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Welcome to Gradient

Gradient is a platform for building and scaling real-world machine learning applications.

Getting Started

Gradient is a Paperspace product geared toward machine learning developers at any stage of the ML development cycle.

The fastest way to get started with Gradient is to follow the tutorial for one of the three Gradient entrypoints.

Notebooks Tutorial
Learn how to create a cloud notebook backed by powerful GPUs and bring along your own code or containers. Best for those who want to do analysis, exploration, or prototyping in an easy-to-configure notebook environment.
Workflows Tutorial
Learn how to create automated pipelines by describing ML tasks in YAML. Best for those interested in MLOps best practices and building production-grade systems.
Deployments Tutorial
Learn how to deploy a model to production and query the endpoint from another application. Best for those looking to serve a model into production as fast as possible.

Gradient Platform

If you're already familiar with Notebooks, Workflows, and Deployments, you might want to dive deeper into how they work.

These resources are filled with specific how-to and reference information for each Gradient Platform primative.

Gradient Notebooks is a web-based Jupyter IDE with free GPUs.
Gradient Workflows provides a simple way to automate machine learning tasks.
Gradient Deployments help you perform effortless model serving.

Gradient Resources

This section of materials deals with the resources that are available across all three Gradient products. Use these guides for practical information on how to how to manage containers, machines, data, models, metrics, and code.

Gradient makes it easy to pull a Docker container for use in a notebook, workflow, or deployment.
Paperspace makes all the best CPU and GPU machines available to Gradient users.
Gradient comes pre-loaded with a few useful datasets and makes it easy to bring your own data.
As you start training models in Gradient, Models helps you manage model training artifacts.
Gradient makes it easy to view performance info about your machines whether you're running a notebook, workflow, or deployment.
Keep your private keys secure in Gradient with Secrets.
GitHub App
Use code commits to a git repo to kickoff training runs.

Gradient Cluster

Gradient clusters are private clusters that run machine learning workloads. Gradient clusters can be created on Paperspace Cloud, on any other cloud provider (AWS, GCP, Azure), or on your own servers via the Gradient Installer.

Gradient Cluster
Start here to learn about Gradient private clusters.


The Gradient CLI & SDK lets you programmatically interact with the Gradient platform.

Learn to use the Gradient CLI with these resources.

Other Resources

Aside from this documentation, some other resources are

  • Check out the ML Showcase for a curated list of interactive ML sample projects.
  • View the Gradient release notes and subscribe to product updates.
  • Please visit the Community to view and post questions.