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ORT format models


What is the ORT model format?

The ORT model format is a reduced-size model format used in constrained environments such as mobile and web applications. ONNX Runtime provides tools to convert ONNX models to ORT format.

Convert ONNX models to ORT format

ONNX models are converted to ORT format using the convert_onnx_models_to_ort script.

The conversion script performs two functions:

  1. Loads and optimizes ONNX format models, and saves them in ORT format
  2. Determines the operators, and optionally data types, required by the optimized models, and saves these in a configuration file for use in a reduced operator build, if required

The conversion script can run on a single ONNX model, or a directory. If run against a directory, the directory will be recursively searched for ‘.onnx’ files to convert.

Each ‘.onnx’ file is loaded, optimized, and saved in ORT format as a file with the ‘.ort’ extension in the same location as the original ‘.onnx’ file.

Outputs of the script

  1. One ORT format model for each ONNX model
  2. A build configuration file (‘required_operators.config’) with the operators required by the optimized ONNX models.

    If type reduction is enabled (ONNX Runtime version 1.7 or later) the configuration file will also include the required types for each operator, and is called ‘required_operators_and_types.config’.

    If you are using a pre-built ONNX Runtime iOS, Android or web package, the build configuration file is not used and can be ignored.

Script location

The ORT model format is supported by version 1.5.2 of ONNX Runtime or later.

Conversion of ONNX format models to ORT format utilizes the ONNX Runtime python package, as the model is loaded into ONNX Runtime and optimized as part of the conversion process.

For ONNX Runtime version 1.8 and later the conversion script is run directly from the ONNX Runtime python package.

For earlier versions, the conversion script is run from the local ONNX Runtime repository.

Install ONNX Runtime

Install the onnxruntime python package from in order to convert models from ONNX format to the internal ORT format. Version 1.5.3 or higher is required.

Install the latest release

pip install onnxruntime

Install a previous release

If you are building ONNX Runtime from source (custom, reduced or minimal builds), you must match the python package version to the branch of the ONNX Runtime repository you checked out.

For example, to use the 1.7 release:

git checkout rel-1.7.2
pip install onnxruntime==1.7.2

If you are using the master branch in the git repository you should use the nightly ONNX Runtime python package

pip install -U -i ort-nightly

Convert ONNX models to ORT format script usage

ONNX Runtime version 1.8 or later:

python -m <onnx model file or dir>


  • onnx mode file or dir is a path to .onnx file or directory containing one or more .onnx models

The current optional arguments are available by running the script with the --help argument. Supported arguments and defaults differ slightly across ONNX Runtime versions.

python -m --help
usage: [-h] [--use_nnapi]
      [--optimization_level {disable,basic,extended,all}]
      [--custom_op_library CUSTOM_OP_LIBRARY] [--save_optimized_onnx_model]

Convert the ONNX model/s in the provided directory to ORT format models. 
All files with a `.onnx` extension will be processed. For each one, an ORT format model will be created in the same directory. 
A configuration file will also be created called `required_operators.config`, and will contain the list of required operators for all converted models. 
This configuration file should be used as input to the minimal build via the `--include_ops_by_config` parameter.

positional arguments:
  model_path_or_dir     Provide path to ONNX model or directory containing ONNX model/s to convert. All files with a .onnx extension, including in subdirectories, will be processed.

optional arguments:
  -h, --help            show this help message and exit
  --optimization_level {disable,basic,extended,all}
                        Level to optimize ONNX model with, prior to converting to ORT format model. 
                        These map to the onnxruntime.GraphOptimizationLevel values. 
                        If the level is 'all' the NCHWc transformer is manually disabled as it contains device specific logic, 
                        so the ORT format model must be generated on the device it will run on. 
                        Additionally, the NCHWc optimizations are not applicable to ARM devices.
                        Add operator specific type information to the configuration file to potentially 
                        reduce the types supported by individual operator implementations.
  --custom_op_library CUSTOM_OP_LIBRARY
                        Provide path to shared library containing custom operator kernels to register.
                        Save the optimized version of each ONNX model. This will have the same optimizations 
                        applied as the ORT format model.

Optional script arguments

Optimization level

Set the optimization level that ONNX Runtime will use to optimize the model prior to saving in ORT format.

For ONNX Runtime version 1.8 and later, all is recommended if the model will be run with the CPU Execution Provider (EP).

For earlier versions, extended is recommended, as the all level previously included device specific optimizations that would limit the portability of the model.

