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.
- 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 releasenotes.md for details on the current release, as well as release history.
Make sure you have a Paperspace account set up. Go to http://paperspace.com to register and generate an API key.
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
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:
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 email@example.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.