Quick Start


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.

Logging into the Paperspace Console for the first time

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.

Create a Notebook

Notebooks can be created on the Notebooks tab. Just select a template, choose your instance type, and then click create.

Check out the FREE GPU option when launching Notebooks!

Select Notebooks > Create a Notebook to enter the notebook create flow

Check out the ML Showcase for a list of projects you can fork into your own account

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.

Learn how to create a Jupyter Notebook. 1m48s.

Advanced MLOps

Create a cluster

Gradient Private Clusters allow you to execute Experiments, Jobs, Deployments, and other workloads. To create a cluster, navigate to the clusters page and select Create Managed Cluster.

Create a managed cluster in the Clusters tab

Create a Project

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.

Select Projects > Create a Project to initiate a new machine learning project

Running your first Experiment

You can run Experiments from the web interface or CLI:

Before creating an experiment using the CLI, you must first install the CLI.

Example command

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.

Note: We recommend stashing your API key with gradient apiKey XXXXXXXXXXXXX or you can add your API key as an option on each Experiment. See Connecting Your Account.

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.

You can also create multi-node and leverage other advanced functionality such as Tensorboards and Metrics. Explore all the advanced options here.

Monitor your Experiment progress

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 🚀

Explore the rest of the platform

From Models to Deployments, there's a lot more to the Gradient platform. We recommend using the Web UI to explore the primary components and also be sure to install the CLI and check out the SDK.

Here's a sample project that exercises most of the components of the platform: