{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-jax","slug":"pypi-jax","name":"jax","type":"framework","url":"https://github.com/jax-ml/jax","page_url":"https://unfragile.ai/pypi-jax","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-jax__cap_0","uri":"capability://data.processing.analysis.numpy.compatible.array.api.with.automatic.differentiation.support","name":"numpy-compatible array api with automatic differentiation support","description":"JAX implements a complete NumPy-compatible API (jax.numpy) that wraps lower-level LAX primitives, enabling users to write familiar NumPy code while maintaining full traceability for automatic differentiation. The implementation maps NumPy operations to JAX's intermediate representation (Jaxpr) through a tracer system that intercepts Python operations, building a computational graph without requiring explicit graph construction syntax. This allows seamless gradient computation and other transformations on NumPy-style code.","intents":["I want to write NumPy code but get automatic gradients without rewriting for a different framework","I need to use standard NumPy operations while maintaining compatibility with JAX transformations","I want to migrate existing NumPy codebases to JAX with minimal refactoring"],"best_for":["ML researchers familiar with NumPy transitioning to JAX","Teams with existing NumPy numerical computing code","Developers building scientific computing applications requiring gradients"],"limitations":["Not all NumPy operations are supported; some edge cases in advanced indexing differ from NumPy semantics","Arrays are immutable by design, requiring functional programming patterns instead of in-place mutations","Dynamic shapes require special handling via vmap or other transformations; static shape inference is preferred for JIT compilation"],"requires":["Python 3.9+","JAX installed via pip (jax, jaxlib)","Compatible CUDA/cuDNN or ROCm for GPU acceleration (optional but recommended)"],"input_types":["Python numerical arrays (lists, tuples, numpy.ndarray)","JAX arrays (jax.Array)","Scalar values"],"output_types":["jax.Array (immutable, device-agnostic arrays)","Python scalars or tuples of arrays"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_1","uri":"capability://data.processing.analysis.automatic.differentiation.via.reverse.mode.and.forward.mode.ad","name":"automatic differentiation via reverse-mode and forward-mode ad","description":"JAX implements automatic differentiation through a tracer-based interpreter system (jax.interpreters.ad) that builds a Jaxpr representation of a function, then applies reverse-mode (backpropagation) or forward-mode differentiation rules to compute gradients. The system supports higher-order derivatives (grad of grad), arbitrary nesting of AD with other transformations, and custom VJP/JVP rules for user-defined operations. Gradients are computed by tracing through the function once to build the computational graph, then applying chain rule transformations.","intents":["I need to compute gradients of arbitrary Python functions for optimization","I want to compute higher-order derivatives (Hessians, Jacobians) efficiently","I need custom gradient rules for operations not in JAX's standard library","I want to compose automatic differentiation with other transformations like JIT or vmap"],"best_for":["ML researchers implementing custom loss functions and optimizers","Scientists computing derivatives for physics simulations or inverse problems","Teams building differentiable programming frameworks on top of JAX"],"limitations":["Reverse-mode AD requires materializing the full computational graph in memory; very large graphs may cause memory issues","Custom VJP/JVP rules must be manually defined for non-standard operations; no automatic symbolic differentiation fallback","AD through control flow (if/while) requires special primitives (lax.cond, lax.while_loop); Python control flow is not automatically differentiable","Differentiation through certain operations (e.g., sorting, argmax) requires custom rules or approximations"],"requires":["Python 3.9+","JAX installed with jaxlib","Understanding of functional programming and chain rule mathematics"],"input_types":["Python functions taking JAX arrays as input","Scalar or array outputs"],"output_types":["jax.Array containing gradient values (same shape as input for scalar outputs)","Tuples of gradients for multi-argument functions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_10","uri":"capability://data.processing.analysis.type.system.and.dtype.handling.with.automatic.promotion","name":"type system and dtype handling with automatic promotion","description":"JAX implements a comprehensive type system (jax.dtypes) that handles numeric types (int32, float32, complex64, etc.) with automatic promotion rules. The system supports weak type promotion (e.g., Python int to int32) and strong type promotion (e.g., int32 to float32 in mixed operations). Type information is preserved through transformations and used by the compiler for optimization. Users can control promotion behavior via jax.numpy.promote_types and explicit casting.","intents":["I want to understand how JAX handles numeric types and promotion","I need to control type promotion in mixed-type operations","I want to ensure numerical precision in my computations","I need to work with specific dtypes (e.g., bfloat16 for TPU efficiency)"],"best_for":["ML engineers optimizing numerical precision and performance","Researchers working with mixed-precision training","Teams deploying models on hardware with specific dtype support (e.g., TPU bfloat16)"],"limitations":["Automatic type promotion can lead to unexpected behavior if not carefully managed; explicit casting is often necessary","Some dtypes (e.g., bfloat16) have limited precision; numerical stability issues may arise","Type information is not always preserved through transformations; users must verify dtype consistency","Weak type promotion can cause subtle bugs; strong typing is recommended for production code"],"requires":["Python 3.9+","JAX with jaxlib","Understanding of numeric types and promotion rules"],"input_types":["JAX arrays with various dtypes","Python scalars (int, float, complex)"],"output_types":["JAX arrays with promoted dtypes","Type information (via jax.numpy.result_type)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_11","uri":"capability://code.generation.editing.xla.compiler.integration.with.mlir.stablehlo.lowering","name":"xla compiler integration with mlir/stablehlo lowering","description":"JAX integrates with Google's XLA compiler by lowering Jaxpr intermediate representations to MLIR (Multi-Level Intermediate Representation) and StableHLO (Stable High-Level Operations). The lowering process converts high-level JAX operations to hardware-independent HLO, which XLA then optimizes and compiles to target-specific code (LLVM for CPU, NVPTX for GPU, HLO for TPU). This architecture enables single-source deployment across heterogeneous hardware without code changes.","intents":["I want to compile JAX code to run on CPUs, GPUs, and TPUs without code changes","I need to understand how JAX code is lowered to hardware-specific instructions","I want to optimize compilation for specific hardware targets","I need to debug compilation issues or performance bottlenecks"],"best_for":["ML engineers deploying models across heterogeneous hardware","Researchers optimizing compilation for specific targets","Teams building production ML systems with hardware flexibility"],"limitations":["XLA compilation can be slow for complex functions; compilation time may exceed execution time for small inputs","Compilation errors from XLA can be difficult to debug; error messages may not clearly indicate the source","Some operations may not be supported on all hardware targets; fallback implementations may be slower","Compiler optimizations are not always predictable; performance may vary across XLA versions"],"requires":["Python 3.9+","JAX with jaxlib compiled with XLA support","XLA compiler (included with jaxlib)","Target hardware (CPU, GPU, or TPU) with appropriate drivers/runtime"],"input_types":["Jaxpr intermediate representations","MLIR/StableHLO code (for advanced users)"],"output_types":["Compiled executables (LLVM for CPU, NVPTX for GPU, HLO for TPU)","MLIR/StableHLO intermediate code (for inspection)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_12","uri":"capability://tool.use.integration.tensorflow.interoperability.via.jax2tf.and.tf2jax.bridges","name":"tensorflow interoperability via jax2tf and tf2jax bridges","description":"JAX provides jax2tf and tf2jax bridges enabling seamless interoperability with TensorFlow. jax2tf converts JAX functions to TensorFlow SavedModel format, enabling deployment in TensorFlow ecosystems. tf2jax wraps TensorFlow operations as JAX functions, allowing mixed JAX/TensorFlow code. The bridges handle dtype conversion, device placement, and gradient flow, enabling gradual migration between frameworks or hybrid workflows.","intents":["I want to deploy JAX models using TensorFlow Serving or other TensorFlow infrastructure","I need to use TensorFlow operations inside JAX code","I want to migrate from TensorFlow to JAX incrementally","I need to combine JAX and TensorFlow code in the same application"],"best_for":["ML engineers migrating from TensorFlow to JAX","Teams with existing TensorFlow infrastructure needing JAX integration","Researchers combining JAX and TensorFlow in hybrid workflows"],"limitations":["jax2tf conversion may not preserve all JAX semantics; some operations may behave differently in TensorFlow","tf2jax wrapping adds overhead; TensorFlow operations may be slower when called from JAX","Gradient flow through TensorFlow operations requires careful handling; some operations may not be differentiable","Device placement and memory management may differ between JAX and TensorFlow, causing performance issues","Interoperability is not always transparent; users must understand both frameworks' semantics"],"requires":["Python 3.9+","JAX with jaxlib","TensorFlow 2.x","jax2tf and tf2jax packages"],"input_types":["JAX functions (for jax2tf)","TensorFlow operations (for tf2jax)"],"output_types":["TensorFlow SavedModel (from jax2tf)","JAX functions wrapping TensorFlow operations (from tf2jax)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_13","uri":"capability://automation.workflow.configuration.and.runtime.behavior.control.via.jax.config","name":"configuration and runtime behavior control via jax.config","description":"JAX provides a configuration system (jax.config) enabling runtime control of behavior without code changes. Users can configure JIT defaults, device placement, dtype promotion, debugging flags, and