Overview
For multi-node Jobs such as distributed training and hyperparameter tuning.
Last updated
For multi-node Jobs such as distributed training and hyperparameter tuning.
Last updated
Experiments have been replaced by a more general framework called Workflows. For the latest info go to Workflows.
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.
Experiments can be run from the Experiment Builder web interface, the 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 and the CLI (command-line interface) is a more advanced tool for launching Experiments. Both options are powerful, flexible, and easy-to-use.
Using Experiments****
GradientCI enables you to submit Experiments directly from a GitHub commit (or branch). You can launch Experiments without ever leaving your code.
GradientCIThe SDK lets you programmatically interact with the Gradient platform. The SDK can be incorporated into any python project and enables more advanced ML pipelines.
Gradient SDK OverviewIn your your CLI command orconfig.yaml, specify the experiment type as singlenode
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:
or
See this article for more info.
Persistent storage is a persistent filesystem located at /storage which is 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 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.
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.
Gradient provides the ability to mount S3 compatible object storage buckets to an experiment at runtime. Learn more here.
Versioned DatasetsThere 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.
Jobs can be chosen to run on a variety of hardware. Pricing and details for all available options can be found here.
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:
State | Name |
| Created |
| Pending |
| Provisioning |
| Provisioned |
| Network Setup |
| Running |
| Network Teardown |
| Stopped (aka Success) |
| Failed |
| Cancelled |
| Error |
