# PyTorch

## Docs

- [Custom Operations](https://mintlify.wiki/pytorch/pytorch/advanced/custom-ops.md): Create custom PyTorch operations and C++/CUDA extensions
- [Distributed Training](https://mintlify.wiki/pytorch/pytorch/advanced/distributed-training.md): Scale PyTorch models across multiple GPUs and machines with DDP and FSDP
- [Mixed Precision Training](https://mintlify.wiki/pytorch/pytorch/advanced/mixed-precision.md): Accelerate training and reduce memory usage with Automatic Mixed Precision (AMP)
- [Quantization](https://mintlify.wiki/pytorch/pytorch/advanced/quantization.md): Reduce model size and accelerate inference with quantization techniques
- [torch.compile](https://mintlify.wiki/pytorch/pytorch/advanced/torch-compile.md): Optimize PyTorch models with compilation for faster execution
- [torch.autograd - Automatic Differentiation](https://mintlify.wiki/pytorch/pytorch/api/autograd.md): PyTorch autograd API reference for gradient computation and automatic differentiation
- [torch.cuda - GPU Acceleration](https://mintlify.wiki/pytorch/pytorch/api/cuda.md): PyTorch CUDA API reference for GPU operations and memory management
- [torch.utils.data - Data Loading](https://mintlify.wiki/pytorch/pytorch/api/data.md): Data loading and processing utilities
- [torch.utils.data.Dataset - Dataset Classes](https://mintlify.wiki/pytorch/pytorch/api/datasets.md): Dataset abstractions and utilities for organizing data
- [torch.distributed](https://mintlify.wiki/pytorch/pytorch/api/distributed.md): Distributed training and communication API
- [torch.export - Model Export](https://mintlify.wiki/pytorch/pytorch/api/export.md): Export PyTorch models to a graph representation for deployment and optimization
- [torch.fft](https://mintlify.wiki/pytorch/pytorch/api/fft.md): Fast Fourier Transform operations
- [torch.jit](https://mintlify.wiki/pytorch/pytorch/api/jit.md): TorchScript JIT compiler API
- [torch.linalg](https://mintlify.wiki/pytorch/pytorch/api/linalg.md): Linear algebra operations
- [torch.optim.lr_scheduler - Learning Rate Schedulers](https://mintlify.wiki/pytorch/pytorch/api/lr-scheduler.md): Learning rate scheduling strategies for optimization
- [torch.nn](https://mintlify.wiki/pytorch/pytorch/api/nn.md): PyTorch neural network module overview and core components
- [torch.nn.functional](https://mintlify.wiki/pytorch/pytorch/api/nn-functional.md): Functional interface for neural network operations
- [torch.nn.init](https://mintlify.wiki/pytorch/pytorch/api/nn-init.md): Weight initialization methods for neural networks
- [torch.nn Modules](https://mintlify.wiki/pytorch/pytorch/api/nn-modules.md): Neural network layers and building blocks
- [torch.optim - Optimizers](https://mintlify.wiki/pytorch/pytorch/api/optim.md): Optimization algorithms for training neural networks
- [torch.quantization](https://mintlify.wiki/pytorch/pytorch/api/quantization.md): Model quantization API for reduced precision training and inference
- [torch.sparse](https://mintlify.wiki/pytorch/pytorch/api/sparse.md): Sparse tensor operations and formats
- [torch.special](https://mintlify.wiki/pytorch/pytorch/api/special.md): Special mathematical functions
- [Tensor - Core Data Structure](https://mintlify.wiki/pytorch/pytorch/api/tensor.md): PyTorch Tensor class API reference with operations and methods
- [torch - Main Module](https://mintlify.wiki/pytorch/pytorch/api/torch.md): PyTorch main module API reference with core functions for tensor operations
- [Data Transforms](https://mintlify.wiki/pytorch/pytorch/api/transforms.md): Data transformation and augmentation utilities
- [torch.utils](https://mintlify.wiki/pytorch/pytorch/api/utils.md): Utility functions and helpers for PyTorch
- [Automatic Differentiation](https://mintlify.wiki/pytorch/pytorch/concepts/autograd.md): Understand PyTorch's autograd system for automatic gradient computation
- [Data Loading](https://mintlify.wiki/pytorch/pytorch/concepts/data-loading.md): Efficiently load and preprocess data with PyTorch's DataLoader and Dataset
- [Neural Networks](https://mintlify.wiki/pytorch/pytorch/concepts/neural-networks.md): Build and train neural networks with PyTorch's torch.nn module
- [Tensors](https://mintlify.wiki/pytorch/pytorch/concepts/tensors.md): Learn about PyTorch tensors - the fundamental building blocks for deep learning computations
- [C++ Autograd API](https://mintlify.wiki/pytorch/pytorch/cpp/autograd.md): Automatic differentiation in the PyTorch C++ API
- [Installing LibTorch](https://mintlify.wiki/pytorch/pytorch/cpp/installation.md): Download and install the PyTorch C++ distribution for your platform
- [C++ API Introduction](https://mintlify.wiki/pytorch/pytorch/cpp/introduction.md): Overview of the PyTorch C++ API (LibTorch) for production and research
- [C++ Module API](https://mintlify.wiki/pytorch/pytorch/cpp/modules.md): Building neural networks with the PyTorch C++ Frontend
- [C++ Tensor API](https://mintlify.wiki/pytorch/pytorch/cpp/tensors.md): Working with tensors in the PyTorch C++ API (ATen)
- [Building Neural Network Models](https://mintlify.wiki/pytorch/pytorch/guides/building-models.md): Build neural network models using torch.nn.Module and PyTorch's built-in layers
- [Model Inference](https://mintlify.wiki/pytorch/pytorch/guides/inference.md): Perform efficient model inference with evaluation mode, no_grad, and optimization techniques
- [Optimizers and Learning Rate Scheduling](https://mintlify.wiki/pytorch/pytorch/guides/optimization.md): Configure optimizers, learning rates, and schedulers to improve model training in PyTorch
- [Saving and Loading Models](https://mintlify.wiki/pytorch/pytorch/guides/saving-loading.md): Save and load PyTorch models, checkpoints, and optimizer states for training resumption and deployment
- [Training Neural Networks](https://mintlify.wiki/pytorch/pytorch/guides/training.md): Train neural networks with loss functions, optimizers, and training loops in PyTorch
- [CUDA Support](https://mintlify.wiki/pytorch/pytorch/hardware/cuda.md): Complete guide to NVIDIA CUDA GPU acceleration in PyTorch
- [MPS Support](https://mintlify.wiki/pytorch/pytorch/hardware/mps.md): Apple Metal Performance Shaders GPU acceleration for PyTorch on Mac
- [ROCm Support](https://mintlify.wiki/pytorch/pytorch/hardware/rocm.md): AMD ROCm GPU acceleration for PyTorch on AMD GPUs
- [XPU Support](https://mintlify.wiki/pytorch/pytorch/hardware/xpu.md): Intel XPU GPU acceleration for PyTorch on Intel GPUs
- [Installation](https://mintlify.wiki/pytorch/pytorch/installation.md): Install PyTorch using pip, conda, or build from source
- [Introduction to PyTorch](https://mintlify.wiki/pytorch/pytorch/introduction.md): Open-source machine learning framework for building neural networks with GPU acceleration
- [Quick Start](https://mintlify.wiki/pytorch/pytorch/quickstart.md): Get started with PyTorch through hands-on examples of tensors and neural networks