thehardwarestore.ai

JAX is a high-performance numerical computing library developed by Google Research. It provides a NumPy-compatible API (jax.numpy), automatic differentiation (jax.grad), just-in-time compilation via XLA (jax.jit), automatic vectorization (jax.vmap), and large-scale parallelization (jax.pmap, jax.shard_map). The library is designed for machine learning research and scientific computing, supporting CPU, GPU (NVIDIA, AMD, Intel), and Google TPU hardware. The system is a hybrid Python/C++ library. The Python layer (jax/) handles function tracing into a JAX intermediate representation (JAXpr), applies transformations (autodiff, batching, sharding), and lowers the computation to MLIR/StableHLO. The C++ backend (jaxlib/) compiles this representation via XLA and manages execution on target hardware. It includes a sophisticated plugin system for hardware backends and a custom kernel DSL (Pallas/Mosaic) for writing high-performance fused operations. While primarily an application/library, JAX has strong research roots. It is explicitly described as a research project, and its experimental/ directory contains rapidly evolving features like custom kernel authoring (Pallas), TensorFlow interop (jax2tf), and sparse array support. The codebase is a blend of production-grade engineering (robust CI, deprecation policies, extensive testing) and cutting-edge research (new autodiff algorithms, novel sharding strategies, experimental hardware support). The repository contains several high-quality, isolated components that are reusable in other Python/C++ projects. These include a file-based LRU cache, a weak-reference LRU cache, thread-safe reentrant hash maps, a lazy module loader, a deprecation framework, and a traceback filtering utility. The core autodiff and JIT compilation patterns are the primary value, but these supporting utilities demonstrate excellent software craftsmanship.

Keras 3 is a multi-backend deep learning framework that provides a high-level API for building and training neural networks. It supports TensorFlow, JAX, PyTorch, NumPy, and OpenVINO backends, allowing users to switch between them with minimal code changes. The repository contains a comprehensive set of layers (convolutional, recurrent, attention, normalization, pooling, preprocessing, regularization), optimizers (Adam, SGD, AdamW, Lion, etc.), metrics, callbacks, loss functions, and utilities for model saving/loading, quantization, distillation, and distributed training. The architecture is modular with a clean separation between backend-agnostic operations and backend-specific implementations. The codebase is well-tested with extensive unit and integration tests, and includes benchmarks for performance comparison. It is actively maintained with frequent commits and responsive issue triage.

AlphaFold3 is Google DeepMind's official command-line application for running AlphaFold 3 biomolecular structure prediction inference. The dominant character is a production-grade scientific application, though it is heavily intertwined with research data pipelines: it ingests a JSON job specification describing protein, RNA, DNA, and ligand chains, runs Jackhmmer/Nhmmer searches over large genetic databases, builds multiple sequence alignments, searches structural templates with HMMER, featurizes the input into JAX/Haiku tensors, samples structures with a diffusion model, and writes predicted structures as mmCIF files plus confidence JSON (pLDDT, PAE, pTM, ipTM). The codebase is not a training repository; it provides inference only and requires separately downloaded model parameters under restrictive legal terms. Architecturally, the system is a multi-stage pipeline driven by the `run_alphafold.py` entry point. Core modules include the data pipeline (`src/alphafold3/data/pipeline.py`), MSA/template tools (`src/alphafold3/data/tools/`), a structure representation layer (`src/alphafold3/structure/`), JAX geometric primitives (`src/alphafold3/jax/geometry/`), the neural network model (`src/alphafold3/model/`), and performance-critical C++ pybind11 extensions for mmCIF parsing, MSA feature extraction, and string/array operations. It depends on external binaries (Jackhmmer, Nhmmer, HMMER suite), RDKit for ligand conformer generation, JAX/Haiku for model execution, and ~630 GB of downloadable sequence and structure databases. The repository is actively maintained, with recent commits and CI running CPU-only data tests. Technical drift is low for a research release: dependencies are pinned via `pyproject.toml` and `uv.lock`, and active maintenance keeps it aligned with its ecosystem, though the C++ build, GPU-specific requirements, and external database URLs remain fragility points.

TensorFlow Probability (TFP) is a production-grade library for probabilistic modeling and Bayesian inference. It provides a comprehensive suite of probability distributions, bijectors (normalizing flows), MCMC kernels (HMC, NUTS, SMC, etc.), variational inference, Gaussian processes, structural time series, and Bayesian optimization. The library supports TensorFlow, JAX, and NumPy backends through a rewrite system, enabling seamless switching between hardware accelerators. The architecture is modular: each distribution, bijector, and kernel inherits from well-defined abstract base classes with `parameter_properties` for automatic shape and constraint inference. Key entry points include `tfp.distributions`, `tfp.bijectors`, `tfp.mcmc`, `tfp.vi`, `tfp.sts`, and `tfp.experimental` for cutting-edge methods. The code is heavily tested across all backends, with property-based tests and numerical stability checks. While primarily an application library, it also includes research-grade components in `discussion/` and `spinoffs/` (Inference Gym, AutoBNN, FunMC) that serve as benchmarks and experimental prototypes.

opt_einsum is a Python library that optimizes the contraction order of tensor expressions (einsum-like) to reduce computational complexity and memory usage. It serves as a drop-in replacement for numpy.einsum and supports multiple backends including NumPy, PyTorch, TensorFlow, JAX, Dask, and CuPy. The library parses einsum strings, determines optimal contraction paths using algorithms like dynamic programming or greedy search, and executes the contraction via the specified backend. Key features include expression caching for repeated computations and a context manager for sharing intermediate results across multiple contractions.

