Kitfile Format for ModelKits

The Kitfile is the blueprint for every AI/ML project packaged with KitOps.

It’s a simple, YAML-based manifest that defines what goes into a ModelKit: models, datasets, notebooks, documentation, and metadata.

ModelKits use Kitfiles to package and version your project so it can be shared, stored, or deployed from any OCI-compatible registry.

✨ Why Use a Kitfile?

A Kitfile helps you:

• Package your AI/ML project with full traceability
• Make your work reproducible across environments
• Collaborate with DevOps, ML engineers, and app developers using a shared format
• Integrate seamlessly into CI/CD pipelines and registries

📚 Kitfile Sections

Each Kitfile includes one or more of the following sections:

Section	Description
package	Project metadata (name, version, license, authors)
code	Paths to notebooks or scripts
model	The serialized model and framework info
datasets	Training, validation, or other datasets
docs	Additional documentation to include

You can extract the Kitfile from any existing ModelKit:

kit unpack [registry/repo:tag] --config -d .

✅ Minimal Kitfile Example

The only required fields are:

• manifestVersion
• At least one of code, model, datasets, or docs section

Example:

manifestVersion: v1.0.0

datasets:
  - name: training data
    path: ./data/train.csv
  - name: validation data
    path: ./data/test.csv

Notes on Kitfile Behavior

• Kitfiles must use relative paths (not absolute)
• ModelKits can include only one model, but multiple datasets or code bases
• You can currently only build ModelKits from local files — but support for remote sources (e.g. DVC, S3) is coming soon

🧱 Example: Full Kitfile

manifestVersion: v1.0.0

package:
  authors:
    - Jozu
  description: Updated model to analyze flight trait and passenger satisfaction data
  license: Apache-2.0
  name: FlightSatML

code:
  - description: Jupyter notebook with model training code in Python
    path: ./notebooks

model:
  description: Flight satisfaction and trait analysis model using Scikit-learn
  framework: Scikit-learn
  license: Apache-2.0
  name: joblib Model
  path: ./models/scikit_class_model_v2.joblib
  version: 1.0.0

datasets:
  - name: training data
    description: Flight traits and traveller satisfaction training data (tabular)
    path: ./data/train.csv
  - name: validation data
    description: validation data (tabular)
    path: ./data/test.csv

Learn More

➡️ Explore the Kitfile structure in detail

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