{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-torch","slug":"pypi-torch","name":"torch","type":"framework","url":"https://pypi.org/project/torch/","page_url":"https://unfragile.ai/pypi-torch","categories":["model-training"],"tags":["pytorch","machine","learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-torch__cap_0","uri":"capability://code.generation.editing.dynamic.computation.graph.compilation.with.torchdynamo.bytecode.capture","name":"dynamic computation graph compilation with torchdynamo bytecode capture","description":"Captures Python function bytecode at runtime and converts it to an intermediate representation without requiring explicit graph definition. TorchDynamo performs frame evaluation and variable tracking via symbolic execution, maintaining guards that detect when recompilation is necessary due to shape changes or type variations. This enables automatic optimization of eager-mode PyTorch code without user annotation.","intents":["Optimize existing PyTorch training loops without rewriting code","Automatically fuse operations and reduce memory overhead in dynamic models","Enable production inference with compiled performance while maintaining development flexibility"],"best_for":["ML engineers with existing PyTorch codebases seeking 2-5x speedups","Teams building models with dynamic control flow (variable batch sizes, conditional branches)","Researchers prototyping models that need both flexibility and performance"],"limitations":["Guard overhead adds ~50-200ms per recompilation when tensor shapes change unexpectedly","Some Python constructs (arbitrary function calls, complex closures) may cause graph breaks and fallback to eager execution","Compilation cache requires disk space; large models can generate 100MB+ of cached artifacts","Debugging compiled code requires understanding TorchDynamo's symbolic variable tracking"],"requires":["Python 3.9+","PyTorch 2.0+","CUDA 11.8+ (for GPU optimization) or CPU-only mode supported"],"input_types":["Python functions with tensor operations","PyTorch nn.Module instances","Dynamic control flow (if/for/while with tensor-dependent conditions)"],"output_types":["Compiled executable graph","Performance metrics and compilation logs","Fallback to eager execution on unsupported operations"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_1","uri":"capability://code.generation.editing.static.graph.export.with.symbolic.shape.inference.and.faketensormode","name":"static graph export with symbolic shape inference and faketensormode","description":"Converts dynamic PyTorch models to static ExportedProgram representations via torch.export, using FakeTensorMode to propagate tensor metadata without allocating real GPU memory. Symbolic shapes track dynamic dimensions as symbolic variables, enabling export of models with variable batch sizes or sequence lengths. AOT Autograd separates forward and backward computation into a functionalized graph suitable for deployment.","intents":["Export trained models for inference on edge devices or non-PyTorch runtimes","Generate portable computation graphs that work across different hardware backends","Optimize models for deployment by separating training and inference graphs"],"best_for":["ML engineers deploying models to mobile, embedded, or cloud inference services","Teams building model serving infrastructure requiring hardware-agnostic representations","Researchers needing to profile and optimize model computation without training overhead"],"limitations":["Symbolic shape inference requires explicit shape annotations for dynamic dimensions; models with data-dependent shapes may fail export","Some PyTorch operations (custom CUDA kernels, Python callbacks) cannot be exported and require reimplementation","Export process adds 30-60 seconds overhead for large models due to FakeTensorMode tracing","Exported graphs lose eager-mode debugging capabilities; errors surface only at runtime"],"requires":["Python 3.9+","PyTorch 2.1+","Models using only supported operations (check torch.export.supported_ops)","Explicit input shape specifications for symbolic dimension tracking"],"input_types":["PyTorch nn.Module with static or symbolically-defined shapes","Traced computation graphs from torch.export","Models without data-dependent control flow"],"output_types":["ExportedProgram serialized object","ONNX format (via torch.export ONNX exporter)","Portable computation graph with symbolic shapes"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_10","uri":"capability://planning.reasoning.profiling.and.performance.analysis.with.kineto.and.memory.visualization","name":"profiling and performance analysis with kineto