script ( encoder ) scripted_decoder = torch. trace ( decoder, ( decoder_input1, decoder_input2, decoder_input3 )) # method 2: using script scripted_encoder = torch. trace ( encoder, ( encoder_input, encoder_hidden )) traced_decoder = torch. zeros ( MAX_LENGTH, hidden_size ) traced_encoder = torch. zeros ( 1, 1, hidden_size ) decoder_input3 = torch. zeros ( 1, 1, hidden_size ) decoder_input1 = torch. n_words ) # method 1: using trace with example inputs encoder_input = torch. n_words, hidden_size ) decoder = AttnDecoderRNN ( hidden_size, output_lang. Image Segmentation DeepLabV3 on AndroidĮncoder = EncoderRNN ( input_lang.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.Writing Distributed Applications with PyTorch.Getting Started with Distributed Data Parallel.Single-Machine Model Parallel Best Practices.(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.(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.(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime.Deploying PyTorch in Python via a REST API with Flask.Text classification with the torchtext library.NLP From Scratch: Translation with a Sequence to Sequence Network and Attention.NLP From Scratch: Generating Names with a Character-Level RNN.NLP From Scratch: Classifying Names with a Character-Level RNN.Sequence-to-Sequence Modeling with nn.Transformer and TorchText.Speech Command Recognition with torchaudio.Optimizing Vision Transformer Model for Deployment.
Transfer Learning for Computer Vision Tutorial.TorchVision Object Detection Finetuning Tutorial.Visualizing Models, Data, and Training with TensorBoard.
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