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Svetlana Karslioglu
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Merge branch 'master' into jingxu10/ipex
2 parents 95c9db0 + 9450a52 commit 8726706

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.jenkins/validate_tutorials_built.py

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"recipes/profiler_recipe",
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"recipes/save_load_across_devices",
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"recipes/warmstarting_model_using_parameters_from_a_different_model",
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"dynamo_tutorial",
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"torch_compile_tutorial_",
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"recipes/dynamic_quantization",
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"recipes/saving_and_loading_a_general_checkpoint",
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"recipes/benchmark",

beginner_source/basics/buildmodel_tutorial.py

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# along with some `background operations <https://github.com/pytorch/pytorch/blob/270111b7b611d174967ed204776985cefca9c144/torch/nn/modules/module.py#L866>`_.
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# Do not call ``model.forward()`` directly!
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#
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# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output. .
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# Calling the model on the input returns a 2-dimensional tensor with dim=0 corresponding to each output of 10 raw predicted values for each class, and dim=1 corresponding to the individual values of each output.
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# We get the prediction probabilities by passing it through an instance of the ``nn.Softmax`` module.
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X = torch.rand(1, 28, 28, device=device)

beginner_source/translation_transformer.py

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tensor_transform) # Add BOS/EOS and create tensor
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# function to collate data samples into batch tesors
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# function to collate data samples into batch tensors
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def collate_fn(batch):
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src_batch, tgt_batch = [], []
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for src_sample, tgt_sample in batch:

index.rst

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What's new in PyTorch tutorials?
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* `Survey: Let us know how the PyTorch Tutorials are working for you! <https://docs.google.com/forms/d/e/1FAIpQLSfM-n7fF-K8AqkOcdSlzS_0g-xd6ith7U_W36xG-C_6Q2B6Mg/viewform>`__
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* `PyTorch Distributed Series <https://pytorch.org/tutorials/beginner/ddp_series_intro.html?utm_source=whats_new_tutorials&utm_medium=ddp_series_intro>`__
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* `Fast Transformer Inference with Better Transformer <https://pytorch.org/tutorials/beginner/bettertransformer_tutorial.html?utm_source=whats_new_tutorials&utm_medium=bettertransformer>`__
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* `Advanced model training with Fully Sharded Data Parallel (FSDP) <https://pytorch.org/tutorials/intermediate/FSDP_adavnced_tutorial.html?utm_source=whats_new_tutorials&utm_medium=FSDP_advanced>`__
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:tags: Model-Optimization,Best-Practice,Ax,TorchX
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.. customcarditem::
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:header: TorchDynamo and TorchInductor Tutorial
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:card_description: Speed up your models with minimal code changes using TorchDynamo and TorchInductor, the latest PyTorch compiler solution.
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:header: torch.compile Tutorial
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:card_description: Speed up your models with minimal code changes using torch.compile, the latest PyTorch compiler solution.
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:image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
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:link: intermediate/dynamo_tutorial.html
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:link: intermediate/torch_compile_tutorial.html
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:tags: Model-Optimization
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.. Parallel-and-Distributed-Training
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intermediate/torchserve_with_ipex_2
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intermediate/nvfuser_intro_tutorial
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intermediate/ax_multiobjective_nas_tutorial
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intermediate/dynamo_tutorial
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intermediate/torch_compile_tutorial
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.. toctree::
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:maxdepth: 2

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