{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"onnx-runtime","slug":"onnx-runtime","name":"ONNX Runtime","type":"framework","url":"https://github.com/microsoft/onnxruntime","page_url":"https://unfragile.ai/onnx-runtime","categories":["deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"onnx-runtime__cap_0","uri":"capability://automation.workflow.multi.backend.inference.execution.with.pluggable.execution.providers","name":"multi-backend inference execution with pluggable execution providers","description":"Executes ONNX models across heterogeneous hardware (CPU, NVIDIA GPU via CUDA, AMD GPU via ROCm, Intel GPU via Level Zero, Apple Silicon via CoreML, Qualcomm NPU via QNN) through a provider bridge architecture that abstracts hardware-specific kernel implementations. The execution provider interface (defined in core/providers) allows runtime selection of compute backends with automatic fallback chains, enabling a single model to run on any supported platform without recompilation.","intents":["Deploy the same ONNX model across CPU, GPU, and specialized hardware without code changes","Automatically select the fastest available execution provider at runtime based on hardware detection","Implement fallback chains so inference continues on CPU if GPU memory is exhausted","Optimize inference latency by leveraging hardware-specific kernels (TensorRT for NVIDIA, CoreML for Apple)"],"best_for":["ML engineers deploying models to heterogeneous infrastructure (cloud + edge + mobile)","Teams requiring single-codebase inference across Windows, Linux, macOS, iOS, Android","Production systems needing automatic hardware acceleration discovery"],"limitations":["Execution provider initialization adds 100-500ms overhead on first inference (provider library loading)","Not all ONNX operators are implemented for all providers — some ops fall back to CPU, causing performance cliffs","Provider-specific quantization formats (e.g., TensorRT INT8) require separate model conversion pipelines","Memory management across providers is manual — IOBinding required for zero-copy GPU inference"],"requires":["ONNX model in opset 7+ format","For CUDA: NVIDIA GPU with compute capability 3.5+, CUDA 11.0+, cuDNN 8.0+","For TensorRT: NVIDIA GPU, TensorRT 8.0+","For CoreML: macOS 11.0+ or iOS 14.0+","For CPU: x86-64 or ARM64 processor"],"input_types":["ONNX model files (.onnx)","Model bytes in memory","Pre-allocated GPU/CPU tensors via IOBinding"],"output_types":["Inference results as CPU or GPU tensors","Execution timing metrics per provider"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_1","uri":"capability://data.processing.analysis.graph.level.optimization.with.operator.fusion.and.memory.planning","name":"graph-level optimization with operator fusion and memory planning","description":"Applies compile-time graph transformations (constant folding, operator fusion, dead code elimination, layout optimization) through a modular optimizer pipeline (onnxruntime/core/optimizer) that rewrites the computation graph before execution. The optimizer analyzes data flow dependencies and fuses multiple operators into single kernels (e.g., Conv+BatchNorm+ReLU → single fused kernel), reducing memory bandwidth and kernel launch overhead. Memory planning assigns tensor lifetimes and reuses buffers across the graph to minimize peak memory usage.","intents":["Reduce model latency by 20-40% through operator fusion without changing model semantics","Lower peak memory consumption by 30-50% via buffer reuse and in-place operations","Eliminate redundant computations (constant folding, dead code removal) before inference","Optimize tensor layouts (NCHW ↔ NHWC) to match hardware-native formats"],"best_for":["Teams deploying large models on memory-constrained devices (mobile, edge)","Latency-critical inference pipelines (real-time video, autonomous systems)","Production systems where 10-20% speedup directly impacts cost/throughput"],"limitations":["Graph optimization is deterministic but opaque — debugging fused operators requires disabling optimization","Some operator fusions are provider-specific (TensorRT fusions differ from CPU MLAS fusions), requiring separate optimization passes","Custom operators bypass the optimizer — fusion only applies to standard ONNX ops","Optimization time adds 50-200ms to session creation (amortized over many inferences but impacts startup latency)"],"requires":["ONNX model with standard operators (custom ops not optimized)","Session creation with optimization level set (SessionOptions.graph_optimization_level)","No dynamic shapes in critical paths (optimizer assumes static tensor dimensions)"],"input_types":["ONNX computation graph","Operator metadata and type information"],"output_types":["Optimized computation graph","Memory allocation plan","Fusion statistics (number of fused ops, memory saved)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_10","uri":"capability://data.processing.analysis.model.profiling.and.performance.analysis.with.per.operator.timing","name":"model profiling and performance analysis with per-operator timing","description":"Provides built-in profiling capabilities (onnxruntime/core/framework/profiler.h) that measure execution time per operator, memory allocation, and provider-specific metrics. The profiler instruments the inference session to collect timing data for each operator kernel execution, memory usage per tensor, and provider-specific counters (GPU utilization, cache hits). Results are exported as JSON or CSV for analysis, enabling identification of performance bottlenecks and optimization opportunities.","intents":["Identify performance bottlenecks by measuring per-operator execution time","Analyze memory usage patterns to optimize memory allocation and buffer reuse","Compare performance across execution providers (CPU vs GPU vs TensorRT)","Profile model optimization impact (measure speedup from fusion, quantization, etc.)"],"best_for":["Performance engineers optimizing model inference latency","Teams comparing execution providers and hardware configurations","Developers validating optimization impact before deployment"],"limitations":["Profiling adds 5-15% overhead due to timing instrumentation","Per-operator timing is approximate — kernel launch overhead and synchronization add noise","Memory profiling is coarse-grained (per tensor, not per allocation)","Provider-specific metrics vary — GPU profiling requires NVIDIA profiling tools for detailed analysis"],"requires":["SessionOptions.enable_profiling = True","Inference session with profiling enabled","Output directory for profiling results"],"input_types":["Inference session with profiling enabled","Input tensors for inference"],"output_types":["Profiling results (JSON/CSV with per-operator timing)","Memory usage statistics","Provider-specific metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_11","uri":"capability://tool.use.integration.cross.language.api.bindings.with.c.c.python.c.and.javascript.support","name":"cross-language api bindings with c/c++, python, c#, and javascript support","description":"Provides language bindings (onnxruntime/core/session/onnxruntime_c_api.h, Python bindings, C# bindings, JavaScript/Node.js bindings) that expose ONNX Runtime functionality across multiple programming languages. The C API (onnxruntime_c_api.h) is the lowest-level interface with stable ABI, while higher-level bindings (Python, C#) provide Pythonic/C#-idiomatic APIs. All bindings share the same underlying C++ engine, ensuring consistent behavior and performance across languages.","intents":["Use ONNX Runtime from Python for ML workflows, C++ for production systems, C# for .NET applications","Integrate ONNX inference into web applications via JavaScript/Node.js bindings","Build language-agnostic inference services with stable C API","Maintain consistent model behavior across different deployment languages"],"best_for":["Teams with polyglot codebases requiring inference across multiple languages","ML engineers using Python for development and C++ for production deployment","Web developers integrating ONNX inference into Node.js or browser applications"],"limitations":["Language bindings have different feature coverage — some advanced features only available in C++","Python bindings add 5-10% overhead due to GIL (Global Interpreter Lock) and type marshaling","JavaScript bindings are limited to Node.js; browser support requires WebAssembly compilation","C# bindings are Windows-focused; Linux/macOS support is limited"],"requires":["ONNX Runtime library compiled for target platform","Language-specific runtime (Python 3.7+, .NET 6.0+, Node.js 14+, etc.)","For C API: C/C++ compiler and linker"],"input_types":["ONNX model file or bytes","Input tensors in language-native format (numpy arrays, C# arrays, etc.)"],"output_types":["Output tensors in language-native format","Inference results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_12","uri":"capability://data.processing.analysis.dynamic.shape.handling.and.symbolic.dimension.inference","name":"dynamic shape handling and symbolic dimension inference","description":"Supports models with dynamic shapes (variable batch sizes, sequence lengths) through symbolic dimension tracking (onnxruntime/core/graph/graph.h) where tensor dimensions can be symbolic variables (e.g., batch_size, seq_len) rather than fixed integers. The shape inference system propagates symbolic dimensions through the graph, computing output shapes as expressions of input dimensions. At runtime, actual shapes are bound to symbolic variables, enabling the same model to handle variable-sized inputs without recompilation.","intents":["Deploy models with variable batch sizes without recompilation or model duplication","Handle variable-length sequences (NLP, time series) with a single model","Optimize memory allocation based on actual input shapes at runtime","Support dynamic batching in inference servers"],"best_for":["Inference servers handling variable batch sizes (dynamic batching)","NLP models processing variable-length sequences","Time series models with variable sequence lengths"],"limitations":["Dynamic shapes complicate graph optimization — some fusions are disabled for dynamic shapes","Memory allocation is less predictable — peak memory usage depends on actual input shapes","Some operators don't support dynamic shapes (e.g., reshape with computed dimensions)","Shape inference is best-effort — some dynamic shapes cannot be inferred statically"],"requires":["ONNX model with symbolic dimensions (e.g., batch_size=None)","Input shapes provided at runtime","Operators that support dynamic shapes"],"input_types":["ONNX model with symbolic dimensions","Actual input tensor shapes at runtime"],"output_types":["Output tensor shapes computed from input shapes","Inference results"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_13","uri":"capability://automation.workflow.multi.threaded.inference.with.inter.op.and.intra.op.parallelism.control","name":"multi-threaded inference with inter-op and intra-op parallelism control","description":"Supports concurrent inference execution through configurable thread pools for inter-op parallelism (parallel execution of independent operators) and intra-op parallelism (parallel execution within a single operator kernel). SessionOptions allows configuration of thread pool sizes, scheduling policies, and affinity settings. The runtime uses a task-based execution model where operators are scheduled as tasks on thread pools, enabling efficient multi-core utilization without explicit thread management.","intents":["Maximize CPU utilization by running independent operators in parallel","Parallelize large matrix operations (GEMM, convolution) across multiple cores","Configure thread pool sizes based on hardware (number of cores, NUMA topology)","Implement CPU-based batching with multi-threaded inference"],"best_for":["Multi-core CPU inference servers maximizing throughput","Latency-sensitive applications on high-core-count CPUs","Teams optimizing for specific hardware topologies (NUMA, heterogeneous cores)"],"limitations":["Thread pool overhead (context switching, synchronization) can exceed benefits for small models","Inter-op parallelism is limited by data dependencies — many models have sequential operator chains","Thread affinity configuration is platform-specific (Linux, Windows, macOS differ)","Oversubscription (too many threads) causes performance degradation due to context switching"],"requires":["Multi-core CPU (2+ cores)","SessionOptions configuration (inter_op_num_threads, intra_op_num_threads)","Models with parallelizable operator structure"],"input_types":["ONNX model","SessionOptions with thread pool configuration"],"output_types":["Inference results","Execution timing and thread utilization metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_2","uri":"capability://data.processing.analysis.quantization.aware.inference.with.mixed.precision.execution","name":"quantization-aware inference with mixed-precision execution","description":"Executes quantized ONNX models (INT8, INT4, float16) with hardware-native quantized kernels through provider-specific quantization operators (QuantizeLinear, DequantizeLinear, QLinearConv, QLinearMatMul). The runtime preserves quantization metadata in the graph and dispatches to optimized quantized kernels on supported hardware (NVIDIA TensorRT INT8, Intel OpenVINO, ARM QNNPACK), falling back to dequantized CPU execution if unavailable. Supports mixed-precision graphs where some layers run in INT8 and others in float32.","intents":["Run quantized models 2-4x faster than float32 with <1% accuracy loss on supported hardware","Deploy models on memory-constrained devices by reducing model size 4x (float32 → INT8)","Leverage hardware quantization engines (TensorRT, OpenVINO) without manual kernel optimization","Mix quantized and float layers in a single model for accuracy-critical operations"],"best_for":["Mobile and edge deployment teams targeting 50-100ms inference latency budgets","Cloud inference services optimizing for throughput and cost (quantization reduces memory bandwidth)","Teams with