{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-onnxruntime","slug":"pypi-onnxruntime","name":"onnxruntime","type":"framework","url":"https://onnxruntime.ai","page_url":"https://unfragile.ai/pypi-onnxruntime","categories":["model-training"],"tags":["onnx","machine","learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-onnxruntime__cap_0","uri":"capability://data.processing.analysis.cross.framework.model.inference.with.automatic.hardware.acceleration","name":"cross-framework model inference with automatic hardware acceleration","description":"Loads ONNX-format models and executes inference through a pluggable execution provider architecture that automatically partitions computation graphs across available hardware accelerators (CPU, GPU, NPU). The InferenceSession abstraction handles model validation, graph optimization, and provider selection without requiring explicit hardware configuration. Supports tensor-based I/O compatible with numpy arrays across Python, C#, C++, Java, JavaScript, and Rust bindings.","intents":["Deploy a PyTorch model trained locally to production inference without framework dependencies","Run the same ONNX model across CPU, GPU, and mobile devices with automatic hardware selection","Execute inference in multiple programming languages from a single trained model artifact","Optimize model latency and throughput through hardware-specific kernels without code changes"],"best_for":["ML engineers deploying models across heterogeneous hardware (cloud, edge, mobile)","Teams requiring cross-language model serving (Python training, C#/.NET production)","Developers building inference pipelines that must run on CPU when GPU unavailable"],"limitations":["Execution provider selection is implicit/automatic — no documented API for explicit provider prioritization or fallback chains","Performance gains are hardware and model-dependent; no guaranteed speedup over native framework inference","Model must be pre-converted to valid ONNX format; runtime does not validate model correctness or numerical accuracy","Malicious ONNX models can trigger excessive memory/compute consumption — user responsible for model provenance validation"],"requires":["Valid ONNX model file (binary format)","Python 3.x for pip install, or language-specific runtime (C# .NET, C++17, Java 8+, Node.js 12+)","Hardware: CPU minimum; GPU drivers (CUDA/cuDNN for NVIDIA, ROCm for AMD) or NPU drivers optional","OS: Linux, Windows, macOS, iOS, Android, or modern web browser (Chromium 90+)"],"input_types":["ONNX model file (binary)","Tensor data (numpy arrays in Python, typed arrays in JavaScript, native arrays in C++)"],"output_types":["Tensor data (numpy arrays in Python, typed arrays in JavaScript, native arrays in C++)","Named output dictionary keyed by model output names"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_1","uri":"capability://data.processing.analysis.framework.agnostic.model.format.conversion.and.import","name":"framework-agnostic model format conversion and import","description":"Accepts pre-trained models from PyTorch, TensorFlow/Keras, TFLite, scikit-learn, and Hugging Face model hub, converting them to ONNX canonical representation for runtime execution. The conversion process validates model structure against ONNX specification and applies graph-level optimizations (operator fusion, constant folding, dead code elimination) before runtime execution. Enables single-model-artifact deployment across frameworks without retraining.","intents":["Export a PyTorch model to ONNX and run it in a C# application without PyTorch dependency","Convert a TensorFlow model to ONNX for inference on mobile devices without TensorFlow Lite conversion","Import a scikit-learn classifier to ONNX for unified inference pipeline with neural network models","Load a Hugging Face transformer model in ONNX format for cross-platform deployment"],"best_for":["ML teams with multi-framework training pipelines (PyTorch + TensorFlow) needing unified inference","Organizations migrating from one framework to another while maintaining model compatibility","Developers building framework-agnostic model serving infrastructure"],"limitations":["Conversion quality depends on framework-specific exporter; some custom layers may not convert automatically","ONNX opset version compatibility must be managed — older models may not run on newer runtime versions","No built-in model validation for numerical accuracy post-conversion; user must benchmark converted model against original","Conversion process is one-way; cannot export ONNX back to original framework"],"requires":["Source model in PyTorch, TensorFlow, TFLite, scikit-learn, or Hugging Face format","Framework-specific exporter (torch.onnx.export for PyTorch, tf2onnx for TensorFlow, etc.)","ONNX opset version compatible with target ONNX Runtime version (typically opset 12-18)"],"input_types":["PyTorch model (.pt, .pth files or model objects)","TensorFlow SavedModel or Keras model (.h5, .pb)","TFLite model (.tflite)","scikit-learn model (pickle or joblib serialized)","Hugging Face model hub identifiers"],"output_types":["ONNX model file (.onnx binary format)","Validated ONNX graph with optimized operator sequences"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_10","uri":"capability://tool.use.integration.model.serving.and.inference.api.with.named.input.output.management","name":"model serving and inference api with named input/output management","description":"Provides InferenceSession API that loads ONNX models and executes inference with named input/output tensors managed as dictionaries. The API abstracts tensor shape and type handling, allowing users to pass numpy arrays (Python), typed arrays (JavaScript), or native arrays (C++) without explicit type conversion. Session manages model state (weights, buffers) and caches optimizations across multiple inference calls. Supports batch inference with variable batch sizes without model reloading.","intents":["Load a model once and run 1000s of inferences without reloading or reoptimizing","Pass input tensors by name without tracking positional argument order","Run inference with variable batch sizes (batch size 1, 32, 128) without model recompilation","Execute multiple inference requests concurrently on same session without thread safety issues"],"best_for":["Inference server implementations requiring efficient session management","Batch inference pipelines processing multiple requests per session","Applications with variable input shapes (dynamic batch sizes, variable sequence lengths)"],"limitations":["Thread safety of InferenceSession is not documented — unclear if multiple threads can call session.run() concurrently","Session state management is implicit — no API to inspect or reset session state","Batch size must be compatible with model — no automatic batching across requests","Named input/output management requires knowing model input/output names — no API to introspect model signature","No built-in request queuing or load balancing — application responsible for managing concurrent requests"],"requires":["ONNX model file","Input tensors with correct shape and type matching model expectations"],"input_types":["Named input dictionary with tensor values (numpy array in Python, typed array in JavaScript)"],"output_types":["Named output dictionary with tensor values"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_11","uri":"capability://data.processing.analysis.model.profiling.and.performance.benchmarking.with.execution.metrics","name":"model profiling and performance benchmarking with execution metrics","description":"Provides profiling capabilities to measure inference latency, memory usage, and per-operator execution time. The profiling system instruments the inference pipeline to collect detailed metrics (operator execution time, memory allocation, cache hits) and generates performance reports. Metrics can be exported for analysis and optimization. Profiling is optional and can be enabled/disabled at runtime without model recompilation.","intents":["Identify performance bottlenecks by measuring per-operator execution time","Compare inference latency across different hardware (CPU vs GPU, NVIDIA vs AMD)","Track memory usage to optimize model for memory-constrained devices","Benchmark model optimization effectiveness by comparing metrics before/after optimization"],"best_for":["Performance engineers optimizing inference latency and memory usage","Teams comparing hardware options (GPU selection, device procurement)","Developers debugging performance regressions or unexpected latency"],"limitations":["Profiling API is not detailed in documentation — unclear how to enable profiling or access metrics","Profiling overhead is not documented — unclear if profiling adds significant latency","Metrics granularity is unknown — unclear if per-operator or per-kernel metrics are available","Export format for metrics is not specified — unclear if JSON, CSV, or other formats are supported","Profiling may not be available on all execution providers or platforms"],"requires":["ONNX Runtime with profiling support enabled","Model to profile"],"input_types":["ONNX model file","Input tensors for inference"],"output_types":["Performance metrics (latency, memory, per-operator times)","Performance report (format unknown)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_12","uri":"capability://automation.workflow.model.export.and.checkpoint.management.for.training.workflows","name":"model export and checkpoint management for training workflows","description":"Supports saving and loading model checkpoints