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[Demonstration of torch.export flow, common challenges and the solutions to address them](https://docs.pytorch.org/tutorials/recipes/torch_export_challenges_solutions.html) - [(beta) Compiling the optimizer with torch.compile](https://docs.pytorch.org/tutorials/recipes/compiling_optimizer.html) - [(beta) Running the compiled optimizer with an LR Scheduler](https://docs.pytorch.org/tutorials/recipes/compiling_optimizer_lr_scheduler.html) - [Using Variable Length Attention in PyTorch](https://docs.pytorch.org/tutorials/intermediate/variable_length_attention_tutorial.html) - [Using User-Defined Triton Kernels with torch.compile](https://docs.pytorch.org/tutorials/recipes/torch_compile_user_defined_triton_kernel_tutorial.html) - [Compile Time Caching in torch.compile](https://docs.pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) - [Reducing torch.compile cold start compilation time with regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) - [torch.export Tutorial](https://docs.pytorch.org/tutorials/intermediate/torch_export_tutorial.html) - [torch.export AOTInductor Tutorial for Python runtime (Beta)](https://docs.pytorch.org/tutorials/recipes/torch_export_aoti_python.html) - [Demonstration of torch.export flow, common challenges and the solutions to address them](https://docs.pytorch.org/tutorials/recipes/torch_export_challenges_solutions.html) - [Introduction to ONNX](https://docs.pytorch.org/tutorials/beginner/onnx/intro_onnx.html) - [Export a PyTorch model to ONNX](https://docs.pytorch.org/tutorials/beginner/onnx/export_simple_model_to_onnx_tutorial.html) - [Extending the ONNX Exporter Operator Support](https://docs.pytorch.org/tutorials/beginner/onnx/onnx_registry_tutorial.html) - [Export a model with control flow to ONNX](https://docs.pytorch.org/tutorials/beginner/onnx/export_control_flow_model_to_onnx_tutorial.html) - [Building a Convolution/Batch Norm fuser with torch.compile](https://docs.pytorch.org/tutorials/intermediate/torch_compile_conv_bn_fuser.html) - [(beta) Building a Simple CPU Performance Profiler with FX](https://docs.pytorch.org/tutorials/intermediate/fx_profiling_tutorial.html) - [Domains](https://docs.pytorch.org/tutorials/domains.html) - [TorchVision Object Detection Finetuning Tutorial](https://docs.pytorch.org/tutorials/intermediate/torchvision_tutorial.html) - [Transfer Learning for Computer Vision Tutorial](https://docs.pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - [Adversarial Example Generation](https://docs.pytorch.org/tutorials/beginner/fgsm_tutorial.html) - [DCGAN Tutorial](https://docs.pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html) - [Spatial Transformer Networks Tutorial](https://docs.pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html) - [Reinforcement Learning (DQN) Tutorial](https://docs.pytorch.org/tutorials/intermediate/reinforcement_q_learning.html) - [Reinforcement Learning (PPO) with TorchRL Tutorial](https://docs.pytorch.org/tutorials/intermediate/reinforcement_ppo.html) - 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[Introduction to Libuv TCPStore Backend](https://docs.pytorch.org/tutorials/intermediate/TCPStore_libuv_backend.html) - [Large Scale Transformer model training with Tensor Parallel (TP)](https://docs.pytorch.org/tutorials/intermediate/TP_tutorial.html) - [Introduction to Distributed Pipeline Parallelism](https://docs.pytorch.org/tutorials/intermediate/pipelining_tutorial.html) - [Customize Process Group Backends Using Cpp Extensions](https://docs.pytorch.org/tutorials/intermediate/process_group_cpp_extension_tutorial.html) - [Getting Started with Distributed RPC Framework](https://docs.pytorch.org/tutorials/intermediate/rpc_tutorial.html) - [Implementing a Parameter Server Using Distributed RPC Framework](https://docs.pytorch.org/tutorials/intermediate/rpc_param_server_tutorial.html) - [Implementing Batch RPC Processing Using Asynchronous Executions](https://docs.pytorch.org/tutorials/intermediate/rpc_async_execution.html) - [Interactive Distributed Applications with Monarch](https://docs.pytorch.org/tutorials/intermediate/monarch_distributed_tutorial.html) - [Combining Distributed DataParallel with Distributed RPC Framework](https://docs.pytorch.org/tutorials/advanced/rpc_ddp_tutorial.html) - [Distributed