Model Format

The Odahu Model Artifact Format (OMAF) describes a format to package, store, and transport ML models.

Models can be built in different languages and use different platform libraries. For example: {Python, Scala, R, …} using {scikit-learn, tensorflow, keras, …}.

An OMAF Artifact is stored as a file-system folder packed into a ZIP file using the Deflate ZIP compression algorithm.

The Artifact contains:

  • odahuflow.model.yaml a YAML file in the root folder. This file contains meta-information about the type of binary model and other model related information (e.g. language, import endpoints, dependencies).
  • Additional folders and files, depending upon meta-information declared in odahuflow.model.yaml.


File structure:

  • binaries - Language and dependencies that should be used to load model binaries
  • binaries.type - Required Odahu Model Environments. See section Odahu Model Environments.
  • binaries.dependencies - Dependency management system, compatible with the selected Odahu Model Environment
  • binaries.<additional> - Model Environment and dependency management system values, for example ‘a path to the requirements file’
  • model - Location of the model artifact Model artifact format depends on Odahu Model Environment.
  • - name of the model, [a-Z0-9\-]+
  • model.version - version of model. Format is <Apache Version>-<Additional suffix>, where Additional suffix is a [a-Z0-9\-\.]+ string.
  • model.workDir - working directory to start model from.
  • model.entrypoint - name of model artifact (e.g. Python module or Java JAR file).
  • odahuflowVersion - OMAF version
  • toolchain - toolchain used for training and preparing the Artifact
  • - name of the toolchain
  • toolchain.version - version of used toolchain.
  • toolchain.<additional> - additional fields, related to used toolchain (e.g. used submodule of toolchain).


Example with GPPI using conda for dependency management, mlflow toolchain.

  type: python
  dependencies: conda
  conda_path: mlflow/model/mlflow_env.yml
  name: wine-quality
  version: 1.0.0-12333122
  workDir: mlflow/model
  entrypoint: entrypoint
odahuflowVersion: '1.0'
  name: mlflow
  version: 1.0.0

Odahu Model Environments

Odahu supports these model environments:

  • General Python Prediction Interface (GPPI). Can import a trained model as a python module and use a predefined function for prediction. Value for binaries.type should be python.
  • General Java Prediction Interface (GJPI). Can import a trained model as a Java Library and use a predefined interfaces for prediction. Value for binaries.type should be java.

Odahu’s General Python Prediction Interface (GPPI)

General Information

Field Value
Name General Python Prediction Interface (GPPI)
Supported languages Python 3.6+
binaries.type "python"
binaries.dependencies "conda"
binaries.conda_path Path to conda env, from artifact root
model.workDir Working directory, PYTHON PATH
model.entrypoint Python import, relative to model.workDir


This interface is an importable Python module with a declared interface (functions with arguments and return types). Toolchains that save models in this format must provide an entrypoint with this interface or they may provide a wrapper around their interface for this interface.

Required Environment variables

  • MODEL_LOCATION – path to model’s file, relative to working directory.

Interface declaration

Interface functions:

Function Description
init Required. Invoked during service boot. Returns prediction mode: object-based or matrix-based.
predict_on_objects Optional. Make prediction based on input objects. Return type is configurable.
get_object_input_type Optional. Get type of input for predict_on_objects. Defaults to List of Dicts if a value is not provided.
get_object_output_type Optional. Get the output type of predict_on_objects. Otherwise it returns a JSON-serializable List of Dicts.
predict_on_matrix Optional. Make prediction based on a value matrix (tuple of tuples). Accepts names of columns. Returns matrix.
get_output_json_serializer Optional. Serialize output, if declared. Otherwise, use default.
get_info Optional. Return OpenAPI description of input and output types (if possible).