during training, enabling resumable training and model versioning. The checkpoint system preserves model weights, optimizer state, and training metadata (epoch, loss, metrics) for recovery from training interruptions. Checkpoints are saved in ONNX format for compatibility with inference runtime. Enables training workflows that span multiple sessions or machines without losing progress.","intents":["Resume training from last checkpoint if training job is interrupted","Save model snapshots at regular intervals to track training progress","Export trained model to ONNX format for inference deployment","Manage multiple model versions from single training run for model selection"],"best_for":["Teams training large models that require multi-day/multi-week training runs","Organizations requiring reproducible training with checkpoint recovery","Developers managing model versioning and experiment tracking"],"limitations":["Checkpoint API is not detailed in documentation — unclear how to save/load checkpoints or what metadata is preserved","Optimizer state preservation is mentioned but not detailed — unclear if all optimizers are supported","Checkpoint format is not specified — unclear if proprietary or standard format","No built-in experiment tracking or model registry — application responsible for checkpoint organization","Checkpoint compatibility across ONNX Runtime versions is not documented"],"requires":["ONNX Runtime with training support","Model in training mode","Storage for checkpoint files"],"input_types":["Model weights and optimizer state"],"output_types":["Checkpoint file (ONNX format or proprietary)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_2","uri":"capability://text.generation.language.large.language.model.inference.with.token.streaming.and.batching","name":"large language model inference with token streaming and batching","description":"The onnxruntime-genai module provides optimized inference for large language models (LLMs) with support for token-by-token streaming, dynamic batching, and state management across inference steps. Implements efficient attention mechanisms (KV-cache management, grouped query attention) and supports popular model families (Llama-2, Phi, Mistral, Qwen) with automatic quantization and graph optimization. Handles variable-length sequences and manages model state (past key-value tensors) across generation steps without explicit user management.","intents":["Run Llama-2-7b inference on consumer GPU with sub-100ms latency per token","Stream LLM responses token-by-token to frontend without buffering full generation","Batch multiple inference requests to a single LLM for throughput optimization","Fine-tune a Hugging Face model locally on-device for personalization without cloud API calls"],"best_for":["Teams building LLM-powered applications requiring low-latency token generation","Edge/on-device AI applications where cloud inference is unavailable or undesirable","Developers optimizing LLM inference cost by running models locally instead of API calls","Organizations requiring model privacy and cannot send data to cloud LLM providers"],"limitations":["Limited to specific model architectures (Llama, Phi, Mistral, Qwen); custom architectures require manual implementation","KV-cache management is automatic but not user-configurable — cannot tune cache size for memory/latency tradeoffs","Quantization is applied automatically; no fine-grained control over quantization strategy (INT8 vs INT4 vs FP16)","On-device training is mentioned but not detailed; unclear if full fine-tuning or LoRA-only","Separate pip package (onnxruntime-genai) — integration with base onnxruntime inference API unclear"],"requires":["onnxruntime-genai pip package (separate from base onnxruntime)","ONNX-format LLM model (Llama-2, Phi, Mistral, Qwen, or compatible architecture)","GPU with sufficient VRAM (7B model ~6GB, 13B model ~12GB; CPU inference possible but slow)","Python 3.8+ for API access"],"input_types":["Text prompt (string)","Model configuration (temperature, top_p, max_tokens parameters)","Optional: previous KV-cache state for multi-turn conversation"],"output_types":["Generated text tokens (streamed or buffered)","Updated KV-cache state for next generation step","Optional: logits or attention weights for advanced use cases"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_3","uri":"capability://data.processing.analysis.on.device.model.fine.tuning.and.personalization","name":"on-device model fine-tuning and personalization","description":"Enables training and fine-tuning of models directly on edge devices (mobile, IoT) or local machines without cloud infrastructure, supporting large model training acceleration and parameter-efficient fine-tuning methods. The training runtime applies graph-level optimizations (gradient