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KitOps Quick Start

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 the ModelKit package to a public or private registry
  • Grab only the assets you need from the ModelKit for testing, integration, local running, or deployment

Before we start...

  1. Make sure you've got the Kit CLI setup on your machine. If you'd like to learn more about ModelKits check out the overview. If you are wondering about specific flags you can check out everything in the CLI command reference

  2. Create and navigate to a new folder on your filesystem. We'd suggest calling it KitStart but any name works.

One more note...with Kit, packing and pushing are separate steps (same with unpacking and pulling). Packing builds the ModelKit using the content outlined in the Kitfile manifest. Pushing takes a built ModelKit from your local registry and sends it to any remote registry. We strongly recommend tagging your ModelKits with a version number and any other tags that will help your team (e.g., challenger, champion, v1.3, dev, production, etc...)

Learning to use the CLI

1/ Check your CLI Version

Check that the Kit CLI is properly installed.

kit version

You'll see information about the version of Kit you're running. If you get an error check to make sure you have Kit installed and in your path.

2/ Login to Your Registry

You can log into any container registry, but below are a few popular ones.

Docker Hub

kit login docker.io

GitHub Registry

kit login ghcr.io

You'll see Log in successful.

3/ Get a Sample ModelKit

Let's unpack a sample ModelKit to our machine that we can play with. In this case we'll unpack the whole thing, but one of the great things about Kit is that you can also selectively unpack only the thigs you need: just the model, the model and dataset, the code, the configuration...whatever you want. Check out the unpack command reference for details.

You can grab any of the ModelKits from our site, but we've chosen a small language model example below.

kit unpack ghcr.io/jozu-ai/modelkit-examples/finetuning_slm:latest

You'll see a set of messages as Kit unpacked the configuration, code, datasets, and serialized model. Now list the directory contents:

ls

You'll see a single file (Kitfile) which is the manifest for our ModelKit, and a set of files or directories including adapters, a Jupyter notebook, and dataset.

4/ Check our Local Repository

Now let's check that we don't have anything in our local repository.

kit list

You'll see the column headings for an empty table with things like REPOSITORY, TAG, etc...

5/ Pack the ModelKit

Since our repository is empty we'll need to create our ModelKit. The ModelKit in your local registry will need to be named the same as your remote registry. So the command will look like:

kit pack . -t [your registry address]/[your repository name]/mymodelkit:latest

In my case I am pushing to my jozubrad repository on Docker Hub, so I use:

kit pack . -t docker.io/jozubrad/mymodelkit:latest

You'll see a set of Saved ... messages as each piece of the ModelKit is saved to the local repository.

Let's check our local registry now:

kit list

You'll see a new entry named the same as your pack command.

6/ Push the ModelKit to a Remote Repository

You're already logged in to your remote repository, so now you can just push. The naming of your ModelKit will need to be the same as what you see in your kit list command (REPOSITORY:TAG). You can even copy and paste it. In my case it looks like:

kit push docker.io/jozubrad/mymodelkit:latest

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:

kit pull docker.io/jozubrad/mymodelkit:latest

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 or join our Discord server.