{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"einops","slug":"einops","name":"Einops","type":"repo","url":"https://github.com/arogozhnikov/einops","page_url":"https://unfragile.ai/einops","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"einops__cap_0","uri":"capability://data.processing.analysis.framework.agnostic.tensor.rearrangement.via.pattern.based.syntax","name":"framework-agnostic tensor rearrangement via pattern-based syntax","description":"Enables reshaping and transposing tensors across NumPy, PyTorch, TensorFlow, JAX, and other frameworks using a unified Einstein-inspired notation (e.g., 'batch height width channels -> batch (height width) channels'). The implementation uses a two-stage compilation pipeline: ParsedExpression extracts axis names and composite axes from pattern strings, then TransformRecipe generates optimized backend-specific transformation instructions. Dual-level LRU caching (256 recipe entries, 1024 shape entries) eliminates recompilation overhead for repeated operations.","intents":["I want to reshape tensors without learning framework-specific APIs for each library","I need to transpose and reshape tensors in a single readable operation","I want automatic shape validation to catch dimension mismatches at runtime","I need to port tensor manipulation code across different deep learning frameworks"],"best_for":["ML engineers working with multiple frameworks in the same codebase","researchers prototyping models that need framework portability","teams migrating from one framework to another"],"limitations":["Pattern syntax has a learning curve despite being more readable than framework APIs","Caching strategy uses fixed LRU sizes (256 recipes, 1024 shapes) which may not scale for extremely dynamic shape scenarios","Composite axis decomposition (e.g., '(height width)') requires explicit pattern specification — cannot infer from tensor shape alone"],"requires":["Python 3.10+","One of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework"],"input_types":["tensor (NumPy ndarray, PyTorch Tensor, TensorFlow Tensor, JAX Array, etc.)","pattern string (Einstein notation)"],"output_types":["tensor (same framework as input)"],"categories":["data-processing-analysis","tensor-operations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_1","uri":"capability://data.processing.analysis.dimension.aware.tensor.reduction.with.named.axes","name":"dimension-aware tensor reduction with named axes","description":"Performs reductions (sum, mean, max, min) along specified dimensions using named axes in Einstein notation (e.g., 'batch height width channels -> batch channels' reduces over height and width). The pattern parser identifies which axes to reduce, and the backend layer translates this into framework-specific reduction operations. Runtime validation ensures all named axes in the pattern match the input tensor's dimensions, preventing silent reduction errors that occur with positional indexing.","intents":["I want to reduce tensors along specific dimensions without counting axis indices","I need to aggregate spatial dimensions while preserving batch and channel dimensions","I want automatic validation that my reduction pattern matches the input shape","I need to apply the same reduction logic across different frameworks"],"best_for":["deep learning practitioners building models with variable input shapes","researchers implementing custom pooling or aggregation operations","teams maintaining multi-framework codebases"],"limitations":["Reduction operation type (sum, mean, max, min) is determined by the backend's default or must be specified separately — not encoded in the pattern string itself","Pattern must explicitly name all axes; cannot use wildcard reductions like 'reduce all except batch'","No support for weighted reductions or custom reduction functions within the pattern syntax"],"requires":["Python 3.10+","One of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework"],"input_types":["tensor (NumPy ndarray, PyTorch Tensor, TensorFlow Tensor, JAX Array, etc.)","pattern string (Einstein notation specifying axes to reduce)"],"output_types":["tensor (same framework as input, with reduced dimensions)"],"categories":["data-processing-analysis","tensor-operations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_10","uri":"capability://data.processing.analysis.array.api.standard.compliance.for.framework.interoperability","name":"array api standard compliance for framework interoperability","description":"Implements support for the Array API standard, enabling einops to work with any framework that implements the Array API specification (NumPy 2.0+, PyTorch, TensorFlow, JAX, etc.). This provides a path toward true framework independence by relying on standardized array operations rather than framework-specific APIs. The implementation detects Array API compliance and uses standard operations when available, falling back to framework-specific implementations when necessary.","intents":["I want to use einops with frameworks that implement the Array API standard","I need to future-proof my code against framework changes","I want to use einops with emerging frameworks that follow Array API","I need to ensure my code works with standardized array operations"],"best_for":["researchers building framework-agnostic