checkpointing, mixed precision) and manages memory constraints on resource-limited devices. Supports personalization workflows where models adapt to user data without uploading sensitive information to cloud services.","intents":["Fine-tune a pre-trained LLM on device-local user data for personalized recommendations","Train a small neural network on edge device for on-device ML without cloud connectivity","Reduce training costs for large models by leveraging local hardware instead of cloud GPUs","Maintain user privacy by keeping training data on-device rather than sending to cloud training services"],"best_for":["Mobile app developers building personalized ML features without cloud backend","IoT/edge device manufacturers requiring on-device learning capabilities","Organizations with privacy-sensitive data that cannot be sent to cloud training services","Teams optimizing training cost by leveraging local hardware (GPU, TPU) instead of cloud"],"limitations":["Training API details are sparse in documentation; unclear if full fine-tuning or parameter-efficient methods (LoRA) only","Memory constraints on mobile/edge devices limit model size and batch size — no documented guidance on model size limits","Training convergence and accuracy on resource-limited devices not benchmarked — unclear if practical for production use","No documented support for distributed training across multiple devices","Unclear if training supports all model architectures or only specific families (LLMs, vision models)"],"requires":["onnxruntime with training support (may require separate build or pip package variant)","Pre-trained model in ONNX format","Training data in memory or accessible from device storage","Sufficient device memory (minimum unclear; likely 2GB+ for small models, 8GB+ for LLMs)","Python 3.8+ or language-specific training API"],"input_types":["Pre-trained ONNX model","Training dataset (text, images, or structured data depending on model type)","Training hyperparameters (learning rate, batch size, epochs, optimizer)"],"output_types":["Fine-tuned ONNX model weights","Training metrics (loss, accuracy, validation metrics)","Updated model checkpoint for resuming training"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_4","uri":"capability://tool.use.integration.multi.platform.model.deployment.with.platform.specific.runtimes","name":"multi-platform model deployment with platform-specific runtimes","description":"Provides platform-specific runtime distributions (ONNX Runtime Mobile for iOS/Android, ONNX Runtime Web for browsers, cloud-optimized builds for Linux/Windows) that package the core inference engine with platform-appropriate dependencies and APIs. Each platform distribution includes language bindings (Swift/Objective-C for iOS, Kotlin/Java for Android, JavaScript for Web, C# for Windows) and applies platform-specific optimizations (CoreML integration on iOS, NNAPI on Android, WebGL/WebAssembly on browsers). Enables single ONNX model to run across desktop, mobile, web, and cloud with minimal code changes.","intents":["Deploy an image classification model to iOS app using native Swift API without PyTorch dependency","Run a text generation model in web browser using WebAssembly without server-side inference","Execute the same ONNX model on Android device using Kotlin with automatic NNAPI acceleration","Build cross-platform inference application (iOS, Android, Web) from single ONNX model artifact"],"best_for":["Mobile app developers (iOS/Android) requiring on-device ML without framework dependencies","Web developers building client-side ML applications (browser-based inference)","Cross-platform teams deploying models to desktop, mobile, and web from single artifact","Organizations requiring offline-first inference without cloud connectivity"],"limitations":["ONNX Runtime Mobile and Web APIs are not detailed in documentation — unclear feature parity with Python/C++ APIs","Platform-specific optimizations vary: iOS has CoreML integration, Android has NNAPI, Web has WebGL/WebAssembly — no unified performance guarantees","Model size constraints on mobile/web: large models may exceed app size limits or browser memory — no documented guidance on model size limits per platform","Web inference performance depends on browser capabilities (WebGL support, WebAssembly performance) — no benchmarks provided","iOS/Android APIs may lag behind Python API in feature completeness (e.g., unclear if mobile supports all execution providers)"],"requires":["ONNX Runtime Mobile for iOS (requires Xcode 12+, iOS 11.0+, Swift 5.0+)","ONNX Runtime Mobile for Android (requires Android API 21+, Kotlin 1.4+ or Java 8+)","ONNX Runtime Web for browsers (requires modern browser with WebAssembly support: Chrome 74+, Firefox 79+, Safari 14+)","ONNX model file compatible with