pre-quantized models from training frameworks (PyTorch, TensorFlow)"],"limitations":["Quantization is provider-specific — INT8 kernels on NVIDIA differ from ARM QNNPACK, requiring separate optimization","Not all operators support quantization — unsupported ops fall back to float32, breaking the quantization chain","Quantization parameters (scale, zero-point) must be pre-computed during model conversion; runtime quantization not supported","Mixed-precision graphs require manual layer-by-layer quantization decisions — no automatic mixed-precision like PyTorch AMP"],"requires":["Pre-quantized ONNX model with QuantizeLinear/DequantizeLinear operators","Quantization parameters (scale, zero-point) embedded in model or provided at runtime","For hardware acceleration: provider-specific quantization support (TensorRT, OpenVINO, QNNPACK)"],"input_types":["Quantized ONNX model (.onnx with INT8/INT4/float16 tensors)","Quantization metadata (scale, zero-point per tensor)"],"output_types":["Quantized inference results (INT8 or float32 depending on output layer)","Quantization statistics (min/max values, scale factors)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_3","uri":"capability://data.processing.analysis.onnx.model.loading.and.graph.serialization.with.shape.inference","name":"onnx model loading and graph serialization with shape inference","description":"Loads ONNX model files (.onnx protobuf format) into an in-memory graph representation (onnxruntime/core/graph/graph.h) with full operator metadata, tensor type information, and shape inference. The loader parses the ONNX protobuf, validates operator signatures against the ONNX opset specification, and runs shape inference to compute output tensor dimensions from input shapes. Supports model serialization back to ONNX format after graph transformations, enabling round-trip optimization and export.","intents":["Load ONNX models from disk or memory into a runtime-optimized graph representation","Validate model correctness (operator signatures, tensor types) before execution","Infer output tensor shapes from input shapes without running inference","Export optimized graphs back to ONNX format for inspection or sharing"],"best_for":["ML engineers validating model compatibility before deployment","Teams building model serving infrastructure that needs shape information for memory allocation","Developers debugging graph transformations and optimizations"],"limitations":["Shape inference is static — dynamic shapes (e.g., batch_size=None) require explicit dimension tracking","Large models (>2GB) load entirely into memory; no streaming or lazy loading","Model validation is strict — non-standard ONNX extensions may fail to load","Shape inference is best-effort — some operators (e.g., with control flow) cannot infer shapes statically"],"requires":["Valid ONNX model file (opset 7+)","ONNX opset definitions for operator validation","Sufficient memory to load entire model graph"],"input_types":["ONNX model file (.onnx)","Model bytes in memory","Input tensor shapes for shape inference"],"output_types":["In-memory graph representation (Graph object)","Inferred output tensor shapes and types","ONNX model file (after optimization)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_4","uri":"capability://automation.workflow.inference.session.management.with.session.configuration.and.state.isolation","name":"inference session management with session configuration and state isolation","description":"Creates and manages inference sessions (onnxruntime/core/session/inference_session.h) that encapsulate model state, execution provider selection, memory allocators, and optimization settings. Each session is independent with isolated memory pools, thread-local execution contexts, and configurable session options (graph optimization level, execution provider order, memory patterns, inter-op/intra-op parallelism). Sessions support both synchronous Run() and asynchronous RunAsync() execution with callback-based result handling.","intents":["Create isolated inference contexts for multi-model or multi-tenant serving scenarios","Configure per-session optimization levels, execution providers, and memory strategies","Run multiple inferences concurrently with thread-safe session state","Implement asynchronous inference pipelines with callback-based result handling"],"best_for":["Production inference servers handling multiple models or concurrent requests","Teams requiring fine-grained control over per-session resource allocation","Latency-sensitive applications needing asynchronous execution"],"limitations":["Session creation overhead is 100-500ms (graph optimization, provider initialization) — reuse sessions across requests","Thread