Gradient notebooks are an interactive environment for developing and running code. Jupyter notebooks (
.ipynb files) are supported natively. You can also treat a Notebook as a full IDE and both write and execute code written in other languages such as Python. You can run Notebook on CPU or GPU instances.
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.
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.
Notebooks run within Docker containers behind the scenes. Gradient includes a handful of pre-built containers and you can easily use a custom container as well. View the list of pre-built containers here.
You can easily generate a link to share your Notebook with friends and colleagues or the general public. Public Notebooks can be forked by others into their own account. To learn more about how Notebooks work, you can fork a public demo Notebook here. The ML Showcase includes several working examples of projects you can run with a couple clicks (project submissions welcome!)
View a quick tutorial on creating a notebook here.