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Master GPU acceleration with 20+ framework guides. Each guide includes installation, code examples, performance tips, and CUDA optimization strategies.
Complete PyTorch CUDA guide. Learn GPU memory management, mixed precision training, custom CUDA kernels, and performance optimization for deep learning.
Complete TensorFlow CUDA guide. Learn XLA compilation, mixed precision, memory growth, distributed training, and TensorRT optimization.
Complete JAX CUDA guide. Learn JIT compilation, automatic vectorization, parallelization with pmap, and XLA optimization for GPU computing.
Complete Horovod guide. Learn distributed training with PyTorch, TensorFlow across multiple GPUs.
Complete DeepSpeed guide. Learn ZeRO optimization, large model training, and inference.
Complete Megatron-LM guide. Learn tensor and pipeline parallelism for training GPT/BERT at scale.
Complete NVIDIA Apex guide. Learn AMP, distributed training utilities, and fused optimizers.
Complete CuPy guide for GPU-accelerated NumPy. Learn array operations, custom kernels, interoperability with PyTorch/TensorFlow, and CUDA optimization.
Complete RAPIDS guide for GPU data science. Learn cuDF for DataFrames, cuML for machine learning, cuGraph for graph analytics, and CUDA optimization.
Complete PyCUDA guide. Learn to write CUDA C kernels and call them from Python.
Complete Triton guide for GPU kernel development. Learn to write custom CUDA kernels in Python with automatic optimization and PyTorch integration.
Complete Numba CUDA guide. Learn JIT compilation, CUDA kernels in Python, GPU arrays, vectorization, and performance optimization for scientific computing.
Complete TVM guide for ML compilation. Learn AutoTVM tuning, Relay IR, CUDA code generation, operator fusion, and deployment optimization.
Complete Numba CUDA guide. Learn JIT compilation, GPU kernels in Python, and performance optimization.
Complete TensorRT guide for optimized inference. Learn layer fusion, precision calibration, dynamic shapes, plugins, and deployment on NVIDIA GPUs.
Complete ONNX Runtime guide for GPU inference. Learn CUDA execution providers, graph optimizations, quantization, and deployment across platforms.
Complete RAPIDS guide. Learn cuDF, cuML, cuGraph for GPU-accelerated data science.
Complete ONNX Runtime CUDA guide. Learn model optimization, TensorRT integration, and deployment.
Complete TensorRT guide. Learn model optimization, quantization, and deployment for NVIDIA GPUs.
Complete cuDF guide. Learn GPU-accelerated DataFrame operations with pandas API.
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