A Developer Guide to ONNX

Harnessing the Power of ONNX Runtime

In this section, we cover:

• The architecture of ONNX Runtime and the concept of execution providers
• How linking strategies (static vs. dynamic) affect deployment and performance
• Practical integration considerations for Python or Rust projects
• Balancing flexibility, efficiency, and maintainability across different platforms

3: How ONNX Runtime Works

3.1: Execution Providers and Their Role in Inference

A cornerstone of ONNX Runtime’s architecture is the concept of Execution Providers (EPs). An execution provider is a pluggable module that implements the execution of ONNX operators on a particular hardware or library. In simpler terms, an EP is like a driver or backend that ONNX Runtime can use to run parts of the model on a specific platform (CPU, CUDA GPU, TensorRT, FPGA, etc.). ONNX Runtime’s core is designed to be extensible – instead of hard-coding all computation, it delegates to whatever EPs are available on the system. This allows ORT to take advantage of specialized hardware without needing to know the low-level details of that hardware.

Each execution provider defines a set of kernels (implementations) for certain ONNX operators, possibly optimized or accelerated for its target device. For example, the CPU Execution Provider includes efficient implementations of all ops on CPU, the CUDA EP provides GPU implementations for many ops using CUDA, and the TensorRT EP can execute whole subgraphs of ops via NVIDIA’s TensorRT engine. At runtime, ONNX Runtime will query each available EP to see which parts of the model they can handle – this is done via a GetCapability() interface where an EP tells ORT which nodes (operations) in the computation graph it can execute.

ONNX Runtime orchestrates inference by partitioning the model’s computational graph among the available execution providers. It essentially fuses the model graph into subgraphs, each subgraph assigned to one provider. For instance, consider a model with various neural network layers: ORT might assign a batch of convolution and activation nodes to run on a CUDA EP (GPU) as one subgraph, and maybe some post-processing ops to the CPU EP as another subgraph, if the GPU EP doesn’t support those ops. These subgraphs are then executed by their respective providers. This partitioning is dynamic and based on what EPs report as supported. Importantly, ONNX Runtime always has a default CPU provider that can execute any op (ensuring no model is unsupported), whereas more specialized EPs might only support a subset of ops. The default CPU EP is used as a fallback for any part of the model that isn’t taken by another EP.

The execution providers are considered in a priority order. ORT will try to “push” as much of the computation as possible onto the most specialized (and usually fastest) provider first, then the next, and so on, with the CPU provider typically last as the catch-all. The goal is to maximize usage of accelerators: for example, if a GPU is present, let it handle everything it can, and only send to CPU what the GPU cannot do. This design allows heterogeneous execution across multiple devices in one seamless flow. The necessary data transfers (e.g. moving tensors between CPU and GPU memory) are handled by ORT so that from the user’s perspective it just works.

To summarize, execution providers in ONNX Runtime abstract the hardware-specific execution of model operations. ORT uses EPs to achieve both flexibility (you can run on different hardware by plugging in different EPs) and performance (by offloading work to faster specialized hardware when available). This modular architecture means as new hardware or libraries emerge (say a new Neural Processing Unit or a new optimization library), support for it can be added to ONNX Runtime as a new EP without disturbing the rest of the system. For developers, it means an ONNX model can automatically benefit from accelerators present on the system, simply by enabling the corresponding execution provider.

3.2: Switching Between CPU and GPU Providers (e.g., CUDA, TensorRT)

One of the practical tasks for library developers using ONNX Runtime is selecting and configuring the execution providers for a given deployment. By default, if you install the standard onnxruntime Python package, it comes with the CPU execution provider enabled (and possibly others if it’s a GPU build). Using the CPU provider requires no special configuration – it’s the fallback and will always be there. However, if you want to leverage a GPU (CUDA) or other accelerators like TensorRT, you need to ensure those providers are available in your ONNX Runtime build and then explicitly configure the runtime to use them.

In Python, switching or specifying execution providers is straightforward. When creating an InferenceSession, you can pass a list of providers in order of priority. For example:

import onnxruntime as ort
session = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider", "CPUExecutionProvider"])

This instructs ONNX Runtime to try the CUDA provider first and use CPU as a fallback (⇒). In this context, CUDAExecutionProvider utilizes the system’s NVIDIA GPU (assuming the onnxruntime package was installed with GPU support, e.g., onnxruntime-gpu), and CPUExecutionProvider is the built-in default. If an operation in the model is supported by CUDA, it will run on the GPU; if not, it will run on CPU (⇒). As of ONNX Runtime 1.10+, the API requires explicitly specifying the GPU provider to use it – leaving the providers list empty defaults to CPU only (⇒). This is a safety measure to avoid unexpected GPU usage; it makes the developer’s intention clear.

For NVIDIA’s TensorRT (another execution provider which uses optimized inference engines), the idea is similar: you need an ONNX Runtime build that includes the TensorRT EP (or you compile ORT with --use_tensorrt). Then you could specify providers=["TensorrtExecutionProvider", "CUDAExecutionProvider", "CPUExecutionProvider"]. In such a configuration, ORT will first try to delegate model parts to TensorRT (which can yield even faster GPU inference for supported models), then fall back to general CUDA, then CPU. It’s worth noting that using TensorRT might require that you provide static shapes or have TensorRT engines built ahead of time, but ONNX Runtime abstracts much of that – it will attempt to build a TensorRT engine for subgraphs it hands off.

In the Rust ecosystem, the ort crate similarly allows selecting execution providers, though the approach is at build-time and initialization rather than at session creation (due to Rust’s static nature). The ort crate comes with certain providers enabled by default (CPU, and possibly CUDA if the feature is turned on). Developers can enable crate features for CUDA, TensorRT, etc., and the crate will include those EPs if available. When using the Rust API, one typically specifies provider options when creating an inference session or uses the default which might already prioritize GPU if compiled in. For example, the SessionBuilder in Rust might have methods to register providers. Under the hood it’s doing the same thing: calling into the ORT C API to set the list of desired providers.

Switching between CPU and GPU thus involves: (1) having ONNX Runtime built with the desired providers, and (2) specifying them in the preferred order when initializing inference. Library developers should ensure they ship or depend on the correct ONNX Runtime package: for instance, use the onnxruntime-gpu Python package if GPU is needed (the CPU-only package won’t have CUDA support), or in Rust enable the cuda feature and link against an ORT build that has CUDA enabled. It’s also a good practice to include CPU as a fallback provider in the list, unless you explicitly want to fail when a GPU op is unsupported. Including CPU ensures that if the accelerator can’t handle something, it will still run (albeit slower) on CPU rather than erroring out (⇒).

In summary, ONNX Runtime makes it easy to switch execution providers: it’s often a one-line configuration change. The heavy lifting of partitioning the model and coordinating between CPU/GPU is handled internally by ORT. For a library developer, the main considerations are building/distributing the runtime with the needed providers and exposing a way for users to choose or configure the provider (for example, allowing a user to select “use GPU” vs “CPU only” in a high-level API). The flexibility of ONNX Runtime here means the same ONNX model can run in a variety of environments – from CPU-only servers to GPU clusters – by simply toggling providers.