libraries","teams adopting emerging frameworks that implement Array API","organizations seeking long-term code stability through standards compliance"],"limitations":["Array API standard is still evolving — not all operations may be standardized","Framework-specific optimizations may be unavailable through Array API","Fallback to framework-specific implementations is still required for full feature support","Performance may be suboptimal when using standardized operations instead of framework-optimized ones"],"requires":["Python 3.10+","Framework with Array API standard implementation (NumPy 2.0+, PyTorch, TensorFlow, JAX, etc.)"],"input_types":["tensor (Array API-compliant array)"],"output_types":["tensor (Array API-compliant array)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_11","uri":"capability://safety.moderation.comprehensive.test.suite.with.shape.validation.and.framework.coverage","name":"comprehensive test suite with shape validation and framework coverage","description":"Includes an extensive test infrastructure that validates einops operations across all supported frameworks (NumPy, PyTorch, TensorFlow, JAX, MLX) with systematic shape testing, edge case coverage, and numerical correctness verification. The test suite uses parameterized tests to cover combinations of frameworks, tensor shapes, and operation types, ensuring consistent behavior across backends. CI/CD pipelines run tests on multiple Python versions and framework versions to catch compatibility issues early.","intents":["I want to verify that einops works correctly on my framework and Python version","I need to ensure shape validation catches errors in my patterns","I want to test my custom einops-based code against multiple frameworks","I need to verify numerical correctness of einops operations"],"best_for":["developers building on top of einops who need confidence in correctness","teams maintaining einops or contributing new features","researchers verifying that einops behaves identically across frameworks"],"limitations":["Test suite is primarily for einops developers — not directly exposed to users","Running full test suite requires all supported frameworks installed","Test coverage may not include all edge cases or custom patterns","CI/CD pipeline is specific to einops repository — not available for user code"],"requires":["Python 3.10+","All supported frameworks (NumPy, PyTorch, TensorFlow, JAX, MLX) for full test coverage"],"input_types":["einops operations and patterns"],"output_types":["test results (pass/fail, numerical correctness verification)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_2","uri":"capability://data.processing.analysis.tensor.repetition.and.broadcasting.via.pattern.expansion","name":"tensor repetition and broadcasting via pattern expansion","description":"Replicates tensor data along new or existing dimensions using Einstein notation (e.g., 'batch height width -> batch height width repeat_count' repeats along a new axis). The pattern parser identifies which axes are new (appear in output but not input) and generates backend-specific repeat/broadcast instructions. This avoids manual broadcasting and explicit repeat calls, providing a declarative alternative to framework-specific APIs like torch.repeat or tf.tile.","intents":["I want to repeat tensor data along new dimensions without explicit repeat/tile calls","I need to broadcast tensors to match shapes in a readable way","I want to expand batch dimensions or create new feature dimensions declaratively","I need consistent repeat semantics across NumPy, PyTorch, TensorFlow, and JAX"],"best_for":["ML engineers building data augmentation pipelines","researchers implementing attention mechanisms or multi-head operations","teams needing framework-agnostic broadcasting logic"],"limitations":["Pattern must explicitly specify all output dimensions — cannot infer repeat counts from context","Repeat count must be a literal integer in the pattern; cannot use dynamic/variable repeat counts","No support for conditional repetition or masked repetition within the pattern syntax"],"requires":["Python 3.10+","One of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework"],"input_types":["tensor (NumPy ndarray, PyTorch Tensor, TensorFlow Tensor, JAX Array, etc.)","pattern string (Einstein notation with new axes)"],"output_types":["tensor (same framework as input, with expanded dimensions)"],"categories":["data-processing-analysis","tensor-operations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_3","uri":"capability://data.processing.analysis.pattern.parsing.and.validation.with.composite.axis.support","name":"pattern parsing and validation with composite axis support","description":"Parses Einstein notation patterns to extract axis names, composite axes (e.g., '(height width)'), and ellipsis operators, then validates that the pattern matches the input tensor's shape at runtime. The ParsedExpression class decomposes patterns into semantic components, and the validation layer checks that all named axes have consistent dimensions across input and output. This prevents silent shape mismatches and provides clear error messages when patterns are invalid.","intents":["I want to validate tensor shapes against a pattern before executing operations","I need to understand what axes a pattern refers