If the model is to be run with the NNAPI EP or CoreML EP, it is recommended to create an ORT format model using the basic optimization level. Performance testing should be done to compare running this model with the NNAPI or CoreML EP enabled vs. running the model optimized to a higher level using the CPU EP to determine the optimal setup.

See the documentation on performance tuning mobile scenarios for more information.

Enable type reduction

With ONNX Runtime version 1.7 and later it is possible to limit the data types the required operators support to further reduce the build size. This pruning is referred to as “operator type reduction” in this documentation. As the ONNX model/s are converted, the input and output data types required by each operator are accumulated and included in the configuration file.

If you wish to enable operator type reduction, the Flatbuffers python package must be installed.

pip install flatbuffers

For example, the ONNX Runtime kernel for Softmax supports both float and double. If your model/s uses Softmax but only with float data, we can exclude the implementation that supports double to reduce the kernel’s binary size.

Custom Operator support

If your ONNX model uses custom operators, the path to the library containing the custom operator kernels must be provided so that the ONNX model can be successfully loaded. The custom operators will be preserved in the ORT format model.

Save optimized ONNX model

Add this flag to save the optimized ONNX model. The optimized ONNX model contains the same nodes and initializers as the ORT format model, and can be viewed in Netron for debugging and performance tuning.

Previous versions of ONNX Runtime

Prior to ONNX Runtime version 1.7, the model conversion script must be run from a cloned source repository:

python <ONNX Runtime repository root>/tools/python/ <onnx model file or dir>

Load and execute a model in ORT format

The API for executing ORT format models is the same as for ONNX models.

See the ONNX Runtime API documentation for details on individual API usage.

APIs by platform

Platform Available APIs
Android C, C++, Java, Kotlin
iOS C, C++, Objective-C (Swift via bridge)
Web JavaScript

ORT format model loading

If you provide a filename for the ORT format model, a file extension of ‘.ort’ will be inferred to be an ORT format model.

If you provide in-memory bytes for the ORT format model, a marker in those bytes will be checked to infer if it’s an ORT format model.

If you wish to explicitly say that the InferenceSession input is an ORT format model you can do so via SessionOptions, although this generally should not be necessary.

Load ORT format model from a file path


Ort::SessionOptions session_options;
session_options.AddConfigEntry("session.load_model_format", "ORT");

Ort::Env env;
Ort::Session session(env, <path to model>, session_options);

Java API

SessionOptions session_options = new SessionOptions();
session_options.addConfigEntry("session.load_model_format", "ORT");

OrtEnvironment env = OrtEnvironment.getEnvironment();
OrtSession session = env.createSession(<path to model>, session_options);

JavaScript API

import * as ort from "onnxruntime-web";

const session = await ort.InferenceSession.create("<path to model>");

Load ORT format model from an in-memory byte array

If a session is created using an input byte array containing the ORT format model data, by default we will copy the model bytes at the time of session creation to ensure the model bytes buffer is valid.

You may also enable the option to use the model bytes directly by setting the Session Options config session.use_ort_model_bytes_directly to 1. This may reduce the peak memory usage of ONNX Runtime Mobile, but you will need to guarantee that the model bytes are valid throughout the lifespan of the ORT session. For ONNX Runtime Web, this option is set by default.


Ort::SessionOptions session_options;
session_options.AddConfigEntry("session.load_model_format", "ORT");
session_options.AddConfigEntry("session.use_ort_model_bytes_directly", "1");

std::ifstream stream(<path to model>, std::ios::in | std::ios::binary);
std::vector<uint8_t> model_bytes((std::istreambuf_iterator<char>(stream)), std::istreambuf_iterator<char>());

Ort::Env env;
Ort::Session session(env,, model_bytes.size(), session_options);

Java API

SessionOptions session_options = new SessionOptions();
session_options.addConfigEntry("session.load_model_format", "ORT");
session_options.addConfigEntry("session.use_ort_model_bytes_directly", "1");

byte[] model_bytes = Files.readAllBytes(Paths.get(<path to model>));

OrtEnvironment env = OrtEnvironment.getEnvironment();
OrtSession session = env.createSession(model_bytes, session_options);

JavaScript API

import * as ort from "onnxruntime-web";

const response = await fetch(modelUrl);
const arrayBuffer = await response.arrayBuffer();
model_bytes = new Uint8Array(arrayBuffer);

const session = await ort.InferenceSession.create(model_bytes);