To begin using Gradient, follow these preliminary steps:
Now you can create Notebooks, Jobs, Projects, Experiments, Models, Deployments, and more!
Note: if you are a Gradient Private Cloud user, please visit the Gradient Private Cloud section for more info on how to utilize private processing site clusters.
When you first log into the Paperspace Console, you'll choose Gradient or Core, depending on whether you want to perform machine learning or to use cloud infrastructure directly.
You can always switch products later by clicking the Product Selector at the top-left of the Console and then selecting Gradient, Core, or your Paperspace Teams & Account settings.
You can stop, start, fork, and swap out the instance type anytime. Choose from a wide selection of pre-configured templates or bring your own.
Projects organize your work. To create a Project, navigate to Gradient > Projects in the UI and click Create Project. Then select Create Standalone Project and provide a project name. Now, you can use the created Project's Project ID in order to create Experiments in that Project via the CLI.
You can run Experiments from the web interface or CLI:
The following command will work and will create and start an Experiment that will display properties of the attached GPU. Be sure to replace
<your-project-id> with your Project ID and
<your-cluster-id> with your Cluster ID.
gradient experiments run singlenode --projectId <your-project-id> --clusterId <your-cluster-id> --container 'Test-Container' --machineType P4000 --command 'nvidia-smi' --name 'test-01' --workspace none --apiKey <your-api-key>
Behind the scenes, your Experiment will be uploaded and executed on your cluster starting with the command you provided. There are several optional Experiment parameters, such as to specify your workspace (the additional files to be used in your experiment). You can always use the
--help option after any command in the CLI for more info.
Experiments states transition from Queued > Pending > Running. Once the Experiment is in the Running state, you can watch your Experiment run in the CLI and web UI. An Experiment can complete with the following states: Success, Cancelled, Error, or Failed.
Congratulations! You ran your first Experiment on Gradient 🚀
Here's a sample project that exercises most of the components of the platform: