![]() ![]() Or latency of predictions substantially by accepting minimally lower Often, we may be able to reduce computational requirements ![]() Inference latency, or model size in order to satisfy deploymentĬonstraints. Simultaneously minimizing competing metrics like power consumption, We may want to maximize model performance (for example, accuracy), while For instance, when deploying models on-device In many NAS applications, there is a natural tradeoff between multiple Larger datasets, we opt for a tutorial that is easily runnableĮnd-to-end on a laptop in less than 20 minutes. Methodology would typically be used for more complicated models and Network model on the popular MNIST dataset. Multi-objective neural architecture search (NAS) for a simple neural In this tutorial, we show how to use Ax to run TorchMultimodal Tutorial: Finetuning FLAVAĪnd the Adaptive Experimentation team at Meta.Image Segmentation DeepLabV3 on Android.Distributed Training with Uneven Inputs Using the Join Context Manager.Training Transformer models using Distributed Data Parallel and Pipeline Parallelism.Training Transformer models using Pipeline Parallelism.Combining Distributed DataParallel with Distributed RPC Framework.Implementing Batch RPC Processing Using Asynchronous Executions.Distributed Pipeline Parallelism Using RPC.Implementing a Parameter Server Using Distributed RPC Framework.Getting Started with Distributed RPC Framework.Customize Process Group Backends Using Cpp Extensions.Advanced Model Training with Fully Sharded Data Parallel (FSDP). ![]() Getting Started with Fully Sharded Data Parallel(FSDP).Writing Distributed Applications with PyTorch.Getting Started with Distributed Data Parallel.Single-Machine Model Parallel Best Practices.Distributed Data Parallel in PyTorch - Video Tutorials.Distributed and Parallel Training Tutorials.(Beta) Implementing High-Performance Transformers with Scaled Dot Product Attention (SDPA).Inductor CPU backend debugging and profiling.Getting Started - Accelerate Your Scripts with nvFuser.Grokking PyTorch Intel CPU performance from first principles (Part 2).Grokking PyTorch Intel CPU performance from first principles.(beta) Static Quantization with Eager Mode in PyTorch.(beta) Quantized Transfer Learning for Computer Vision Tutorial.(beta) Dynamic Quantization on an LSTM Word Language Model.Extending dispatcher for a new backend in C++.Registering a Dispatched Operator in C++.Extending TorchScript with Custom C++ Classes.Extending TorchScript with Custom C++ Operators.Fusing Convolution and Batch Norm using Custom Function.Jacobians, Hessians, hvp, vhp, and more: composing function transforms.Forward-mode Automatic Differentiation (Beta).(beta) Channels Last Memory Format in PyTorch.(beta) Building a Simple CPU Performance Profiler with FX.(beta) Building a Convolution/Batch Norm fuser in FX.Real Time Inference on Raspberry Pi 4 (30 fps!).(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime. ![]()
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