPU support.paperspace/nb-tensorflow:22.02-tf2-py3DockerHub
Paperspace + Paperspace's template is built for getting up and running with the enormously popular online MOOC.paperspace/fastai:2.0-fastbook-2022-05-09-rc3GitHub
Hugging Face TransformersA state-of-the-art NLP library from Hugging Facepaperspace/nb-transformers:4.17.0DockerHub
Analytics Vidhya CVAnalytics Vidhya containerjalfaizy/cv_docker:latestGitHubN/A
TensorFlow (1.14 GPU)Official docker images for TensorFlow version 1paperspace/dl-containers:tensorflow1140-py36-cu100-cdnn7-jupyterDockerHubN/A
JupyterLab Data Science StackJupyter Notebook Data Science Stackjupyter/datascience-notebookDockerHubN/A
JupyterLab Data R StackJupyter Notebook R Stackjupyter/r-notebookDockerHub

How to specify a custom workspace

Gradient provides the ability to import a workspace during notebook creation.

Let's say we're interested in working with GFP-GAN, an exciting library that helps us restore and upscale images. Rather than clone the files from GitHub after we create the notebook, we can instantiate the notebook with the files already there.

All we need to do is specify the GFP-GAN GitHub repo in the Workspace URL parameter of Advanced options during notebook creation.

To import a GitHub repository when creating a notebook we can include the repo URL in the Workspace URL field.

Gradient automatically copies all of the files from the GitHub repo into the notebook IDE.

The resulting notebook looks like this:

GFPGAN files have successfully imported into the notebook.

A custom workspace can be specified at the time of notebook creation only.

It's also possible to pull files from private or password-protected repositories by filling in Workspace Username and Workspace Password fields.

The Workspace Username and Workspace Password fields are useful for pulling files from private GitHub repositories.

The same logic can be applied to containers, which is demonstrated in the next section.

How to specify a custom container

A container is a disk image pre-loaded with files and dependencies. Gradient provides the ability to import an image from a container registry such as DockerHub during notebook creation.

In this example, we'll tell Gradient to pull the latest container from NVIDIA RAPIDS by specifying the Docker image in the Container Name field.

To pull the latest NVIDIA RAPIDS image, we can specify the DockerHub address in the Container Name field.

Note that the larger the container, the longer it will take for Gradient to pull it into a notebook.

How to create a custom Docker container to use with Gradient Notebooks

Paperspace recommends using Docker to get the container image from the local machine to Gradient.

jupyter must be run on port 8888 and connections from ip address must be allowed.

If running jupyter notebook, the following flags must be included in the Command field to support the Gradient Notebooks IDE:

--no-browser --NotebookApp.trust_xheaders=True --NotebookApp.disable_check_xsrf=False --NotebookApp.allow_remote_access=True --NotebookApp.allow_origin='*'

If running jupyter lab, these flags should be used in the Command field instead:

--no-browser --LabApp.trust_xheaders=True --LabApp.disable_check_xsrf=False --LabApp.allow_remote_access=True --LabApp.allow_origin='*'

Custom container reference

The following fields are available in the Container section of Advanced options when creating a new Gradient Notebook:

The Container section of Advanced options in the Create a notebook flow in Gradient.

Container NametruePath and tags of image from DockerHub or NVIDIA Container Registry. E.g. ufoym/deepo:all-jupyter-py36
Registry UsernamefalsePrivate container registry username. Can be left blank for public images.
Registry PasswordfalsePrivate container registry password. Can be left blank for public images. Secrets may be used in this field using the substitution syntax secret:
CommandfalseMust be Jupyter compatible. If left blank, defaults to jupyter notebook --allow-root --ip= --no-browser --NotebookApp.trust_xheaders=True --NotebookApp.disable_check_xsrf=False --NotebookApp.allow_remote_access=True --NotebookApp.allow_origin='*'
Container UserfalseOptional user. Defaults to 'root' if left blank.


When using a container from Nvidia, make sure to use a username ($oauthtoken) and password (API key) when submitting the custom container reference. You can create a Nvidia API key here. You will need to be logged in to create an API key.