wip

Pat Mellon · Pat Mellon · commit 26aa53839c01 · 2020-08-27T16:21:39.000-04:00
diff --git a/_posts/2017-5-11-a-tour-of-pytorch-internals-1.md b/_posts/2017-5-11-a-tour-of-pytorch-internals-1.md
@@ -5,6 +5,7 @@ author: "Trevor Killeen"
 date: 2017-05-11 12:00:00 -0500
 redirect_from: /2017/05/11/Internals.html
 image: /assets/images/bert2.png
+tags: [one]
 ---
 
 The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:
diff --git a/_posts/2017-6-27-a-tour-of-pytorch-internals-2.md b/_posts/2017-6-27-a-tour-of-pytorch-internals-2.md
@@ -5,6 +5,7 @@ author: "Trevor Killeen"
 date: 2017-06-27 12:00:00 -0500
 redirect_from: /2017/06/27/Internals2.html
 image: /assets/images/bert2.png
+tags: [one]
 ---
 
 In the first [post]({{ site.baseurl }}{% link _posts/2017-5-11-a-tour-of-pytorch-internals-1.md %}) I explained how we generate a `torch.Tensor` object that you can use in your Python interpreter. Next, I will explore the build system for PyTorch. The PyTorch codebase has a variety of components:
diff --git a/_posts/2018-01-19-a-year-in.md b/_posts/2018-01-19-a-year-in.md
@@ -5,6 +5,7 @@ author: "The PyTorch Team"
 date: 2018-01-19 12:00:00 -0500
 redirect_from: /2018/01/19/a-year-in.html
 image: /assets/images/bert2.png
+tags: [one]
 ---
 
 Today marks 1 year since PyTorch was released publicly. It's been a wild ride — our quest to build a flexible deep learning research platform. Over the last year, we've seen an amazing community of people using, contributing to and evangelizing PyTorch — thank you for the love.
diff --git a/_posts/2018-03-5-tensor-comprehensions.md b/_posts/2018-03-5-tensor-comprehensions.md
@@ -4,6 +4,7 @@ title: 'Tensor Comprehensions in PyTorch'
 author: Priya Goyal (FAIR), Nicolas Vasilache (FAIR), Oleksandr Zinenko (Inria & DI ENS), Theodoros Theodoridis (ETH Zürich), Zachary DeVito (FAIR), William S. Moses (MIT CSAIL), Sven Verdoolaege (FAIR), Andrew Adams (FAIR), Albert Cohen (Inria & DI ENS & FAIR)
 redirect_from: /2018/03/05/tensor-comprehensions.html
 image: /assets/images/bert2.png
+tags: [two]
 ---
 
 Tensor Comprehensions (TC) is a tool that lowers the barrier for writing high-performance code. It generates GPU code from a simple high-level language and autotunes the code for specific input sizes.
diff --git a/_posts/2018-04-22-pytorch-0_4_0-migration-guide.md b/_posts/2018-04-22-pytorch-0_4_0-migration-guide.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch 0.4.0 Migration Guide'
 redirect_from: /2018/04/22/0_4_0-migration-guide.html
 image: /assets/images/bert2.png
+tags: [two]
 ---
 
 Welcome to the migration guide for PyTorch 0.4.0. In this release we introduced [many exciting new features and critical bug fixes](https://github.com/pytorch/pytorch/releases/tag/v0.4.0), with the goal of providing users a better and cleaner interface. In this guide, we will cover the most important changes in migrating existing code from previous versions:
diff --git a/_posts/2018-05-2-the-road-to-1_0.md b/_posts/2018-05-2-the-road-to-1_0.md
@@ -4,6 +4,7 @@ title: 'The road to 1.0: production ready PyTorch'
 author: The PyTorch Team
 redirect_from: /2018/05/02/road-to-1.0.html
 image: /assets/images/bert2.png
+tags: [two]
 ---
 
 We would like to give you a preview of the roadmap for PyTorch 1.0 , the next release of PyTorch. Over the last year, we've had 0.2, 0.3 and 0.4 transform PyTorch from a [Torch+Chainer]-like interface into something cleaner, adding double-backwards, numpy-like functions, advanced indexing and removing Variable boilerplate. At this time, we're confident that the API is in a reasonable and stable state to confidently release a 1.0.
diff --git a/_posts/2019-05-08-model-serving-in-pyorch.md b/_posts/2019-05-08-model-serving-in-pyorch.md
@@ -4,6 +4,7 @@ title: 'Model Serving in PyTorch'
 author: Jeff Smith
 redirect_from: /2019/05/08/model-serving-in-pyorch.html
 image: /assets/images/bert2.png
+tags: [three]
 ---
 
