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https://github.com/jozu-ai/kitops

An open source DevOps tool for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI artifact.
https://github.com/jozu-ai/kitops

ai code datasets devops devops-tools gguf hacktoberfest kubernetes kubernetes-deployment ml mlops mlops-tools model-interpretability model-serving models opensource platform-engineering pytorch sklearn tensorflow

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An open source DevOps tool for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI artifact.

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README 

        

KitOps



## Standards-based packaging and versioning system for AI/ML projects.



[![LICENSE](https://img.shields.io/badge/License-Apache%202.0-yellow.svg)](https://github.com/myscale/myscaledb/blob/main/LICENSE)

[![Language](https://img.shields.io/badge/Language-go-blue.svg)](https://go.dev/)

[![Discord](https://img.shields.io/discord/1098133460310294528?logo=Discord)](https://discord.gg/Tapeh8agYy)

[![Twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Twitter)](https://twitter.com/kit_ops)

[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fjozu-ai%2Fkitops&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)



[![Official Website]()](https://kitops.ml?utm_source=github&utm_medium=kitops-readme)



[![Use Cases]()](https://kitops.ml/docs/get-started.html?utm_source=github&utm_medium=kitops-readme)



### What is KitOps?



KitOps is a packaging, versioning, and sharing system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using, and can be stored in your enterprise container registry. It's AI/ML platform engineering teams' preferred solution for securely packaging and versioning assets.



KitOps creates a ModelKit for your AI/ML project which includes everything you need to reproduce it locally or deploy it into production. You can even **selectively unpack a ModelKit** so different team members can save time and storage space by only grabbing what they need for a task. Because ModelKits are immutable, signable, and live in your existing container registry they're easy for organizations to track, control, and audit.



ModelKits [simplify the handoffs between data scientists, application developers, and SREs](https://www.youtube.com/watch?v=j2qjHf2HzSQ) working with LLMs and other AI/ML models. Teams and enterprises use KitOps as a secure storage throughout the AI/ML project lifecycle.



Use KitOps to speed up and de-risk all types of AI/ML projects:

* Predictive models

* Large language models

* Computer vision models

* Multi-modal models

* Audio models

* etc...



### 🇪🇺 EU AI Act Compliance 🔒

For our friends in the EU - ModelKits are the perfect way to create a library of model versions for EU AI Act compliance because they're tamper-proof, signable, and auditable.



### 😍 What's New? ✨



* 🚢 Create a **[runnable container from a ModelKit](https://tinyurl.com/5b76p5u3)** with one command! Read [KitOps deploy docs](https://kitops.ml/docs/deploy.html) for details.

* 🥂 Get the most out of KitOps' ModelKits by using them with the **[Jozu Hub](https://jozu.ml/)** repository. Or, continue using ModelKits with your existing OCI registry (even on-premises and air-gapped).

* 🛠️ Use KitOps with Dagger pipelines using our modules from the [Daggerverse](https://daggerverse.dev/mod/github.com/jozu-ai/daggerverse/kit).

* ⛑️ [KitOps works great with Red Hat](https://developers.redhat.com/articles/2024/09/16/enhance-llms-instructlab-kitops) InstructLab and Quay.io products.



### Features



* 🎁 **[Unified packaging](https://kitops.ml/docs/modelkit/intro.html):** A ModelKit package includes models, datasets, configurations, and code. Add as much or as little as your project needs.

* 🏭 **[Versioning](https://kitops.ml/docs/cli/cli-reference.html#kit-tag):** Each ModelKit is tagged so everyone knows which dataset and model work together.

* 🔒 **[Tamper-proofing](https://kitops.ml/docs/modelkit/spec.html):** Each ModelKit package includes an SHA digest for itself, and every artifact it holds.

* 🤩 **[Selective-unpacking](https://kitops.ml/docs/cli/cli-reference.html#kit-unpack):** Unpack only what you need from a ModelKit with the `kit unpack --filter` command - just the model, just the dataset and code, or any other combination.

* 🤖 **[Automation](https://github.com/marketplace/actions/setup-kit-cli):** Pack or unpack a ModelKit locally or as part of your CI/CD workflow for testing, integration, or deployment (e.g. [GitHub Actions](https://github.com/marketplace/actions/setup-kit-cli) or [Dagger](https://daggerverse.dev/mod/github.com/jozu-ai/daggerverse/kit).

* 🐳 **[Deploy containers](https://kitops.ml/docs/deploy.html):** Generate a basic or custom docker container from any ModelKit.

