tensorflow/swiftArchived

Features

• Swift Development - Provides a custom Swift toolchain for building and training TensorFlow models with automatic differentiation.
• Automatic Differentiation - Implements compiler-integrated automatic differentiation for computing gradients of Swift functions.
• Automatic Differentiation Frameworks - Ships a compiler-integrated system that computes gradients of Swift functions and types for optimization.
• Compile-Time Gradient Computations - Provides compiler-integrated automatic differentiation that computes gradients during compilation without runtime tracing.
• Differentiable Type Declarations - Allows marking custom Swift types as differentiable for automatic gradient computation.
• Swift Compiler Differentiations - Computes gradients of Swift functions and types using compiler-integrated differential operators.
• Compile-Time Graph Extractions - Converts Swift functions into static computational graphs at compile time for optimization.
• Custom Differentiation Rules - Provides compiler-integrated custom differentiation rules for user-defined Swift functions.
• End-to-End Training Pipelines - Runs complete training loops with gradient computation and parameter updates on datasets.
• Iterative Parameter Optimizations - Applies gradient-based optimization algorithms to update model parameters during training loops.
• Swift Interoperability - Enables Swift code to call Python ML libraries directly, reusing existing machine learning tooling.
• Swift Python Interoperability - Enables direct reuse of Python machine learning libraries from Swift code without bindings.
• Compile-Time Graph Extractions - Converts Swift functions into static computational graphs at compile time for optimized execution on hardware.
• Compiler-Integrated Differentiable Programmings - Uses compiler-integrated automatic differentiation to compute gradients without runtime tracing.
• Language Extensions - Extends Swift with first-class automatic differentiation and differentiable types for gradient-based computation.
• XLA Backend Compilations - Accelerates tensor operations by compiling and executing them on XLA-compatible hardware for high-performance computation.
• Swift Custom Toolchains - Provides a custom Swift toolchain integrating the compiler with TensorFlow runtime and XLA backend for ML.
• Native Syntax Interoperability - Imports and calls Python libraries directly from Swift using native syntax, bridging ecosystems without a binding layer.
• Swift Native Interoperability - Provides native Swift syntax to import and call Python libraries directly, bridging both ecosystems.
• Swift Native Syntax - Provides native Swift syntax to import and call Python libraries directly, bridging both ecosystems.
• Compile-Time Graph Extractions - Converts Swift functions into static computational graphs at compile time for optimized execution.
• XLA Backends - Compiles and executes tensor operations on XLA-compatible hardware for high-performance computation.
• XLA Hardware Accelerations - Accelerates tensor computations by compiling and executing on XLA-compatible hardware.
• Key-Path-Based Parameter Optimizations - Iterates over model properties using key paths for generic parameter access and hierarchical optimization.
• Jupyter Notebook Training Environments - Ships a Jupyter kernel with autocomplete for live model training and API exploration.
• XLA Accelerated - Compiles and executes tensor operations on XLA-compatible hardware for high-performance machine learning.
• Hierarchical Parameter Optimizations - Traverses nested parameter structures to apply optimizers for complex model architectures.
• Runtime Property Iterations - Iterates over model properties at runtime using key paths for generic optimization and access.
• XLA Backend Executions - Runs tensor operations through XLA for hardware-accelerated compilation and execution.
• XLA Backends - Compiles and executes tensor operations on XLA-compatible hardware for high-performance computation.
• Low-Level Operator Invocations - Invokes low-level TensorFlow operations directly from Swift to build custom computation graphs.
• Swift Notebook Environments - Provides a Jupyter notebook environment with Swift kernel support for interactive model training and API exploration.
• Swift Jupyter Kernels - Provides a Swift kernel for Jupyter notebooks with autocomplete for interactive model training.
• Swift Language Kernels - Ships a Swift language kernel for Jupyter notebooks enabling interactive model training with autocomplete.
• Swift Jupyter Training Environments - Runs Swift machine learning code in Jupyter notebooks with autocomplete for live experimentation.
• Key-Path Property Iterations - Enables runtime traversal of type properties using key paths for generic parameter access and hierarchical optimization.
• Property Iterators - Enables runtime iteration over model properties using key paths for generic optimization.
• Key-Path Property Iterations - Walks over a type's stored properties at runtime using key paths for generic parameter manipulation.
• Key-Path Property Iterations - Enables runtime traversal of type properties using key paths for generic parameter manipulation.
• Deep Learning Frameworks - Swift language integration for deep learning workflows.
• Machine Learning Frameworks - Next-generation platform for differentiable programming and machine learning.

Star history