Packaging Olive artifacts

What is Olive Packaging

Olive will output multiple candidate models based on metrics priorities. It also can package output artifacts when the user required. Olive packaging can be used in different scenarios. There is only one packaging type: Zipfile.


Zipfile packaging will generate a ZIP file which includes 3 folders: CandidateModels, SampleCode and ONNXRuntimePackages in the output_dir folder (from Engine Configuration):

  • CandidateModels: top ranked output model set

    • Model file

    • Olive Pass run history configurations for candidate model

    • Inference settings (onnx model only)

  • SampleCode: code sample for ONNX model

    • C++

    • C#

    • Python

  • ONNXRuntimePackages: ONNXRuntime package files with the same version that were used by Olive Engine in this workflow run.


CandidateModels includes k folders where k is the number of output models, with name BestCandidateModel_1, BestCandidateModel_2, … and BestCandidateModel_k. The order is ranked by metrics priorities. e.g., if you have 3 metrics metric_1, metric_2 and metric_3 with priority 1, 2 and 3. The output models will be sorted firstly by metric_1. If the value of metric_1 of 2 output models are same, they will be sorted by metric_2, and followed by next lower priority metric.

Each BestCandidateModel folder will include model file/folder. The folder also includes a json file which includes the Olive Pass run history configurations since input model, a json file with performance metrics and a json file for inference settings for the candidate model if the candidate model is an ONNX model.

Inference config file

The inference config file is a json file including execution_provider and session_options. e.g.:

    "execution_provider": [
    "session_options": {
        "execution_mode": 1,
        "graph_optimization_level": 99,
        "extra_session_config": null,
        "inter_op_num_threads": 1,
        "intra_op_num_threads": 64


Olive will only provide sample codes for ONNX model. Sample code supports 3 different programming languages: C++, C# and Python. And a code snippet introducing how to use Olive output artifacts to inference candidate model with recommended inference configurations.

How to package Olive artifacts

Olive packaging configuration is configured in PackagingConfig in Engine configuration. If not specified, Olive will not package artifacts.

  • PackagingConfig

    • type [PackagingType]: Olive packaging type. Olive will package different artifacts based on type.

    • name [str]: For PackagingType.Zipfile type, Olive will generate a ZIP file with name prefix: <name>.zip. By default, the output artifacts will be named as

    • export_in_mlflow_format [bool]: Export model in mlflow format. This is false by default.

You can add PackagingConfig to Engine configurations. e.g.:

"engine": {
    "search_strategy": {
        "execution_order": "joint",
        "search_algorithm": "tpe",
        "search_algorithm_config": {
            "num_samples": 5,
            "seed": 0
    "evaluator": "common_evaluator",
    "host": "local_system",
    "target": "local_system",
    "packaging_config": {
        "type": "Zipfile",
        "name": "OutputModels"
    "clean_cache": true,
    "cache_dir": "cache"