Einops is a Python library that provides a uniform, readable, and reliable API for tensor manipulation across multiple deep learning frameworks (NumPy, PyTorch, TensorFlow, JAX, and others). It uses Einstein-like notation (e.g., 'b c h w -> b h w c') to express axis reordering, composition, decomposition, reduction, and repetition in a single, explicit string. The library is built around a core transformation engine that parses axis patterns into a cached 'TransformRecipe', which is then applied via a backend abstraction layer. This architecture allows einops to support 10+ frameworks with minimal code duplication, while also providing framework-specific layer wrappers (Rearrange, Reduce, EinMix) for seamless integration into model definitions. The library is designed for production and research use, with a focus on reducing errors from manual view/permute/transpose operations.

SGLang-JAX is a high-performance LLM inference engine optimized for TPU/GPU hardware using JAX. It implements continuous batching, radix tree KV cache management, FlashAttention integration, tensor parallelism, and disaggregated serving (prefill-decode separation). The system provides OpenAI-compatible API endpoints for text generation, chat, embeddings, and tool use, with support for Mixture-of-Experts models, LoRA adaptation, quantization, and speculative decoding. Architecture features modular components: tokenizer manager, scheduler, model executor, memory hierarchy (including HiCache host-device transfer), optimized JAX/Pallas kernels, and LoRA adapter management. Designed for production serving workloads on TPU infrastructure with first-party support for Qwen, Llama, Gemma, DeepSeek, and multimodal models.

TensorFlow Datasets (TFDS) is a library providing ready-to-use public datasets for machine learning, primarily integrated with TensorFlow but also supporting JAX and PyTorch. It offers a standardized API (tfds.load, tfds.builder) for downloading, preparing, and loading datasets as tf.data.Dataset objects or numpy arrays. The architecture consists of core components (dataset builders, feature connectors, download management) and hundreds of dataset-specific implementations following a consistent pattern. Key entry points include the tfds CLI for dataset operations and the main tfds module for programmatic access. Data flows involve downloading raw data, generating TFRecord files via builders, and yielding examples through tf.data pipelines. External dependencies include TensorFlow, absl-py, numpy, pandas, and dataset-specific extras like apache-beam for large-scale processing or huggingface_hub for community datasets.

KerasHub is a production-grade Keras 3 library providing pretrained model architectures, layers, tokenizers, metrics, and sampling utilities for NLP, vision, multimodal, and audio tasks. The codebase is organized around a consistent Backbone → Task → Preprocessor pattern, where each model family (e.g., BERT, Gemma, ViT, Whisper) defines a backbone, a preprocessor (tokenizer + optional media converter), and task-specific heads (CausalLM, ImageClassifier, etc.). All models expose a unified `from_preset()` API for loading pretrained weights hosted on Kaggle Models, and `save_to_preset()` for export. The library includes extensive checkpoint conversion tooling from HuggingFace and TIMM, export to HuggingFace format and OpenVINO, and integration with vLLM for serving. While predominantly an application library, it has strong data/research character through its pretrained weight infrastructure, benchmark scripts, and research-oriented architectures (e.g., FNet, RWKV7, BLIP-2).

Dynamiqs is a high-performance Python library for simulating quantum systems, built on JAX for GPU acceleration and automatic differentiation. It provides solvers for the Schrödinger equation, Lindblad master equation, stochastic master equations (diffusive and jump), and Floquet integration, with support for time-dependent Hamiltonians, batching over parameters, and gradient computation (including Hessians). The architecture is modular: an abstract integrator hierarchy (with concrete solvers using Diffrax, matrix-exponential, low-rank, Rouchon, and Monte Carlo methods), custom quantum array types (dense and sparse diagonal DIA layout), a clean `TimeQArray` abstraction for time-dependent operators, and utilities for state/operator construction, plotting, and quantum information metrics. The library is designed for quantum optimal control and parameter estimation, and is used in production at Alice & Bob. It features extensive automated tests (including numerical gradient/Hessian validation) and is actively maintained with a growing community.