 PyTorch has seen a lot of adoption in research, but people can get confused about how well PyTorch models can be taken into production. This blog post is meant to clear up any confusion people might have about the road to production in PyTorch.
diff --git a/_posts/2019-05-1-pytorch-adds-new-dev-tools.md b/_posts/2019-05-1-pytorch-adds-new-dev-tools.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch adds new dev tools as it hits production scale'
 author: The PyTorch Team
 image: /assets/images/bert2.png
+tags: [three]
 ---
 
 _This is a partial re-post of the original blog post on the Facebook AI Blog. The full post can be [viewed here](https://ai.facebook.com/blog/pytorch-adds-new-dev-tools-as-it-hits-production-scale/)_
diff --git a/_posts/2019-06-10-towards-reproducible-research-with-pytorch-hub.md b/_posts/2019-06-10-towards-reproducible-research-with-pytorch-hub.md
@@ -4,6 +4,7 @@ title: 'Towards Reproducible Research with PyTorch Hub'
 author: Team PyTorch
 redirect_from: /2019/06/10/pytorch_hub.html
 image: /assets/images/bert2.png
+tags: [three]
 ---
 
 Reproducibility is an essential requirement for many fields of research including those based on machine learning techniques. However, many machine learning publications are either not reproducible or are difficult to reproduce. With the continued growth in the number of research publications, including tens of thousands of papers now hosted on arXiv and submissions to conferences at an all time high, research reproducibility is more important than ever. While many of these publications are accompanied by code as well as trained models which is helpful but still leaves a number of steps for users to figure out for themselves.
diff --git a/_posts/2019-07-18-pytorch-ecosystem.md b/_posts/2019-07-18-pytorch-ecosystem.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch Adds New Ecosystem Projects for Encrypted AI and Quantum Computing, Expands PyTorch Hub'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [four]
 ---
 
 The PyTorch ecosystem includes projects, tools, models and libraries from a broad community of researchers in academia and industry, application developers, and ML engineers. The goal of this ecosystem is to support, accelerate, and aid in your exploration with PyTorch and help you push the state of the art, no matter what field you are exploring. Similarly, we are expanding the recently launched PyTorch Hub to further help you discover and reproduce the latest research.
diff --git a/_posts/2019-07-23-mapillary-research.md b/_posts/2019-07-23-mapillary-research.md
@@ -4,6 +4,7 @@ title: 'Mapillary Research: Seamless Scene Segmentation and In-Place Activated B
 author: Lorenzo Porzi, Mapillary
 redirect_from: /2019/07/23/mapillary-research.html
 image: /assets/images/bert2.png
+tags: [four]
 ---
 
 With roads in developed countries like the US changing up to 15% annually, Mapillary addresses a growing demand for keeping maps updated by combining images from any camera into a 3D visualization of the world. Mapillary's independent and collaborative approach enables anyone to collect, share, and use street-level images for improving maps, developing cities, and advancing the automotive industry.
diff --git a/_posts/2019-08-08-pytorch-1.2-and-domain-api-release.md b/_posts/2019-08-08-pytorch-1.2-and-domain-api-release.md
@@ -4,6 +4,7 @@ title: 'New Releases: PyTorch 1.2, torchtext 0.4, torchaudio 0.3, and torchvisio
 author: Team PyTorch
 redirect_from: /2019/08/06/pytorch_aug2019_releases.html
 image: /assets/images/bert2.png
+tags: [four]
 ---
 
 Since the release of PyTorch 1.0, we’ve seen the community expand to add new tools, contribute to a growing set of models available in the PyTorch Hub, and continually increase usage in both research and production.
diff --git a/_posts/2019-10-10-pytorch-1-dot-3-adds-mobile-privacy-quantization-and-named-tensors.md b/_posts/2019-10-10-pytorch-1-dot-3-adds-mobile-privacy-quantization-and-named-tensors.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch 1.3 adds mobile, privacy, quantization, and named tensors'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [four]
 ---
 
 PyTorch continues to gain momentum because of its focus on meeting the needs of researchers, its streamlined workflow for production use, and most of all because of the enthusiastic support it has received from the AI community. PyTorch citations in papers on ArXiv [grew 194 percent in the first half of 2019 alone, as noted by O’Reilly](https://www.oreilly.com/ideas/one-simple-graphic-researchers-love-pytorch-and-tensorflow?fbclid=IwAR3kYmlyD7zky37IYFu0cafQn7yemhl8P-7MNyB30z0q5RDzxcTOrP8kxDk), and the number of contributors to the platform has grown more than 50 percent over the last year, to nearly 1,200. Facebook, Microsoft, Uber, and other organizations across industries are increasingly using it as the foundation for their most important machine learning (ML) research and production workloads.
diff --git a/_posts/2019-12-06-openmined-and-pytorch-launch-fellowship-funding-for-privacy-preserving-ml.md b/_posts/2019-12-06-openmined-and-pytorch-launch-fellowship-funding-for-privacy-preserving-ml.md
@@ -4,6 +4,7 @@ title: 'OpenMined and PyTorch partner to launch fellowship funding for privacy-p
 author: Andrew Trask (OpenMined/U.Oxford), Shubho Sengupta, Laurens van der Maaten, Joe Spisak
 excerpt: Many applications of machine learning (ML) pose a range of security and privacy challenges.
 image: /assets/images/bert2.png
+tags: [four]
 ---
 