target platform (may require quantization for mobile/web)"],"input_types":["ONNX model file (binary)","Platform-specific tensor representation (CVPixelBuffer on iOS, Bitmap on Android, TypedArray on Web)"],"output_types":["Platform-specific tensor representation (CVPixelBuffer on iOS, Bitmap on Android, TypedArray on Web)","Named output dictionary keyed by model output names"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_5","uri":"capability://data.processing.analysis.graph.level.model.optimization.with.automatic.operator.fusion","name":"graph-level model optimization with automatic operator fusion","description":"Applies compile-time graph optimizations to ONNX models before execution, including operator fusion (combining multiple operators into single fused kernel), constant folding (pre-computing constant subexpressions), dead code elimination, and layout optimization. The optimization pipeline is applied uniformly across all execution providers and hardware targets, reducing memory bandwidth, improving cache locality, and decreasing kernel launch overhead. Optimizations are transparent to user code — no explicit API calls required.","intents":["Reduce model latency by 20-40% through automatic operator fusion without model retraining","Decrease model memory footprint by pre-computing constant expressions at load time","Improve inference throughput by optimizing tensor layouts for target hardware","Eliminate unused model branches automatically without manual model editing"],"best_for":["Teams deploying models to latency-sensitive applications (real-time inference, mobile)","Organizations optimizing inference cost by reducing memory bandwidth and compute","Developers requiring consistent performance across hardware targets without per-hardware tuning"],"limitations":["Optimization strategies are not user-configurable — no API to enable/disable specific optimizations or tune aggressiveness","Optimization effectiveness varies by model architecture and hardware — no predictable performance improvement guarantees","Optimized model is not exportable — cannot inspect or debug optimized graph structure","Some custom operators may not be fusible — unclear which operators are supported for fusion","No documentation on optimization pass order or interaction between passes"],"requires":["Valid ONNX model file","ONNX opset version compatible with optimization passes (typically opset 12+)"],"input_types":["ONNX model file (binary)"],"output_types":["Optimized ONNX model (in-memory representation, not exported)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_6","uri":"capability://data.processing.analysis.quantization.aware.model.inference.with.automatic.precision.selection","name":"quantization-aware model inference with automatic precision selection","description":"Supports inference on quantized models (INT8, INT4, FP16) with automatic precision selection based on hardware capabilities and model requirements. The runtime handles dequantization transparently during inference, applying quantized operations on hardware that supports them (e.g., INT8 on NVIDIA GPUs) and falling back to higher precision on unsupported hardware. Quantized models reduce memory footprint and improve inference latency without requiring explicit quantization code from users.","intents":["Run a quantized INT8 model on NVIDIA GPU with 4x memory reduction and 2-3x latency improvement","Deploy a large language model on mobile device using INT4 quantization to fit within app size limits","Automatically select FP16 precision on GPU and INT8 on CPU for hardware-optimal inference","Reduce model serving cost by 50% through quantization without retraining"],"best_for":["Teams deploying large models to memory-constrained devices (mobile, edge, embedded)","Organizations optimizing inference latency and cost through quantization","Developers requiring hardware-optimal precision selection without manual configuration"],"limitations":["Quantization must be applied before ONNX Runtime (via external tools like ONNX quantizer or framework-native quantization) — ONNX Runtime does not provide quantization API","Quantization accuracy loss is model and data-dependent — no guaranteed accuracy preservation","Automatic precision selection is implicit — no API to force specific precision or inspect selected precision","Limited to INT8, INT4, FP16 — no support for other quantization schemes (binary, ternary)","Quantization support varies by execution provider — unclear which providers support which precisions"],"requires":["Pre-quantized ONNX model (INT8, INT4, or FP16 precision)","Hardware supporting target precision (INT8 support on most modern GPUs, INT4 support varies)","Quantization tool to prepare model (ONNX quantizer, PyTorch quantization, TensorFlow quantization)"],"input_types":["Quantized