pytorch
diff --git a/‎_data/ecosystem/pted/2021/posters.yaml
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diff --git a/‎_mobile/android.md
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diff --git a/‎_mobile/ios.md
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diff --git a/‎_posts/2018-03-5-tensor-comprehensions.md
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diff --git a/‎_posts/2019-05-08-model-serving-in-pyorch.md
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diff --git a/‎_posts/2019-06-10-towards-reproducible-research-with-pytorch-hub.md
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diff --git a/‎_posts/2019-07-18-pytorch-ecosystem.md
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diff --git a/‎_posts/2019-08-08-pytorch-1.2-and-domain-api-release.md
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@@ -10,7 +10,7 @@
     are provided as a Torch tensor with a defined gradient. We highlight how this
     functionality can be used to explore new paradigms in machine learning, including
     the use of hybrid models for transfer learning.
-  link: http://www.pennylane.ai
+  link: http://pennylane.ai
   poster_link: https://s3.amazonaws.com/assets.pytorch.org/pted2021/posters/K1.png
   section: K1
   thumbnail_link: https://s3.amazonaws.com/assets.pytorch.org/pted2021/posters/thumb-K1.png
@@ -321,7 +321,7 @@
     supports accelerated mixed precision training. AMD also provides hardware support
     for the PyTorch community build to help develop and maintain new features. This
     poster will highlight some of the work that has gone into enabling PyTorch support.
-  link: www.amd.com/rocm
+  link: https://www.amd.com/rocm
   poster_link: https://s3.amazonaws.com/assets.pytorch.org/pted2021/posters/K8.png
   section: K8
   thumbnail_link: https://s3.amazonaws.com/assets.pytorch.org/pted2021/posters/thumb-K8.png
 
@@ -94,7 +94,7 @@ Tensor inputTensor = TensorImageUtils.bitmapToFloat32Tensor(bitmap,
     TensorImageUtils.TORCHVISION_NORM_MEAN_RGB, TensorImageUtils.TORCHVISION_NORM_STD_RGB);
 ```
 `org.pytorch.torchvision.TensorImageUtils` is part of `org.pytorch:pytorch_android_torchvision` library.
-The `TensorImageUtils#bitmapToFloat32Tensor` method creates tensors in the [torchvision format](https://pytorch.org/docs/stable/torchvision/models.html) using `android.graphics.Bitmap` as a source.
+The `TensorImageUtils#bitmapToFloat32Tensor` method creates tensors in the [torchvision format](https://pytorch.org/vision/stable/models.html) using `android.graphics.Bitmap` as a source.
 
 > All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224.
 > The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]` and `std = [0.229, 0.224, 0.225]`
 
@@ -23,7 +23,7 @@ HelloWorld is a simple image classification application that demonstrates how to
 
 ### Model Preparation
 
-Let's start with model preparation. If you are familiar with PyTorch, you probably should already know how to train and save your model. In case you don't, we are going to use a pre-trained image classification model - [MobileNet v2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/), which is already packaged in [TorchVision](https://pytorch.org/docs/stable/torchvision/index.html). To install it, run the command below.
+Let's start with model preparation. If you are familiar with PyTorch, you probably should already know how to train and save your model. In case you don't, we are going to use a pre-trained image classification model - [MobileNet v2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/), which is already packaged in [TorchVision](https://pytorch.org/vision/stable/index.html). To install it, run the command below.
 
 > We highly recommend following the [Pytorch Github page](https://github.com/pytorch/pytorch) to set up the Python development environment on your local machine.
 
 
@@ -34,7 +34,7 @@ conda install -c pytorch -c tensorcomp tensor_comprehensions
 
 At this time we only provide Linux-64 binaries which have been tested on Ubuntu 16.04 and CentOS7.
 
-TC depends on heavyweight C++ projects such as [Halide](http://halide-lang.org/), [Tapir-LLVM](https://github.com/wsmoses/Tapir-LLVM) and [ISL](http://isl.gforge.inria.fr/). Hence, we rely on Anaconda to distribute these dependencies reliably. For the same reason, TC is not available via PyPI.
+TC depends on heavyweight C++ projects such as [Halide](http://halide-lang.org/), [Tapir-LLVM](https://github.com/wsmoses/Tapir-LLVM) and ISL. Hence, we rely on Anaconda to distribute these dependencies reliably. For the same reason, TC is not available via PyPI.
 
 #### 2. Import the python package
 
@@ -74,8 +74,6 @@ The autotuner is your biggest friend. You generally do not want to use a `tc` fu
 
 When the autotuning is running, the current best performance is displayed. If you are satisfied with the current result or you are out of time, stop the tuning procedure by pressing `Ctrl+C`.
 
-![tc-autotuner](https://pytorch.org/static/img/tc_autotuner.gif)
-
 `cache` saves the results of the autotuned kernel search and saves it to the file `fcrelu_100_128_100.tc`. The next time you call the same line of code, it loads the results of the autotuning without recomputing it.
