Docs for containerizing and Kubernetes deployment (#579)

* add deploy page

* Added new deploy page outlining how to use containers and Kubernetes with KitOps ModelKits
* Updated a few pages to include links to issues and Discord for help

* added updates

* feedback

* addressed PR feedback
* fixed all the Discord links

docs/.vitepress/config.mts

@@ -84,6 +84,7 @@ export default defineConfig({
           { text: 'Overview', link: '/docs/overview' },
           { text: 'Get Started', link: '/docs/get-started' },
           { text: 'Next Steps', link: '/docs/next-steps' },
+          { text: 'Deploy ModelKits', link: '/docs/deploy' },
           { text: 'Kit Dev', link: '/docs/dev-mode' },
           { text: 'Why KitOps?', link: '/docs/why-kitops' },
           { text: 'How it is Used', link: '/docs/use-cases' },

docs/src/docs/deploy.md

@@ -0,0 +1,112 @@
+# Deploying ModelKits
+
+This page outlines how to use `init` or Kit CLI containers to deploy a ModelKit-packaged AI/ML project to Kubernetes or any other container runtime. The KitOps repo provides pre-built ModelKits that can be used for both semi-turnkey solutions, and more DIY options.
+
+## Pre-built Containers
+
+There are currently two pre-built containers:
+
+1. Init container: https://github.com/jozu-ai/kitops/blob/main/build/dockerfiles/init/README.md
+1. Kit CLI container: https://github.com/jozu-ai/kitops/blob/main/build/dockerfiles/README.md 
+
+## Init Container
+
+The init container unpacks the model reference from a ModelKit to a specific path and then exits. This makes it useful as a Kubernetes `init` container. This container also supports verifying signatures for containers automatically from key-based or keyless signers.
+
+The container is configurable via environment variables:
+
+`$MODELKIT_REF`: The ModelKit to pull (required).
+`$UNPACK_PATH`: Where to unpack the ModelKit (normally you’d want a `volumeMount` here). This is required and will default to `/home/user/modelkit`.
+`$UNPACK_FILTER`: Optional filter to limit what is unpacked (e.g., just the model, or model + code). The filter format is the same as the [--filter command line argument](./cli/cli-reference.md) for the Kit CLI.
+`$COSIGN_KEY`: Path to the key that should be used for verification, mounted inside the init container (e.g., from a Kubernetes secret).
+`$COSIGN_CERT_IDENTITY`: Signing identity for keyless signing.
+`$COSIGN_CERT_OIDC_ISSUER`: OIDC issuer for keyless signer identity.
+
+### Example Kubernetes YAML
+
+```
+ apiVersion: v1
+ kind: Pod
+ metadata:
+   name: my-modelkit-pod
+ spec:
+   containers:
+     - name: model-server
+       image: "" # Some container that expects your modelkit
+       # Share a volumeMount between the init container and this one
+       volumeMounts:
+         - name: modelkit-storage
+           mountPath: /my-modelkit
+
+   # Run the init container to unpack the ModelKit into the volume mount and make
+   # it available to the main container
+   initContainers:
+     - name: kitops-init
+       image: ghcr.io/jozu-ai/kitops-init-container:latest
+       env:
+         - name: MODELKIT_REF
+           value: "ghcr.io/jozu-ai/my-modelkit:latest"
+         - name: UNPACK_PATH
+           value: /tmp/my-modelkit
+       volumeMounts:
+         - name: modelkit-storage
+           mountPath: /tmp/my-modelkit
+
+   # Define a volume to store the ModelKit
+   volumes:
+     - name: modelkit-storage
+       emptyDir: {}       
+```
+
+## Using the Kit CLI Container
+
+The containerized Kit CLI can be used to tailor the running of a ModelKit because you can run any Kit CLI command. This gives you flexibility, but more manual work (the world is your oyster, but it may be hard to shuck).
+
+This container runs `kit` as its entrypoint, accepting Kit CLI arguments. So you could run the container instead of downloading and installing the Kit CLI, although you’ll need to mount a docker volume.
+
+Docker run example:
+
+`docker run ghcr.io/jozu-ai/kit:latest pull jozu.ml/jozu/llama3-8b:8B-instruct-q5_0`
+
+Kubernetes example:
+
+```
+ apiVersion: v1
+ kind: Pod
+ metadata:
+   name: my-modelkit-pod
+ spec:
+   containers:
+     - name: me-using-kit
+       image: ghcr.io/jozu-ai/kit:latest
+       args: # You can put whatever you want; args is an array
+         - pull
+         - jozu.ml/jozu/llama3-8b:8B-instruct-q5_0
+```
+
+## Creating a Custom Container
+
+Going a step further you can use the Kit CLI container to create your own bespoke ModelKit containers.
+
+Example `dockerfile` for a custom container that has <my-modelkit> built into it:
+
+```
+ # Staged build to grab the ModelKit so we can use it later
+ FROM ghcr.io/jozu-ai/kit:latest AS modelkit-download
+
+ # Download your ModelKit into the container
+ RUN kit unpack <my-modelkit> /tmp/my-modelkit
+
+ # Actual build stage; this just uses Alpine but you would build whatever 
+ # container you need here
+ FROM alpine:latest
+
+ # Normal container build + setup -- depends on your use case
+ # ...
+ # ...
+
+ # Copy the downloaded ModelKit into this container
+ COPY --from=modelkit-download /tmp/my-modelkit /home/user/modelkit-data
+```
+
+**Questions or suggestions?** Drop an [issue in our GitHub repository](https://github.com/jozu-ai/kitops/issues) or join [our Discord server](https://discord.gg/Tapeh8agYy) to get support or share your feedback.

