README.md

ort - ONNX Runtime Rust bindings

ort is yet another ONNX Runtime wrapper for Rust based on onnxruntime-rs. ort is updated for ONNX Runtime 1.13.1 and contains many API improvements & fixes.

See the docs and examples/ for more detailed information.

Cargo features

• fetch-models: Enables fetching models from the ONNX Model Zoo; not recommended for production.
• generate-bindings: Update/generate ONNX Runtime bindings with bindgen. Requires libclang.
• copy-dylibs: Copy dynamic libraries to the Cargo target folder.
• prefer-system-strategy: Uses the system compile strategy by default; requires users to provide ONNX Runtime libraries.
  • prefer-dynamic-libs: By default, if the path pointed to by ORT_LIB_LOCATION contains static libraries, ort will link to them rather than dynamic libraries. This feature prefers linking to dynamic libraries instead.
• prefer-compile-strategy: Uses the compile strategy by default; will take a very long time, but allows for easy static linking, avoiding the DLL hell.
  • 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).
• Execution providers: These are required for both building and using execution providers. Do not enable any of these features unless you are using the compile strategy or you are using the system strategy with binaries that support these execution providers, otherwise you'll run into linking errors.
  • 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 v11.6+ and TensorRT v8.4+.
  • openvino: Enables the OpenVINO execution provider for 6th+ generation Intel Core CPUs.
  • onednn: Enables the 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.
  • rocm: Enables the ROCm execution provider for AMD ROCm-enabled GPUs.
  • 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.
  • migraphx: Enables the MIGraphX execution provider for Windows x86/x64 targets with dedicated AMD GPUs.
  • 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.
• half: Builds support for float16/bfloat16 ONNX tensors.

Strategies

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. These binaries may collect telemetry.
• compile: Clones & compiles ONNX Runtime from source. This is currently untested and extremely slow! It's recommended to use system instead.
• system: Links to ONNX Runtime binaries provided by the system or a path pointed to by the ORT_LIB_LOCATION environment variable. ort will link to static or dynamic libraries depending on what is available in the ORT_LIB_LOCATION folder (see the prefer-dynamic-libs feature).

Execution providers

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. After that, configuring & enabling these execution providers can be done through SessionBuilder::execution_providers().

Requesting an execution provider via e.g. ExecutionProviderBuilder::cuda() will silently fail if that EP is not available on the system or encounters an error and falls back to the next requested execution provider or to the CPU provider if no requested providers are available. If you must know why the execution provider is unavailable, use ExecutionProviderBuilder::try_*(), e.g. try_cuda().

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. No other execution providers as prebuilt binaries, and thus enabling other EP features will fail when ORT_STRATEGY=download. To use other execution providers, you must build ONNX Runtime from source.

Shared library hell

Because compiling ONNX Runtime from source takes so long (and static linking is not recommended by Microsoft), it may be easier to compile ONNX Runtime as a shared library or use prebuilt DLLs. However, this can cause some issues with library paths and load orders.

Windows

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 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.

Linux

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

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" ]