3.3: Model Optimization Features in ONNX Runtime

Beyond hardware acceleration, ONNX Runtime provides a suite of model optimization techniques that can significantly improve inference performance. These optimizations operate on the ONNX computation graph (often called graph optimizations or graph transformations). The idea is to transform the model graph into a more efficient but functionally equivalent form before executing it. ORT performs many of these optimizations automatically when you load a model, and it also offers APIs to control or run optimizations explicitly.

Some of the key optimization features are:

• Graph Optimizations: ONNX Runtime categorizes its graph-level transformations into levels like Basic, Extended, and Layout optimizations (⇒). Basic optimizations include things like constant folding (pre-computing parts of the graph that are static constants, so they don’t have to be computed at runtime) and eliminating redundant nodes (removing identity operations, etc.) (⇒). These are semantics-preserving changes that simplify the graph. Extended optimizations go further to fuse multiple nodes into one for efficiency (for example, combining a sequence of operations into a single kernel call when possible). Layout optimizations might reorder weights or change tensor layouts to match the target hardware preferences. By default, ONNX Runtime enables all these optimization levels (it will try to do the most advanced optimizations it knows how to do) (⇒). The result is often a faster graph execution due to fewer operations and better use of memory locality. Notably, these happen before the execution providers see the graph – so both CPU and GPU EPs benefit from a cleaner, optimized graph.

• Online vs Offline Optimization: ORT can optimize the model at runtime just-in-time (online) or produce an optimized model file for reuse (offline). In the online mode, when you create an InferenceSession, ORT will apply the graph optimizations in memory and then run the model. This incurs a bit of overhead on session initialization but ensures you’re running an optimized graph. In offline mode, ORT provides tools (or APIs) to save the optimized graph to disk (sometimes called an ORT format model). This way, you can preprocess a model – e.g., convert model.onnx to model.opt.onnx – and then at runtime load the already optimized model, saving initialization time (⇒). This is useful for scenarios like mobile or embedded deployment where you want to minimize load latency or if you want to distribute an already optimized model to customers. The Python API, for instance, has optimize_model functions or you can use onnxruntime.tools scripts to produce an optimized model.

• Precision Reduction and Quantization: ONNX Runtime also supports model optimizations that reduce numeric precision to gain speed. For example, it has features for FP16 conversion – converting a model’s weights (and ops that support it) from 32-bit floats to 16-bit floats to leverage faster half-precision math on GPUs – and quantization – reducing weights and possibly activations to int8 or uint8 with minimal accuracy loss, which can greatly accelerate CPU and some GPUs. These are typically offline optimizations (you run a quantization script on an ONNX model to produce a quantized ONNX model). ORT provides a quantization tool and runtime support for executing quantized models (with dedicated kernels for int8 ops). This way, a library developer can take a trained model, quantize it using ONNX Runtime’s tooling (or encourage users to do so), and then infer with ORT to achieve lower latency especially on CPU deployments (⇒). Quantization is a trade-off (speed vs. accuracy), but ORT’s support makes it fairly straightforward to implement if desired.

• Memory and Execution Optimizations: Additionally, ONNX Runtime takes care of optimizing memory usage and threading. It will reuse memory buffers for intermediate tensors when safe (to reduce memory footprint). It also has settings for intra-op and inter-op thread pools – by default, the CPU EP will use multiple threads to execute different parts of the graph in parallel or to parallelize operations, which can significantly speed up inference on multi-core machines. There are session options to configure thread counts and affinities (⇒), which advanced users can tweak for optimal performance. But out-of-the-box, ORT tries to make good use of available compute resources.

All these optimization features mean that simply by choosing ONNX Runtime, a lot of performance tuning is handled for you under the hood. A library developer doesn’t necessarily need to implement custom fusions or worry about manually optimizing the computation graph – ORT will handle general best practices. However, it is useful to be aware of these features. For example, if you know your model has segments that could be fused, you can check if ORT is already fusing them (it often does). If you require absolute maximum performance, you might ship an offline-optimized model or use ORT’s API to disable certain optimizations (rarely needed, but there are cases like debugging where you might turn them off). In practice, enabling ORT’s highest optimization level (which is the default ORT_ENABLE_ALL in C++ API or just default behavior in Python) is recommended for production inference. It’s also worth noting that as ONNX Runtime evolves, new optimization passes (like emerging compiler techniques, better operator fusion for transformer models, etc.) are added, and users automatically benefit by upgrading ORT. This continuous improvement is one reason ONNX Runtime often shows better performance with each release.

4: Deployment Considerations and Linking Strategies

4.1: Static vs. Dynamic Linking of ONNX Runtime

When integrating ONNX Runtime into a library or application (especially in languages like C++ or Rust), one important consideration is how the ONNX Runtime library is linked: statically or dynamically.

• Static Linking means that the ONNX Runtime code is compiled into your application’s binary. The result is one larger executable (or library) that contains both your code and ONNX Runtime. The advantage of static linking is that you don’t have to distribute a separate ONNX Runtime DLL/.so file with your app – everything is self-contained. This can simplify deployment and avoid issues where the runtime might be missing or not found on the target system. In Rust, static linking is often the preferred method (Rust tooling favors static libs for dependencies). The maintainers of the ort Rust crate note that “you should prefer static linking if your execution providers support it, as it avoids many issues and follows de facto Rust practices.” (⇒). Static linking can also yield slightly faster load times since there’s no dynamic symbol resolution at startup, and it ensures the exact version of ORT you built against is used at runtime. To use static linking, one must compile ONNX Runtime with the option to build static libraries (for example, running ORT’s build with --build_shared_lib off to get .a or .lib files) (⇒). Many of ORT’s official build configurations do support this.

• Dynamic Linking means that ONNX Runtime is included as a separate shared library (e.g., onnxruntime.dll on Windows or libonnxruntime.so on Linux) that your application loads at runtime. Your application’s binary will be smaller, and multiple applications can potentially share one copy of the ONNX Runtime library on the system. This is the typical approach in Python: the onnxruntime Python package includes the ORT shared library, which the Python code loads. Dynamic linking is sometimes necessary; for example, certain execution providers (like CUDA or TensorRT or some proprietary DSP libs) might only be available or supported as dynamic libraries (⇒). In those cases, even if you statically link the core ORT, it may internally still load dynamic libraries for those providers. The downside of dynamic linking is the dependency management: you must ensure the correct version of the ONNX Runtime .dll/.so is present on the target system in a known location. If it’s missing or mismatched, you get runtime errors. It “doesn’t play well with the Rust ecosystem” by default (⇒) because Rust favors static linking, but the ort crate provides mechanisms to handle it. In C/C++ scenarios, dynamic linking requires careful handling of library paths (using RPATH on Linux or bundling DLLs on Windows).