experimental features. Configuration can be set via environment variables, Python API, or context managers, enabling flexible control of JAX behavior for different use cases (development, testing, production).","intents":["I want to disable JIT for debugging without changing code","I need to control device placement and memory allocation","I want to enable experimental features for testing","I need to set different configurations for development vs production"],"best_for":["ML engineers debugging JAX code","Teams managing JAX deployments across environments","Researchers experimenting with JAX features"],"limitations":["Configuration changes may not take effect for already-compiled functions; recompilation may be necessary","Some configurations conflict with each other; users must understand interactions","Configuration is global; thread-local or process-local configuration is not fully supported","Documentation of configuration options is incomplete; users must explore source code"],"requires":["Python 3.9+","JAX with jaxlib"],"input_types":["Configuration keys (strings)","Configuration values (booleans, strings, integers)"],"output_types":["Configuration state (via jax.config.read)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_2","uri":"capability://code.generation.editing.just.in.time.compilation.to.xla.with.staged.compilation.pipeline","name":"just-in-time compilation to xla with staged compilation pipeline","description":"JAX's jit decorator traces a Python function to produce a Jaxpr intermediate representation, lowers it to MLIR/StableHLO, and compiles via XLA to hardware-specific executables (LLVM for CPU, NVPTX for GPU, HLO for TPU). The compilation pipeline exposes three stages (Traced, Lowered, Compiled) via jax.stages, allowing inspection and debugging of the compilation process. JIT compilation caches compiled functions by input shape and dtype, enabling fast re-execution of the same computation with different data.","intents":["I want to compile Python functions to native machine code for 10-100x speedup","I need to understand what's happening during compilation for debugging performance issues","I want to compile functions once and reuse them across multiple data batches","I need to compile code for deployment on TPUs, GPUs, or CPUs without code changes"],"best_for":["ML engineers optimizing training loops and inference pipelines","Researchers running large-scale simulations requiring compiled performance","Teams deploying JAX models to production on heterogeneous hardware"],"limitations":["JIT requires static shapes at compile time; dynamic shapes require vmap or other workarounds, adding compilation overhead","First call to a jitted function incurs compilation latency (seconds to minutes for complex functions); subsequent calls with same shape are fast","Python control flow (if/while) is not supported in jitted functions; must use lax.cond, lax.while_loop, or other JAX primitives","Debugging jitted code is harder; print statements and breakpoints don't work inside jitted functions","Compilation cache is keyed by input shape/dtype; shape changes trigger recompilation, which can be expensive in dynamic scenarios"],"requires":["Python 3.9+","JAX with jaxlib compiled for target hardware (CPU, GPU, or TPU)","XLA compiler (included with jaxlib)","Understanding of static vs dynamic shapes"],"input_types":["Python functions with JAX array inputs","Optional static_argnums parameter to specify which arguments don't affect compilation"],"output_types":["Compiled executable (jax.stages.Compiled)","jax.Array outputs with same semantics as uncompiled execution"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_3","uri":"capability://data.processing.analysis.vectorization.via.vmap.with.automatic.batching","name":"vectorization via vmap with automatic batching","description":"JAX's vmap (vectorized map) transformation automatically vectorizes functions across a batch dimension by tracing the function once and generating SIMD/batched operations. Instead of writing explicit loops over batch dimensions, users annotate which axis to vectorize, and vmap generates efficient batched code that runs on vector units or tensor cores. The implementation uses a batching interpreter that transforms scalar operations into batched equivalents, composing with JIT for compiled vectorized kernels.","intents":["I want to apply a function to a batch of inputs without writing explicit loops","I need to vectorize operations across multiple dimensions (e.g., batch and feature dimensions)","I want to compose batching with JIT compilation for efficient batch processing","I need to compute Jacobians or other per-sample derivatives efficiently"],"best_for":["ML engineers processing batches of data in training loops","Researchers computing batch Jacobians or per-sample gradients","Teams building vectorized numerical simulations"],"limitations":["vmap requires the function to be written for a single input; operations that depend on batch size (e.g., batch normalization statistics) require special handling via vmap's in_axes parameter","Nested vmap calls can be inefficient if not carefully composed; multiple levels of vectorization may not fully utilize hardware parallelism","vmap doesn't automatically handle ragged or variable-length inputs; padding or masking is required","Some operations (e.g., sorting, argmax) may not