and memory visualization","description":"Provides comprehensive performance profiling via Kineto profiler (GPU-aware, captures CUDA kernels and collectives) and autograd profiler (operation-level timing). Generates timeline traces compatible with Chrome DevTools and TensorBoard for interactive visualization. Memory profiler tracks allocation/deallocation patterns and identifies memory bottlenecks.","intents":["Identify performance bottlenecks in training and inference pipelines","Analyze GPU utilization and kernel launch overhead","Optimize memory usage by visualizing allocation patterns and identifying leaks"],"best_for":["ML engineers optimizing model training and inference performance","Teams debugging GPU utilization issues and communication overhead","Researchers analyzing performance characteristics of different model architectures"],"limitations":["Profiling adds 5-20% overhead to training; results may not reflect production performance","Kineto profiler requires CUDA 11.0+ and specific GPU drivers; older hardware may have limited kernel visibility","Memory profiler tracks CPU memory accurately but GPU memory tracking may miss some allocations","Trace files can be 100MB+ for long training runs; requires significant disk space and memory to visualize"],"requires":["Python 3.9+","PyTorch 2.0+","CUDA 11.0+ (for Kineto GPU profiling)","Optional: TensorBoard or Chrome DevTools for trace visualization"],"input_types":["PyTorch training or inference code","Profiling configuration (activities, record shapes, etc.)"],"output_types":["Timeline traces (JSON format compatible with Chrome DevTools)","Operation-level timing statistics","Memory allocation/deallocation logs","GPU kernel execution traces"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_11","uri":"capability://code.generation.editing.custom.operator.registration.and.library.extension.via.torchgen.code.generator","name":"custom operator registration and library extension via torchgen code generator","description":"Enables extension of PyTorch with custom operators through torchgen, which auto-generates C++ bindings, Python wrappers, and dispatcher code from YAML operator definitions. Supports custom CUDA kernels, CPU implementations, and automatic differentiation via custom autograd functions. AOTI C Shim provides stable ABI for binary compatibility across PyTorch versions.","intents":["Implement custom operations (CUDA kernels, specialized algorithms) that integrate seamlessly with PyTorch","Extend PyTorch with domain-specific operations (vision, NLP, scientific computing) without forking the framework","Build reusable operator libraries with automatic Python bindings and type checking"],"best_for":["ML engineers implementing specialized operations for custom models","Teams building domain-specific PyTorch extensions (vision, NLP, scientific computing)","Researchers prototyping novel operators with automatic differentiation support"],"limitations":["torchgen requires understanding YAML operator definitions and C++ implementation details; steep learning curve","Custom operators must be registered per PyTorch version; binary compatibility requires AOTI C Shim","Debugging custom operators requires C++ debugger and understanding of dispatcher mechanism","Performance of custom operators depends on implementation quality; poorly written kernels may underperform PyTorch built-ins"],"requires":["Python 3.9+","PyTorch 2.0+","C++ compiler (GCC 9+, Clang 10+, MSVC 2019+)","CUDA toolkit (for GPU operators)","torchgen (included with PyTorch source)"],"input_types":["YAML operator definitions","C++ kernel implementations","Custom autograd function definitions"],"output_types":["Compiled operator library (.so/.pyd)","Python bindings and type stubs","Dispatcher registration code"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_12","uri":"capability://automation.workflow.inference.runtime.optimization.via.nativert.and.aotinductor","name":"inference runtime optimization via nativert and aotinductor","description":"Optimizes inference through NativeRT (native runtime) and AOTInductor, which execute ExportedProgram graphs with minimal overhead. NativeRT uses compiled kernels from TorchInductor without Python interpreter, reducing latency by 50-80% compared to eager execution. AOTInductor generates standalone C++ code for deployment without PyTorch runtime dependency.","intents":["Deploy models with minimal latency by eliminating Python interpreter overhead","Generate standalone inference binaries that don't require PyTorch installation","Optimize inference for edge devices and cloud serving with compiled execution"],"best_for":["ML engineers deploying