For Python users, this choice is abstracted away – using the pip package means you’re effectively using dynamic linking (the package ships a shared library). For Rust or C++ library developers, deciding static vs dynamic is an important build configuration. Static linking tends to be easier for end-users of your library (no external dependency to install), but it can increase your binary size. Dynamic linking can keep binaries smaller and allow swapping out the ORT library without recompiling your app (for instance, updating ORT separately), but introduces more points of failure in deployment.

There is also a hybrid approach in Rust that the ort crate supports: a feature called “load dynamic”. This is where you compile your Rust code without directly linking against ORT, and instead, at runtime, you dynamically load (dlopen) the ONNX Runtime library. This gives flexibility to decide which ORT binary to load (maybe different ones on different machines) and avoids the application failing to start if the library isn’t present – you can catch that error at runtime and handle it more gracefully (⇒) (⇒). The load-dynamic feature in the Rust crate is recommended when dynamic linking is needed because it lets you control the loading process and path, rather than letting the OS loader handle it blindly (⇒). In C/C++, one could implement a similar plugin loading approach, but it’s more manual.

In summary, static linking of ONNX Runtime yields a self-contained artifact and is often preferred for maximum reliability (especially in Rust), whereas dynamic linking is necessary in some cases and can be convenient for sharing libraries or updating them independently. The decision may depend on the execution providers you need (if all your needed EPs support static linking, that path is attractive; if not, dynamic can’t be avoided). It’s important for library developers to provide guidance or options to users: for instance, the Rust crate allows both methods via features, and a C++ library might offer CMake options for static vs dynamic linkage. One must also consider license implications (ORT is MIT licensed, so not problematic to static link) and compliance (some environments might require dynamic linking for security updates). But purely technically, both linking strategies are supported by ONNX Runtime.

4.2: How Linking Choices Affect Portability and Performance

Linking strategy has implications for portability, distribution size, and even to a small extent performance:

• Portability: Static linking can improve portability because the resulting binary carries its dependencies with it. For example, if you build a Rust executable that statically links ORT, you can drop that executable on another machine of the same OS/architecture and it will run, even if ONNX Runtime is not installed there. Dynamic linking, by contrast, requires that the target system have the correct ONNX Runtime library deployed in a known location (or that you bundle the .so/.dll alongside your app). If a user forgets to include the .dll or places it incorrectly, the app won’t launch. There’s also the matter of symbol/name mismatches if different versions get mixed up. In practice, many developers using dynamic linking will ship the .dll with the application installer or containerize the app with the needed library, which mitigates the issue. Static linking just avoids that whole class of issues. On the other hand, dynamic linking allows one library to be used by many apps, which on a system level means you could update ONNX Runtime in one place and multiple programs benefit (assuming ABI compatibility). For example, if a critical bug in ORT is fixed, updating the shared lib fixes it for all apps using that lib. Static-linked apps would each need to be rebuilt or redeployed.

• Binary size: Static linking will increase the size of your binary since it includes ORT’s code. ORT, especially with multiple execution providers, can be tens of megabytes (the CPU-only ORT is a few MB, but with CUDA, TensorRT, etc., it can grow significantly). This might be a concern for lightweight scenarios (like mobile apps or browser-based via WebAssembly). Dynamic linking keeps your binary lighter and puts the size into a separate file (which might be shared). However, if you have to ship that separate file anyway, the total size difference is often negligible – it’s more about whether you allow sharing or not.

• Performance: At runtime, the inference performance (throughput/latency of model execution) is generally the same whether ORT is statically or dynamically linked. The actual math and memory operations dominate execution time, not the way the binary was linked. There might be a tiny overhead for dynamic linking in function calls (PLT indirection) but in modern systems that’s usually not noticeable in the context of heavy computation like neural nets. One area where performance differs is startup time: a dynamically linked program needs to load the shared library and resolve symbols, which is a bit of extra I/O and CPU at launch. For long-running services this is trivial, but for short-lived CLI tools or serverless functions that spin up frequently, static linking can reduce cold-start time. Another aspect is memory: if multiple processes use the same dynamic library, the OS can load one copy into memory and let them share it, whereas statically linked instances mean each process has its own copy in memory. So for running many instances, dynamic linking could save RAM by sharing the code pages.

In essence, the linking choice doesn’t affect the core inference speed but does affect the deployment characteristics. Library developers should weigh these trade-offs. Often, providing both options is best: e.g., publish a Python wheel (dynamic) for ease of use, and also publish a statically linked binary for C++ or Rust usage where single-file deployment is valued.

From a user perspective, transparency and documentation are key. If your library uses ONNX Runtime, document if users need to install anything. For example, a Rust library might say “this comes bundled with ONNX Runtime statically linked for CPU – if you want GPU support, enable this feature and ensure you have the appropriate CUDA libraries available.” Or a C++ application might check at startup for the presence of onnxruntime.dll and give a clear error message if not found. ONNX Runtime’s own documentation and the ort crate maintainers explicitly call out these linking nuances so developers can avoid common pitfalls (like missing DLLs or unresolved symbols) (⇒) (⇒).

In summary, static linking tends to simplify portability at the cost of larger binaries, while dynamic linking can reduce footprint and allow shared usage at the cost of more complicated deployment. Performance differences are usually minor, except in memory usage when scaling out many processes or slight startup latency differences. Knowing these factors, you can make an informed decision that aligns with your project’s needs (e.g., a command-line tool might favor static for simplicity, whereas a system package might favor dynamic to integrate with system libraries).

4.3: Deploying ONNX Models Across Different Architectures

One of ONNX Runtime’s strengths is that it is cross-platform and supports various processor architectures – but to leverage that, library developers need to plan for how they’ll deploy ORT on different targets (x86, ARM, etc.). An ONNX model (.onnx file) is itself platform-neutral (it’s just data – a graph of ops). The question is ensuring that an ONNX Runtime implementation is available on the architecture where you want to run that model.

ONNX Runtime supports architectures like x86_64, ARM64, and others. For example, you may want to run on desktop CPUs (x86_64 Windows/Linux/macOS), mobile devices (ARM64 Android or iOS), or even specialized chips (like NVIDIA Jetson’s ARM64 CPU with CUDA GPU). The official ONNX Runtime project provides pre-built binaries for the most common combinations (often x64 Windows/Linux with CPU and GPU, some ARM builds, etc.), and the community (or the Rust crate maintainers) provides others (⇒) (⇒). If a pre-built package is not available for your target (say you need ONNX Runtime on a Raspberry Pi with ARM32), you have the option to compile from source for that architecture (⇒). The build system supports cross-compilation flags (there are even docs for cross-compiling to ARM or other platforms (⇒)).

When deploying across architectures, consider the following:

• Binary Compatibility: An ONNX Runtime binary is specific to an architecture and OS (e.g., an .so built for Linux aarch64 won’t run on Windows or on x86). So you may need to produce multiple builds of your library: one for each target platform. For Python, this might mean producing wheels for different platforms (many projects do this – e.g., a wheel for Linux x86_64, one for Windows x86_64, one for Mac, one for Linux ARM64, etc.). The onnxruntime PyPI packages actually do this behind the scenes: if you install onnxruntime on a Raspberry Pi (ARM32/64), pip will try to find a compatible wheel or you might have to compile it. For Rust, the ort crate can compile ORT for the target if you provide the build or it might offer precompiled for some targets (⇒). As a library dev, you might want to bundle the correct ORT binary for each platform you support. This can be handled by build scripts or conditional dependencies.

