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Gradient CLI

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Gradient is an an end-to-end MLOps platform that enables individuals and organizations to quickly develop, train, and deploy Deep Learning models. The Gradient software stack runs on any infrastructure e.g. AWS, GCP, on-premise and low-cost Paperspace GPUs. Leverage automatic versioning, distributed training, built-in graphs & metrics, hyperparameter search, GradientCI, 1-click Jupyter Notebooks, our Python SDK, and more.

Key components:

  • Notebooks: 1-click Jupyter Notebooks.
  • Workflows: Train models at scale with composable actions.
  • Inference: Deploy models as API endpoints.

Gradient supports any ML/DL framework (TensorFlow, PyTorch, XGBoost, etc).

See for details on the current release, as well as release history.

Getting Started

  1. Make sure you have a Paperspace account set up. Go to to register and generate an API key.

  2. Use pip, pipenv, or conda to install the gradient package, e.g.:

    pip install -U gradient

    To install/update prerelease (Alpha/Beta) version version of gradient, use:

    pip install -U --pre gradient

  3. Set your api key by executing the following:

    gradient apiKey <your-api-key-here>

    Note: your api key is cached in ~/.paperspace/config.json

    You can remove your cached api key by executing:

    gradient logout

Executing tasks on Gradient

The Gradient CLI follows a standard [command] [--options] syntax

For example, to create a new Deployment use:

gradient workflows create [type] [--options]

For a full list of available commands run gradient workflows --help. You can also view more info about Workflows in the docs.


Want to contribute? Contact us at

Pre-Release Testing

Have a Paperspace QA tester install your change directly from the branch to test it. They can do it with pip install git+