ble_backward_tutorial.html)
  * [ Fusing Convolution and Batch Norm using Custom Function ](intermediate/custom_function_conv_bn_tutorial.html)
  * [ Custom C++ and CUDA Extensions ](advanced/cpp_extension.html)
  * [ Extending TorchScript with Custom C++ Operators ](advanced/torch_script_custom_ops.html)
  * [ Extending TorchScript with Custom C++ Classes ](advanced/torch_script_custom_classes.html)
  * [ Registering a Dispatched Operator in C++ ](advanced/dispatcher.html)
  * [ Extending dispatcher for a new backend in C++ ](advanced/extend_dispatcher.html)
  * [ Facilitating New Backend Integration by PrivateUse1 ](advanced/privateuseone.html)

Model Optimization

  * [ Profiling your PyTorch Module ](beginner/profiler.html)
  * [ PyTorch Profiler With TensorBoard ](intermediate/tensorboard_profiler_tutorial.html)
  * [ Hyperparameter tuning with Ray Tune ](beginner/hyperparameter_tuning_tutorial.html)
  * [ Optimizing Vision Transformer Model for Deployment ](beginner/vt_tutorial.html)
  * [ Parametrizations Tutorial ](intermediate/parametrizations.html)
  * [ Pruning Tutorial ](intermediate/pruning_tutorial.html)
  * [ (beta) Dynamic Quantization on an LSTM Word Language Model ](advanced/dynamic_quantization_tutorial.html)
  * [ (beta) Dynamic Quantization on BERT ](intermediate/dynamic_quantization_bert_tutorial.html)
  * [ (beta) Quantized Transfer Learning for Computer Vision Tutorial ](intermediate/quantized_transfer_learning_tutorial.html)
  * [ (beta) Static Quantization with Eager Mode in PyTorch ](advanced/static_quantization_tutorial.html)
  * [ Grokking PyTorch Intel CPU performance from first principles ](intermediate/torchserve_with_ipex.html)
  * [ Grokking PyTorch Intel CPU performance from first principles (Part 2) ](intermediate/torchserve_with_ipex_2.html)
  * [ Getting Started - Accelerate Your Scripts with nvFuser ](intermediate/nvfuser_intro_tutorial.html)
  * [ Multi-Objective NAS with Ax ](intermediate/ax_multiobjective_nas_tutorial.html)
  * [ Introduction to ` torch.compile  ` ](intermediate/torch_compile_tutorial.html)
  * [ Inductor CPU backend debugging and profiling ](intermediate/inductor_debug_cpu.html)
  * [ (Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA) ](intermediate/scaled_dot_product_attention_tutorial.html)
  * [ Using SDPA with ` torch.compile  ` ](intermediate/scaled_dot_product_attention_tutorial.html#using-sdpa-with-torch-compile)
  * [ Using SDPA with attn_bias subclasses` ](intermediate/scaled_dot_product_attention_tutorial.html#using-sdpa-with-attn-bias-subclasses)
  * [ Conclusion ](intermediate/scaled_dot_product_attention_tutorial.html#conclusion)
  * [ Knowledge Distillation Tutorial ](beginner/knowledge_distillation_tutorial.html)

Parallel and Distributed Training

  * [ Distributed and Parallel Training Tutorials ](distributed/home.html)
  * [ PyTorch Distributed Overview ](beginner/dist_overview.html)
  * [ Distributed Data Parallel in PyTorch - Video Tutorials ](beginner/ddp_series_intro.html)
  * [ Single-Machine Model Parallel Best Practices ](intermediate/model_parallel_tutorial.html)
  * [ Getting Started with Distributed Data Parallel ](intermediate/ddp_tutorial.html)
  * [ Writing Distributed Applications with PyTorch ](intermediate/dist_tuto.html)
  * [ Getting Started with Fully Sharded Data Parallel(FSDP) ](intermediate/FSDP_tutorial.html)
  * [ Advanced Model Training with Fully Sharded Data Parallel (FSDP) ](intermediate/FSDP_adavnced_tutorial.html)
  * [ Large Scale Transformer model training with Tensor Parallel (TP) ](intermediate/TP_tutorial.html)
  * [ Customize Process Group Backends Using Cpp Extensions ](intermediate/process_group_cpp_extension_tutorial.html)
  * [ Getting Started with Distributed RPC Framework ](intermediate/rpc_tutorial.html)
  * [ Implementing a Parameter Server Using Distributed RPC Framework ](intermediate/rpc_param_server_tutorial.html)
  * [ Distributed Pipeline Parallelism Using RPC ]