• Execution Provider Availability: Not all execution providers are available on all architectures. For example, CUDA and TensorRT are only for NVIDIA GPUs (mostly x86_64 and ARM64 with Linux in the case of Jetson). DirectML is only on Windows (for DirectX12-capable GPUs). CoreML is only on Apple devices. So when deploying to different hardware, you must consider which EPs make sense. On a mobile ARM device, you might use the NNAPI EP (Android) or CoreML EP (iOS) instead of CUDA. ONNX Runtime’s modular EP system means the core runtime is the same, but you include different EPs for each platform. Practically, this could mean you compile ORT with --use_coreml for iOS, with --use_nnapi for Android, with --use_cuda for a PC with NVIDIA, etc., or use pre-built packages that correspond to those. When distributing, you’d ensure the right variant is used on the right device. For example, if your library is running on Windows on ARM (say the new Windows on ARM PCs), you’d need an ORT build for ARM Windows (perhaps using the default CPU and the DirectML EP for GPU if any).

• Portable Model Considerations: ONNX as a format abstracts the differences in hardware, but occasionally you have to be mindful of ops that might not perform well everywhere. For instance, some operators might be supported by one EP but not another. If you deploy the same ONNX model to both an x86 server (with GPU) and an ARM edge device (CPU only), the model will run in both cases (since CPU can handle anything), but performance characteristics differ. If the model is large and was really designed for GPU, the ARM CPU might be too slow. So deployment across architectures might involve model optimization per target. You might choose to quantize the model for the ARM device to make it faster, or use a smaller model architecture altogether for edge vs. cloud. This isn’t an ONNX Runtime issue per se, but something to consider: ONNX makes it possible to run the same model everywhere, but whether it’s optimal to do so is a separate question. Best practice is to test your ONNX model on the weakest target device and see if performance is acceptable; if not, consider model changes or optimizations for that device.

• Testing and CI: To ensure smooth cross-architecture deployment, it’s wise to incorporate multiple architectures in your testing. For example, if you develop on Linux x64, also test on an ARM64 board or emulator to ensure that your packaging includes the right ORT and that everything runs. ONNX Runtime’s core is fairly consistent across platforms, so model results should be the same (barring minor floating-point differences). But testing will catch things like missing libraries (maybe you forgot to include the OpenMP library on ARM, etc.) or misconfigured EPs.

In practical terms, many library developers leverage containerization or pre-built distributions. For instance, if deploying to an Nvidia Jetson (ARM64 with GPU), one might use NVIDIA’s JetPack which includes ONNX Runtime with GPU support, or use Docker containers that have ORT built in. If targeting iOS, one would include ORT as a static library in the Xcode project (the ORT project provides an XCFramework for iOS). These platform-specific deployment steps need to be documented for your library’s users.

To sum up, ONNX Runtime is designed to be cross-platform, but the onus is on the library/application developer to package or compile the runtime for each target architecture they intend to support. Static vs dynamic linking, as discussed, also plays a role here: static linking can simplify deploying to different OS/arch by baking in the bits, whereas dynamic linking means you need to include the right .so for each platform. The good news is the ONNX model itself doesn’t need changes – it’s all about the runtime support. By planning for multi-architecture support (perhaps using continuous integration to build for several platforms and verifying the inference correctness and performance), a library developer can deliver a robust solution that uses ONNX Runtime to run models anywhere from cloud servers to mobile phones (⇒), fulfilling ONNX’s promise of flexible deployment.

5: Execution Providers and Performance Trade-offs

5.1: Overview of Available Execution Providers

ONNX Runtime boasts a wide array of execution providers, each tapping into different hardware or software accelerators. The CPU Execution Provider is the default built-in provider that uses the host CPU (with optimizations like multi-threading and vectorized instructions). In addition, there are many others for hardware acceleration:

• CUDA Execution Provider (NVIDIA GPU): Uses NVIDIA’s CUDA toolkit and cuDNN to execute ONNX ops on Nvidia GPUs. This provider can accelerate general compute-intensive models on a GPU using high-performance kernel implementations. It requires an NVIDIA GPU and the appropriate drivers/CUDA runtime.

• TensorRT Execution Provider (NVIDIA GPU): Integrates NVIDIA’s TensorRT, which is an optimizer and runtime specifically for neural network inference. The TensorRT EP takes whole subgraphs of the model and compiles them into highly optimized GPU executables, often yielding better performance than CUDA EP for supported models (especially CNNs and other well-supported architectures). It has the constraint that models (or subgraphs) must be supported by TensorRT – if they are, speed-ups can be significant (TensorRT specializes in low-latency, high-throughput inferencing). The trade-off is a longer initialization time (building engines) and sometimes needing static input shapes. Many production NVIDIA deployments use TensorRT for maximum throughput.

• DirectML Execution Provider (Cross-vendor GPU on Windows): DirectML allows hardware-accelerated inference on Windows 10/11 using any DirectX12-capable GPU (NVIDIA, AMD, or Intel integrated graphics). It’s particularly useful for Windows applications where you want GPU acceleration but not necessarily CUDA (e.g., an AMD GPU). It runs through the DirectML API which under the hood will use the GPU’s drivers.

• Intel OpenVINO Execution Provider: Targets Intel CPUs, iGPUs, and VPUs (like Intel’s Movidius sticks) by using the OpenVINO toolkit. OpenVINO is optimized for Intel architectures; it can speed up inference on Intel CPUs by using the MKL-DNN (oneDNN) library and on Intel GPUs or VPUs via specialized code. This EP might fuse operations and use low precision optimizations that are specifically tuned for Intel hardware, often giving an edge over the generic CPU provider on those platforms.

• oneDNN Execution Provider: (Previously called MKL-DNN or DNNL) – this is an alternative CPU EP that uses Intel’s oneDNN library for acceleration. In some ORT builds, instead of using ORT’s built-in MLAS (Microsoft Linear Algebra Subsystem) for CPU, you can use oneDNN for potentially better performance on certain CPU architectures (especially with Intel AVX512, etc.). It’s basically a faster CPU kernel implementation for many ops.

• NNAPI (Android Neural Networks API) Execution Provider: Allows usage of mobile SoC AI accelerators on Android devices. Many Android phones have DSPs or NPUs accessible via NNAPI. If an ONNX model contains ops that NNAPI can execute on specialized hardware (like Qualcomm Hexagon DSP), this EP will offload those, otherwise it falls back. It’s useful for mobile deployment to utilize on-device acceleration beyond the CPU.

