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beginner_source/hyperparameter_tuning_tutorial.py

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@@ -184,7 +184,7 @@ def forward(self, x):
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# inputs, labels = inputs.to(device), labels.to(device)
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#
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# The code now supports training on CPUs, on a single GPU, and on multiple GPUs. Notably, Ray
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# also supports `fractional GPUs <https://docs.ray.io/en/master/using-ray-with-gpus.html#fractional-gpus>`_
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# also supports `fractional GPUs <https://docs.ray.io/en/latest/ray-core/scheduling/accelerators.html#fractional-accelerators>`_
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# so we can share GPUs among trials, as long as the model still fits on the GPU memory. We'll come back
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# to that later.
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#

index.rst

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@@ -612,20 +612,6 @@ Welcome to PyTorch Tutorials
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:link: advanced/static_quantization_tutorial.html
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:tags: Quantization
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.. customcarditem::
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:header: Grokking PyTorch Intel CPU Performance from First Principles
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:card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch.
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:image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
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:link: intermediate/torchserve_with_ipex
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:tags: Model-Optimization,Production
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.. customcarditem::
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:header: Grokking PyTorch Intel CPU Performance from First Principles (Part 2)
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:card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch (Part 2).
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:image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
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:link: intermediate/torchserve_with_ipex_2
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:tags: Model-Optimization,Production
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.. customcarditem::
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:header: Multi-Objective Neural Architecture Search with Ax
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:card_description: Learn how to use Ax to search over architectures find optimal tradeoffs between accuracy and latency.
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intermediate/dynamic_quantization_bert_tutorial
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intermediate/quantized_transfer_learning_tutorial
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advanced/static_quantization_tutorial
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intermediate/torchserve_with_ipex
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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/torch_compile_tutorial

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