Run AI, ML, and Scientific Research Code on Any Cloud or On-Prem Cluster with a Single Line
pip install covalent --upgrade
Check our Quick Start Guide for setup instructions or dive into your First Experiment. Learn more on the Concepts.
What is Covalent?
Covalent is a Python library for AI/ML engineers, developers, and researchers. It provides a straightforward approach to running compute jobs, like LLMs, generative AI, and scientific research, on various cloud platforms or on-prem clusters.
Run Code Anywhere: Execute Python functions in any cloud or on-prem cluster by changing just a single line of code.
It is as simple as swapping the decorator with our executor plugins. Choose from existing plugins or create custom ones for tailored interactions with any infrastructure.
Abstraction of Infrastructure Management: Abstract the complexities of cloud consoles, terraform, or IaC in the background.
Serverless Infrastructure: Automatically converts any infrastructure, including on-prem SLURM clusters or cloud compute, into a serverless setup.
If you find Covalent useful or interesting, feel free to give us a ⭐ on GitHub! Your support helps us to continue developing and improving this framework.
| For AI/ML Practitioners and Developers |
For Researchers |
- Robust Compute Backend: Ideal as a backend compute framework for AI/ML applications, Large Language Models (LLMs), Generative AI, and more.
- Cloud-Agnostic Execution: Execute high-compute tasks seamlessly across different cloud environments.
- Infrastructure Abstraction: Directly use computing resources while keeping your business code independent from the infrastructure/resource definitions.
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- Local-Like Access: Effortlessly connect to compute resources from your laptop, eliminating the need for SSH or complex scripts.
- Unified Interface Across Environments: Consistent experience with on-prem HPC clusters and cloud platforms like SLURM, PBS, LSF, AWS, GCP, Azure.
- Real-Time Monitoring Monitoring: User-friendly UI for real-time monitoring, enabling cost-effective and iterative R&D.
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If you find Covalent useful or interesting, feel free to give us a ⭐ on GitHub! Your support helps us to continue developing and improving this framework.
Explore Covalent Through Examples
Jump right into practical examples to see Covalent in action. These tutorials cover a range of applications, giving you a hands-on experience:
Explore Our Extensive Plugin Ecosystem
Covalent integrates seamlessly with a variety of platforms. Discover our range of plugins to enhance your Covalent experience:
Key Features at a Glance
Get a quick overview of what Covalent offers. Our infographic summarizes the main features, providing you with a snapshot of our capabilities:
Know More About Covalent
For a more in-depth description of Covalent’s features and how they work, see the Concepts page in the documentation.
Installation
Covalent is developed using Python on Linux and macOS. The easiest way to install Covalent is by using the PyPI package manager.
pip install covalent --upgrade
For other methods of installation, please check the docs.
Deployments
Covalent offers flexible deployment options, from Docker image/AMIs for self-hosting to pip package for local installations, accommodating various use cases
Contributing
To contribute to Covalent, refer to the Contribution Guidelines. We use GitHub’s issue tracking to manage known issues, bugs, and pull requests. Get started by forking the develop branch and submitting a pull request with your contributions. Improvements to the documentation, including tutorials and how-to guides, are also welcome from the community. For more information on adding tutorials, check the Tutorial Guidelines. Participation in the Covalent community is governed by the Code of Conduct.
Citation
Please use the following citation in any publications.
https://doi.org/10.5281/zenodo.5903364
License
Covalent is licensed under the Apache 2.0 License. See the LICENSE file or contact the support team for more details.
For a detailed history of changes and new features, see the Changelog.
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