• CoreML Execution Provider: Uses Apple’s CoreML framework to run models on Apple devices (iPhones, iPads, Macs). CoreML can leverage the Apple Neural Engine (ANE) on newer devices or GPU. So this EP is key for iOS deployments to get acceleration on the neural engine or Metal GPU.

• ROCm and MIGraphX (AMD GPUs): There are providers for AMD’s ROCm stack and for AMD’s MIGraphX (which is a graph optimizer for AMD GPUs). These are analogous to CUDA/TensorRT but for AMD hardware, though they are less commonly used unless you’re specifically targeting AMD in a data center scenario.

• XNNPACK Execution Provider: XNNPACK is a highly optimized library for mobile/ARM CPUs (used by TensorFlow Lite). ORT can use XNNPACK to accelerate ops like convolutions on ARM CPUs, which is great for Android/iOS devices without using a GPU – it’s faster than the default implementation for many ops on those platforms.

• Custom / Community EPs: Because ORT is extensible, there are also community-contributed EPs for various specialized hardware (e.g., Qualcomm’s QNN for Snapdragon DSPs, Huawei’s CANN for Ascend AI processors, Xilinx’s Vitis AI for FPGAs, etc.) (⇒). Some are official, some in preview. Essentially, if there’s a piece of hardware for accelerating neural nets, chances are an ONNX Runtime EP either exists or can be written for it.

In practice, the most commonly used EPs (as of today) are CPU, CUDA, TensorRT, and DirectML, with OpenVINO and NNAPI also popular in their respective domains. The ONNX Runtime documentation provides a summary table of supported EPs, indicating which are stable vs in preview (⇒). The variety of EPs means ORT is very flexible: the same ONNX model could run with different EPs on different devices – for example, CUDA EP on an Nvidia server, OpenVINO EP on an Intel-based edge device, or NNAPI on an Android phone.

For library developers, it’s important to know which EPs you want to support or include. Including every single EP is often not feasible because that could bloat the binary (each EP adds code and sometimes third-party dependencies). Instead, you’d pick based on your audience: e.g., if your library is for server-side inference with Nvidia GPUs, you’d include CUDA and TensorRT EPs. If it’s for mobile, you’d include NNAPI or CoreML EPs. The onnxruntime Python packages are split (there’s onnxruntime-gpu for CUDA/TensorRT, etc.) to keep size manageable. In Rust, you enable features for the EPs you need (the ort crate might mark some EP support as optional features, so you don’t pay the cost if you don’t use them).

5.2: Impact of Execution Provider Selection on Performance

Choosing the right execution provider can have a dramatic impact on performance. The general rule is: use the most specialized provider available for your hardware, as long as it supports your model’s operations. Here are a few considerations and trade-offs:

• CPU vs GPU: If running on a machine with a capable GPU and a large neural network model, using a GPU EP (like CUDA) will typically significantly outperform the CPU EP. GPUs excel at parallel math operations, so for CNNs, transformers, etc., you might see orders-of-magnitude speedup vs CPU. However, for very small models or models with lots of decision logic (think small trees or sparse models), the overhead of using a GPU (memory transfer, kernel launch latency) might not pay off. The CPU EP is often quite fast for moderate workloads, especially when using all threads. There is a crossover point where the model size/complexity justifies GPU. For library developers, it may be wise to benchmark typical models on CPU vs GPU to know when GPU is actually beneficial. Also, using GPU requires the whole deployment environment to have a GPU and drivers, which is a constraint.

• CUDA vs TensorRT (on NVIDIA): The CUDA EP is more general (it will run any op that has a CUDA implementation, one op at a time), whereas the TensorRT EP aims to maximize throughput by optimizing the model as a whole (fusing nodes, etc.). When TensorRT supports the model, it often yields better performance – for example, NVIDIA has reported cases where TensorRT doubles or triples throughput compared to naive CUDA execution, due to optimizations like layer fusion and precision calibration. But TensorRT doesn’t support every ONNX op or every model (especially custom or very new ops). So if you use TensorRT EP, you may still need a fallback (CUDA or CPU) for unsupported parts. That fallback can introduce overhead if the execution has to hop from TensorRT to CPU for a few ops. Therefore, performance might actually degrade if only a small portion of the model runs in TensorRT and the rest falls back with a lot of back-and-forth. A best practice is to check compatibility: there are tools to see if TensorRT will support your model fully. If it does, it’s likely the best choice for NVIDIA inference. If it partially does, weigh the cost of fragmentation. ORT will handle partitioning (e.g., one subgraph on TensorRT, remainder on CUDA or CPU). The fewer partitions the better for performance.

• Specialized accelerators vs general processors: Execution providers like OpenVINO, NNAPI, CoreML can give huge speedups on their respective hardware by using neural engine, DSP instructions, etc. For instance, OpenVINO EP might use Intel’s AVX512 and give a decent boost over the default CPU EP (MLAS) for certain models on Xeon processors (⇒) (⇒). But sometimes the difference is small or even negative if the EP introduces overhead or isn’t optimized for a particular model. A concrete example: oneDNN (MKL-DNN) EP might accelerate conv and matmul heavy models, but if your model is light on those and heavy on control flow or data manipulation, MLAS might suffice. So performance gains are model-dependent.

• Coverage of operators: Performance is not just raw speed of an EP but also how much of the model it can cover. An EP that can run the entire model will likely perform better than one that runs 90% and leaves 10% to CPU, because when mixing providers, there’s overhead in handing off data between them. Each boundary between EPs may incur memory copies (like CPU to GPU transfer) and synchronization barriers. Therefore, sometimes it can be faster to run entirely on CPU than split between CPU and GPU if very frequent small ops ping-pong between them. ORT’s partitioner tries to minimize fragmentation, but it’s constrained by what EPs report. As a developer, you might have to decide: do I allow partial acceleration or force a single device execution? ORT allows you to set the EP priority list; if you only list one provider (e.g., only TensorRT), then if something’s unsupported, it either errors or (if you include CPU as secondary) falls back. Including CPU as a fallback ensures correct execution, but with potential performance loss if fallback happens often. Some advanced users inspect the ORT execution plan (there are profiling tools) to see which providers ran which nodes, to identify if fallback is hurting performance.

• Multi-threading and parallelism: CPU EP can use multi-threading; GPU EP inherently uses massive parallelism on device. One should know that by default, ORT CPU will utilize multiple threads for ops like matrix multiplication, etc., and also can execute independent branches of the graph concurrently. If you use multiple CPU EPs (like oneDNN vs default), tuning thread numbers might affect results. For GPU, typically you drive them with one stream per inference. If you do batched processing, GPUs shine more. So the nature of your workload (single query vs batch of 16, etc.) might influence which EP is more performant.

In practice, benchmarking is crucial. ONNX Runtime provides performance tuning guidelines (⇒), but a lot comes down to trying different EPs for your specific model. For example, a bert-like transformer model might benefit greatly from NVIDIA’s TensorRT or OpenVINO with int8, whereas a simple logistic regression sees no benefit beyond CPU. A combination often used in deployment is running multiple providers in a prioritized list: e.g., ["CUDAExecutionProvider", "CPUExecutionProvider"] as mentioned, which gives the best of both – use GPU when possible, otherwise CPU (⇒). This typically ensures flexibility (works on machines with or without GPU) and performance (take advantage of GPU if present). The ONNX Runtime team suggests always including CPU as backup to guarantee execution (⇒).