models in production inference services","Teams building edge inference applications with strict latency requirements","Researchers benchmarking inference performance across different optimization strategies"],"limitations":["NativeRT requires static computation graphs; dynamic control flow disables optimization","AOTInductor generates C++ code that must be compiled for target platform; cross-compilation requires additional setup","Debugging NativeRT/AOTInductor requires understanding compiled kernel execution; no Python-level debugging","Memory overhead of compiled kernels may exceed eager execution for small models"],"requires":["Python 3.9+","PyTorch 2.1+","ExportedProgram from torch.export","C++ compiler (for AOTInductor code generation)"],"input_types":["ExportedProgram from torch.export","Input tensor specifications","Inference configuration (batch size, device)"],"output_types":["Compiled inference executable","Inference latency and throughput metrics","Standalone C++ code (for AOTInductor)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_13","uri":"capability://code.generation.editing.attention.mechanism.optimization.and.transformer.specific.kernels","name":"attention mechanism optimization and transformer-specific kernels","description":"Provides optimized implementations of attention mechanisms (scaled dot-product attention, multi-head attention) with fused kernels that reduce memory bandwidth and kernel launch overhead. Includes flash attention variants for different hardware (NVIDIA, AMD, TPU) and automatic selection based on input shapes and device. Integrates with model compilation for end-to-end optimization.","intents":["Accelerate transformer model training and inference by 2-4x through fused attention kernels","Reduce memory usage of attention computation by 50-70% through kernel fusion","Deploy attention-based models efficiently across different hardware platforms"],"best_for":["ML engineers training and deploying large language models and vision transformers","Teams optimizing inference latency for transformer-based applications","Researchers studying attention mechanism efficiency and hardware-specific optimizations"],"limitations":["Fused attention kernels require specific input shapes and data types; fallback to unfused implementation for unsupported cases","Flash attention variants have different numerical stability characteristics; may require fine-tuning for specific models","Attention optimization adds 10-50ms overhead for kernel selection and compilation on first run","Debugging fused attention kernels requires understanding kernel implementation details"],"requires":["Python 3.9+","PyTorch 2.0+","CUDA 11.8+ (for NVIDIA flash attention) or compatible GPU","Transformer models using standard attention patterns"],"input_types":["Query, key, value tensors","Attention mask (optional)","Dropout probability (optional)"],"output_types":["Fused attention output","Performance metrics (latency, memory usage)","Kernel selection trace"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_14","uri":"capability://data.processing.analysis.sparse.tensor.operations.and.structured.sparsity.support","name":"sparse tensor operations and structured sparsity support","description":"Enables efficient computation on sparse tensors through sparse tensor data structures (COO, CSR, CSC) and sparse-dense operations. Supports structured sparsity patterns (block sparsity, N:M sparsity) that leverage hardware acceleration. Integrates with quantization and pruning for model compression.","intents":["Reduce model size and inference latency through structured sparsity (N:M, block sparsity)","Accelerate sparse matrix operations on specialized hardware (NVIDIA Ampere sparse tensor cores)","Implement sparse neural networks with automatic sparsity pattern optimization"],"best_for":["ML engineers compressing models through pruning and sparsity","Teams deploying sparse models on hardware with sparsity acceleration","Researchers studying sparsity patterns and their impact on model accuracy"],"limitations":["Sparse tensor operations have limited kernel coverage; many operations fall back to dense computation","Structured sparsity patterns (N:M) may reduce model accuracy by 1-3% compared to unstructured sparsity","Sparse tensor overhead (indexing, format conversion) may exceed dense computation for small models","Debugging sparse operations requires understanding sparse tensor formats and indexing"],"requires":["Python 3.9+","PyTorch 2.0+","CUDA 11.0+ (for sparse tensor core acceleration)","Models with sufficient sparsity (>50%) for efficiency gains"],"input_types":["Dense or sparse tensors","Sparsity patterns (N:M, block sparsity)","Sparse-dense operation specifications"],"output_types":["Sparse tensor results","Sparsity metrics (compression ratio, speedup)","Sparse tensor format conversions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_2","uri":"capability://code.generation.editing.multi.backend.kernel.code.generation.and.autotuning.via.torchinductor","name":"multi-backend kernel code generation and autotuning via torchinductor","description":"Lowers optimized computation graphs to hardware-specific kernels through TorchInductor's IR, which performs operation fusion, memory layout optimization, and scheduling. Generates code for Triton (GPU), CUTLASS (NVIDIA tensor cores), Pallas (TPU), and C++ (CPU), with built-in autotuning that benchmarks multiple kernel implementations and selects the fastest. Compilation cache stores generated kernels to avoid recompilation.","intents":["Achieve near-hand-optimized performance on GPUs without writing custom CUDA kernels","Automatically fuse element-wise operations and reduce memory bandwidth bottlenecks","Deploy models across heterogeneous hardware (NVIDIA, AMD, Intel, TPU) with single codebase"],"best_for":["ML engineers optimizing model inference latency on production GPUs","Teams building custom training loops requiring kernel-level performance tuning","Researchers experimenting with novel operator fusions and memory layouts"],"limitations":["Autotuning adds 5-30 seconds per unique kernel on first compilation; subsequent runs use cached results","Generated Triton kernels may underperform hand-written CUTLASS for specialized operations (e.g., sparse matrix multiply)","Kernel selection heuristics are conservative; some fusion opportunities are missed for complex graphs","Debugging generated code requires understanding Triton/CUTLASS assembly; no symbolic debugger support"],"requires":["Python 3.9+","PyTorch 2.0+","CUDA 11.8+ (for Triton) or compatible GPU driver","Triton compiler (installed automatically with PyTorch)","Optional: CUTLASS headers for tensor core optimization"],"input_types":["Fused computation graphs from torch.compile or torch.export","ATen operator sequences","Memory layout specifications (NCHW, NHWC, etc.)"],"output_types":["Compiled Triton/CUTLASS/C++ kernels","Performance benchmarks and autotuning results","Kernel cache artifacts (binary .so files)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_3","uri":"capability://automation.workflow.distributed.training.with.dtensor.sharding.and.automatic.communication.planning","name":"distributed training with dtensor sharding and automatic communication planning","description":"Abstracts multi-GPU/multi-node training through DTensor (Distributed Tensor), which annotates tensors with placement specifications (replicated, sharded across dimensions, or hybrid). Sharding propagation automatically determines how operations should execute across devices, and redistribution planning generates optimal collective communication (all-reduce, all-gather, reduce-scatter) to move data between sharding schemes. Integrates with c10d backend for NCCL/Gloo communication.","intents":["Scale training to multiple GPUs/nodes without manual communication code","Automatically optimize communication patterns based on model structure and hardware topology","Experiment with different sharding strategies (data parallel, tensor parallel, pipeline parallel) without rewriting training loops"],"best_for":["ML engineers training large models (>1B parameters) on multi-GPU clusters","Teams exploring tensor parallelism or hybrid parallelism strategies","Researchers prototyping distributed training algorithms with automatic communication optimization"],"limitations":["Sharding propagation adds 100-500ms overhead per forward/backward pass due to redistribution planning","Some operations (custom kernels, dynamic shapes) cannot be automatically sharded and require manual specification","Communication planning assumes static computation graphs; dynamic control flow may cause suboptimal collectives","Debugging distributed training requires understanding DTensor placement semantics and collective communication traces"],"requires":["Python 3.9+","PyTorch 2.0+","NCCL 2.10+ (for NVIDIA GPUs) or Gloo backend","Multi-GPU setup (2+ GPUs) or multi-node cluster","torch.distributed initialized with process group"],"input_types":["PyTorch nn.Module with tensor operations","DTensor placement specifications (Replicate, Shard, Partial)","DeviceMesh topology definitions"],"output_types":["Distributed computation graphs with automatic collectives","Communication traces and bandwidth utilization metrics","Sharded model