Gradient Jobs execute generic tasks on remote infrastructure and can be used to perform a variety of functions from compiling a model to running an ETL operation. When you run an Experiment within Gradient, a job is created and executed (or multiple jobs in the case of distributed training or a hyperparameter sweep).
Gradient Jobs can be created using the Web UI or via the CLI, and they form part of a larger suite of tools that work seamlessly with Gradient Notebooks and other features of the Gradient platform.
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
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 more.
Jobs have access to your persistent storage (
/storage) and can output files to /artifacts. Learn more about Gradient storage here.
Jobs can be chosen to run on a variety of hardware. Pricing and details for all available options can be found here.