Note We are rolling out a new streamlined Paperspace CLI and recommend using this new CLI for all new projects.

Get started: Create Account • Install CLI • Tutorials • Docs

Resources: Website • Blog • Support • Contact Sales

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.
• Inference: Deploy models as API endpoints.

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

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

1. Make sure you have a Paperspace account set up. Go to http://paperspace.com 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

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

For example, to create a new Workflow in a project use:

gradient projects list
gradient workflows create --name <name> --projectId <project-id>

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 hello@paperspace.com

Have a Paperspace QA tester install your change directly from the branch to test it. They can do it with pip install git+https://github.com/Paperspace/gradient-cli.git@MYBRANCH.