The Gradient Jobs are designed for executing code (such as training a deep neural network) on a cluster of GPUs without managing any infrastructure.

Gradient Jobs part of a larger suite of tools that work seamlessly with Gradient Notebooks.

A Job consists of:

  1. a collection of files (code, resources, etc) from your local computer or GitHub

  2. a container (with code dependencies and packages pre-installed)

  3. a command to execute (i.e. python main.py or nvidia-smi)

Running a Job in Gradient

There are several ways to run a Job in Gradient: You can use the Job Builder, a web interface for submitting Jobs, the Paperspace CLI, or you can clone a Public Job.

Optional Job features

There are many features you will want to check out like outputting your model to the /artifacts directory, the persistent data layer at /storage, graphing with Job Metrics, sharing Jobs with the Public Jobs feature, and opening ports eg for accessing TensorBoard.

Compute Types

Jobs can be chosen to run on a variety of hardware. Pricing and details for all available options can be found here.