# apachecn/pytorch-doc-zh

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [awesome-repositories.com](https://awesome-repositories.com/repository/apachecn-pytorch-doc-zh).**

4,224 stars · 986 forks · Shell

## Links

- GitHub: https://github.com/apachecn/pytorch-doc-zh
- Homepage: https://pytorch.apachecn.org
- awesome-repositories: https://awesome-repositories.com/repository/apachecn-pytorch-doc-zh.md

## Topics

`deep-learning` `documentation` `python` `pytorch`

## Description

This project is a Chinese language translation of the technical guides and API references for the PyTorch deep learning framework. It serves as a localized knowledge base and reference material to make deep learning documentation accessible to non-English speakers.

The documentation covers a comprehensive range of PyTorch capabilities, including neural network model development, automatic differentiation, and the implementation of backend kernels. It provides detailed guidance on distributed training strategies, model deployment through formats like ONNX and C++, and various model optimization and quantization techniques.

The project utilizes a community-driven translation pipeline and a distributed contribution model to maintain version-synced content. Technical materials are organized using markdown and rendered into a navigable website via static site generation.

## Tags

### Education & Learning Resources

- [Localized Technical Documentation](https://awesome-repositories.com/f/education-learning-resources/localized-technical-documentation.md) — Provides a comprehensive Chinese translation of PyTorch technical guides and API references for non-English speakers.
- [Technical Document Translations](https://awesome-repositories.com/f/education-learning-resources/technical-document-translations.md) — Provides comprehensive Chinese translations of technical deep learning guides and API references. ([source](https://pytorch.apachecn.org/2.0/docs/))
- [Community-Translated Documentation](https://awesome-repositories.com/f/education-learning-resources/technical-document-translations/community-translated-documentation.md) — Provides a structured community-driven pipeline for translating technical documentation into multiple languages.
- [Technical Reference Materials](https://awesome-repositories.com/f/education-learning-resources/technical-reference-materials.md) — Ships translated technical documentation covering tensor operations, automatic differentiation, and model construction.

### Mobile Development

- [Framework Documentation Translations](https://awesome-repositories.com/f/mobile-development/android-ecosystem/android-development/chinese-learning-resources/machine-learning-theory-translations/framework-documentation-translations.md) — Provides a localized Chinese translation of the official PyTorch API references and technical implementation guides.

