PyTorch 示例 --------------------------------------------- Recipes are bite-sized, actionable examples of how to use specific PyTorch features, different from our full-length tutorials. 与入门教程不同，此系列通过简洁实用的示例，展示了如何使用PyTorch的特性。 .. raw:: html
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Basics .. customcarditem:: :header: PyTorch 加载数据 :card_description: 学习如何使用 PyTorch 来准备和加载常见的数据集。 :image: ../_static/img/thumbnails/cropped/loading-data.PNG :link: ../recipes/recipes/loading_data_recipe.html :tags: Basics .. customcarditem:: :header: PyTorch 创建神经网络 :card_description: 学习如何使用torch.nn，为MNIST数据集创建一个神经网络。 :image: ../_static/img/thumbnails/cropped/defining-a-network.PNG :link: ../recipes/recipes/defining_a_neural_network.html :tags: Basics .. customcarditem:: :header: PyTorch 中 state_dict 是什么 :card_description: 学习如何使用 `state_dict` 对象和 Python 字典在 PyTorch 中保存或加载模型。 :image: ../_static/img/thumbnails/cropped/what-is-a-state-dict.PNG :link: ../recipes/recipes/what_is_state_dict.html :tags: Basics .. customcarditem:: :header: PyTorch 保存和加载模型 :card_description: 在PyTorch中保存和加载模型用于推理的两种方式 - state_dict和完整模型。 :image: ../_static/img/thumbnails/cropped/saving-and-loading-models-for-inference.PNG :link: ../recipes/recipes/saving_and_loading_models_for_inference.html :tags: Basics .. customcarditem:: :header: PyTorch 保存和加载通用检查点 :card_description: 保存和加载一个通用的检查点模型,可以帮助您从上次停止的地方继续推理或训练。在这个示例中,探索如何保存和加载多个检查点。 :image: ../_static/img/thumbnails/cropped/saving-and-loading-general-checkpoint.PNG :link: ../recipes/recipes/saving_and_loading_a_general_checkpoint.html :tags: Basics .. customcarditem:: :header: PyTorch 在一个文件中保存和加载多个模型 :card_description: 在这个示例中,学习保存和加载多个模型,有助于重用您之前训练过的模型。 :image: ../_static/img/thumbnails/cropped/saving-multiple-models.PNG :link: ../recipes/recipes/saving_multiple_models_in_one_file.html :tags: Basics .. customcarditem:: :header: PyTorch 使用不同模型的参数对模型进行热启动 :card_description: 了解如何通过部分加载模型或加载部分模型方式来热启动训练过程,这可以帮助您的模型比从头开始训练收敛得更快。 :image: ../_static/img/thumbnails/cropped/warmstarting-models.PNG :link: ../recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html :tags: Basics .. customcarditem:: :header: PyTorch 跨设备保存和加载模型 :card_description: 了解如何使用PyTorch在不同设备(CPU和GPU)之间保存和加载模型。 :image: ../_static/img/thumbnails/cropped/saving-and-loading-models-across-devices.PNG :link: ../recipes/recipes/save_load_across_devices.html :tags: Basics .. customcarditem:: :header: PyTorch 清零梯度 :card_description: 了解何时应该清零梯度,以及这样做如何有助于提高模型的精度。 :image: ../_static/img/thumbnails/cropped/zeroing-out-gradients.PNG :link: ../recipes/recipes/zeroing_out_gradients.html :tags: Basics .. customcarditem:: :header: PyTorch Benchmark :card_description: 学习如何使用 PyTorch Benchmark 模块来测量和比较代码性能 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/benchmark.html :tags: Basics .. customcarditem:: :header: PyTorch Benchmark Timer 快速入门 :card_description: 学习如何测量代码片段的运行时间和收集指令。 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/timer_quick_start.html :tags: Basics .. customcarditem:: :header: PyTorch Profiler :card_description: 学习如何使用 PyTorch Profiler 来测量算子的时间和内存消耗。 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/profiler_recipe.html :tags: Basics .. customcarditem:: :header: PyTorch Profiler with Instrumentation and Tracing Technology API (ITT API) support :card_description: 学习如何使用支持 Instrumentation and Tracing Technology API (ITT API) 的 PyTorch Profiler,在 Intel® VTune™ Profiler GUI 中可视化算子标签 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/profile_with_itt.html :tags: Basics .. customcarditem:: :header: Torch Compile IPEX 后端 :card_description: 学习如何使用 torch.compile IPEX 后端 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/torch_compile_backend_ipex.html :tags: Basics .. customcarditem:: :header: 在 PyTorch 中推理形状 :card_description: 学习如何使用 meta 设备来推理模型中的形状。 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/recipes/reasoning_about_shapes.html :tags: Basics .. customcarditem:: :header: 从检查点加载 nn.Module 的技巧 :card_description: 学习从检查点加载 nn.Module 的技巧。 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/recipes/module_load_state_dict_tips.html :tags: Basics .. customcarditem:: :header: (beta) 使用 TORCH_LOGS 观察 torch.compile :card_description: 学习如何使用 torch 日志 API 观察编译过程。 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/torch_logs.html :tags: Basics .. customcarditem:: :header: nn.Module 中用于加载 state_dict 和张量子类的扩展点 :card_description: nn.Module 中的新扩展点。 