docs/src/docs/dev-mode.md

@@ -39,3 +39,5 @@ When you're done don't forget to stop the Kit dev server:
 ```sh
 kit dev stop
 ```
+
+**Questions or suggestions?** Drop an [issue in our GitHub repository](https://github.com/jozu-ai/kitops/issues) or join [our Discord server](https://discord.gg/Tapeh8agYy) to get support or share your feedback.

docs/src/docs/get-started.md

@@ -8,6 +8,7 @@ In this guide, we'll use ModelKits and the kit CLI to easily:
 * Package up a model, notebook, and datasets into a single ModelKit you can share through your existing tools
 * Push that versioned ModelKit package to a registry
 * Grab only the assets you need from the ModelKit for testing, integration, local running, or deployment
+* Package the ModelKit as a container or Kubernetes deployment
 
 ## Before we start...
 
@@ -121,6 +122,10 @@ kit push jozu.ml/brad/quick-start:latest
 
 Note that some registries, like Jozu Hub, don't automatically create a repository. If you receive an error from your `push` command, make sure you have created the repository in your target registry and that you have push rights to the repository.
 
+### ModelKit to Container or Kubernetes
+
+You can build a container or Kubernetes deployment that pulls artifacts directly from the ModelKit. This makes automating container creation and Kubernetes deployment simple. Read more in our [deployment documentation](./deploy.md).
+
 ### Congratulations
 
 You've learned how to unpack a ModelKit, pack one up, and push it. Anyone with access to your remote repository can now pull your new ModelKit and start playing with your model using the `kit pull` or `kit unpack` commands.
@@ -134,4 +139,4 @@ If you'd like to learn more about using Kit, try our [Next Steps with Kit](./nex
 
 Or, if you want to run an LLM-based ModelKit locally try our [dev mode](./dev-mode.md)
 
-Thanks for taking some time to play with Kit. We'd love to hear what you think. Feel free to drop us an [issue in our GitHub repository](https://github.com/jozu-ai/kitops/issues) or join [our Discord server](https://discord.gg/3eDb4yAN).
+Thanks for taking some time to play with Kit. We'd love to hear what you think. Feel free to drop us an [issue in our GitHub repository](https://github.com/jozu-ai/kitops/issues) or join [our Discord server](https://discord.gg/Tapeh8agYy).

docs/src/docs/kitfile/kf-overview.md

@@ -88,4 +88,6 @@ datasets:
 - description: validation data (tabular)
   name: validation data
   path: ./data/test.csv
-```
+```
+
+**Questions or suggestions?** Drop an [issue in our GitHub repository](https://github.com/jozu-ai/kitops/issues) or join [our Discord server](https://discord.gg/Tapeh8agYy) to get support or share your feedback.

docs/src/docs/modelkit/intro.md

@@ -21,3 +21,5 @@ ModelKit revolutionizes the way AI/ML artifacts are shared and managed throughou
 **Optimized for AI/ML Workflows:** ModelKits are tailor-made for AI/ML projects, addressing specific needs such as versioning and environment configuration.
 
 ModelKit is not just a packaging format; it's a building block for innovation, simplifying the complexities of AI/ML development and deployment. By adopting ModelKit, teams can focus more on creating value and less on managing the intricacies of artifact storage and sharing.
+
+**Questions or suggestions?** Drop an [issue in our GitHub repository](https://github.com/jozu-ai/kitops/issues) or join [our Discord server](https://discord.gg/Tapeh8agYy) to get support or share your feedback.

docs/src/docs/overview.md

@@ -6,7 +6,7 @@ KitOps is an innovative open-source project designed to enhance collaboration am
 
 ### 🎁 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.
+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. You can even [deploy ModelKits to containers or Kubernetes](./deploy.md).
 
 ### 📄 Kitfile 
 
@@ -37,15 +37,19 @@ KitOps enables you to innovate in AI/ML without the usual infrastructure distrac
 KitOps is not just another tool; it's a comprehensive CLI and packaging system specifically designed for the AI/ML workflow. It acknowledges the nuanced needs of AI/ML projects, such as:
 
 ### 📊 Management of Unstructured Datasets
+
 AI/ML projects often deal with large, unstructured datasets, such as images, videos, and audio files. KitOps simplifies the versioning and sharing of these datasets, making them as manageable as traditional code.
 
 ### 🤝 Synchronized Data and Code Versioning
+
 One of the core strengths of KitOps is its ability to keep data and code versions in sync. This crucial feature solves the reproducibility issues that frequently arise in AI/ML development, ensuring consistency and reliability across project stages.
 
 ### 🚀 Deployment Ready
-Designed with a focus on deployment, ModelKits package assets in standard formats so they're compatible with nearly any tool - helping you get your model to production faster and more efficiently.
+
+Designed with a focus on deployment, ModelKits package assets in standard formats so you can depoloy them as [containers or to Kubernetes](./deploy.md). They're also [compatible with nearly any tool](./modelkit/compatibility.md) - helping you get your model to production faster and more efficiently.
 
 ### 🏭 Standards-Based Approach
+
 KitOps champions openness and interoperability through its core components, ensuring seamless integration into your existing workflows:
 
 ModelKits are designed as OCI (Open Container Initiative) artifacts, making them fully compatible with the Docker image registries and other OCI-compliant storage solutions you already use. This compatibility allows for an easy and familiar integration process. By adhering to widely accepted standards, KitOps ensures you're not tied to a single vendor or platform. This flexibility gives you the freedom to choose the best tools and services for your needs without being restricted by proprietary formats.