 
 The autotuner has a few hyperparameters (just like your ConvNet has learning rate, number of layers, etc.). We pick reasonable defaults, but you can read about using advanced options [here](https://facebookresearch.github.io/TensorComprehensions/framework/pytorch_integration/writing_layers.html#specifying-mapping-options).
@@ -146,7 +144,7 @@ Note: the syntax for passing in scalars is subject to change in the next release
 
 ## torch.nn layers
 
-We added some sugar-coating around the basic PyTorch integration of TC to make it easy to integrate TC into larger `torch.nn` models by defining the forward and backward TC expressions and taking `Variable` inputs / outputs. Here is an [example](https://github.com/facebookresearch/TensorComprehensions/blob/master/test_python/layers/test_convolution_train.py) of defining a convolution layer with TC.
+We added some sugar-coating around the basic PyTorch integration of TC to make it easy to integrate TC into larger `torch.nn` models by defining the forward and backward TC expressions and taking `Variable` inputs / outputs. 
 
 ## Some essentials that you will miss (we're working on them)
 
@@ -183,12 +181,12 @@ You cannot write this operation in TC: `torch.matmul(...).view(...).mean(...)`.
 ## Getting Started
 
 - [Walk through Tutorial](https://facebookresearch.github.io/TensorComprehensions/framework/pytorch_integration/writing_layers.html) to quickly get started with understanding and using Tensor Comprehensions PyTorch package.
-- [Over 20 examples](https://github.com/facebookresearch/TensorComprehensions/tree/master/test_python/layers) of various ML layers with TC, including `avgpool`, `maxpool`, `matmul`, matmul - give output buffers and `batch-matmul`, `convolution`, `strided-convolution`, `batchnorm`, `copy`, `cosine similarity`, `Linear`, `Linear + ReLU`, `group-convolutions`, strided `group-convolutions`, `indexing`, `Embedding` (lookup table), small-mobilenet, `softmax`, `tensordot`, `transpose`
+- Over 20 examples of various ML layers with TC, including `avgpool`, `maxpool`, `matmul`, matmul - give output buffers and `batch-matmul`, `convolution`, `strided-convolution`, `batchnorm`, `copy`, `cosine similarity`, `Linear`, `Linear + ReLU`, `group-convolutions`, strided `group-convolutions`, `indexing`, `Embedding` (lookup table), small-mobilenet, `softmax`, `tensordot`, `transpose`
 - [Detailed docs](https://facebookresearch.github.io/TensorComprehensions/framework/pytorch_integration/getting_started.html) on Tensor Comprehensions and integration with PyTorch.
 
 ## Communication
 
-- [Slack](https://tensorcomprehensions.herokuapp.com/): For discussion around framework integration, build support, collaboration, etc. join our slack channel.
+- Slack: For discussion around framework integration, build support, collaboration, etc. join our slack channel.
 - Email: [email protected]
 - [GitHub](https://github.com/facebookresearch/TensorComprehensions): bug reports, feature requests, install issues, RFCs, thoughts, etc.
 
 
@@ -52,7 +52,7 @@ If you can't use the cloud or prefer to manage all services using the same techn
 
 If you want to manage multiple models within a non-cloud service solution, there are teams developing PyTorch support in model servers like [MLFlow](https://mlflow.org/), [Kubeflow](https://www.kubeflow.org/), and [RedisAI.](https://oss.redislabs.com/redisai/) We're excited to see innovation from multiple teams building OSS model servers, and we'll continue to highlight innovation in the PyTorch ecosystem in the future.
 
-If you can use the cloud for your application, there are several great choices for working with models in the cloud. For AWS Sagemaker, you can start find a guide to [all of the resources from AWS for working with PyTorch](https://docs.aws.amazon.com/sagemaker/latest/dg/pytorch.html), including docs on how to use the [Sagemaker Python SDK](https://sagemaker.readthedocs.io/en/stable/using_pytorch.html). You can also see [some](https://youtu.be/5h1Ot2dPi2E) [talks](https://youtu.be/qc5ZikKw9_w) we've given on using PyTorch on Sagemaker. Finally, if you happen to be using PyTorch via FastAI, then they've written a [really simple guide](https://course.fast.ai/deployment_amzn_sagemaker.html) to getting up and running on Sagemaker. 
+If you can use the cloud for your application, there are several great choices for working with models in the cloud. For AWS Sagemaker, you can start find a guide to [all of the resources from AWS for working with PyTorch](https://docs.aws.amazon.com/sagemaker/latest/dg/pytorch.html), including docs on how to use the [Sagemaker Python SDK](https://sagemaker.readthedocs.io/en/stable/using_pytorch.html). You can also see [some](https://youtu.be/5h1Ot2dPi2E) [talks](https://youtu.be/qc5ZikKw9_w) we've given on using PyTorch on Sagemaker. Finally, if you happen to be using PyTorch via FastAI, then they've written a really simple guide to getting up and running on Sagemaker. 