to without manual inspection","I want clear error messages when my reshape/reduce pattern doesn't match the tensor shape","I need to support composite axes like '(height width)' for flattening spatial dimensions"],"best_for":["developers building tensor manipulation pipelines with shape validation","researchers debugging shape mismatches in complex models","teams implementing custom tensor operations on top of einops"],"limitations":["Pattern syntax is strict — whitespace and parenthesis placement matter, requiring careful pattern construction","Ellipsis (...) support is limited; cannot use multiple ellipsis in a single pattern","Composite axis decomposition requires explicit parentheses; implicit flattening is not supported","Error messages may be verbose for complex patterns with many axes"],"requires":["Python 3.10+","Understanding of Einstein notation syntax"],"input_types":["pattern string (Einstein notation)","tensor shape (tuple of integers)"],"output_types":["ParsedExpression object (internal representation of pattern structure)","validation errors (if pattern is invalid)"],"categories":["data-processing-analysis","validation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_4","uri":"capability://data.processing.analysis.recipe.compilation.and.caching.for.repeated.operations","name":"recipe compilation and caching for repeated operations","description":"Compiles patterns into optimized TransformRecipe objects that encode the exact transformation steps, then caches recipes using a 256-entry LRU cache to avoid recompilation on repeated operations. The caching layer operates at two levels: recipe caching (pattern → transformation instructions) and shape caching (1024 entries) for frequently seen tensor shapes. This architecture eliminates parsing and compilation overhead for operations that use the same pattern multiple times, critical for performance in training loops.","intents":["I want to avoid recompiling patterns in tight training loops","I need to understand the transformation steps einops will execute","I want to optimize performance for repeated tensor operations","I need to debug what operations einops is actually performing"],"best_for":["ML engineers optimizing training performance","researchers profiling tensor operation overhead","teams building custom layers on top of einops"],"limitations":["LRU cache size is fixed at 256 recipes and 1024 shapes — may cause cache thrashing with extremely diverse patterns","Cache is not persistent across Python sessions; each run rebuilds the cache","No cache statistics or introspection API to monitor hit rates or debug cache behavior","Cache invalidation is automatic but opaque — no control over cache eviction policy"],"requires":["Python 3.10+","Repeated operations using the same pattern (to benefit from caching)"],"input_types":["pattern string (Einstein notation)","tensor shape (tuple of integers)"],"output_types":["TransformRecipe object (internal representation of transformation steps)","cached recipe (on subsequent calls with same pattern)"],"categories":["data-processing-analysis","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_5","uri":"capability://data.processing.analysis.dynamic.backend.detection.and.framework.agnostic.execution","name":"dynamic backend detection and framework-agnostic execution","description":"Automatically detects the input tensor's framework (NumPy, PyTorch, TensorFlow, JAX, MLX, etc.) and dispatches operations to the appropriate backend implementation without user configuration. The backend abstraction layer wraps framework-specific operations (reshape, transpose, reduce, etc.) with a unified interface, enabling identical einops code to execute on any supported framework. This design eliminates the need for framework-specific imports or conditional logic in user code.","intents":["I want to write tensor code once and run it on multiple frameworks","I need to support both NumPy and PyTorch without duplicating logic","I want to automatically use the right framework operations without explicit backend selection","I need to migrate code between frameworks with minimal changes"],"best_for":["ML engineers building framework-agnostic libraries","researchers prototyping models that need to run on multiple backends","teams maintaining codebases that support multiple frameworks"],"limitations":["Backend detection is based on tensor type inspection — cannot explicitly override backend selection","Some framework-specific optimizations may not be available through the abstraction layer","New frameworks require explicit backend implementation; no automatic fallback mechanism","Performance characteristics vary across backends — einops cannot guarantee identical execution speed"],"requires":["Python 3.10+","At least one of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework installed"],"input_types":["tensor (NumPy ndarray, PyTorch Tensor, TensorFlow Tensor, JAX Array, MLX Array, etc.)"],"output_types":["tensor (same framework as input)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_6","uri":"capability://data.processing.analysis.einstein.summation.einsum.with.pattern.based.syntax","name":"einstein summation (einsum) with pattern-based syntax","description":"Provides Einstein summation notation for complex tensor contractions (e.g., 'ij,jk->ik' for