One trade-off to highlight: including many EPs can increase load time and memory usage. If you enable everything (CUDA, TensorRT, OpenVINO, etc.) in one runtime, the initialization will load various drivers and libraries which has overhead. If you know your environment (e.g., this build is for GPU machines only), you can trim down to just needed EPs for efficiency.

In summary, execution provider selection is a key lever for performance tuning in ONNX Runtime. The fastest option is to use the most optimized EP available for the target hardware – usually a vendor-specific accelerator. But you must consider coverage and overhead. Often a combination like “specialized EP + CPU fallback” is used to balance performance with completeness. The ONNX Runtime architecture ensures that if an EP can speed up even part of the model, it will do so, which generally yields performance improvements as long as the accelerated part is significant enough to outweigh any transition overhead. Best practice is to test your particular model with different EP configurations to see which yields the best performance on your target hardware, and then deploy with that configuration.

5.3: Best Practices for Balancing Performance and Flexibility

Balancing performance and flexibility when using ONNX Runtime often means making your solution run fast on high-end hardware while still being able to run (perhaps more slowly) on lower-end hardware or in different environments. Here are some best practices for library developers:

• Use Provider Fallbacks Intelligently: As mentioned, configure multiple providers in priority order. For example, if you distribute a library that can use GPU, consider defaulting to ["CUDAExecutionProvider", "CPUExecutionProvider"]. This way, if the user has a GPU and the onnxruntime with CUDA, they get speed, and if not, it gracefully falls back to CPU. This dual-list approach is common because it provides flexibility without requiring separate code paths. However, be mindful that if performance on CPU is drastically worse, you might warn users or allow them to disable GPU-specific features. Conversely, if using an EP like TensorRT, which might not be present everywhere, you might keep it optional.

• Allow Configuration: Provide an easy way for users of your library to select or override the execution provider configuration. For instance, in a Python library that uses onnxruntime, you could allow an argument like device='cpu'|'cuda'|'tensorrt' or an environment variable to choose the EP. In Rust or C++, you might read a config or expose functions to register different EPs. This flexibility lets advanced users optimize for their scenario (maybe they know their model isn’t supported by TensorRT and want to skip it, etc.).

• Profile and Tune: Use ONNX Runtime’s profiling tools to understand where time is spent. ORT can output profiling data that shows each operator’s execution provider and time. This can reveal, for example, if a supposed GPU-run model is constantly falling back to CPU for certain ops, creating a bottleneck. If so, you might decide to transform the model to avoid that op or update ORT/EP to support it. It can also show if multi-threading is effective or if perhaps you should adjust the thread pool size. ORT allows controlling parallel threads via SessionOptions (e.g., intra_op_num_threads). For CPU-bound scenarios, tuning thread counts (especially in multi-model deployment) can yield better throughput.

• Match the Model to the Hardware: If you have influence over the model design (or provide guidelines to users exporting models), ensure the model is exportable to ONNX in a form that the desired EP can consume efficiently. Sometimes small changes (like using ops that fuse well, or avoiding dynamic shapes if possible for TensorRT, etc.) can let the EP shine. ONNX has optional post-training quantization – if you know the target is, say, an ARM CPU, providing a quantized model option (INT8) could improve performance a lot. This goes a bit beyond just ORT usage into model optimization, but it’s within the scope of using ORT effectively.

• Minimize Data Transfer Overheads: If your application needs to do pre- or post-processing, try to do it on the same device as the majority of the model to avoid moving data around. For example, if using a GPU EP, it can be efficient to feed input data that’s already on the GPU (ORT supports an API for binding pre-allocated device memory). Similarly, retrieve outputs on GPU if you plan to use them on GPU next. ORT’s I/O binding can help pin inputs/outputs to devices, avoiding unnecessary copy to CPU (⇒) (⇒). This is an advanced optimization but can be important for high-performance systems where PCIe transfers are costly.

• Stay Updated with ORT Releases: ONNX Runtime is actively developed, and each release often brings performance improvements, expanded EP support, and new optimizations. For instance, newer versions may support newer CUDA versions or have improved graph optimizations for transformer models. Keeping your library’s ORT version up to date can automatically give users a boost. The flip side is to be cautious of changes in EP behaviors and test your critical models with the new version.

• Testing and Fallback Logic: If including many EPs, test scenarios where some EPs aren’t available. E.g., if your library tries CUDA first, ensure it properly falls back to CPU if no CUDA (the onnxruntime session creation will throw if it can’t find the CUDA EP or the CUDA driver). You might catch exceptions and retry with CPU. The Python API typically just works if you list both, as long as you installed the GPU package. But say you had a single binary with both, you might do something like: try to load CUDA EP, if it fails, log a warning and use CPU. This way, the library is robust across environments.

• Documentation of Requirements: Clearly document what is needed for peak performance. If using TensorRT EP, note that “NVIDIA TensorRT 8.x must be installed and an NVIDIA GPU present” etc. If users run without meeting those requirements, they’ll fall back to CPU and might wonder about performance – documentation helps set expectations.

• Avoid Over-Engineering for Unused Flexibility: While flexibility is good, not every deployment needs every EP. It’s acceptable to have separate builds or installation options for different scenarios (that’s what ONNX Runtime itself does with different packages). For example, you might release a CPU-only version of your library that’s smaller and a GPU-enabled version that’s larger. Users then pick what they need. Trying to make one artifact that does absolutely everything can lead to complexity and bloat. So balance the need: if 99% of your users will either use CPU or an NVIDIA GPU, you might not need to include every other EP in your distributed package.

Balancing performance and flexibility is essentially about graceful degradation: get maximum acceleration when available, but don’t break or behave poorly when it isn’t. ONNX Runtime, by virtue of its design, helps a lot with this by allowing multiple providers in priority order and by always having a safe CPU fallback that supports all ops. By following the above practices, a library can deliver great performance on say, a powerful GPU workstation, while still functioning on a laptop with no GPU (perhaps slower, but correctly). The users can then decide to upgrade hardware or adjust settings if they need more speed, without needing a different codebase or model.

6: ONNX Runtime in Practice

6.1: Real-world Usage in Libraries and Applications

ONNX Runtime has been widely adopted in both open-source libraries and commercial applications as the inference engine of choice for ONNX models. Some notable examples:

• Microsoft Products: Given Microsoft’s development of ORT, it’s no surprise that many of their products use it. ONNX Runtime powers ML features in Bing Search and Ads, Office 365 (e.g., intelligent features in Word/Excel), and Windows. In Windows, the Windows ML API uses ONNX Runtime under the hood to allow Windows apps to do local ML inference using ONNX models. In the .NET ecosystem, ML.NET uses ONNX Runtime to score ONNX format models, taking advantage of hardware acceleration when available. These production uses show ORT’s ability to handle large-scale, latency-sensitive workloads; for example, Bing saw a ~2x improvement in inference performance by using ONNX Runtime for models that were originally implemented in other frameworks.

