KitOps is your toolkit for transforming how you package, share, and deploy AI/ML models. Say goodbye to compatibility concerns and hello to smooth AI/ML collaboration.
KitOps simplifies the handoffs between data scientists, application developers, and SREs working on self-hosted AI/ML models (including LLMs). KitOps' ModelKits create a unified package for models, their dependencies, configurations, and environments. The ModelKit is portable and uses open standards for compatibility with the tools you already use.
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ModelKit: At the heart of KitOps is the ModelKit, an OCI-compliant packaging format that enables the seamless sharing of all necessary artifacts involved in the AI/ML model lifecycle. This includes datasets, code, configurations, and the models themselves. By standardizing the way these components are packaged, ModelKit facilitates a more streamlined and collaborative development process that is compatible with nearly any tool.
Kitfile: Complementing the ModelKit is the Kitfile, your AI/ML project's blueprint. It's a YAML-based configuration file that simplifies the sharing of model, dataset, and code configurations. Kitfiles are designed with both ease of use and security in mind, ensuring that configurations can be efficiently packaged and shared without compromising on safety or governance.
Kit CLI: Your magic wand for AI/ML collaboration. The Kit CLI not only enables users to create, manage, run, and deploy ModelKits... it lets you pull only the pieces you need. Just need the serialized model for deployment? Use unpack --model or maybe you just want the training datasets? unpack --datasets. So, whether you are packaging a new model for development or deploying an existing model into production, the Kit CLI provides the flexibility and power to streamline your workflow.
Dive into the world of KitOps with ease! Whether you're looking to streamline your AI/ML workflows or explore the power of ModelKits, getting started with Kit is straightforward.
First, download the Kit CLI. Choose the latest tagged version for the most stable release, or explore the next tag for our development builds.
For installation instructions and selecting the right binary for your platform, please refer to our Installation Guide.
To launch Kit, simply open a terminal and type:
kitThis command will display a list of available actions to supercharge your AI/ML projects.
For those who prefer to build from the source, follow these steps to get the latest version directly from our repository:
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Clone the Repository: Clone the KitOps source code to your local machine:
git clone https://github.com/jozu-ai/kitops.git cd kitops -
Build the Kit CLI: Compile the source code into an executable named kit:
go build -o kit
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Run Your Build: Execute the built CLI to see all available commands:
./kit
Or, for direct execution during development:
go run .The easiest way to get introduced to Kit is with our quick start guide
Your insights help Kit evolve as an open standard for AI/ML. We deeply value the issues and feature requests we get from users in our community 💖. To contribute your thoughts,navigate to the Issues tab and hitting the New Issue green button. Our templates guide you in providing essential details to address your request effectively.
We ❤️ our Kit community and contributors. To learn more about the many ways you can contribute (you don't need to be a coder) and how to get started see our Contributor's Guide. Please read our Governance and our Code of Conduct before contributing.
If you need help there are several ways to reach our community and Maintainers outlined in our support doc
At KitOps, inclusivity, empathy, and responsibility are at our core. Please read our Code of Conduct to understand the values guiding our community.
For support, release updates, and general KitOps discussion, please join the KitOps Discord.