Training with Uneven Inputs Using the Join Context Manager](https://docs.pytorch.org/tutorials/advanced/generic_join.html) - [Distributed training at scale with PyTorch and Ray Train](https://docs.pytorch.org/tutorials/beginner/distributed_training_with_ray_tutorial.html) - [Deep Dive](https://docs.pytorch.org/tutorials/deep-dive.html) - [Profiling your PyTorch Module](https://docs.pytorch.org/tutorials/beginner/profiler.html) - [Parametrizations Tutorial](https://docs.pytorch.org/tutorials/intermediate/parametrizations.html) - [Pruning Tutorial](https://docs.pytorch.org/tutorials/intermediate/pruning_tutorial.html) - [Inductor CPU backend debugging and profiling](https://docs.pytorch.org/tutorials/intermediate/inductor_debug_cpu.html) - [(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA)](https://docs.pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html) - [Knowledge Distillation Tutorial](https://docs.pytorch.org/tutorials/beginner/knowledge_distillation_tutorial.html) - [Channels Last Memory Format in PyTorch](https://docs.pytorch.org/tutorials/intermediate/memory_format_tutorial.html) - [Forward-mode Automatic Differentiation (Beta)](https://docs.pytorch.org/tutorials/intermediate/forward_ad_usage.html) - [Jacobians, Hessians, hvp, vhp, and more: composing function transforms](https://docs.pytorch.org/tutorials/intermediate/jacobians_hessians.html) - [Model ensembling](https://docs.pytorch.org/tutorials/intermediate/ensembling.html) - [Per-sample-gradients](https://docs.pytorch.org/tutorials/intermediate/per_sample_grads.html) - [Using the PyTorch C++ Frontend](https://docs.pytorch.org/tutorials/advanced/cpp_frontend.html) - [Autograd in C++ Frontend](https://docs.pytorch.org/tutorials/advanced/cpp_autograd.html) - [Extension](https://docs.pytorch.org/tutorials/extension.html) - [PyTorch Custom Operators](https://docs.pytorch.org/tutorials/advanced/custom_ops_landing_page.html) - [Custom Python Operators](https://docs.pytorch.org/tutorials/advanced/python_custom_ops.html) - [Custom C++ and CUDA Operators](https://docs.pytorch.org/tutorials/advanced/cpp_custom_ops.html) - [Double Backward with Custom Functions](https://docs.pytorch.org/tutorials/intermediate/custom_function_double_backward_tutorial.html) - [Fusing Convolution and Batch Norm using Custom Function](https://docs.pytorch.org/tutorials/intermediate/custom_function_conv_bn_tutorial.html) - [Registering a Dispatched Operator in C++](https://docs.pytorch.org/tutorials/advanced/dispatcher.html) - [Extending dispatcher for a new backend in C++](https://docs.pytorch.org/tutorials/advanced/extend_dispatcher.html) - [Facilitating New Backend Integration by PrivateUse1](https://docs.pytorch.org/tutorials/advanced/privateuseone.html) - [Ecosystem](https://docs.pytorch.org/tutorials/ecosystem.html) - [Hyperparameter tuning using Ray Tune](https://docs.pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html) - [Serve PyTorch models at scale with Ray Serve](https://docs.pytorch.org/tutorials/beginner/serving_tutorial.html) - [Multi-Objective NAS with Ax](https://docs.pytorch.org/tutorials/intermediate/ax_multiobjective_nas_tutorial.html) - [PyTorch Profiler With TensorBoard](https://docs.pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html) - [Real Time Inference on Raspberry Pi 4 and 5 (40 fps!)](https://docs.pytorch.org/tutorials/intermediate/realtime_rpi.html) - [Mosaic: Memory Profiling for PyTorch](https://docs.pytorch.org/tutorials/beginner/mosaic_memory_profiling_tutorial.html) - [Distributed training at scale with PyTorch and Ray Train](https://docs.pytorch.org/tutorials/beginner/distributed_training_with_ray_tutorial.html) - [Recipes](https://docs.pytorch.org/tutorials/recipes_index.html) - [Defining a Neural Network in PyTorch](https://docs.pytorch.org/tutorials/recipes/recipes/defining_a_neural_network.html) - [(beta) Using TORCH\_LOGS python API with torch.compile](https://docs.pytorch.org/tutorials/recipes/torch_logs.html) - [What is a state\_dict in PyTorch](https://docs.pytorch.org/tutorials/recipes/recipes/what_is_state_dict.html) - [Warmstarting model using parameters from a different model in PyTorch](https://docs.pytorch.org/tutorials/recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html) - [Zeroing out gradients in