• Hugging Face and NLP Models: The Hugging Face Transformers community has embraced ONNX Runtime as a way to speed up Transformer model inference. Through the Optimum library, users can export transformer models (BERT, GPT-2, etc.) to ONNX and run them with ORT for acceleration (⇒). In fact, ONNX Runtime can accelerate over 130,000 models on Hugging Face’s model hub across various model types (⇒). Many popular transformer architectures (BERT, GPT-2, T5, etc.) are fully supported by ORT (⇒). This means an NLP library can offer ONNX Runtime as a backend for faster inference, especially beneficial when serving models in production. For example, Hugging Face’s Transformers.js (which runs models in the browser) uses ONNX Runtime WebAssembly backend to execute ONNX models in a browser environment (⇒). This is a testament to ORT’s flexibility – the same engine design can even run in a web browser (with a JS/WebASM EP).

• Open-Source Libraries and Frameworks: Many libraries outside of Microsoft’s ecosystem use ORT. For instance, the deep learning framework Chainer (by Preferred Networks) used ONNX Runtime to test its ONNX export and even built a wrapper called Menoh to run ONNX models, choosing ORT as the backend for execution. This shows that even framework developers trust ORT to run models independently of their own runtime. Another example is the ONNX Model Zoo – models in the zoo are often benchmarked or verified with ONNX Runtime to ensure they run correctly and efficiently. In the computer vision space, some projects use ORT to deploy models for image recognition, object detection, etc., taking advantage of EPs like TensorRT to meet real-time requirements. There are also community projects like OpenCV’s DNN module, which can import ONNX models and (while OpenCV has its own inference engine) some opt to use ORT for improved performance on certain ops.

• Industry and Enterprise: Many enterprises have integrated ONNX Runtime into their ML pipelines. For example, Qualcomm has worked with ORT to enable it on Snapdragon chips (via the QNN EP). NXP (IoT chip maker) supports ORT for their processors, giving their customers flexibility to run models from various frameworks using ORT on their edge devices. Cloud providers (besides Azure) also allow or use ORT – for instance, AWS has tutorials for running ONNX models on Lambda or EC2 with ONNX Runtime. In robotics, ORT is used to run models on constrained hardware using EPs like OpenVINO or TensorRT to get the needed speed. Essentially, ORT appears in any scenario where inference speed and framework interoperability are important.

• Rust Community Usage: The ort Rust crate being community-driven has seen uptake in Rust projects that need ML inference. Many of these projects highlight using ORT for its performance and the safety of Rust. Examples include integrating ONNX models in games (there’s a Bevy game engine plugin that uses ort for ML), or command-line tools that do things like OCR or object detection using ONNX models and ORT under the hood. The Rust binding’s maintainers note that “Many commercial, open-source, & research projects use ort in serious production scenarios to boost inference performance” (⇒) – implying that even outside the Python mainstream, ORT is considered reliable enough for production by Rust developers.

These real-world usages demonstrate that ONNX Runtime isn’t just an academic or experimental tool; it’s battle-tested in scenarios ranging from cloud services to mobile apps. For library developers, leveraging ORT means you are building on the same engine that powers large-scale systems, giving confidence in its stability and performance. It also means there’s a community of practice and knowledge – you can find examples of similar use cases to learn from. For instance, if you’re developing a cross-platform app in Flutter and want to use ORT, Microsoft even published a blog on how Pieces.app used ONNX Runtime in a Flutter app (⇒), indicating community knowledge on mobile integration.

6.2: Integration Challenges and Cross-Platform Deployment Solutions

Integrating ONNX Runtime into a library or application, especially across multiple platforms, can introduce some challenges. However, there are known solutions and patterns for each:

• Managing Native Binaries in Different Environments: ONNX Runtime is a native library, so when you use it in Python or Rust (or any language), you need to have the compiled binaries for each OS/architecture. In Python, pip wheels take care of this (you install onnxruntime and get a pre-compiled .dll/.so). The challenge is if you need GPU support, you have to install the GPU-specific package (onnxruntime-gpu), and that package has to match your CUDA version. Some users might not realize they need a different install for GPU. A solution is to clearly document or even programmatically check: e.g., if your library detects an available CUDA but the CPU-only ORT, it could warn or prompt to install the GPU package. In Rust, the crate can either vendor the ORT binaries or build them. The ort crate chose to supply prebuilt ORT for some common targets and an easy path to compile for others (⇒) (⇒). A challenge here is large file sizes or linking complexities (as discussed in linking strategies). The solution was environment variables like ORT_LIB_LOCATION to point to custom builds (⇒), and features to toggle linking modes.

• Cross-Platform API Consistency: ONNX Runtime’s API is intended to be consistent across platforms, but sometimes certain EPs are only available on some platforms, which might lead to conditional code. For example, if you try to register the NNAPI EP on a non-Android system, it will error. So your integration might need #ifdef or runtime checks before enabling an EP. A solution is to abstract that in your code: e.g., only add the NNAPI provider in your providers list if sys.platform indicates Android. The Python onnxruntime package actually does this internally to some extent – it won’t list unavailable EPs. But if you manually call them you’d see errors. So defensive programming (trying an EP and catching exception) is a way to handle it. In cross-platform deployment, one often ends up with a matrix of supported combinations. The ONNX Runtime documentation and the crate docs help identify which EPs are supported where (⇒), so use that to guide what to enable.

• Hardware Drivers and Dependencies: Using certain EPs requires that the target system has the appropriate drivers or libraries. For instance, the CUDA EP needs the NVIDIA CUDA driver and compatible GPU. The TensorRT EP needs the TensorRT library installed on the system (ORT calls into it). If those aren’t present, the EP will not initialize. This can cause confusion if, say, a user has onnxruntime-gpu installed but no GPU – ORT might fall back to CPU silently, which is fine, but if TensorRT is enabled and not present, you might get an init error. The key is to handle these scenarios. Solutions include: packaging required libraries with your app (some do that for convenience, though NVIDIA’s EULA might restrict bundling CUDA in some cases), or clearly instructing users to install prerequisites for advanced EPs. ORT tries to give descriptive errors when an EP fails to load. A library could catch these and provide a clearer message like "TensorRT not found, please install it or remove it from the provider list."

• Model Conversion and Compatibility: Getting a model into ONNX form that is well-supported by ONNX Runtime can be a hurdle. Different frameworks have different ONNX exporter maturity. A library might want to include pre-converted ONNX models to sidestep that. If you expect users to bring their own models, you may need to assist with conversion (perhaps using onnxruntime.tools or recommending tf2onnx, skl2onnx, etc.). Testing the ONNX model with ONNX Runtime (perhaps using onnx.checker and a test run) can catch incompatibilities early. As a library developer, you might maintain a list of “supported models” or patterns known to work with ORT to set user expectations.