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/recipes/swap_tensors.html :tags: Basics .. Interpretability .. customcarditem:: :header: 使用 Captum 进行模型可解释性 :card_description: 学习如何使用 Captum 将图像分类器的预测归因于相应的图像特征,并可视化归因结果。 :image: ../_static/img/thumbnails/cropped/model-interpretability-using-captum.png :link: ../recipes/recipes/Captum_Recipe.html :tags: Interpretability,Captum .. customcarditem:: :header: 如何在 PyTorch 中使用 TensorBoard :card_description: 学习在 PyTorch 中使用 TensorBoard 的基本用法,以及如何在 TensorBoard UI 中可视化数据 :image: ../_static/img/thumbnails/tensorboard_scalars.png :link: ../recipes/recipes/tensorboard_with_pytorch.html :tags: Visualization,TensorBoard .. Quantization .. customcarditem:: :header: 动态量化 :card_description: 对一个简单的 LSTM 模型应用动态量化。 :image: ../_static/img/thumbnails/cropped/using-dynamic-post-training-quantization.png :link: ../recipes/recipes/dynamic_quantization.html :tags: Quantization,Text,Model-Optimization .. Production Development .. customcarditem:: :header: 部署时使用 TorchScript :card_description: 学习如何将训练好的模型导出为 TorchScript 格式,以及如何在 C++ 中加载 TorchScript 模型并进行推理。 :image: ../_static/img/thumbnails/cropped/torchscript_overview.png :link: ../recipes/torchscript_inference.html :tags: TorchScript .. customcarditem:: :header: 使用 Flask 进行部署 :card_description: 学习如何使用轻量级 Web 服务器 Flask 快速从训练好的 PyTorch 模型搭建 Web API。 :image: ../_static/img/thumbnails/cropped/using-flask-create-restful-api.png :link: ../recipes/deployment_with_flask.html :tags: Production,TorchScript .. customcarditem:: :header: PyTorch 移动端性能优化技巧 :card_description: 在移动端(Android 和 iOS)使用 PyTorch 时的一些性能优化技巧。 :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/mobile_perf.html :tags: Mobile,Model-Optimization .. customcarditem:: :header: 制作使用 PyTorch Android 预编译库的 Android 原生应用 :card_description: 学习如何从头开始制作使用 LibTorch C++ API 和 TorchScript 模型(带自定义 C++ 算子)的 Android 应用。 :image: ../_static/img/thumbnails/cropped/android.png :link: ../recipes/android_native_app_with_custom_op.html :tags: Mobile .. customcarditem:: :header: 融合模块技巧 :card_description: 学习如何在量化之前将一系列 PyTorch 模块融合为单个模块,以减小模型大小。 :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/fuse.html :tags: Mobile .. customcarditem:: :header: 移动端量化技巧 :card_description: 学习如何在不太损失精度的情况下减小模型大小并加快运行速度。 :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/quantization.html :tags: Mobile,Quantization .. customcarditem:: :header: 为移动端脚本化和优化 :card_description: 学习如何将模型转换为 TorchScript,并(可选)为移动应用优化。 :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/script_optimized.html :tags: Mobile .. customcarditem:: :header: iOS 端模型准备技巧 :card_description: 学习如何将模型添加到 iOS 项目中,以及如何使用 PyTorch pod for iOS。 :image: ../_static/img/thumbnails/cropped/ios.png :link: ../recipes/model_preparation_ios.html :tags: Mobile .. customcarditem:: :header: Android 端模型准备技巧 :card_description: 学习如何将模型添加到 Android 项目中,以及如何使用 PyTorch library for Android。 :image: ../_static/img/thumbnails/cropped/android.png :link: ../recipes/model_preparation_android.html :tags: Mobile .. customcarditem:: :header: Android 和 iOS 上的移动端解释器工作流程 :card_description: 学习如何在 iOS 和 Android 设备上使用移动端解释器。 :image: ../_static/img/thumbnails/cropped/mobile.png :link: ../recipes/mobile_interpreter.html :tags: Mobile .. customcarditem:: :header: 分析基于 PyTorch RPC 的工作负载 :card_description: 如何使用 PyTorch Profiler 分析基于 RPC 的工作负载。 :image: ../_static/img/thumbnails/cropped/profile.png :link: ../recipes/distributed_rpc_profiling.html :tags: Production .. Automatic Mixed Precision .. customcarditem:: :header: 自动混合精度 :card_description: 使用 torch.cuda.amp 在 NVIDIA GPU 上减少运行时间并节省内存。 :image: ../_static/img/thumbnails/cropped/amp.png :link: ../recipes/recipes/amp_recipe.html :tags: Model-Optimization .. Performance .. customcarditem:: :header: 性能优化指南 :card_description: 实现最佳性能的技巧。 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/recipes/tuning_guide.html :tags: Model-Optimization .. customcarditem:: :header: 在 AWS Graviton 处理器上优化 PyTorch 推理性能 :card_description: 在 AWS Graviton CPU 上实现最佳推理性能的技巧 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/inference_tuning_on_aws_graviton.html :tags: Model-Optimization .. Leverage Advanced Matrix Extensions .. customcarditem:: :header: 利用 Intel® 高级矩阵扩展 :card_description: 学习如何利用 Intel® 高级矩阵扩展。 