PyTorch](https://docs.pytorch.org/tutorials/recipes/recipes/zeroing_out_gradients.html) - [PyTorch Profiler](https://docs.pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) - [Model Interpretability using Captum](https://docs.pytorch.org/tutorials/recipes/recipes/Captum_Recipe.html) - [How to use TensorBoard with PyTorch](https://docs.pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html) - [Automatic Mixed Precision](https://docs.pytorch.org/tutorials/recipes/recipes/amp_recipe.html) - [Performance Tuning Guide](https://docs.pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - [(beta) Compiling the optimizer with torch.compile](https://docs.pytorch.org/tutorials/recipes/compiling_optimizer.html) - [Timer quick start](https://docs.pytorch.org/tutorials/recipes/recipes/timer_quick_start.html) - [Shard Optimizer States with ZeroRedundancyOptimizer](https://docs.pytorch.org/tutorials/recipes/zero_redundancy_optimizer.html) - [Getting Started with CommDebugMode](https://docs.pytorch.org/tutorials/recipes/distributed_comm_debug_mode.html) - [Demonstration of torch.export flow, common challenges and the solutions to address them](https://docs.pytorch.org/tutorials/recipes/torch_export_challenges_solutions.html) - [PyTorch Benchmark](https://docs.pytorch.org/tutorials/recipes/recipes/benchmark.html) - [Tips for Loading an nn.Module from a Checkpoint](https://docs.pytorch.org/tutorials/recipes/recipes/module_load_state_dict_tips.html) - [Reasoning about Shapes in PyTorch](https://docs.pytorch.org/tutorials/recipes/recipes/reasoning_about_shapes.html) - [Extension points in nn.Module for load\_state\_dict and tensor subclasses](https://docs.pytorch.org/tutorials/recipes/recipes/swap_tensors.html) - [torch.export AOTInductor Tutorial for Python runtime (Beta)](https://docs.pytorch.org/tutorials/recipes/torch_export_aoti_python.html) - [How to use TensorBoard with PyTorch](https://docs.pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html) - [(beta) Utilizing Torch Function modes with torch.compile](https://docs.pytorch.org/tutorials/recipes/torch_compile_torch_function_modes.html) - [(beta) Running the compiled optimizer with an LR Scheduler](https://docs.pytorch.org/tutorials/recipes/compiling_optimizer_lr_scheduler.html) - [Explicit horizontal fusion with foreach\_map and torch.compile](https://docs.pytorch.org/tutorials/recipes/foreach_map.html) - [Using User-Defined Triton Kernels with torch.compile](https://docs.pytorch.org/tutorials/recipes/torch_compile_user_defined_triton_kernel_tutorial.html) - [Compile Time Caching in torch.compile](https://docs.pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) - [Compile Time Caching Configuration](https://docs.pytorch.org/tutorials/recipes/torch_compile_caching_configuration_tutorial.html) - [Reducing torch.compile cold start compilation time with regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_compilation.html) - [Reducing AoT cold start compilation time with regional compilation](https://docs.pytorch.org/tutorials/recipes/regional_aot.html) - [Ease-of-use quantization for PyTorch with Intel® Neural Compressor](https://docs.pytorch.org/tutorials/recipes/intel_neural_compressor_for_pytorch.html) - [Getting Started with DeviceMesh](https://docs.pytorch.org/tutorials/recipes/distributed_device_mesh.html) - [Getting Started with Distributed Checkpoint (DCP)](https://docs.pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html) - [Asynchronous Saving with Distributed Checkpoint (DCP)](https://docs.pytorch.org/tutorials/recipes/distributed_async_checkpoint_recipe.html) - [DebugMode: Recording Dispatched Operations and Numerical Debugging](https://docs.pytorch.org/tutorials/recipes/debug_mode_tutorial.html) - [Unstable](https://docs.pytorch.org/tutorials/unstable_index.html) - [Introduction to Context Parallel](https://docs.pytorch.org/tutorials/unstable/context_parallel.html) - [Flight Recorder for Debugging Stuck Jobs](https://docs.pytorch.org/tutorials/unstable/flight_recorder_tutorial.html) - [TorchInductor C++ Wrapper Tutorial](https://docs.pytorch.org/tutorials/unstable/inductor_cpp_wrapper_tutorial.html) - [How to use torch.compile on Windows CPU/XPU](https://docs.pytorch.org/tutorials/unstable/inductor_windows.html) - [torch.vmap](https://docs.pytorch.org/tutorials/unstable/vmap_recipe.html) - [Getting Started with