vectorize efficiently across all dimensions"],"requires":["Python 3.9+","JAX with jaxlib","Understanding of batch dimensions and axis notation"],"input_types":["Python functions taking JAX arrays","in_axes parameter specifying which axes to vectorize (int or nested structure)","out_axes parameter specifying output batch axes"],"output_types":["jax.Array with batched results","Same structure as original function output, with batch dimension added"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_4","uri":"capability://automation.workflow.multi.device.parallelization.via.pmap.with.automatic.sharding","name":"multi-device parallelization via pmap with automatic sharding","description":"JAX's pmap (parallel map) distributes a function across multiple devices (GPUs, TPUs) by tracing the function and automatically generating sharded computation graphs. Each device receives a slice of the input along a specified axis, executes the function independently, and results are gathered. The system handles device placement, communication (all-reduce, all-gather), and synchronization transparently. pmap composes with JIT and grad, enabling distributed training and inference without explicit communication code.","intents":["I want to parallelize training across multiple GPUs or TPUs without writing distributed code","I need to run the same function on different data shards across devices","I want to use collective operations (all-reduce, all-gather) for distributed algorithms","I need to compute gradients across multiple devices for distributed training"],"best_for":["ML engineers training large models on multi-GPU or multi-TPU systems","Researchers running distributed simulations","Teams building distributed machine learning systems"],"limitations":["pmap requires static device count at compile time; dynamic device addition/removal requires recompilation","Communication overhead (all-reduce, all-gather) can dominate for small batch sizes or high-latency networks","Debugging pmap is difficult; errors on individual devices may not be clearly reported","pmap is being superseded by jit with sharding annotations (pjit); pmap is less flexible for complex sharding patterns","Requires multiple devices; single-device execution is inefficient"],"requires":["Python 3.9+","JAX with jaxlib compiled for multi-device support (CUDA/cuDNN for multi-GPU, TPU runtime for TPUs)","Multiple GPUs, TPUs, or other devices visible to JAX","Understanding of data parallelism and collective operations"],"input_types":["Python functions taking JAX arrays","axis parameter specifying which axis to parallelize across","Optional in_axes/out_axes for non-standard sharding"],"output_types":["jax.Array with results gathered from all devices","Same structure as original function output"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_5","uri":"capability://code.generation.editing.custom.kernel.development.via.pallas.with.tpu.gpu.code.generation","name":"custom kernel development via pallas with tpu/gpu code generation","description":"JAX's Pallas subsystem enables writing custom kernels in a Python-like DSL that compiles to TPU (Mosaic) or GPU (Triton/Mosaic GPU) code. Pallas provides a lower-level abstraction than JAX's high-level operations, allowing fine-grained control over memory layout, communication patterns, and hardware-specific optimizations. Kernels written in Pallas integrate seamlessly with JAX's transformation system (grad, vmap, jit), enabling custom operations with automatic differentiation support.","intents":["I need to implement a custom operation not available in JAX's standard library","I want to optimize a critical kernel for specific hardware (TPU or GPU)","I need fine-grained control over memory access patterns and communication","I want to implement a custom operation with automatic differentiation support"],"best_for":["ML systems engineers optimizing performance-critical kernels","Researchers implementing novel algorithms requiring custom operations","Teams building specialized hardware accelerators"],"limitations":["Pallas requires understanding of low-level hardware concepts (memory hierarchy, communication patterns, synchronization)","TPU Mosaic and GPU Triton have different APIs and capabilities; code is not portable between them","Debugging Pallas kernels is difficult; profiling and error messages are less informative than high-level JAX code","Pallas is still experimental; APIs may change and documentation is limited","Custom VJP/JVP rules must be manually defined for automatic differentiation support"],"requires":["Python 3.9+","JAX with jaxlib compiled for target hardware (TPU or GPU)","Understanding of low-level hardware concepts and memory hierarchies","For TPU: Mosaic compiler and TPU runtime","For GPU: Triton compiler (for NVIDIA) or Mosaic GPU compiler"],"input_types":["Pallas kernel definitions (Python functions with pallas.BlockSpec annotations)","Input arrays and scalar parameters"],"output_types":["jax.Array with kernel results","Compiled kernel executable"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_6","uri":"capability://planning.reasoning.control.flow.primitives.with.automatic.differentiation.support","name":"control flow primitives with automatic differentiation support","description":"JAX provides lax.cond, lax.while_loop, lax.for_loop, and lax.scan primitives that enable control flow within