Gradient CLI

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.
  • Experiments: Run experiments from a web interface, CLI, SDK, or GradientCI bot.
  • Models: Store, analyze, and version models.
  • Inference: Deploy models as API endpoints.

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

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 experiment use:

gradient experiments create [type] [--options]

The two available experiment types are singlenode and multinode. Various command options include setting the instance type, container, project, etc. Note that some options are required to create new experiment.

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