matrix multiplication) with automatic translation to framework-specific einsum implementations. The pattern parser converts Einstein notation into the appropriate backend einsum call, supporting implicit and explicit summation semantics. This unifies einsum syntax across frameworks that have different einsum APIs (PyTorch vs TensorFlow vs NumPy).","intents":["I want to express tensor contractions using Einstein notation","I need to compute matrix multiplications, batch matrix multiplications, or complex tensor products","I want to use einsum syntax that works identically across frameworks","I need to implement attention mechanisms or other contraction-heavy operations"],"best_for":["researchers implementing attention mechanisms and transformer layers","ML engineers building custom tensor contraction operations","teams needing framework-agnostic einsum semantics"],"limitations":["Pattern syntax must follow strict Einstein notation rules — invalid patterns produce cryptic errors","Performance varies significantly across frameworks; PyTorch einsum may be slower than explicit operations","No automatic optimization of contraction order; complex patterns may execute inefficiently","Implicit summation semantics can be ambiguous for users unfamiliar with Einstein notation"],"requires":["Python 3.10+","One of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework with einsum support"],"input_types":["multiple tensors (NumPy ndarray, PyTorch Tensor, TensorFlow Tensor, JAX Array, etc.)","pattern string (Einstein summation notation)"],"output_types":["tensor (same framework as input, result of contraction)"],"categories":["data-processing-analysis","tensor-operations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_7","uri":"capability://data.processing.analysis.tensor.packing.and.unpacking.for.variable.length.sequences","name":"tensor packing and unpacking for variable-length sequences","description":"Provides pack() and unpack() operations to efficiently handle variable-length sequences by concatenating them into a single tensor with a companion packed_shapes array, then reconstructing the original sequences. This avoids padding and masking overhead for operations on ragged/variable-length data. The pack operation flattens multiple tensors along a specified axis and records shape information, while unpack reverses this process using the recorded shapes.","intents":["I want to process variable-length sequences without padding","I need to concatenate multiple tensors of different shapes efficiently","I want to avoid masking overhead in models that handle ragged data","I need to reconstruct original sequences after processing a packed tensor"],"best_for":["NLP engineers processing variable-length sequences","researchers building models for ragged/sparse data","teams optimizing memory usage for variable-length inputs"],"limitations":["Packed shapes metadata must be preserved and passed to unpack — no automatic shape recovery","Pack/unpack operations add overhead for small tensors or short sequences","Not all operations can be efficiently applied to packed tensors — some require unpacking first","Debugging packed tensors is harder than working with padded tensors"],"requires":["Python 3.10+","One of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework"],"input_types":["multiple tensors of different shapes (NumPy ndarray, PyTorch Tensor, TensorFlow Tensor, JAX Array, etc.)","axis specification (which dimension to pack along)"],"output_types":["packed tensor (concatenated along specified axis)","packed_shapes metadata (tuple of original shapes)"],"categories":["data-processing-analysis","tensor-operations"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_8","uri":"capability://code.generation.editing.framework.specific.neural.network.layers.pytorch.tensorflow.keras","name":"framework-specific neural network layers (pytorch, tensorflow, keras)","description":"Provides drop-in neural network layer implementations (einops.layers.torch.Rearrange, einops.layers.tensorflow.Rearrange, etc.) that apply einops operations within model architectures. These layers wrap rearrange, reduce, and repeat operations as nn.Module subclasses, enabling einops patterns to be used directly in model definitions without explicit function calls. The EinMix layer provides learnable linear transformations along specified axes, combining einops rearrangement with weight matrices.","intents":["I want to use einops patterns directly in PyTorch or TensorFlow model definitions","I need to apply tensor rearrangement as a layer in a neural network","I want to implement learnable transformations along specific axes (EinMix)","I need to replace reshape/transpose layers with readable einops patterns"],"best_for":["ML engineers building PyTorch or TensorFlow models","researchers implementing custom architectures with complex tensor manipulations","teams migrating models to use einops for readability"],"limitations":["Layers are framework-specific — separate implementations for PyTorch, TensorFlow, Keras required","EinMix layer adds learnable parameters but may not be as optimized as framework-native linear layers","Layer serialization/deserialization may require custom handling for model checkpointing","Pattern validation happens at layer instantiation, not at forward pass — shape