Merge branch 'main' into add-new-theme

Author: svekars

beginner_source/hyperparameter_tuning_tutorial.py

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

index.rst

@@ -613,20 +613,6 @@ Welcome to PyTorch Tutorials
    :link: advanced/static_quantization_tutorial.html
    :tags: Quantization
 
-.. customcarditem::
-   :header: Grokking PyTorch Intel CPU Performance from First Principles
-   :card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch.
-   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
-   :link: intermediate/torchserve_with_ipex
-   :tags: Model-Optimization,Production
-
-.. customcarditem::
-   :header: Grokking PyTorch Intel CPU Performance from First Principles (Part 2)
-   :card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch (Part 2).
-   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
-   :link: intermediate/torchserve_with_ipex_2
-   :tags: Model-Optimization,Production
-
 .. customcarditem::
    :header: Multi-Objective Neural Architecture Search with Ax
    :card_description: Learn how to use Ax to search over architectures find optimal tradeoffs between accuracy and latency.

intermediate_source/rpc_async_execution.rst

@@ -15,8 +15,7 @@ Prerequisites:
 This tutorial demonstrates how to build batch-processing RPC applications with
 the `@rpc.functions.async_execution <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.functions.async_execution>`__
 decorator, which helps to speed up training by reducing the number of blocked
-RPC threads and consolidating CUDA operations on the callee. This shares the
-same idea as `Batch Inference with TorchServe <https://pytorch.org/serve/batch_inference_with_ts.html>`__.
+RPC threads and consolidating CUDA operations on the callee.
 
 .. note:: This tutorial requires PyTorch v1.6.0 or above.