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 can be created using the GUI or via the CLI, and they form part of a larger suite of tools that work seamlessly with Gradient Notebooks.
a collection of files (code, resources, etc) from your local computer or GitHub
a container (with code dependencies and packages pre-installed)
a command to execute (i.e. python main.py or nvidia-smi)
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.
Jobs can be chosen to run on a variety of hardware. Pricing and details for all available options can be found here.