checkpoints"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_4","uri":"capability://automation.workflow.fully.sharded.data.parallel.fsdp.with.parameter.management.and.communication.compute.overlap","name":"fully sharded data parallel (fsdp) with parameter management and communication-compute overlap","description":"Distributes model parameters across GPUs and reconstructs them on-demand during forward/backward passes via all-gather collectives, then discards them to save memory. Bucketing strategy groups parameters to overlap communication and computation, reducing idle time. Handles gradient accumulation, mixed-precision training, and automatic gradient checkpointing to further reduce memory footprint.","intents":["Train models larger than single-GPU memory by sharding parameters across multiple GPUs","Reduce training time by overlapping parameter communication with computation","Implement memory-efficient training with gradient checkpointing and mixed precision"],"best_for":["ML engineers training models with 10B+ parameters on multi-GPU clusters","Teams optimizing memory efficiency for cost-constrained cloud training","Researchers implementing large-scale language model and vision transformer training"],"limitations":["All-gather communication adds 20-40% overhead compared to data-parallel training; effective only for models >10B parameters","Bucketing strategy requires tuning bucket size for optimal communication-compute overlap; suboptimal buckets reduce speedup by 10-20%","Gradient checkpointing trades memory for compute, adding 20-30% training time overhead","Debugging FSDP requires understanding parameter sharding state and collective communication traces"],"requires":["Python 3.9+","PyTorch 2.0+","NCCL 2.10+ for multi-GPU or multi-node training","Multi-GPU setup (4+ GPUs recommended for efficiency)","torch.distributed initialized with NCCL backend"],"input_types":["PyTorch nn.Module with trainable parameters","Optimizer state (Adam, SGD, etc.)","Training loop with loss computation"],"output_types":["Sharded model state dict","Distributed optimizer state","Training metrics (throughput, memory usage, communication overhead)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_5","uri":"capability://code.generation.editing.automatic.differentiation.with.aot.autograd.and.functionalization","name":"automatic differentiation with aot autograd and functionalization","description":"Separates forward and backward computation into a static graph via AOT (Ahead-of-Time) Autograd, which traces the backward pass without executing it. Functionalization converts in-place operations to functional equivalents, enabling graph-level optimization and memory layout analysis. Autograd caching stores backward graphs to avoid retracing for repeated forward patterns.","intents":["Optimize backward pass computation separately from forward pass","Enable memory layout optimization by understanding full forward-backward flow","Reduce autograd overhead for models with repeated forward patterns"],"best_for":["ML engineers optimizing training performance for large models","Teams implementing custom training algorithms requiring backward graph inspection","Researchers studying gradient computation patterns and memory efficiency"],"limitations":["AOT tracing adds 50-200ms overhead per unique forward pattern; amortized over training but impacts first iteration","Functionalization may increase memory usage for models with many in-place operations due to intermediate tensor materialization","Autograd caching requires memory for storing backward graphs; large models may consume 1-10GB of cache","Debugging functionalized graphs requires understanding how in-place ops map to functional equivalents"],"requires":["Python 3.9+","PyTorch 2.0+","Models using standard autograd (no custom autograd functions without registration)"],"input_types":["PyTorch computation graphs with differentiable operations","Forward pass with in-place operations","Loss computation and backward() calls"],"output_types":["Separated forward and backward graphs","Functionalized operation sequences","Autograd cache artifacts"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_6","uri":"capability://code.generation.editing.fx.graph.intermediate.representation.with.composable.transformations","name":"fx graph intermediate representation with composable transformations","description":"Represents PyTorch models as symbolic computation graphs (FX graphs) with nodes for operations and edges for data dependencies. Enables composable graph passes (dead code elimination, constant folding, operation fusion) that transform the graph without executing it. Node API provides fine-grained