safety is per-session; sharing a session across threads requires external synchronization","Memory allocators are session-scoped — no cross-session memory sharing or pooling","Asynchronous execution requires manual callback management; no built-in promise/future abstraction"],"requires":["ONNX model loaded into memory","SessionOptions configuration (execution providers, optimization level)","For async: callback function signature matching ORT's async interface"],"input_types":["ONNX model (Graph object)","SessionOptions configuration","Input tensors (CPU or GPU)"],"output_types":["InferenceSession object","Output tensors","Execution timing and profiling data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_5","uri":"capability://tool.use.integration.custom.operator.registration.and.extension.system","name":"custom operator registration and extension system","description":"Allows developers to register custom operators (not in standard ONNX opset) through a plugin architecture (onnxruntime/core/session/custom_ops.cc) where custom kernels implement a standardized interface (CustomOpBase) and are registered per execution provider. Custom operators can be implemented in C++ or loaded from external libraries (.dll, .so), enabling domain-specific optimizations (e.g., custom attention kernels, proprietary image processing ops). The registration system integrates custom ops into the graph optimizer and execution pipeline.","intents":["Implement proprietary or domain-specific operators not in standard ONNX (e.g., custom attention, image filters)","Optimize critical operators with hand-tuned kernels for specific hardware","Extend ONNX Runtime with operators from external libraries without modifying core code","Support models trained with custom layers from PyTorch or TensorFlow"],"best_for":["Teams with proprietary models requiring custom operators","Performance-critical applications needing hand-optimized kernels for specific ops","Researchers prototyping novel operators before standardization"],"limitations":["Custom operators bypass graph optimization — fusion and memory planning don't apply","Custom ops must be registered per execution provider; a single custom op may need multiple implementations (CPU, CUDA, etc.)","Type inference for custom ops is manual — no automatic shape/type propagation","Custom operator libraries must be loaded at session creation; dynamic loading not supported"],"requires":["C++ implementation of CustomOpBase interface","Operator schema definition (input/output types, attributes)","Compilation to shared library (.dll, .so) or static linking","Registration via SessionOptions.AppendExecutionProvider or custom op library loading"],"input_types":["Custom operator implementation (C++ class inheriting CustomOpBase)","Operator schema (input/output tensor types, attributes)","Compiled shared library or static code"],"output_types":["Registered custom operator available in graph execution","Custom operator results (tensors)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_6","uri":"capability://data.processing.analysis.cpu.optimized.kernels.via.mlas.math.linear.algebra.subroutines","name":"cpu-optimized kernels via mlas (math linear algebra subroutines)","description":"Provides hand-optimized CPU kernels for common operations (GEMM, convolution, element-wise ops, quantized operations) through the MLAS library (onnxruntime/core/mlas), which implements SIMD-accelerated kernels for x86-64 (AVX2, AVX-512) and ARM64 (NEON, SVE). MLAS kernels are auto-tuned for different CPU architectures and cache hierarchies, providing 2-10x speedup over generic implementations. The CPU execution provider dispatches operators to MLAS kernels when available, falling back to reference implementations for unsupported ops.","intents":["Achieve 2-10x CPU inference speedup through SIMD-optimized kernels without GPU","Deploy models on CPU-only infrastructure (servers, edge devices) with competitive latency","Support diverse CPU architectures (x86-64, ARM64) with architecture-specific optimizations","Reduce model latency on cost-constrained deployments where GPU is unavailable"],"best_for":["Teams deploying inference on CPU-only servers or edge devices","Cost-sensitive deployments where GPU acceleration is not economical","Latency-critical applications on ARM64 devices (mobile, IoT)"],"limitations":["MLAS kernels are limited to common operations (GEMM, Conv, element-wise); specialized ops fall back to reference implementations","Performance is architecture-dependent — AVX-512 kernels 2-3x faster than AVX2, but not all CPUs support AVX-512","Memory bandwidth is the bottleneck for many operations; SIMD optimization provides limited speedup for memory-bound