ONNX model file (INT8, INT4, or FP16)"],"output_types":["Inference results in full precision (dequantized)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_7","uri":"capability://tool.use.integration.multi.language.api.bindings.with.unified.inference.interface","name":"multi-language api bindings with unified inference interface","description":"Provides language-specific API bindings (Python, C#/.NET, C++, Java, JavaScript/Node.js, Rust) that wrap the core C++ inference engine with language-idiomatic interfaces. Each binding implements the same InferenceSession abstraction (load model, run inference, retrieve outputs) with language-specific conventions (numpy arrays in Python, typed arrays in JavaScript, native arrays in C++). Enables teams to use ONNX Runtime across polyglot codebases without learning framework-specific APIs.","intents":["Use ONNX Runtime in Python for model development and C# for production inference without API relearning","Build inference microservice in C++ for low-latency serving and JavaScript frontend for client-side inference","Integrate ONNX Runtime into Java backend application without Python dependency","Deploy model to Rust application for memory-safe inference without garbage collection overhead"],"best_for":["Polyglot teams using multiple programming languages in same codebase","Organizations migrating inference from one language to another (e.g., Python to C# for production)","Developers requiring language-specific performance characteristics (Rust for memory safety, C++ for latency)"],"limitations":["API feature parity across languages is not documented — unclear if all languages support all capabilities (e.g., does Rust binding support execution provider selection?)","Language-specific bindings may lag behind Python API in feature completeness","Performance characteristics vary by language (Python has GIL overhead, JavaScript has WebAssembly overhead) — no benchmarks provided","Rust binding is mentioned but no examples or documentation provided","Java binding support for execution providers unclear"],"requires":["Language-specific runtime: Python 3.x, .NET 6.0+, C++17, Java 8+, Node.js 12+, Rust 1.56+","Language-specific ONNX Runtime package (pip for Python, NuGet for C#, npm for JavaScript, cargo for Rust)"],"input_types":["ONNX model file (binary)","Language-specific tensor representation (numpy array in Python, typed array in JavaScript, native array in C++)"],"output_types":["Language-specific tensor representation","Named output dictionary keyed by model output names"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_8","uri":"capability://safety.moderation.model.validation.and.security.scanning.for.malicious.onnx.artifacts","name":"model validation and security scanning for malicious onnx artifacts","description":"Validates ONNX models against specification compliance and scans for potentially malicious patterns (excessive memory allocation, unbounded loops, unsafe operations) before execution. The validation process checks model structure, operator compatibility, tensor shape consistency, and data type correctness. Security scanning identifies models that could trigger denial-of-service attacks through resource exhaustion (memory bombs, infinite loops) or unsafe operations. Validation is applied at InferenceSession creation time before model execution.","intents":["Prevent denial-of-service attacks by scanning untrusted ONNX models before loading","Validate model correctness before production deployment to catch conversion errors","Detect incompatible operators or unsupported model features early in development","Ensure model conforms to ONNX specification before cross-platform deployment"],"best_for":["Teams deploying models from untrusted sources (model marketplaces, user-uploaded models)","Organizations with security requirements for model provenance and validation","Developers debugging model conversion issues and compatibility problems"],"limitations":["Validation is implicit — no explicit API to access validation results or configure validation strictness","Security scanning is mentioned but not detailed — unclear which attack patterns are detected","No API to whitelist/blacklist specific operators or model features","Validation does not check numerical correctness or accuracy — only structural compliance","No documentation on validation performance impact (latency added to model loading)"],"requires":["Valid ONNX model file","ONNX opset version compatible with runtime"],"input_types":["ONNX model file (binary)"],"output_types":["Validation errors/warnings (if any)","Loaded InferenceSession (if validation passes)"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-onnxruntime__cap_9","uri":"capability://tool.use.integration.execution.provider.abstraction.with.hardware.specific.kernel.optimization","name":"execution