Experiments are intended to be used for intensive computational tasks like neural network training. Gradient supports single-node experiments as well as distributed training through multinode experiments.
Experiments can be run from the Experiment Builder web interface, our CLI, the GradientCI bot, or our new SDK. Here is a quick overview and instructions for each option:
The web interface is great for getting familiar with Experiments and running sample projects.
The CLI (command-line interface) is the most popular tool for launching Experiments. It's powerful, flexible, and easy-to-use.
The SDK let's you programmatically interact with the Gradient platform. The SDK can be incorporated into any python project and enables more advanced ML pipelines.
GradientCI enables you to submit Experiments directly from a GitHub commit (or branch). You can launch Experiments without ever leaving your code.
In your your CLI command or
config.yaml, specify the experiment type as
Gradient supports both gRPC and MPI protocols for distributed TensorFlow model training. In your CLI command or
config.yaml, specify the experiment type as either
multinode. The two types are:
An experiment goes through a number of "states" between being submitted to Gradient (through the CLI, SDK, the GradientCI GitHub App, or Job Builder GUI). These states are enumerated below:
Stopped (aka Success)