jitted and differentiated code. These primitives are implemented as special traced operations that build a Jaxpr representation of the control flow, enabling automatic differentiation and JIT compilation. Unlike Python's if/while statements, which are not traceable, lax primitives produce functional control flow that can be optimized and compiled.","intents":["I need to use conditional logic (if/else) inside a jitted function","I want to implement loops (for/while) that are JIT-compiled and differentiable","I need to scan over a sequence of inputs with a stateful function","I want to implement iterative algorithms (e.g., Newton's method) with automatic differentiation"],"best_for":["ML researchers implementing custom optimization algorithms","Scientists building iterative numerical solvers","Teams implementing control flow-heavy algorithms (e.g., RNNs, tree search)"],"limitations":["lax.cond requires both branches to have the same output shape and dtype; dynamic shapes require vmap or other workarounds","lax.while_loop requires a fixed maximum iteration count or early termination logic; unbounded loops are not supported","Debugging control flow primitives is difficult; errors in branches may not be clearly reported","Performance of lax primitives depends on the compiler's ability to optimize; some patterns may be slower than hand-written code","lax.scan is less intuitive than Python loops; users must explicitly manage state and accumulation"],"requires":["Python 3.9+","JAX with jaxlib","Understanding of functional programming and immutable state"],"input_types":["Python functions (branches for cond, body for while_loop/for_loop/scan)","Condition values (boolean for cond, carry state for loops)","Sequence of inputs (for scan)"],"output_types":["jax.Array with results from selected branch or loop iterations","Tuple of (carry, outputs) for scan"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_7","uri":"capability://data.processing.analysis.random.number.generation.with.deterministic.seeding.and.transformation.composition","name":"random number generation with deterministic seeding and transformation composition","description":"JAX implements a stateless random number generation system (jax.random) that uses explicit seed/key management instead of global state. The system provides a threefry/philox counter-based PRNG that is deterministic, reproducible, and composable with transformations (vmap, pmap, jit). Keys are split and threaded through code, enabling parallel RNG streams without synchronization. The design avoids global state, making random operations safe in jitted and distributed code.","intents":["I want reproducible random number generation in my ML code","I need to generate different random streams across devices in distributed training","I want random operations to work correctly inside jitted functions","I need to compose random sampling with vmap for batch operations"],"best_for":["ML engineers building reproducible training pipelines","Researchers running distributed experiments requiring deterministic randomness","Teams implementing stochastic algorithms (dropout, data augmentation)"],"limitations":["Explicit key management is more verbose than NumPy's global random state; users must thread keys through code","Key splitting adds overhead; performance may be slower than NumPy's random for simple operations","Some NumPy random functions are not available in jax.random; users must use available distributions","Seeding is not global; each function call requires an explicit key, making migration from NumPy code tedious"],"requires":["Python 3.9+","JAX with jaxlib","Understanding of key splitting and stateless RNG"],"input_types":["jax.random.PRNGKey (seed value)","Shape and dtype parameters for random array generation","Distribution parameters (mean, std, etc.)"],"output_types":["jax.Array with random values","Tuple of (key, array) for functions that return updated key"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_8","uri":"capability://automation.workflow.distributed.computing.with.automatic.sharding.and.collective.operations","name":"distributed computing with automatic sharding and collective operations","description":"JAX's distributed computing system (jax.experimental.pjit and sharding annotations) enables automatic data and model parallelism across multiple devices. Users annotate arrays with sharding specifications (e.g., PartitionSpec), and the compiler automatically generates communication code (all-reduce, all-gather, reduce-scatter) to synchronize computations. The system integrates with XLA's collective operations and handles device placement transparently, enabling distributed training and inference without explicit communication code.","intents":["I want to train large models across multiple devices with automatic sharding","I need to implement model parallelism (splitting model across devices)","I want to use data parallelism with automatic gradient synchronization","I need to optimize communication patterns for distributed training"],"best_for":["ML engineers training large-scale models on multi-device clusters","Researchers implementing distributed algorithms","Teams building production ML systems with distributed inference"],"limitations":["Sharding annotations require understanding of device mesh topology and partition specifications; incorrect annotations can cause