Nested Tensors](https://docs.pytorch.org/tutorials/unstable/nestedtensor.html) - [MaskedTensor Overview](https://docs.pytorch.org/tutorials/unstable/maskedtensor_overview.html) - [MaskedTensor Sparsity](https://docs.pytorch.org/tutorials/unstable/maskedtensor_sparsity.html) - [MaskedTensor Advanced Semantics](https://docs.pytorch.org/tutorials/unstable/maskedtensor_advanced_semantics.html) - [Efficiently writing “sparse” semantics for Adagrad with MaskedTensor](https://docs.pytorch.org/tutorials/unstable/maskedtensor_adagrad.html) - [Autoloading Out-of-Tree Extension](https://docs.pytorch.org/tutorials/unstable/python_extension_autoload.html) - [Using Max-Autotune Compilation on CPU for Better Performance](https://docs.pytorch.org/tutorials/unstable/max_autotune_on_CPU_tutorial.html) [Go to pytorch.org](https://pytorch.org/) - [X](https://x.com/PyTorch) - [GitHub](https://github.com/pytorch/tutorials) - [Discourse](https://dev-discuss.pytorch.org/) - [PyPi](https://pypi.org/project/torch/) Section Navigation Getting Started with PyTorch - [Learn the Basics](https://docs.pytorch.org/tutorials/beginner/basics/intro.html) - [Quickstart](https://docs.pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html) - [Tensors](https://docs.pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html) - [Datasets & DataLoaders](https://docs.pytorch.org/tutorials/beginner/basics/data_tutorial.html) - [Transforms](https://docs.pytorch.org/tutorials/beginner/basics/transforms_tutorial.html) - [Build the Neural Network](https://docs.pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html) - [Automatic Differentiation with `torch.autograd`](https://docs.pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html) - [Optimizing Model Parameters](https://docs.pytorch.org/tutorials/beginner/basics/optimization_tutorial.html) - [Save and Load the Model](https://docs.pytorch.org/tutorials/beginner/basics/saveloadrun_tutorial.html) - [Introduction to PyTorch - YouTube Series](https://docs.pytorch.org/tutorials/beginner/introyt/introyt_index.html) - [Introduction to PyTorch](https://docs.pytorch.org/tutorials/beginner/introyt/introyt1_tutorial.html) - [Introduction to PyTorch Tensors](https://docs.pytorch.org/tutorials/beginner/introyt/tensors_deeper_tutorial.html) - [The Fundamentals of Autograd](https://docs.pytorch.org/tutorials/beginner/introyt/autogradyt_tutorial.html) - [Building Models with PyTorch](https://docs.pytorch.org/tutorials/beginner/introyt/modelsyt_tutorial.html) - [PyTorch TensorBoard Support](https://docs.pytorch.org/tutorials/beginner/introyt/tensorboardyt_tutorial.html) - [Training with PyTorch](https://docs.pytorch.org/tutorials/beginner/introyt/trainingyt.html) - [Model Understanding with Captum](https://docs.pytorch.org/tutorials/beginner/introyt/captumyt.html) Learning PyTorch - [Deep Learning with PyTorch: A 60 Minute Blitz](https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) - [Tensors](https://docs.pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html) - [A Gentle Introduction to `torch.autograd`](https://docs.pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html) - [Neural Networks](https://docs.pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) - [Training a Classifier](https://docs.pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) - [Learning PyTorch with Examples](https://docs.pytorch.org/tutorials/beginner/pytorch_with_examples.html) - [Warm-up: numpy](https://docs.pytorch.org/tutorials/beginner/examples_tensor/polynomial_numpy.html) - [PyTorch: Tensors](https://docs.pytorch.org/tutorials/beginner/examples_tensor/polynomial_tensor.html) - [PyTorch: Tensors and autograd](https://docs.pytorch.org/tutorials/beginner/examples_autograd/polynomial_autograd.html) - [PyTorch: Defining New autograd Functions](https://docs.pytorch.org/tutorials/beginner/examples_autograd/polynomial_custom_function.html) - [PyTorch: nn](https://docs.pytorch.org/tutorials/beginner/examples_nn/polynomial_nn.html) - [PyTorch: optim](https://docs.pytorch.org/tutorials/beginner/examples_nn/polynomial_optim.html) - [PyTorch: Custom nn Modules](https://docs.pytorch.org/tutorials/beginner/examples_nn/polynomial_module.html) - [PyTorch: Control Flow + Weight Sharing](https://docs.pytorch.org/tutorials/beginner/examples_nn/dynamic_net.html) - [What is