### Artificial Intelligence & ML

- [Distributed Training Sharding](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-sharding.md) — Details strategies for scaling model training across compute nodes using data parallelism, pipeline parallelism, and sharding. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/sharding/))
- [Hardware Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/gpu-training-accelerators/hardware-acceleration.md) — Documents managing the movement of tensors and modules between CPU and GPU for hardware acceleration. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))
- [PyTorch Backends](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-backends.md) — Provides guides on extending PyTorch through custom C++ operators, backend kernels, and hardware integration.
- [PyTorch Model Development](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-model-development.md) — Provides comprehensive translated documentation for designing, implementing, and training neural networks with PyTorch.
- [Learning Resources](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-model-development/learning-resources.md) — Serves as a localized knowledge base for learning about backend kernels, distributed training, and model quantization.
- [Distributed Parallelism Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-learning-models/sequence-model-training/transformer-training-configurations/text-sequence-model-training/distributed-parallelism-strategies.md) — Documents scaling sequence-to-sequence model training across multiple nodes using data and pipeline parallelism. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/ddp_pipeline/))
- [Automatic Differentiation](https://awesome-repositories.com/f/artificial-intelligence-ml/automatic-differentiation.md) — Describes the mechanisms for calculating gradients through backpropagation in neural networks. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/dispatcher/))
- [Dataset Batch Loading](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-batch-loading.md) — Covers multi-threaded data loading in fixed-size batches to optimize training performance. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))
- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training.md) — Provides utilities to handle uneven input queues in distributed training to prevent system crashes. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/generic_join/))
- [Combined Parallel Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/data-parallel-training/combined-parallel-strategies.md) — Explains how to integrate data and model parallelism to train models with large sparse embeddings. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/rpc_ddp_tutorial/))
- [Embedding Table Sharding](https://awesome-repositories.com/f/artificial-intelligence-ml/embedding-adaptation-utilities/vocabulary-embedding-adapters/embedding-table-sharding.md) — Provides techniques for distributing embedding tables across multiple devices to balance load. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/sharding/))
- [Gradient Computation](https://awesome-repositories.com/f/artificial-intelligence-ml/gradient-computation.md) — Covers the automatic derivation of gradients for model training and optimization. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_autograd/))
- [Gradient Tracking Suppressions](https://awesome-repositories.com/f/artificial-intelligence-ml/gradient-tracking-suppressions.md) — Covers methods to disable gradient recording to optimize memory usage and inference speed. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_autograd/))
- [Automatic Precision Casting](https://awesome-repositories.com/f/artificial-intelligence-ml/half-precision-inference/automatic-precision-casting.md) — Explains the use of autocast wrappers for automatic precision conversion to improve training and inference performance. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/dispatcher/))
- [Hardware Dispatchers](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-acceleration-kernels/hardware-dispatchers.md) — Details how to extend the dispatcher to route operations to custom hardware backends. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/extend_dispatcher/))
- [Hardware Backend Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-backend-integrations.md) — Provides guidance on binding custom backend modules to namespaces for standardized hardware API calls. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/privateuseone/))
- [Hardware Operator Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-operator-integrations.md) — Guides the integration of third-party operators via backend-specific kernels and dispatcher registration. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/extend_dispatcher/))
- [Custom Autograd Functions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/automatic-differentiation-systems/functional-autograd/custom-autograd-functions.md) — Explains the implementation of custom backward kernels to support automatic differentiation for new backends. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/extend_dispatcher/))
- [ONNX Model Exporters](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/serialization-and-export-formats/onnx-model-exporters.md) — Provides instructions on converting PyTorch models into the standardized ONNX format for cross-platform deployment. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/super_resolution_with_onnxruntime/))
- [ONNX Runtime Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-inference-serving/inference-engines/onnx-runtime-inference.md) — Details how to execute exported models using the cross-platform ONNX runtime for high-efficiency predictions. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/super_resolution_with_onnxruntime/))
- [Model Parallelism](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/model-training-pipelines/model-parallelism.md) — Covers the optimal placement of model parameters across clusters based on hardware topology. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/sharding/))
- [Model Inference Execution](https://awesome-repositories.com/f/artificial-intelligence-ml/model-inference-execution.md) — Provides documentation on executing the model forward pass to generate predictions. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))
- [Quantization-Aware Training](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization/quantization-aware-training.md) — Explains techniques for simulating quantization noise during training to maintain accuracy in compressed models. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/static_quantization_tutorial/))
- [C++ Inference Exports](https://awesome-repositories.com/f/artificial-intelligence-ml/model-training/model-exporting/c-inference-exports.md) — Documents the process of exporting trained models for C++ runtime deployment to remove Python dependencies. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_export/))
- [Sequential Model Builders](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-construction/sequential-model-builders.md) — Provides documentation on constructing neural networks by stacking layers in a linear sequence. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))
- [Neural Network Modules](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-modules.md) — Guides the creation of reusable neural network modules for managing parameters and forward logic. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))
- [Parameter Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-optimizers.md) — Provides documentation on using optimization algorithms to update model weights and minimize loss. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))
- [PyTorch Model Export](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-model-export.md) — Provides translated guides on exporting models to interoperable formats like ONNX and C++ for deployment.
- [Weight Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization.md) — Explains how to convert weights of specific layers to lower-precision integers to reduce memory usage. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/dynamic_quantization_tutorial/))
- [Post-Training Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization/post-training-quantization.md) — Provides guidance on converting weights and activations to integers using data distribution observation after training. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/static_quantization_tutorial/))
- [Reinforcement Learning Environments](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-environments.md) — Explains how to define custom simulation environments for reinforcement learning tasks. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/pendulum/))
- [Inference Speed Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/token-optimization-utilities/generation-speed-optimizers/transcription-speed-optimizers/inference-speed-optimizers.md) — Offers methods to reduce memory footprint and execution time by converting models to quantized formats. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/static_quantization_tutorial/))
- [Training Data Transformations](https://awesome-repositories.com/f/artificial-intelligence-ml/training-data-transformations.md) — Provides methods for modifying simulation inputs and outputs to prepare raw data for training. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/pendulum/))

### Software Engineering & Architecture

- [Distributed Contribution Models](https://awesome-repositories.com/f/software-engineering-architecture/project-management-governance/project-governance/contribution-guidelines/distributed-contribution-models.md) — Utilizes a distributed contribution model allowing external community members to submit translations via version control.

### Part of an Awesome List

- [Checkpoint Saving and Restoration](https://awesome-repositories.com/f/awesome-lists/ai/model-training-and-fine-tuning/checkpoint-saving-and-restoration.md) — Explains how to serialize model parameters and optimizer states for checkpointing and recovery. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))

### Data & Databases

- [Data Transformation Functions](https://awesome-repositories.com/f/data-databases/data-transformation-functions.md) — Provides guidance on normalizing and reshaping dataset tensors using mapping functions. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_frontend/))

### Operating Systems & Systems Programming

- [CUDA Compute Kernels](https://awesome-repositories.com/f/operating-systems-systems-programming/cuda-compute-kernels.md) — Details the construction of custom CUDA kernels to parallelize heavy mathematical computations on GPUs. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_extension/))
- [Operator Kernel Implementations](https://awesome-repositories.com/f/operating-systems-systems-programming/kernel-core-internals/kernel-development/kernel-driver-implementation/operator-kernel-implementations.md) — Describes the registration of high-performance operator kernels and mixed precision implementations. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/extend_dispatcher/))

### Programming Languages & Runtimes

- [C++ Model Interfaces](https://awesome-repositories.com/f/programming-languages-runtimes/c-model-interfaces.md) — Provides technical references for loading serialized model files via native C++ APIs for inference. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_export/))
- [C++ Engine Extensions](https://awesome-repositories.com/f/programming-languages-runtimes/high-performance-c-libraries/c-engine-extensions.md) — Provides guidance on implementing high-performance operators in C++ and exposing them via a bridge API. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/cpp_extension/))
- [Operator Dispatchers](https://awesome-repositories.com/f/programming-languages-runtimes/runtime-execution-environments/runtime-environments/runtimes/type-definition-systems/runtime-type-dispatching/universal-function-dispatchers/operator-dispatchers.md) — Details the use of operator dispatchers to map a single schema to multiple device-specific implementations. ([source](https://pytorch.apachecn.org/2.0/tutorials/advanced/dispatcher/))