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/amx.html :tags: Model-Optimization .. (beta) Compiling the Optimizer with torch.compile .. customcarditem:: :header: (beta) 使用 torch.compile 编译优化器 :card_description: 使用 torch.compile 加速优化器 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/compiling_optimizer.html :tags: Model-Optimization .. (beta) Running the compiled optimizer with an LR Scheduler .. customcarditem:: :header: (beta) 使用学习率调度器运行编译后的优化器 :card_description: 使用 LRScheduler 和 torch.compiled 优化器加速训练 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/compiling_optimizer_lr_scheduler.html :tags: Model-Optimization .. Using User-Defined Triton Kernels with ``torch.compile`` .. customcarditem:: :header: 在 ``torch.compile`` 中使用用户定义的 Triton 内核 :card_description: 学习如何在 ``torch.compile`` 中使用用户定义的内核 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/torch_compile_user_defined_triton_kernel_tutorial.html :tags: Model-Optimization .. Reducing Cold Start Compilation Time with Regional Compilation .. customcarditem:: :header: 通过区域编译减少 torch.compile 冷启动编译时间 :card_description: 了解如何使用区域编译来控制冷启动编译时间 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/regional_compilation.html :tags: Model-Optimization .. Intel(R) Extension for PyTorch* .. customcarditem:: :header: Intel® Extension for PyTorch* :card_description: Intel® Extension for PyTorch* 介绍 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/intel_extension_for_pytorch.html :tags: Model-Optimization .. Intel(R) Neural Compressor for PyTorch* .. customcarditem:: :header: Intel® Neural Compressor for PyTorch :card_description: 使用 Intel® Neural Compressor 轻松量化 PyTorch。 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/intel_neural_compressor_for_pytorch.html :tags: Quantization,Model-Optimization .. Distributed Training .. customcarditem:: :header: DeviceMesh 入门 :card_description: 学习如何使用 DeviceMesh :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/distributed_device_mesh.html :tags: Distributed-Training .. customcarditem:: :header: 使用 ZeroRedundancyOptimizer 分片优化器状态 :card_description: 如何使用 ZeroRedundancyOptimizer 减少内存消耗。 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/zero_redundancy_optimizer.html :tags: Distributed-Training .. customcarditem:: :header: 使用 TensorPipe RPC 实现直接设备间通信 :card_description: 如何使用支持直接 GPU 到 GPU 通信的 RPC。 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/cuda_rpc.html :tags: Distributed-Training .. customcarditem:: :header: 支持 TorchScript 的分布式优化器 :card_description: 如何为分布式优化器启用 TorchScript 支持。 :image: ../_static/img/thumbnails/cropped/profiler.png :link: ../recipes/distributed_optim_torchscript.html :tags: Distributed-Training,TorchScript .. customcarditem:: :header: 分布式检查点 (DCP) 入门 :card_description: 学习如何使用分布式检查点包检查点分布式模型。 :image: ../_static/img/thumbnails/cropped/Getting-Started-with-DCP.png :link: ../recipes/distributed_checkpoint_recipe.html :tags: Distributed-Training .. TorchServe .. customcarditem:: :header: 将 PyTorch Stable Diffusion 模型部署为 Vertex AI 端点 :card_description: 学习如何使用 TorchServe 在 Vertex AI 中部署模型 :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png :link: ../recipes/torchserve_vertexai_tutorial.html :tags: Production .. End of tutorial card section .. raw:: html
.. ----------------------------------------- .. Page TOC .. ----------------------------------------- .. toctree:: :hidden: /recipes/recipes/loading_data_recipe /recipes/recipes/defining_a_neural_network /recipes/torch_logs /recipes/recipes/what_is_state_dict /recipes/recipes/saving_and_loading_models_for_inference /recipes/recipes/saving_and_loading_a_general_checkpoint /recipes/recipes/saving_multiple_models_in_one_file /recipes/recipes/warmstarting_model_using_parameters_from_a_different_model /recipes/recipes/save_load_across_devices /recipes/recipes/zeroing_out_gradients /recipes/recipes/profiler_recipe /recipes/recipes/profile_with_itt /recipes/recipes/Captum_Recipe /recipes/recipes/tensorboard_with_pytorch /recipes/recipes/dynamic_quantization /recipes/recipes/amp_recipe /recipes/recipes/tuning_guide /recipes/recipes/intel_extension_for_pytorch /recipes/compiling_optimizer /recipes/torch_compile_backend_ipex /recipes/torchscript_inference /recipes/deployment_with_flask /recipes/distributed_rpc_profiling /recipes/zero_redundancy_optimizer /recipes/cuda_rpc /recipes/distributed_optim_torchscript /recipes/mobile_interpreter