torch.nn *really*?](https://docs.pytorch.org/tutorials/beginner/nn_tutorial.html) - [Understanding requires\_grad, retain\_grad, Leaf, and Non-leaf Tensors](https://docs.pytorch.org/tutorials/beginner/understanding_leaf_vs_nonleaf_tutorial.html) - [NLP from Scratch](https://docs.pytorch.org/tutorials/intermediate/nlp_from_scratch_index.html) - [Visualizing Models, Data, and Training with TensorBoard](https://docs.pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) - [A guide on good usage of `non_blocking` and `pin_memory()` in PyTorch](https://docs.pytorch.org/tutorials/intermediate/pinmem_nonblock.html) - [Visualizing Gradients](https://docs.pytorch.org/tutorials/intermediate/visualizing_gradients_tutorial.html) - [Intro](https://docs.pytorch.org/tutorials/intro.html) - Deep... Rate this Page ★ ★ ★ ★ ★ beginner/deep\_learning\_60min\_blitz [ Run in Google Colab Colab]() [ Download Notebook Notebook]() [ View on GitHub GitHub]() # Deep Learning with PyTorch: A 60 Minute Blitz[\#](https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html#deep-learning-with-pytorch-a-60-minute-blitz "Link to this heading") Created On: Mar 24, 2017 \| Last Updated: May 31, 2023 \| Last Verified: Nov 05, 2024 **Author**: [Soumith Chintala](http://soumith.ch/) ## What is PyTorch?[\#](https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html#what-is-pytorch "Link to this heading") PyTorch is a Python-based scientific computing package serving two broad purposes: - A replacement for NumPy to use the power of GPUs and other accelerators. - An automatic differentiation library that is useful to implement neural networks. ## Goal of this tutorial:[\#](https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html#goal-of-this-tutorial "Link to this heading") - Understand PyTorch’s Tensor library and neural networks at a high level. - Train a small neural network to classify images To run the tutorials below, make sure you have the [torch](https://github.com/pytorch/pytorch), [torchvision](https://github.com/pytorch/vision), and [matplotlib](https://github.com/matplotlib/matplotlib) packages installed. Tensors In this tutorial, you will learn the basics of PyTorch tensors. Code [blitz/tensor\_tutorial.html](https://docs.pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html) A Gentle Introduction to torch.autograd Learn about autograd. Code [blitz/autograd\_tutorial.html](https://docs.pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html) Neural Networks This tutorial demonstrates how you can train neural networks in PyTorch. Code [blitz/neural\_networks\_tutorial.html](https://docs.pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) Training a Classifier Learn how to train an image classifier in PyTorch by using the CIFAR10 dataset. Code [blitz/cifar10\_tutorial.html](https://docs.pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) Rate this Page ★ ★ ★ ★ ★ Send Feedback [previous Model Understanding with Captum](https://docs.pytorch.org/tutorials/beginner/introyt/captumyt.html "previous page") [next Tensors](https://docs.pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html "next page") Built with the [PyData Sphinx Theme](https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html) 0.15.4. 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Rate this Page ★ ★ ★ ★ ★ Created On: Mar 24, 2017 \| Last Updated: May 31, 2023 \| Last Verified: Nov 05, 2024 **Author**: [Soumith Chintala](http://soumith.ch/) ## What is PyTorch?[\#](https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html#what-is-pytorch "Link to this heading") PyTorch is a Python-based scientific computing package serving two broad purposes: - A replacement for NumPy to use the power of GPUs and other accelerators. - An automatic differentiation library that is useful to implement neural networks. ## Goal of this tutorial:[\#](https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html#goal-of-this-tutorial "Link to this heading") - Understand PyTorch’s Tensor library and neural networks at a high level. - Train a small neural network to classify images To run the tutorials below, make sure you have the [torch](https://github.com/pytorch/pytorch), [torchvision](https://github.com/pytorch/vision), and [matplotlib](https://github.com/matplotlib/matplotlib) packages installed.