Quick Start


To begin using Gradient, follow these preliminary steps:

  1. Optional: Create a team to invite collaborators

Now you can create Notebooks, Workflows, Deployments, and more!

Logging in for the first time

When you first log into the Paperspace Console, you can select Gradient from the product dropdown:

First Create a Project

Projects help organize your work. To get started, just choose a name and click create.

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.

Create a Workflow

You can automate machine learning tasks using Workflows. You can define a workflow once and use it repeatedly to perform simple or complex MLOps activities, such as pre-processing data, training models, and creating or updating deployments.

Follow the instructions on the page to install the Gradient Github App into your Github account, and select the git repo you want to associate with project. Alternatively you can select one of the sample repos.

If you choose to use your own git repo, you will be prompted to add a YAML file to your repo that defines the Workflow steps.

Run a Workflow from the CLI (advanced)

  1. Create a Workflow

    This step is only needed if you didn't already create a default demo workflow in the web interface. Specify a name for the Workflow and a projectId. Use the projectId from the project you created earlier.

    gradient workflows create \
    --name <your-workflow-name> \
    --projectId <your-project-id>

    To see a list of your projects and projectIds run gradient projects list. For a list of Workflows within a project run gradient workflows list --projectId <your-project-id>.

  2. Download or copy the sample Workflow Spec to your computer

    Here is the Workflow Spec for reference:

    instance-type: C5
    type: volume
    url: https://github.com/NVlabs/stylegan2.git
    instance-type: P4000
    - CloneRepo
    repo: CloneRepo.outputs.repo
    type: dataset
    ref: demo-dataset
    script: |-
    pip install scipy==1.3.3
    pip install requests==2.22.0
    pip install Pillow==6.2.1
    cp -R /inputs/repo /stylegan2
    cd /stylegan2
    python run_generator.py generate-images \
    --network=gdrive:networks/stylegan2-ffhq-config-f.pkl \
    --seeds=6600-6605 \
    --truncation-psi=0.5 \
    image: tensorflow/tensorflow:1.14.0-gpu-py3

    Place the contents in a file named workflow.yaml.

  3. Run the Workflow from the CLI

    The following command will run an instance of the Workflow in your project. Be sure to replace <your-workflow-id> with your Workflow ID.

    gradient workflows run \
    --id <your-workflow-id> \
    --path ./workflow.yaml

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

Behind the scenes, your Workflow will be executed on the Gradient public cluster. Congratulations! You ran your first Workflow on Gradient πŸš€

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.