DeePMD-kit is a comprehensive, production-grade deep learning package for many-body interatomic potential energy representation and molecular dynamics simulation. It provides a unified framework for training and deploying machine learning interatomic potentials (MLIPs) across multiple backends: TensorFlow, PyTorch, JAX, and PaddlePaddle, with a backend-agnostic core (dpmodel) that ensures numerical consistency. The package supports a wide range of descriptor architectures (SeA, SeR, SeT, DPA1/2/3/4, attention-based, hybrid) and fitting targets (energy, force, virial, dipole, polarizability, density of states, spin, property). It integrates tightly with LAMMPS via custom pair styles and plugins, and also supports ASE, i-PI, GROMACS, and other MD engines. The codebase is highly modular, with a plugin registration system for models, descriptors, and losses, and offers extensive serialization capabilities for model export and compression. Pretrained models (DPA-3, DPA-4) are available for direct download and fine-tuning. The project is actively maintained with a large community, comprehensive test suites, and continuous integration across CPU and GPU platforms.

DMFF (Deep Modeling Force Field) is a JAX-based Python library for differentiable molecular force field parameterization and energy/force evaluation. It supports classical force fields (bonded, Lennard-Jones, Coulomb), advanced polarizable multipole models (ADMP with PME, Thole damping, Slater damping), and machine learning potentials (sGNN, EANN). The library provides a generator pattern for constructing differentiable energy functions from XML force field definitions, an operator pattern for atom typing and virtual site placement, and a parameter management system (ParamSet) that integrates with JAX's automatic differentiation for gradient-based optimization. It also includes an OpenMM plugin (C++/CUDA) that can load trained JAX models via TensorFlow for production molecular dynamics simulations. The architecture is modular: the core `dmff` package contains subpackages for ADMP (polarizable multipole), classical forces, sGNN/EANN, common utilities (neighbor lists, constants), and an API layer (Hamiltonian, topology, parameter sets). The backend includes a C++ OpenMM plugin with Reference and CUDA platforms, serialization support, and a TensorFlow export path via `jax2tf`. The library is designed for research in force field development, enabling parameter fitting, free energy calculations (MBAR), and integration with machine learning models. While primarily a software library, it has strong research characteristics: it is accompanied by a published paper, includes example workflows for water models and PEG systems, and provides reference data for validation. The codebase demonstrates high craftsmanship in areas like PME implementation, subgraph neural network construction, and differentiable programming patterns, though some parts (e.g., the OpenMM plugin's TensorFlow dependency) show potential drift risks.

XLB is a differentiable, massively parallel Lattice Boltzmann Method (LBM) library written in Python, designed for physics-based machine learning and computational fluid dynamics (CFD). It provides a modular operator-based architecture with abstract base classes and backend-specific implementations for JAX (CPU/GPU/TPU), NVIDIA Warp (single-GPU CUDA), and Autodesk Neon (multi-GPU, multi-resolution). The library supports 2D and 3D simulations with multiple velocity sets (D2Q9, D3Q19, D3Q27), collision models (BGK, KBC, Smagorinsky LES), boundary conditions (equilibrium, bounce-back, regularized, Zou-He, hybrid, extrapolation outflow), and immersed boundary method (IBM) for fluid-structure interaction. A key differentiator is its native differentiability via JAX and Warp autodiff, enabling gradient-based optimization of initial conditions, geometry, or control parameters. The codebase also includes experimental out-of-core support for simulations exceeding GPU memory, multi-resolution grid refinement, and distributed computation across multiple devices. While primarily a software library for CFD practitioners and ML researchers, it is accompanied by research validation data and academic publications, giving it a hybrid application/research character.

Jaxformer is a JAX-based library for training large language models (LLMs) on TPUs with data and model parallelism. It provides a complete training loop supporting distributed training across TPU cores, checkpointing, and resumable data loading from TFRecord files. The codebase includes model definitions for transformer-based architectures (like CodeGen), utilities for learning rate scheduling and optimization, and integration with Hugging Face for model conversion and sampling. The system is designed for scalability, supporting models up to 6B parameters via sharding strategies, and includes tools for preprocessing large datasets into TFRecord format. While the core training functionality is tightly coupled to the TPU environment, several components like the resumable data iterator and checkpointing mechanism demonstrate reusable patterns.

This repository is primarily an extensive educational compendium covering mathematics, computer science, and artificial intelligence from first principles to state-of-the-art topics. It contains over 100 markdown files organized into 20 chapters, each with detailed explanations, diagrams, and interactive coding tasks (primarily in Python/JAX). The repo also includes auxiliary tooling: a MkDocs-based documentation site with MathJax and a Pyodide in-browser Python runner that includes a JAX compatibility shim, and a TypeScript MCP (Model Context Protocol) server that provides programmatic access to the compendium content for AI assistants. While the vast majority of the repository is educational content rather than runnable software, the MCP server and interactive runner are functional application components. The compendium is well-structured and current, but the code examples are pedagogical (simplified, not production-ready) and the JAX shim is a fragile polyfill. The project is not a data research pipeline—it contains no datasets, experiment logs, or trained models.