Experiments are used to train machine learning models. Gradient supports single-node experiments as well as distributed training through multi-node experiments. Gradient also supports Hyperparameter Search.

Creating Experiments

Experiments can be run from the Experiment Builder web interface, our CLI, the GradientCI bot, or the SDK. Here is a quick overview and instructions for each option:

The web UI 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.

Experiment Modes

Single-node

In your your CLI command orconfig.yaml, specify the experiment type as singlenode

Multi-node

Gradient supports both gRPC and MPI protocols for distributed TensorFlow model training. In your CLI command orconfig.yaml, specify the experiment type as either multinode. The two types are:

type: "multi-grpc"

or

type: "multi-mpi"

Hyperparamater Tuning

See this article for more info.

Storage

Persistent Storage

Persistent storage is a persistent filesystem automatically mounted on every Experiment, Job, and Notebook and is ideal for storing data like images, datasets, model checkpoints, and more. Learn more here.

Artifact Storage

Artifact storage is collected and made available after the Experiment or Job run in the CLI and web interface. You can download any files that your job has placed in the /artifacts directory from the CLI or UI. If you need to get result data from a job run out of Gradient, use the Artifacts directory. Learn more here.

Workspace Storage

The Workspace storage is typically imported from the local directory in which you started your job. The contents of that directory are zipped up and uploaded to the container in which your job runs. The Workspace exists for the duration of the job run. If you need to push code up to Gradient and run it, using the Workspace storage is the way to do it. Learn more here.

Private Datasets

Gradient provides the ability to mount S3 compatible object storage buckets to an experiment at runtime. Learn more here.

Optional Experiment features

There are many features you will want to check out like outputting your models, the persistent data layer at /storage, graphing with Experiment Metrics, sharing underling Jobs with the Public Jobs feature, sourcing your code from private GitHub repositories, and opening ports.

Instance Types

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

State Transitions

An experiment goes through a number of "states" between being submitted to Gradient (through the CLI, SDK, the GradientCI GitHub App, or Job Builder Web UI). These states are enumerated below: