Overview
Last updated
Last updated
This section of the documentation covers our previous generation of Gradient. For the current version go to Gradient Next.
Gradient notebooks are an interactive environment (based on Jupyter Notebook or Jupyter Lab) for developing and running code. You can run Jupyter notebooks on a GPU, CPU, or even a TPU.
A Gradient **Notebook gives you access to a full Jupyter Notebook environment. Within the Notebook, you can store an unlimited number of documents and other files. You can think of a Gradient Notebook as your persistent, on-demand workspace in the cloud.
NEW! Visit the new ML Showcase for a list of sample projects you can fork into your own account.
Any data stored in /storage will be preserved for you across restarts. Persistent storage is backed by a filesystem and is ideal for storing data like images, datasets, model checkpoints etc. Learn more about persistent storage here.
Because everything is running in a Docker container behind the scenes, we support any kernel you would like. We have a handful of pre-built containers and you can easily add a custom container or build one from a base template, such as the Jupyter R stack.
View the list of pre-built containers here.
There are a number of environment variables loaded into a notebook's environment, which you can access and use. Probably most common is is PS_API_KEY , which will contain your most recently created API key (if you've created one). In combination with the Gradient SDK, this allows you to programmatically interact with Gradient.