 
 The story is similar across other major clouds. On Google Cloud, you can follow [these instructions](https://cloud.google.com/deep-learning-vm/docs/pytorch_start_instance) to get access to a Deep Learning VM with PyTorch pre-installed. On Microsoft Azure, you have a number of ways to get started from [Azure Machine Learning Service](https://azure.microsoft.com/en-us/services/machine-learning-service/) to [Azure Notebooks](https://notebooks.azure.com/pytorch/projects/tutorials) showing how to use PyTorch.
 
 
@@ -106,7 +106,7 @@ Users can list all available entrypoints in a repo using the ```torch.hub.list()
  'vgg19_bn']
  ```
 
-Note that PyTorch Hub also allows auxillary entrypoints (other than pretrained models), e.g. ```bertTokenizer``` for preprocessing in the [BERT](https://pytorch.org/hub/huggingface_pytorch-pretrained-bert_bert/) models, to make the user workflow smoother.
+Note that PyTorch Hub also allows auxillary entrypoints (other than pretrained models), e.g. ```bertTokenizer``` for preprocessing in the BERT models, to make the user workflow smoother.
 
 
 ### Load a model
@@ -164,7 +164,7 @@ forward(input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=No
 ...
 ```
 
-Have a closer look at the [BERT](https://pytorch.org/hub/huggingface_pytorch-pretrained-bert_bert/) and [DeepLabV3](https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/) pages, where you can see how these models can be used once loaded.
+Have a closer look at the BERT and [DeepLabV3](https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/) pages, where you can see how these models can be used once loaded.
 
 ### Other ways to explore
 
 
@@ -45,7 +45,7 @@ If you would like to have your project included in the PyTorch ecosystem and fea
 
 ## PyTorch Hub for reproducible research | New models
 
-Since [launching](https://pytorch.org/blog/towards-reproducible-research-with-pytorch-hub/) the PyTorch Hub in beta, we’ve received a lot of interest from the community including the contribution of many new models. Some of the latest include [U-Net for Brain MRI](https://pytorch.org/hub/mateuszbuda_brain-segmentation-pytorch_unet/) contributed by researchers at Duke University, [Single Shot Detection](https://pytorch.org/hub/nvidia_deeplearningexamples_ssd/) from NVIDIA and [Transformer-XL](https://pytorch.org/hub/huggingface_pytorch-pretrained-bert_transformerXL/) from HuggingFace.
+Since [launching](https://pytorch.org/blog/towards-reproducible-research-with-pytorch-hub/) the PyTorch Hub in beta, we’ve received a lot of interest from the community including the contribution of many new models. Some of the latest include [U-Net for Brain MRI](https://pytorch.org/hub/mateuszbuda_brain-segmentation-pytorch_unet/) contributed by researchers at Duke University, [Single Shot Detection](https://pytorch.org/hub/nvidia_deeplearningexamples_ssd/) from NVIDIA and Transformer-XL from HuggingFace.
 
 We’ve seen organic integration of the PyTorch Hub by folks like [paperswithcode](https://paperswithcode.com/), making it even easier for you to try out the state of the art in AI research. In addition, companies like [Seldon](https://github.com/axsaucedo/seldon-core/tree/pytorch_hub/examples/models/pytorchhub) provide production-level support for PyTorch Hub models on top of Kubernetes.
 
 
@@ -115,9 +115,9 @@ We are excited to see an active community around torchaudio and eager to further
 
 ## Torchtext 0.4 with supervised learning datasets
 
-A key focus area of torchtext is to provide the fundamental elements to help accelerate NLP research. This includes easy access to commonly used datasets and basic preprocessing pipelines for working on raw text based data. The torchtext 0.4.0 release includes several popular supervised learning baselines with "one-command" data loading. A [tutorial](https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html) is included to show how to use the new datasets for text classification analysis. We also added and improved on a few functions such as [get_tokenizer](https://pytorch.org/text/data.html?highlight=get_tokenizer#torchtext.data.get_tokenizer) and [build_vocab_from_iterator](https://pytorch.org/text/vocab.html#build-vocab-from-iterator) to make it easier to implement future datasets. Additional examples can be found [here](https://github.com/pytorch/text/tree/master/examples/text_classification).
+A key focus area of torchtext is to provide the fundamental elements to help accelerate NLP research. This includes easy access to commonly used datasets and basic preprocessing pipelines for working on raw text based data. The torchtext 0.4.0 release includes several popular supervised learning baselines with "one-command" data loading. A [tutorial](https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html) is included to show how to use the new datasets for text classification analysis. We also added and improved on a few functions such as get_tokenizer and build_vocab_from_iterator to make it easier to implement future datasets. Additional examples can be found [here](https://github.com/pytorch/text/tree/master/examples/text_classification).
 
-Text classification is an important task in Natural Language Processing with many applications, such as sentiment analysis. The new release includes several popular [text classification datasets](https://pytorch.org/text/datasets.html?highlight=textclassification#torchtext.datasets.TextClassificationDataset) for supervised learning including:
+Text classification is an important task in Natural Language Processing with many applications, such as sentiment analysis. The new release includes several popular text classification datasets for supervised learning including:
 
 * AG_NEWS
 * SogouNews