mismatches may not be caught until runtime"],"requires":["Python 3.10+","PyTorch 1.9+ (for torch layers) OR TensorFlow 2.0+ (for TensorFlow/Keras layers)"],"input_types":["tensor (PyTorch Tensor, TensorFlow Tensor, etc.)","pattern string (Einstein notation)"],"output_types":["tensor (same framework as input)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__cap_9","uri":"capability://code.generation.editing.pytorch.compilation.support.via.torch.compile","name":"pytorch compilation support via torch.compile","description":"Enables einops operations to be compiled and optimized by PyTorch's torch.compile() mechanism, allowing patterns to be fused with surrounding operations for improved performance. The implementation ensures that einops operations are compatible with PyTorch's symbolic tracing and graph compilation, enabling end-to-end model compilation without einops being a compilation bottleneck. This is particularly valuable for production inference where compilation overhead is amortized across many forward passes.","intents":["I want to compile my entire model including einops operations with torch.compile","I need to optimize einops performance for production inference","I want einops operations to be fused with surrounding layers","I need to avoid einops being a compilation bottleneck in torch.compile workflows"],"best_for":["ML engineers optimizing PyTorch models for production inference","researchers using torch.compile for end-to-end model optimization","teams deploying models where compilation overhead is amortized"],"limitations":["Compilation support is PyTorch-specific — not available for TensorFlow or JAX backends","Compilation overhead may not be worth it for models with few einops operations","Symbolic tracing may fail for complex patterns or dynamic shapes","Compiled performance gains depend on surrounding operations and hardware"],"requires":["Python 3.10+","PyTorch 2.0+ (for torch.compile support)"],"input_types":["PyTorch model using einops operations","torch.compile configuration"],"output_types":["compiled PyTorch model (optimized graph)"],"categories":["code-generation-editing","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"einops__headline","uri":"capability://data.processing.analysis.einstein.inspired.tensor.manipulation.library","name":"einstein-inspired tensor manipulation library","description":"Einops is a flexible and powerful library for tensor operations that uses a readable, pattern-based syntax inspired by Einstein notation, enabling seamless manipulation across multiple deep learning frameworks like NumPy, PyTorch, TensorFlow, and JAX.","intents":["best tensor manipulation library","tensor operations for deep learning","Einstein notation for tensor manipulation","cross-framework tensor operations","tensor reshaping and reducing tools"],"best_for":["deep learning practitioners","data scientists","researchers"],"limitations":[],"requires":["Python 3.10+"],"input_types":["tensors"],"output_types":["manipulated tensors"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","One of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework","Framework with Array API standard implementation (NumPy 2.0+, PyTorch, TensorFlow, JAX, etc.)","All supported frameworks (NumPy, PyTorch, TensorFlow, JAX, MLX) for full test coverage","Understanding of Einstein notation syntax","Repeated operations using the same pattern (to benefit from caching)","At least one of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework installed","One of: NumPy, PyTorch, TensorFlow, JAX, MLX, or compatible framework with einsum support","PyTorch 1.9+ (for torch layers) OR TensorFlow 2.0+ (for TensorFlow/Keras layers)","PyTorch 2.0+ (for torch.compile support)"],"failure_modes":["Pattern syntax has a learning curve despite being more readable than framework APIs","Caching strategy uses fixed LRU sizes (256 recipes, 1024 shapes) which may not scale for extremely dynamic shape scenarios","Composite axis decomposition (e.g., '(height width)') requires explicit pattern specification — cannot infer from tensor shape alone","Reduction operation type (sum, mean, max, min) is determined by the backend's default or must be specified separately — not encoded in the pattern string itself","Pattern must explicitly name all axes; cannot use wildcard reductions like 'reduce all except batch'","No support for weighted reductions or custom reduction functions within the pattern syntax","Array API standard is still evolving — not all operations may be standardized","Framework-specific optimizations may be unavailable through Array API","Fallback to framework-specific implementations is still required for full feature support","Performance may be suboptimal when using standardized operations instead of framework-optimized ones","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"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=einops","compare_url":"https://unfragile.ai/compare?artifact=einops"}},"signature":"fkYNMIzJ1Jl/PffD5VDSSG05Qok6FlBOc0FdfYqH62zbv7uYDgnPsKscdQ1XrKkHqMfzWDQI1H8U0tQZd8M3Dg==","signedAt":"2026-06-22T18:30:50.974Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/einops","artifact":"https://unfragile.ai/einops","verify":"https://unfragile.ai/api/v1/verify?slug=einops","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"}}