ort - ONNX Runtime Rust bindings

ort is an ONNX Runtime 1.14 wrapper for Rust based on onnxruntime-rs. ONNX Runtime accelerates ML inference on both CPU & GPU.

See the docs and examples/ for more detailed information.

Note: For developers using ort in a library (if you are developing an app, you can skip this part), it is heavily recommended to use default-features = false to avoid bringing in unnecessary bloat. Cargo features are additive. Users of a library that requires ort with default features enabled will not be able to remove those features, and if the library isn't using them, it's just adding unnecessary bloat. This is especially true for the fetch-models feature, which you should almost never use in production. Instead, you should enable ort's default features in your dev dependencies only, and instruct downstream users to include ort = { version = "...", features = [ "download-binaries" ] } in their dependencies if they need it.

• fetch-models (default): Enables fetching models from the ONNX Model Zoo. Useful for quick testing with some common models. Not recommended in production.
• download-binaries (default): Enables downloading binaries via the download strategy. If disabled, the default behavior will be the system strategy.
• copy-dylibs (default): Copy dynamic libraries to the Cargo target folder.
• half (default): Builds support for float16/bfloat16 ONNX tensors.
• generate-bindings: Update/generate ONNX Runtime bindings with bindgen. Requires libclang.
• Execution providers: These are required to use some execution providers. If you are using an execution provider not provided for your platform by the download strategy, you must use the compile or system strategies with binaries that support those execution providers, otherwise you'll run into linking errors.
  • Some EPs are not currently implemented due to a lack of hardware for testing. Please open an issue if your desired EP has a ⚠️
  • cuda: Enables the CUDA execution provider for Maxwell (7xx) NVIDIA GPUs and above. Requires CUDA v11.6+.
  • tensorrt: Enables the TensorRT execution provider for GeForce 9xx series NVIDIA GPUs and above; requires CUDA v11.4+ and TensorRT v8.4+.
  • ⚠️ openvino: Enables the OpenVINO execution provider for 6th+ generation Intel Core CPUs.
  • onednn: Enables the Intel oneDNN execution provider for x86/x64 targets.
  • directml: Enables the DirectML execution provider for Windows x86/x64 targets with dedicated GPUs supporting DirectX 12.
  • ⚠️ snpe: Enables the SNPE execution provider for Qualcomm Snapdragon CPUs & Adreno GPUs.
  • ⚠️ nnapi: Enables the Android Neural Networks API (NNAPI) execution provider.
  • coreml: Enables the CoreML execution provider for macOS/iOS targets.
  • ⚠️ xnnpack: Enables the XNNPACK backend for WebAssembly and Android.
  • ⚠️ migraphx: Enables the MIGraphX execution provider AMD GPUs.
  • ⚠️ rocm: Enables the ROCm execution provider for AMD ROCm-enabled GPUs. (#16)
  • acl: Enables the ARM Compute Library execution provider for multi-core ARM v8 processors.
  • ⚠️ armnn: Enables the ArmNN execution provider for ARM v8 targets.
  • ⚠️ tvm: Enables the preview Apache TVM execution provider.
  • ⚠️ rknpu: Enables the RKNPU execution provider for Rockchip NPUs.
  • ⚠️ vitis: Enables Xilinx's Vitis-AI execution provider for U200/U250 accelerators.
  • ⚠️ cann: Enables the Huawei Compute Architecture for Neural Networks (CANN) execution provider.
• Compile strategy features - These features only apply when using the compile strategy.
  • compile-static: Compiles ONNX Runtime as a static library.
  • mimalloc: Uses the (usually) faster mimalloc memory allocation library instead of the platform default.
  • experimental: Compiles Microsoft experimental operators.
  • minimal-build: Builds ONNX Runtime without RTTI, .onnx model format support, runtime optimizations, or dynamically-registered EP kernels. Drastically reduces binary size, recommended for release builds (if possible).

There are 3 'strategies' for obtaining and linking ONNX Runtime binaries. The strategy can be set with the ORT_STRATEGY environment variable.

• download (default): Downloads prebuilt ONNX Runtime from Microsoft. Only a few execution providers are available for download at the moment, namely CUDA and TensorRT. These binaries may collect telemetry. In the future, pyke may provide binaries with telemetry disabled and more execution providers available.
• system: Links to ONNX Runtime binaries provided by the system or a path pointed to by the ORT_LIB_LOCATION environment variable. ort will automatically link to static or dynamic libraries depending on what is available in the ORT_LIB_LOCATION folder.
• compile: Clones & compiles ONNX Runtime from source. This is extremely slow! It's recommended to use system instead.

To use other execution providers, you must explicitly enable them via their Cargo features. Using the compile strategy, everything should just work™️. If using the system strategy, ensure that the binaries you are linking to have been built with the execution providers you want to use, otherwise you may get linking errors. Configuring & enabling execution providers can be done through SessionBuilder::execution_providers().

Execution providers will attempt to be registered in the order they are passed, silently falling back to the CPU provider if none of the requested providers are available. If you must know whether an EP is available, you can use ExecutionProvider::cuda().is_available().

For prebuilt Microsoft binaries, you can enable the CUDA or TensorRT execution providers for Windows and Linux via the cuda and tensorrt Cargo features respectively. Microsoft does not provide prebuilt binaries for other execution providers, and thus enabling other EP features will fail when ORT_STRATEGY=download. To use other execution providers, you must build ONNX Runtime from source.

If using shared libraries (as is the default with ORT_STRATEGY=download), you may need to make some changes to avoid issues with library paths and load orders.

Some versions of Windows come bundled with an older vesrion of onnxruntime.dll in the System32 folder, which will cause an assertion error at runtime:

The given version [13] is not supported, only version 1 to 10 is supported in this build.
thread 'main' panicked at 'assertion failed: `(left != right)`
  left: `0x0`,
 right: `0x0`', src\lib.rs:50:5
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace

The fix is to copy the ONNX Runtime DLLs into the same directory as the binary. ort can automatically copy the DLLs to the Cargo target folder when the copy-dylibs feature is enabled, though this only fixes binary Cargo targets. When running tests/benchmarks/examples for the first time, you'll have to manually copy the target/debug/onnxruntime*.dll files to target/debug/deps/ for tests & benchmarks or target/debug/examples/ for examples.

Running a binary via cargo run should work without copy-dylibs. If you'd like to use the produced binaries outside of Cargo, you'll either have to copy libonnxruntime.so to a known lib location (e.g. /usr/lib) or enable rpath to load libraries from the same folder as the binary and place libonnxruntime.so alongside your binary.

In Cargo.toml:

[profile.dev]
rpath = true

[profile.release]
rpath = true

# do this for all profiles

In .cargo/config.toml:

[target.x86_64-unknown-linux-gnu]
rustflags = [ "-Clink-args=-Wl,-rpath,\\$ORIGIN" ]

# do this for all Linux targets as well

macOS has the same limitations as Linux. If enabling rpath, note that the rpath should point to @loader_path rather than $ORIGIN:

# .cargo/config.toml
[target.x86_64-apple-darwin]
rustflags = [ "-Clink-args=-Wl,-rpath,@loader_path" ]

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