mlx
MLX: An array framework for Apple silicon
Explore how MLX's autograd engine powers automatic differentiation in transforms for machine learning. Understand dynamic computation graphs and gradient computation.
How to Benchmark MLX Operations and Compare Performance Against PyTorchBenchmark MLX operations and compare performance directly against PyTorch using the built-in Python benchmarking suite. Measure operator latency, warm-up runs, and forced evaluation for accurate results.
Differences Between MLX's C++ and Python APIs: A Complete Technical GuideExplore the technical differences between MLX C++ and Python APIs. Understand language semantics memory management and compilation requirements for optimal MLX development.
How MLX Primitives Form the Backend of All OperationsDiscover how MLX primitives power all MLX operations. Learn about their role as low-level computational units handling execution, gradients, and batching via a unified C++ interface.
How MLX Computation Graph Optimization Works Internally: A Deep Dive into the Compiler PipelineDiscover MLX's five-stage compilation pipeline: tracing, tape construction, simplification, kernel fusion, and caching. Optimize MLX computation graphs for efficient device kernels.
How to Generate Random Numbers Using MLX's Random ModuleLearn to generate random numbers with MLX's random module. Explore PRNG keys, distributions & GPU acceleration for CPU, CUDA, and Metal.
How to Use FFT Operations in MLX for Signal ProcessingUnlock powerful signal processing with MLX FFT operations. Learn to implement complex and real-valued transforms efficiently across CPU, CUDA, and Metal backends.
How MLX's Memory Allocator and Buffer Management Work: A Deep Dive into the Core SubsystemExplore MLX's memory allocator and buffer management. Understand how it handles CPU, Metal, and CUDA memory with specialized optimization for high performance.
How to Implement Distributed Training with MLX's Distributed Module: A Complete GuideLearn how to implement distributed training with MLX's distributed module. This guide covers tensor sharding and gradient synchronization for multi-device setups.
How to Write Custom Metal Kernels Using MLX's fast.h ModuleLearn to write custom Metal kernels with MLX fast.h. Explore a JIT compilation interface for direct Python access to Metal compute shaders and streamline your GPU programming.
How MLX's Compile Function Works and When to Use Compile ModeDiscover how MLX's compile function optimizes Python code into efficient kernels. Learn when to use CompileMode for performance gains and memory savings in your ML projects.
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