# Internal APIs Internally monarch is implemented using a Rust library for actors called hyperactor. [This book](books/hyperactor-book/src/introduction) provides more details about its design. This page provides access to the Rust API documentation for Monarch. The Monarch project consists of several Rust crates, each with specialized functionality: ### Core Framework - **hyperactor** - Core actor framework for distributed computing - **hyperactor_macros** - Procedural macros for the hyperactor framework - **hyperactor_mesh** - Mesh networking for hyperactor clusters - **hyperactor_mesh_macros** - Macros for hyperactor mesh functionality - **hyperactor_config** - Configuration framework for hyperactor - **hyperactor_telemetry** - Telemetry and monitoring for hyperactor ### CUDA and GPU Computing - **nccl-sys** - NCCL (NVIDIA Collective Communications Library) bindings - **torch-sys2** - Simplified PyTorch Python API bindings for Rust - **torch-sys-cuda** - CUDA-specific PyTorch FFI bindings - **monarch_tensor_worker** - High-performance tensor processing worker ### RDMA and High-Performance Networking - **monarch_rdma** - Remote Direct Memory Access (RDMA) support for high-speed networking - **rdmaxcel-sys** - Low-level RDMA acceleration bindings ### Monarch Python Integration - **monarch_hyperactor** - Python bindings bridging hyperactor to Monarch's Python API - **monarch_extension** - Python extension module for Monarch functionality - **monarch_messages** - Message types for Monarch actor communication ### System and Utilities - **hyper** - Mesh admin CLI and HTTP utilities - **ndslice** - N-dimensional array slicing and manipulation - **typeuri** - Type URI system for message serialization - **wirevalue** - Wire-level value serialization for actor messages - **serde_multipart** - Zero-copy multipart serialization ## Architecture Overview The Rust implementation provides a comprehensive framework for distributed computing with GPU acceleration: - **Actor Model**: Built on the hyperactor framework for concurrent, distributed processing - **GPU Integration**: Native CUDA support for high-performance computing workloads - **Mesh Networking**: Efficient communication between distributed nodes - **Tensor Operations**: Optimized tensor processing with PyTorch integration - **Multi-dimensional Arrays**: Advanced slicing and manipulation of n-dimensional data For complete technical details, API references, and usage examples, explore the individual crate documentation above.