kernels","MLAS kernels are single-threaded; multi-threaded parallelism requires external thread pool (OpenMP, TBB)"],"requires":["x86-64 CPU with AVX2 support (minimum) or ARM64 CPU with NEON support","CPU execution provider enabled in SessionOptions","For best performance: modern CPU with AVX-512 or ARM SVE support"],"input_types":["Tensors in CPU memory","Operator parameters (weights, biases)"],"output_types":["Computed tensors in CPU memory","Execution timing per kernel"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_7","uri":"capability://tool.use.integration.iobinding.for.zero.copy.gpu.inference.with.pre.allocated.memory","name":"iobinding for zero-copy gpu inference with pre-allocated memory","description":"Enables zero-copy GPU inference by allowing pre-allocated GPU tensors to be bound directly to model inputs/outputs, bypassing CPU-GPU memory transfers. IOBinding (onnxruntime/core/framework/iobinding.h) maps input/output names to GPU memory addresses, allowing the inference engine to read from and write to GPU memory without intermediate CPU copies. Supports both CUDA and other GPU backends, enabling efficient batched inference and integration with GPU-based data pipelines.","intents":["Eliminate CPU-GPU memory transfer overhead for GPU inference (10-30% latency reduction)","Integrate ONNX Runtime into GPU-based data processing pipelines without CPU bottlenecks","Implement efficient batched inference with pre-allocated GPU memory pools","Support real-time inference on GPU-resident data (video frames, sensor streams)"],"best_for":["High-throughput GPU inference servers processing batches","Real-time applications with GPU-resident data (video processing, autonomous systems)","Teams optimizing for latency-critical inference with GPU acceleration"],"limitations":["IOBinding requires manual memory management — developers must allocate and manage GPU memory","Tensor shapes must be known at binding time; dynamic shapes require rebinding","IOBinding is provider-specific (CUDA IOBinding differs from other GPU providers)","Incorrect memory layout or shape mismatches cause silent failures or crashes"],"requires":["GPU execution provider enabled (CUDA, TensorRT, etc.)","Pre-allocated GPU memory (via CUDA malloc, cuDNN, or provider-specific allocators)","Knowledge of input/output tensor shapes and memory layout"],"input_types":["GPU memory pointers (void*)","Tensor shape and data type information","Input/output tensor names"],"output_types":["Inference results in pre-allocated GPU memory","Execution status and timing"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_8","uri":"capability://code.generation.editing.ortmodule.for.pytorch.training.integration.with.gradient.computation","name":"ortmodule for pytorch training integration with gradient computation","description":"Integrates ONNX Runtime into PyTorch training pipelines via ORTModule (onnxruntime/training/ortmodule), which wraps PyTorch models and executes the forward pass through ONNX Runtime while computing gradients via automatic differentiation. ORTModule exports the PyTorch model to ONNX, builds a gradient graph for backpropagation, and optimizes both forward and backward passes. This enables training acceleration through ONNX optimizations (operator fusion, memory planning) while maintaining PyTorch's training API.","intents":["Accelerate PyTorch model training 20-40% through ONNX graph optimizations and fused kernels","Reduce training memory consumption via ONNX memory planning and gradient checkpointing","Leverage hardware-specific training optimizations (TensorRT, OpenVINO) during training","Maintain PyTorch training code while benefiting from ONNX Runtime optimizations"],"best_for":["Teams training large models where 20-40% speedup significantly reduces training time","Memory-constrained training scenarios (large batch sizes on limited GPU memory)","Researchers exploring hardware-specific training optimizations"],"limitations":["ORTModule requires model export to ONNX — some PyTorch ops (control flow, dynamic shapes) may not export cleanly","Gradient computation adds overhead — speedup is model-dependent and may be <10% for small models","Debugging is harder because gradients are computed in ONNX, not PyTorch — stack traces are opaque","Custom PyTorch layers require custom ONNX operators; not all PyTorch ops have ONNX equivalents"],"requires":["PyTorch model that exports to ONNX (opset 12+)","ONNX Runtime with training support compiled","CUDA 11.0+ for GPU training","PyTorch 1.9+ compatible with ORTModule"],"input_types":["PyTorch model (nn.Module)","Training data (tensors)","Loss function"],"output_types":["Trained model weights","Gradients for backpropagation","Training metrics (loss, accuracy)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__cap_9","uri":"capability://tool.use.integration.operator.kernel.registration.and.dispatch.system","name":"operator kernel registration and dispatch system","description":"Manages a registry of operator kernels (onnxruntime/core/framework/op_kernel.h) where each ONNX operator has multiple implementations (CPU, CUDA, TensorRT, etc.) registered per execution provider. The kernel dispatch system (onnxruntime/core/framework/kernel_registry.h) selects the appropriate kernel at graph execution time based on the execution provider and tensor data types. Supports operator versioning (opset 7, 8, 9, etc.) with automatic version selection based on model opset.","intents":["Register custom or optimized operator implementations for specific hardware backends","Automatically select the best kernel implementation based on execution provider and data type","Support multiple ONNX opset versions without code duplication","Enable provider-specific operator optimizations (e.g., TensorRT fused kernels)"],"best_for":["Teams implementing custom operators for specific hardware","Framework developers extending ONNX Runtime with new operators","Hardware vendors optimizing operators for their accelerators"],"limitations":["Kernel registration is static — no dynamic kernel loading at runtime","Type dispatch is limited to tensor data types (float32, int8, etc.); no dispatch on tensor shapes or values","Operator versioning requires separate kernel implementations per opset version","Kernel selection is deterministic but not customizable — no user-defined dispatch policies"],"requires":["Operator kernel implementation (class inheriting OpKernel)","Kernel registration macro (ONNX_OPERATOR_KERNEL_EX)","Execution provider context (CPU, CUDA, etc.)"],"input_types":["Operator kernel implementation","Operator schema (inputs, outputs, attributes)","Execution provider identifier"],"output_types":["Registered kernel available for dispatch","Kernel execution results"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"onnx-runtime__headline","uri":"capability://deployment.infra.cross.platform.inference.engine.for.onnx.models","name":"cross-platform inference engine for onnx models","description":"ONNX Runtime is a high-performance, cross-platform inference engine that accelerates the execution of ONNX models on various hardware, including CPUs, GPUs, and specialized accelerators, making it ideal for deploying machine learning models in production environments.","intents":["best inference engine for ONNX models","ONNX model deployment solutions","high-performance ONNX runtime","cross-platform ONNX model execution","accelerate ONNX model inference"],"best_for":["enterprise-level deployments","high-performance computing","multi-platform support"],"limitations":[],"requires":[],"input_types":["ONNX models"],"output_types":["inference results"],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":60,"verified":false,"data_access_risk":"high","permissions":["ONNX model in opset 7+ format","For CUDA: NVIDIA GPU with compute capability 3.5+, CUDA 11.0+, cuDNN 8.0+","For TensorRT: NVIDIA GPU, TensorRT 8.0+","For CoreML: macOS 11.0+ or iOS 14.0+","For CPU: x86-64 or ARM64 processor","ONNX model with standard operators (custom ops not optimized)","Session creation with optimization level set (SessionOptions.graph_optimization_level)","No dynamic shapes in critical paths (optimizer assumes static tensor dimensions)","SessionOptions.enable_profiling = True","Inference session with profiling enabled"],"failure_modes":["Execution provider initialization adds 100-500ms overhead on first inference (provider library loading)","Not all ONNX operators are implemented for all providers — some ops fall back to CPU, causing performance cliffs","Provider-specific quantization formats (e.g., TensorRT INT8) require separate model conversion pipelines","Memory management across providers is manual — IOBinding required for zero-copy GPU inference","Graph optimization is deterministic but opaque — debugging fused operators requires disabling optimization","Some operator fusions are provider-specific (TensorRT fusions differ from CPU MLAS fusions), requiring separate optimization passes","Custom operators bypass the optimizer — fusion only applies to standard ONNX ops","Optimization time adds 50-200ms to session creation (amortized over many inferences but impacts startup latency)","Profiling adds 5-15% overhead due to timing instrumentation","Per-operator timing is approximate — kernel launch overhead and synchronization add noise","builder identity is not verified yet","no observed match outcomes 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