control over graph structure, enabling custom optimization passes.","intents":["Analyze and optimize model computation graphs programmatically","Implement custom graph transformations (fusion, quantization, pruning)","Visualize model computation structure and identify bottlenecks"],"best_for":["ML engineers implementing custom model optimization passes","Teams building model analysis and profiling tools","Researchers experimenting with novel graph transformations"],"limitations":["FX tracing requires models to be traceable (no data-dependent control flow); dynamic models may require retracing for different inputs","Graph passes are sequential; complex transformations may require multiple passes, increasing compilation time","Debugging graph transformations requires understanding symbolic execution and node dependencies","Some PyTorch operations (custom kernels, Python callbacks) cannot be represented in FX graphs"],"requires":["Python 3.9+","PyTorch 2.0+","Models using only FX-traceable operations"],"input_types":["PyTorch nn.Module instances","Traced FX graphs","Custom graph pass implementations"],"output_types":["Transformed FX graphs","Graph visualization (Graphviz, TensorBoard)","Optimization metrics (operation count, memory usage)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_7","uri":"capability://automation.workflow.multi.backend.device.support.with.native.operation.dispatch.and.cuda.memory.optimization","name":"multi-backend device support with native operation dispatch and cuda memory optimization","description":"Abstracts hardware differences through ATen (A Tensor Library) native function system, which registers operations for CUDA, CPU, MPS (Metal), and XPU backends. Native function dispatch routes operations to backend-specific implementations at runtime. CUDA backend includes caching allocator for memory pooling, CUDA graph capture for kernel launch overhead reduction, and BLAS/matrix multiplication optimization via cuBLAS and cuDNN.","intents":["Write hardware-agnostic PyTorch code that runs on NVIDIA, AMD, Intel, and Apple GPUs","Optimize GPU memory usage through caching allocation and graph capture","Leverage backend-specific optimizations (cuDNN, cuBLAS, Metal Performance Shaders) automatically"],"best_for":["ML engineers deploying models across heterogeneous hardware","Teams optimizing GPU memory efficiency for large-scale training","Researchers implementing custom operations for multiple backends"],"limitations":["Backend dispatch adds 1-5% overhead per operation due to runtime type checking and function lookup","CUDA caching allocator may fragment memory for models with highly variable tensor sizes; manual memory management may be needed","CUDA graph capture requires static computation graphs; dynamic control flow disables optimization","MPS and XPU backends have fewer optimized operations than CUDA; some operations fall back to CPU"],"requires":["Python 3.9+","PyTorch 2.0+","CUDA 11.8+ (for NVIDIA GPUs) or compatible GPU driver","Optional: cuDNN 8.0+, cuBLAS, Metal SDK (for MPS)"],"input_types":["PyTorch tensor operations","Device specifications (cuda, cpu, mps, xpu)","Custom native function implementations"],"output_types":["Computed tensors on specified device","Memory allocation traces and fragmentation metrics","CUDA graph artifacts"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_8","uri":"capability://data.processing.analysis.quantization.with.post.training.and.qat.support.via.pt2e.framework","name":"quantization with post-training and qat support via pt2e framework","description":"Reduces model size and inference latency through quantization (INT8, FP8, etc.) via PT2E (PyTorch 2 Export) quantization framework. Supports post-training quantization (PTQ) for quick optimization without retraining, and quantization-aware training (QAT) for higher accuracy. Integrates with torch.export to generate quantized computation graphs suitable for deployment.","intents":["Reduce model size by 4-8x through INT8 quantization for deployment on resource-constrained devices","Accelerate inference by 2-4x using lower-precision arithmetic on quantization-capable hardware","Maintain model accuracy through QAT while achieving deployment efficiency"],"best_for":["ML engineers optimizing models for mobile and edge deployment","Teams reducing inference latency and cost on cloud GPUs","Researchers studying quantization-accuracy tradeoffs for different model architectures"],"limitations":["Post-training quantization may reduce accuracy by 1-5% depending on model and calibration data","QAT requires retraining for 5-10% of original training time to recover accuracy","Quantized models