performance degradation","Communication overhead (all-reduce, all-gather) can dominate for small batch sizes or high-latency networks","Debugging distributed code is difficult; errors on individual devices may not be clearly reported","pjit is still experimental; APIs may change and documentation is limited","Collective operations require synchronization; asynchronous execution is not supported"],"requires":["Python 3.9+","JAX with jaxlib compiled for multi-device support","Multiple devices (GPUs, TPUs) visible to JAX","Understanding of device mesh topology and sharding specifications"],"input_types":["Python functions with JAX array inputs","PartitionSpec annotations specifying sharding strategy","Device mesh specification"],"output_types":["jax.Array with results gathered from all devices","Same structure as original function output"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-jax__cap_9","uri":"capability://code.generation.editing.functional.transformations.composition.with.jaxpr.intermediate.representation","name":"functional transformations composition with jaxpr intermediate representation","description":"JAX's core architecture is built on composable function transformations (grad, jit, vmap, pmap) that operate on a pure functional intermediate representation called Jaxpr. Each transformation traces a function to produce a Jaxpr, applies interpretation rules (AD rules, batching rules, etc.), and produces a new function. Transformations can be arbitrarily nested and composed without special-casing, enabling powerful abstractions like grad(jit(vmap(f))) or vmap(grad(f)). The Jaxpr representation is a directed acyclic graph of primitive operations with explicit data flow.","intents":["I want to compose multiple transformations (e.g., JIT + grad + vmap) without performance penalties","I need to inspect the intermediate representation of my code for debugging or optimization","I want to implement custom transformations that compose with JAX's built-in transformations","I need to understand how JAX transforms my code for performance analysis"],"best_for":["ML researchers building advanced optimization algorithms requiring transformation composition","Framework developers implementing custom transformations on top of JAX","Teams optimizing complex numerical code with multiple transformations"],"limitations":["Jaxpr representation is not human-readable; debugging requires understanding the intermediate format","Custom transformations require implementing interpretation rules for all primitive operations; incomplete implementations can cause errors","Composition of transformations can lead to unexpected behavior if interpretation rules interact poorly","Jaxpr is immutable; modifying the intermediate representation requires rebuilding the entire graph"],"requires":["Python 3.9+","JAX with jaxlib","Understanding of functional programming and intermediate representations","Knowledge of JAX's primitive operations and interpretation rules"],"input_types":["Python functions","Jaxpr intermediate representations (for custom transformations)"],"output_types":["Transformed Python functions","Jaxpr representations (via jax.make_jaxpr)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","JAX installed via pip (jax, jaxlib)","Compatible CUDA/cuDNN or ROCm for GPU acceleration (optional but recommended)","JAX installed with jaxlib","Understanding of functional programming and chain rule mathematics","JAX with jaxlib","Understanding of numeric types and promotion rules","JAX with jaxlib compiled with XLA support","XLA compiler (included with jaxlib)","Target hardware (CPU, GPU, or TPU) with appropriate drivers/runtime"],"failure_modes":["Not all NumPy operations are supported; some edge cases in advanced indexing differ from NumPy semantics","Arrays are immutable by design, requiring functional programming patterns instead of in-place mutations","Dynamic shapes require special handling via vmap or other transformations; static shape inference is preferred for JIT compilation","Reverse-mode AD requires materializing the full computational graph in memory; very large graphs may cause memory issues","Custom VJP/JVP rules must be manually defined for non-standard operations; no automatic symbolic differentiation fallback","AD through control flow (if/while) requires special primitives (lax.cond, lax.while_loop); Python control flow is not automatically differentiable","Differentiation through certain operations (e.g., sorting, argmax) requires custom rules or approximations","Automatic type promotion can lead to unexpected behavior if not carefully managed; explicit casting is often necessary","Some dtypes (e.g., bfloat16) have limited precision; numerical stability issues may arise","Type information is not always preserved through transformations; users must verify dtype consistency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.295Z","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-jax","compare_url":"https://unfragile.ai/compare?artifact=pypi-jax"}},"signature":"M5oLDLFDCpfjBFSyCJJlh96biB5Zpu6FsccYSA7sR7X21MPi8dPw8h50Bxcj8aBNbVoc6R7r4Ammhrr5SNWxCQ==","signedAt":"2026-06-21T03:48:18.279Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-jax","artifact":"https://unfragile.ai/pypi-jax","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-jax","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"}}