tinygrad is a deep learning framework that provides a full-stack solution from tensor operations and automatic differentiation to a custom compiler stack that generates optimized GPU kernels for multiple backends (CUDA, HIP, Metal, WebGPU, etc.). It includes a JIT compiler (TinyJit), a universal operation IR (UOp) for graph rewriting and custom kernel authoring, and a suite of neural network layers, optimizers, and datasets. The repository also contains extensive MLPerf benchmark implementations, a LLM server with streaming, and low-level hardware interaction tools (AMD firmware flashing, GPU profiling). It is primarily a software library for training and inference, with a strong research component in kernel optimization and hardware exploration.
ArrayFire is a mature, production-grade tensor and array computing library implemented in C++, exposing both a C API (af_* functions) and a C++ API (af::array). It provides hundreds of accelerated numerical operations — dense and sparse linear algebra (LU, QR, SVD, Cholesky, solve), FFT and signal processing, image transforms and filtering, computer vision feature detection (FAST, SIFT, ORB, SUSAN, homography), statistics, and random number generation — across four hardware backends: CPU, CUDA, OpenCL, and oneAPI/SYCL. The architecture is layered: thin C API wrappers and C++ classes in src/api/c and src/api/cpp dispatch through a unified runtime layer (src/api/unified) that lazily loads backend shared libraries via a symbol manager; each backend implements the same templated operation set over device-resident arrays with backend-specific kernels under src/backend/*/kernel. The genuinely interesting engineering lives in src/backend/common: a JIT expression-tree system that fuses array operations into compiled kernels, deterministic hash-based kernel caching, a memory manager with JIT-pressure eviction heuristics, FFT plan caching, and cross-platform dynamic library loading. The test suite is unusually rigorous for a numerical library — well over 100 gtest programs covering correctness across all dtypes, large dimensions, random-number quality via PractRand, and death tests — while examples span image processing, machine learning, financial Monte Carlo, and PDE simulation. Maintenance is active (commits through 2026, CI workflows, source releases), but technical drift is medium: the oneAPI backend is explicitly incomplete with many operations stubbed as ONEAPI_NOT_SUPPORTED, deprecated compatibility APIs remain (include/af/compatible.h), the four backends duplicate a large amount of near-identical code, and some packaging artifacts (Tegra toolchain pinned to gcc-5, vendored vcpkg LAPACK patches, Debian postinst hardcoding MKL/CUDA paths) are stale. This is a software library rather than a data pipeline — there are no notebooks, datasets, or analytical artifacts — though its algorithms are directly applicable to computer vision and scientific computing workloads.
TorchFort is a library that bridges PyTorch with Fortran and C/C++ programs, primarily targeting HPC/scientific computing applications. It provides a C API that allows users to load PyTorch models (via TorchScript or built-in MLP), train them, and perform inference directly on user-provided arrays without data copying. The library supports supervised learning as well as reinforcement learning (RL) with several state-of-the-art algorithms: DDPG, TD3, SAC, and PPO. It includes comprehensive RL infrastructure such as replay buffers, rollout buffers, online running normalizers, and distributed training via MPI. Integration with Weights & Biases is available for experiment tracking. Architecturally, TorchFort consists of a thin C API layer, a set of C++ implementations using LibTorch, and Fortran bindings generated via ISO_C_BINDING. The core C++ code is organized into modules for model management, training, distributed communication, CUDA utilities, and RL subsystems. Configuration is externalized via YAML files, and the library is designed to be used as a static or shared library linked into user applications. Notable components include a prioritized replay buffer with sum-tree implementation (replay_buffer.h), an online running normalizer using Welford's algorithm with MPI synchronization (running_normalizer.h), and a tensor list abstraction for handling multiple typed arrays (tensor_list.h). The codebase demonstrates good engineering practices with extensive test coverage, error handling via return codes, and support for both CPU and GPU execution. While primarily an application/library for integrating PyTorch into HPC codes, the repository also contains research-quality RL algorithm implementations and data preprocessing components, giving it a hybrid character.