• Performance Tuning for Each Platform: Once integrated, you might face performance differences across platforms (as discussed earlier). For cross-platform deployment, ensure you’re using the best EP each offers. That could mean compiling multiple variants of your library: e.g., one with TensorRT for x86_64 Linux, one with NNAPI for Android, etc. Alternatively, a single binary could include multiple EPs and choose at runtime, but that increases size. Some companies solve this by having platform-specific builds or plugins. For instance, an app could have a plugin system: the core uses ORT CPU (works everywhere), and if on Android, load an NNAPI plugin, if on Windows with GPU, load DirectML plugin. This modular approach keeps flexibility high.

• Memory and Resource Constraints: On mobile/edge, memory is at a premium. ONNX Runtime on those platforms can be built with fewer ops (using a reduced op set) to save space. If you deploy to such constrained environments, consider using ORT’s ability to compile a custom build that includes only the ops your model needs, drastically reducing binary size. This is a bit advanced as it requires analysis of the model and building ORT from source with a custom config. But it’s a solution to the challenge of ORT’s size on tiny devices. ORT also has an “ORT format” for models that loads faster and is more size-efficient, which can be used in those scenarios (⇒).

In essence, cross-platform deployment with ONNX Runtime is a solved problem in many ways – numerous projects have done it, so there are established patterns. Using continuous integration to build/test on each target platform is extremely helpful: it catches issues early. For example, if your library CI builds on Linux, Windows, Mac, and runs a sample ONNX model through ORT on each, you can verify that all needed pieces are in place. Containerization is another helpful tool: you can ship a Docker image with ORT set up properly for those who want a turnkey solution.

Community forums (GitHub issues, ONNX Runtime’s GitHub, StackOverflow) are full of Q&A on integrating ORT in various scenarios, which can be a resource. As a library developer, embracing ORT’s cross-platform nature means you can support a wide user base – from cloud VMs to smartphones – but it does require careful planning of packaging and thorough testing in each environment.

6.3: Best Practices for Efficient and Maintainable ONNX Runtime Usage

To wrap up, here are some best practices for using ONNX Runtime in a way that is efficient (performance-wise) and maintainable (easy to support and update) within libraries or applications:

• Encapsulate ORT Usage: Hide the details of ONNX Runtime behind an abstraction in your library. For example, write a wrapper class that handles session creation, provider selection, input/output preparation, etc. This way, if you need to swap out or update anything about ORT (say, new provider APIs or session options), you do it in one place. Users of your library just call a simple interface like result = model.run(input) without needing to know it’s ONNX Runtime under the hood. This also means if one day you wanted to allow an alternative runtime, you could slot it in, but likely ORT will serve all needs.

• Resource Management: ONNX Runtime sessions are meant to be reused. It’s best to initialize the InferenceSession once (which does model loading and optimizations) and then reuse it for multiple inferences. Creating a session is somewhat expensive, so don’t do it per inference if you can avoid it. In a library, you might load the model at startup or the first time it’s needed, then cache that session. Also, be mindful to release resources if needed (in Python, sessions are closed when garbage-collected; in C++/Rust, make sure to drop them when no longer needed to free memory). ORT also has an Env singleton for thread pools – use a single Env if doing multiple sessions to reuse threads (the Python API handles this internally).

• Stay Aligned with ONNX Standards: Ensure the ONNX models you use or recommend comply with the official ONNX opsets and datatypes supported by ONNX Runtime. Avoid custom ONNX ops if possible, because then you’d have to implement custom kernels. ONNX Runtime does allow custom ops and custom EPs, but that complicates maintainability. It’s easier to stick to standard ops that ORT supports out-of-the-box so you benefit from its ongoing improvements. If you have a use-case that absolutely needs a custom operation, you can write a custom operator and register it with ORT, but weigh the maintenance cost (you’d have to maintain that code as ORT updates).

• Use Stable APIs and Monitor Deprecations: ONNX Runtime’s APIs (especially in higher-level languages) are fairly stable. But occasionally, provider names or options might change (e.g., a new version might deprecate an old EP name or change default behaviors like the requirement to explicitly specify providers from 1.10 onward (⇒)). Keep an eye on release notes of new ORT versions. The maintainability tip is to not pin to a very old ORT if possible; try to update ORT versions in your library periodically (after testing). The longer you stay on an old version, the more effort it might be later to jump to the latest. Minor version bumps usually don’t break things, but it’s good to test.

• Leverage ONNX Runtime Features: Use the features ORT provides instead of reinventing them. For instance, if you need to run multiple models in parallel, consider using ORT’s built-in thread management or running separate sessions on different threads (ORT is thread-safe for inference). If you need to run on GPU but memory is a concern, use the memory arena and allocator APIs ORT provides to potentially pool memory. For maintainability, using ORT’s ecosystem (like their optimization scripts, quantization tool, etc.) can save you from maintaining your own optimization code.

• Logging and Debugging: ORT can log a lot of useful information when enabled (through session options or environment variables). In a production library, you’d keep logging off or minimal, but for debugging user issues it can be helpful to allow a debug flag that turns on ORT verbose logging. This can output which EP is doing what, if any ops are falling back, if any optimizations were not applied, etc., which can greatly speed up diagnosing performance or correctness problems. In maintainability terms, this is your insight into the black box when something goes wrong. Encourage users (or internally, use it) to provide ORT logs when reporting an issue.

• Security Considerations: ONNX Runtime is a native codebase executing potentially untrusted model files (if you load models from outside). Make sure to use a recent version as security fixes do come (e.g., to ONNX parsing). Also, consider the source of ONNX models – like any binary format, malformed ONNX could crash the runtime. If your library accepts arbitrary ONNX from users, you might want to wrap session creation in a try/except and handle failures gracefully. The maintainers of ORT actively address such issues, but it’s good to be aware.

• Community and Support: To maintain your integration well, keep connected with the ONNX/ORT community. If you encounter a bug or performance issue in ORT, don’t hesitate to check the GitHub issues or file a new one – the project is quite active. Sometimes what you think is an issue in your code might be a known quirk in a specific ORT version with a workaround or fix in the pipeline. Likewise, share your usage: if your library is popular, the ORT maintainers are keen to know and sometimes incorporate feedback that benefits your scenario.

In conclusion, using ONNX Runtime in a library involves considerations at both development-time (setting up the build, choosing EPs, writing the integration code) and run-time (performance tuning, handling different environments). By understanding its architecture and using its features as intended, you can create a solution that is both fast and robust. ONNX Runtime effectively abstracts away a lot of complexity of multi-hardware inference, letting you focus on the higher-level functionality of your library. By following the outlined practices – from correct provider configuration to cross-platform testing and optimization – library developers can harness the full power of ONNX Runtime’s efficient inference while keeping their codebase clean and maintainable for the long term. The end result is delivering to users a flexible AI inference capability: they can take a model from anywhere, and with your library and ONNX Runtime, run it everywhere. (⇒) (⇒)

Key References

• ONNX Runtime: High level design