provider abstraction with hardware-specific kernel optimization","description":"Pluggable execution provider architecture that abstracts hardware-specific inference implementations (CPU, NVIDIA GPU, AMD GPU, Intel GPU, Apple Neural Engine, ARM NNAPI, Qualcomm Hexagon NPU) behind unified interface. Each provider implements hardware-specific optimized kernels for common operators (convolution, matrix multiplication, attention) and applies provider-specific graph optimizations. The runtime automatically selects available providers and partitions computation graph across multiple providers if beneficial. Providers are loaded dynamically at runtime without recompilation.","intents":["Automatically use NVIDIA CUDA when available, fall back to CPU when GPU unavailable","Partition model execution across CPU and GPU to optimize latency and memory usage","Deploy same model to NVIDIA, AMD, and Intel GPUs with automatic provider selection","Leverage Apple Neural Engine on iOS without explicit CoreML integration code"],"best_for":["Teams deploying models across heterogeneous hardware (cloud with multiple GPU types, edge with CPU/NPU mix)","Organizations requiring automatic hardware utilization without manual device management","Developers building inference infrastructure that must work on any available hardware"],"limitations":["Execution provider names and capabilities not documented — unclear which providers are available and what operators they support","Provider selection is implicit/automatic — no documented API for explicit provider prioritization or fallback chains","Graph partitioning across multiple providers is automatic — no API to control partitioning strategy or inspect partition boundaries","Provider-specific optimizations are opaque — no documentation on which providers implement which optimizations","Custom operators may not be supported by all providers — unclear error handling when operator is unavailable on selected provider"],"requires":["Hardware-specific drivers/libraries: CUDA/cuDNN for NVIDIA, ROCm for AMD, oneAPI for Intel, Metal for Apple, NNAPI for Android","ONNX Runtime built with provider support (may require separate build or pip package variant)"],"input_types":["ONNX model file (binary)"],"output_types":["Inference results (provider-agnostic)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Valid ONNX model file (binary format)","Python 3.x for pip install, or language-specific runtime (C# .NET, C++17, Java 8+, Node.js 12+)","Hardware: CPU minimum; GPU drivers (CUDA/cuDNN for NVIDIA, ROCm for AMD) or NPU drivers optional","OS: Linux, Windows, macOS, iOS, Android, or modern web browser (Chromium 90+)","Source model in PyTorch, TensorFlow, TFLite, scikit-learn, or Hugging Face format","Framework-specific exporter (torch.onnx.export for PyTorch, tf2onnx for TensorFlow, etc.)","ONNX opset version compatible with target ONNX Runtime version (typically opset 12-18)","ONNX model file","Input tensors with correct shape and type matching model expectations","ONNX Runtime with profiling support enabled"],"failure_modes":["Execution provider selection is implicit/automatic — no documented API for explicit provider prioritization or fallback chains","Performance gains are hardware and model-dependent; no guaranteed speedup over native framework inference","Model must be pre-converted to valid ONNX format; runtime does not validate model correctness or numerical accuracy","Malicious ONNX models can trigger excessive memory/compute consumption — user responsible for model provenance validation","Conversion quality depends on framework-specific exporter; some custom layers may not convert automatically","ONNX opset version compatibility must be managed — older models may not run on newer runtime versions","No built-in model validation for numerical accuracy post-conversion; user must benchmark converted model against original","Conversion process is one-way; cannot export ONNX back to original framework","Thread safety of InferenceSession is not documented — unclear if multiple threads can call session.run() concurrently","Session state management is implicit — no API to inspect or reset session state","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:16.568Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-onnxruntime","compare_url":"https://unfragile.ai/compare?artifact=pypi-onnxruntime"}},"signature":"CvWivR7oIA/UoUNcwujskn2FcUkvWaxkTDMOpwk32gR9gJWycRdiSmjkN7Iasyyv+9fth0F2aow7SRrLj7McDA==","signedAt":"2026-06-22T13:21:14.529Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-onnxruntime","artifact":"https://unfragile.ai/pypi-onnxruntime","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-onnxruntime","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}