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.
- 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).
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 experiment use:
gradient experiments create [type] [--options]
The two available experiment types are
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.