* 🚢 **[Kubernetes-ready](https://kitops.ml/docs/deploy.html):** Generate a Kubernetes / KServe deployment config from any ModelKit.

* 🪛 **[LLM fine-tuning](https://dev.to/kitops/fine-tune-your-first-large-language-model-llm-with-lora-llamacpp-and-kitops-in-5-easy-steps-1g7f):** Use KitOps to fine-tune a large language model using LoRA.

* 🎯 **[RAG pipelines](https://www.codeproject.com/Articles/5384392/A-Step-by-Step-Guide-to-Building-and-Distributing):** Create a RAG pipeline for tailoring an LLM with KitOps.

* 📝 **[Artifact signing](https://kitops.ml/docs/next-steps.html):** ModelKits and their assets can be signed so you can be confident of their provenance.

* 🌈 **[Standards-based](https://kitops.ml/docs/modelkit/compatibility.html):** Store ModelKits in any OCI 1.1-compliant container or artifact registry.

* 🥧 **[Simple syntax](https://kitops.ml/docs/kitfile/kf-overview.html):** Kitfiles are easy to write and read, using a familiar YAML syntax.

* 🩰 **[Flexible](https://kitops.ml/docs/kitfile/format.html#model):** Reference base models using `model parts`, or store key-value pairs (or any YAML-compatible JSON data) in your Kitfile - use it to keep features, hyperparameters, links to MLOps tool experiments, or validation output.

* 🏃‍♂️‍➡️ **[Run locally](./docs/src/docs/dev-mode.md):** Kit's Dev Mode lets you run an LLM locally, configure it, and prompt/chat with it instantly.

* 🤗 **Universal:** ModelKits can be used with any AI, ML, or LLM project - even multi-modal models.



### See KitOps in Action



There's a video of KitOps in action on the [KitOps site](https://kitops.ml/).



## 🚀 Try KitOps in under 15 Minutes



1. [Install the CLI](https://kitops.ml/docs/cli/installation.html) for your platform.

2. Follow the [Getting Started](https://kitops.ml/docs/get-started.html) docs to learn to pack, unpack, and share a ModelKit.

3. Test drive one of our [ModelKit Quick Starts](https://jozu.ml/organization/jozu-quickstarts) that includes everything you need to run your model including a codebase, dataset, documentation, and of course the model.



For those who prefer to build from the source, follow [these steps](https://kitops.ml/docs/cli/installation.html#🛠️-install-from-source) to get the latest version from our repository.



## What is in the box?



**[ModelKit](https://kitops.ml/docs/modelkit/intro.html):** At the heart of KitOps is the ModelKit, an OCI-compliant packaging format for sharing all AI project artifacts: datasets, code, configurations, and models. By standardizing the way these components are packaged, versioned, and shared, ModelKits facilitate a more streamlined and collaborative development process that is compatible with any MLOps or DevOps tool.



**[Kitfile](https://kitops.ml/docs/kitfile/kf-overview.html):** A ModelKit is defined by a Kitfile - your AI/ML project's blueprint. It uses YAML to describe where to find each of the artifacts that will be packaged into the ModelKit. The Kitfile outlines what each part of the project is.



**[Kit CLI](https://kitops.ml/docs/cli/cli-reference.html):** 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`.



## Need Help?



### Join KitOps community



For support, release updates, and general KitOps discussion, please join the [KitOps Discord](https://discord.gg/Tapeh8agYy). Follow [KitOps on X](https://twitter.com/Kit_Ops) for daily updates.



If you need help there are several ways to reach our community and [Maintainers](./MAINTAINERS.md) outlined in our [support doc](./SUPPORT.md)



### Reporting Issues and Suggesting Features



Your insights help KitOps evolve as an open standard for AI/ML. We *deeply value* the issues and feature requests we get from users in our community :sparkling_heart:. 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.



### Joining the KitOps Contributors



We ❤️ our KitOps 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](./CONTRIBUTING.md). Please read our [Governance](./GOVERNANCE.md) and our [Code of Conduct](./CODE-OF-CONDUCT.md) before contributing.



### A Community Built on Respect



At KitOps, inclusivity, empathy, and responsibility are at our core. Please read our [Code of Conduct](./CODE-OF-CONDUCT.md) to understand the values guiding our community.



## Roadmap



We [share our roadmap openly](./ROADMAP.md) so anyone in the community can provide feedback and ideas. Let us know what you'd like to see by pinging us on Discord or creating an issue.



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