require hardware support (INT8 cores on modern GPUs); older hardware may not benefit","Debugging quantization requires understanding scale/zero-point calibration and per-channel vs per-tensor quantization"],"requires":["Python 3.9+","PyTorch 2.1+","Calibration dataset for post-training quantization (100-1000 samples typical)","Optional: quantization-aware hardware (NVIDIA Tensor Cores, Apple Neural Engine)"],"input_types":["Trained PyTorch models","Calibration dataset for scale/zero-point estimation","Quantization configuration (bit-width, per-channel vs per-tensor)"],"output_types":["Quantized model weights and activations","Quantized computation graphs (via torch.export)","Accuracy metrics and inference latency comparisons"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-torch__cap_9","uri":"capability://code.generation.editing.onnx.export.with.torchscript.and.torch.export.backends","name":"onnx export with torchscript and torch.export backends","description":"Exports PyTorch models to ONNX (Open Neural Network Exchange) format for cross-framework compatibility via two backends: TorchScript ONNX exporter (legacy, supports dynamic shapes) and torch.export ONNX exporter (modern, leverages static graph export). Handles operator mapping, shape inference, and opset version selection for target runtime compatibility.","intents":["Deploy PyTorch models in non-PyTorch runtimes (ONNX Runtime, TensorRT, CoreML, etc.)","Enable model interoperability across frameworks (TensorFlow, JAX, etc.)","Optimize inference using runtime-specific backends (TensorRT for NVIDIA, CoreML for Apple)"],"best_for":["ML engineers deploying models to heterogeneous inference infrastructure","Teams building cross-framework model serving pipelines","Researchers comparing model performance across different runtimes"],"limitations":["ONNX operator coverage is incomplete; some PyTorch operations require custom ONNX operator definitions","Shape inference may fail for models with dynamic shapes; explicit shape annotations required","ONNX export adds 10-30 seconds overhead for large models due to graph traversal and operator mapping","Debugging ONNX export requires understanding operator compatibility and opset version constraints"],"requires":["Python 3.9+","PyTorch 2.0+","onnx package (pip install onnx)","Target runtime (ONNX Runtime, TensorRT, etc.) for validation"],"input_types":["PyTorch nn.Module or traced models","Input shape specifications","Opset version (default: latest supported)"],"output_types":["ONNX model file (.onnx)","Operator compatibility report","Shape inference results"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch 2.0+","CUDA 11.8+ (for GPU optimization) or CPU-only mode supported","PyTorch 2.1+","Models using only supported operations (check torch.export.supported_ops)","Explicit input shape specifications for symbolic dimension tracking","CUDA 11.0+ (for Kineto GPU profiling)","Optional: TensorBoard or Chrome DevTools for trace visualization","C++ compiler (GCC 9+, Clang 10+, MSVC 2019+)","CUDA toolkit (for GPU operators)"],"failure_modes":["Guard overhead adds ~50-200ms per recompilation when tensor shapes change unexpectedly","Some Python constructs (arbitrary function calls, complex closures) may cause graph breaks and fallback to eager execution","Compilation cache requires disk space; large models can generate 100MB+ of cached artifacts","Debugging compiled code requires understanding TorchDynamo's symbolic variable tracking","Symbolic shape inference requires explicit shape annotations for dynamic dimensions; models with data-dependent shapes may fail export","Some PyTorch operations (custom CUDA kernels, Python callbacks) cannot be exported and require reimplementation","Export process adds 30-60 seconds overhead for large models due to FakeTensorMode tracing","Exported graphs lose eager-mode debugging capabilities; errors surface only at runtime","Profiling adds 5-20% overhead to training; results may not reflect production performance","Kineto profiler requires CUDA 11.0+ and specific GPU drivers; older hardware may have limited kernel visibility","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:13.888Z","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-torch","compare_url":"https://unfragile.ai/compare?artifact=pypi-torch"}},"signature":"OHaOp1h95Umfsx2GskI5QFxIIbiBCWOXangr4uHq33/8tHTrisT1YuszkNO15/cyzgGY3URy33Gx3myCF1ONBA==","signedAt":"2026-06-15T13:30:00.199Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-torch","artifact":"https://unfragile.ai/pypi-torch","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-torch","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"}}