Tutorials

The PyTorch Tutorials repository is a collection of educational resources that provides step-by-step guidance on building, training, and deploying neural networks using the PyTorch framework. It covers the complete machine learning workflow, from data loading and model definition through optimization loops and model persistence, with dedicated guides for distributed training, model fine-tuning, and deployment.

The tutorials offer practical demonstrations of adapting pre-trained models to new tasks through transfer learning, scaling training across multiple GPUs or machines using PyTorch's distributed primitives, and serving trained models behind scalable HTTP endpoints for production inference. They also include examples of using TensorBoard for real-time inspection of training metrics, model architecture, and gradient flow to diagnose issues like vanishing or exploding gradients.

Additional content covers performance optimization techniques such as configuring parallel data loading with optimal worker counts and memory pinning to maximize throughput, as well as memory profiling to identify allocation bottlenecks during training. The repository provides walkthroughs for hyperparameter search and quantization-aware training simulation, rounding out the set of capabilities needed to take a model from development to production.

Features

PyTorch Training Frameworks - Provides step-by-step guides that teach how to build, train, and deploy neural networks using the PyTorch framework.

Data-Parallel Training - Provides step-by-step guides for replicating models across devices and synchronizing gradients via all-reduce.

Distributed Training Scaling Utilities - Provides guides for distributing model training across multiple processes or machines to reduce wall-clock time.

Distributed Training - Distributes model training across multiple processes or machines to reduce wall-clock time.

Pre-trained Model Transfer - Demonstrates adapting pre-trained PyTorch models to new tasks by retraining final layers.

Pre-training Transfer Learning - Demonstrates adapting pre-trained neural networks to new tasks by retraining final layers on custom data.

Differentiable Programming - Provides tutorials on automatic gradient computation via PyTorch's autograd system for neural network training.

Neural Networks and Deep Learning - Provides walkthroughs covering data loading, model definition, loss functions, and optimization loops for deep learning.

Model Training Guides - Provides step-by-step tutorials covering the complete machine learning workflow from data loading to model saving.

Gradient Flow Analysis - Visualizes gradient propagation through network layers to identify vanishing or exploding gradients.

Multi-Process Data Loading - Optimizes DataLoader settings like worker count and memory pinning to maximize training throughput.

Quantization-Aware Training - Includes walkthroughs for simulating low-precision arithmetic during training to improve quantized model accuracy.

TorchScript Exports - Provides tutorials on compiling PyTorch models into TorchScript for production deployment without Python.

PyTorch Model Export - Provides instructions for serving trained PyTorch models behind scalable HTTP endpoints for inference.

TensorBoard Event Generators - Demonstrates writing scalar and histogram events to log files for real-time TensorBoard visualization.

Training Data Prefetchers - Ships tutorials on configuring multiprocess data prefetching to hide I/O latency during training.

Model Serving & Deployment - Demonstrates deploying trained models behind scalable HTTP endpoints for low-latency inference.

Model Fine-Tuning - Demonstrates adapting pre-trained PyTorch models to new tasks by retraining final layers on custom data.

TensorBoard Dashboards - Shows how to use TensorBoard dashboards to inspect training metrics, model architecture, and data.

Training Throughput Optimizations - Covers optimizing data loading, memory usage, and gradient flow to maximize training throughput.

GPU Kernel Fusions - Demonstrates CUDA kernel fusion techniques to optimize GPU memory bandwidth and reduce launch overhead.

Memory Profilers - Records memory allocation and deallocation events to identify bottlenecks and leaks during training.

Model Training Metrics - Uses TensorBoard to inspect data, model architecture, and training metrics during development.

Training Gradient Visualizations - Provides examples of using TensorBoard to inspect gradients, memory usage, and training metrics.

Tutorials - Listed in the “Tutorials” section of the The Incredible Pytorch awesome list.

pytorchtutorials

Features

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