 <div class="text-center">
diff --git a/_posts/2019-12-06-pytorch-adds-new-tools-and-libraries-welcomes-preferred-networks-to-its-community.md b/_posts/2019-12-06-pytorch-adds-new-tools-and-libraries-welcomes-preferred-networks-to-its-community.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch adds new tools and libraries, welcomes Preferred Networks to its community'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [four]
 ---
 
 PyTorch continues to be used for the latest state-of-the-art research on display at the NeurIPS conference next week, making up nearly [70% of papers](https://chillee.github.io/pytorch-vs-tensorflow/) that cite a framework. In addition, we’re excited to welcome Preferred Networks, the maintainers of the Chainer framework, to the PyTorch community. Their teams are moving fully over to PyTorch for developing their ML capabilities and services.
diff --git a/_posts/2019-4-29-stochastic-weight-averaging-in-pytorch.md b/_posts/2019-4-29-stochastic-weight-averaging-in-pytorch.md
@@ -4,6 +4,7 @@ title: 'Stochastic Weight Averaging in PyTorch'
 author: Pavel Izmailov and Andrew Gordon Wilson
 redirect_from: /2019/04/29/road-to-1.0.html
 image: /assets/images/bert2.png
+tags: [two]
 ---
 
 In this blogpost we describe the recently proposed Stochastic Weight Averaging (SWA) technique [1, 2], and its new implementation in [`torchcontrib`](https://github.com/pytorch/contrib).  SWA is a simple procedure that improves generalization in deep learning over Stochastic Gradient Descent (SGD) at no additional cost, and can be used as a drop-in replacement for any other optimizer in PyTorch. SWA has a wide range of applications and features:
diff --git a/_posts/2019-5-1-optimizing-cuda-rnn-with-torchscript.md b/_posts/2019-5-1-optimizing-cuda-rnn-with-torchscript.md
@@ -4,6 +4,7 @@ title: "Optimizing CUDA Recurrent Neural Networks with TorchScript"
 author: "The PyTorch Team"
 date: 2019-05-01 8:00:00 -0500
 image: /assets/images/bert2.png
+tags: [three]
 ---
 
 This week, we officially released PyTorch 1.1, a large feature update to PyTorch 1.0. One of the new features we've added is better support for fast, custom Recurrent Neural Networks (fastrnns) with TorchScript (the PyTorch JIT) (https://pytorch.org/docs/stable/jit.html).
diff --git a/_posts/2019-5-22-torchvision03.md b/_posts/2019-5-22-torchvision03.md
@@ -4,6 +4,7 @@ title: 'torchvision 0.3: segmentation, detection models, new datasets and more..
 author: Francisco Massa
 redirect_from: /2019/05/23/torchvision03.html
 image: /assets/images/bert2.png
+tags: [three]
 ---
 
 PyTorch domain libraries like torchvision provide convenient access to common datasets and models that can be used to quickly create a state-of-the-art baseline. Moreover, they also provide common abstractions to reduce boilerplate code that users might have to otherwise repeatedly write. The torchvision 0.3 release brings several new features including models for semantic segmentation, object detection, instance segmentation, and person keypoint detection, as well as custom C++ / CUDA ops specific to computer vision.
diff --git a/_posts/2020-07-28-accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision.md b/_posts/2020-07-28-accelerating-training-on-nvidia-gpus-with-pytorch-automatic-mixed-precision.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'Introducing native PyTorch automatic mixed precision for faster training on NVIDIA GPUs'
 author: Mengdi Huang, Chetan Tekur, Michael Carilli
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 Most deep learning frameworks, including PyTorch, train with 32-bit floating point (FP32) arithmetic by default. However this is not essential to achieve full accuracy for many deep learning models. In 2017, NVIDIA researchers developed a methodology for [mixed-precision training](https://developer.nvidia.com/blog/mixed-precision-training-deep-neural-networks/), which combined [single-precision](https://blogs.nvidia.com/blog/2019/11/15/whats-the-difference-between-single-double-multi-and-mixed-precision-computing/) (FP32) with half-precision (e.g. FP16) format when training a network, and achieved the same accuracy as FP32 training using the same hyperparameters, with additional performance benefits on NVIDIA GPUs:
diff --git a/_posts/2020-07-28-microsoft-becomes-maintainer-of-the-windows-version-of-pytorch.md b/_posts/2020-07-28-microsoft-becomes-maintainer-of-the-windows-version-of-pytorch.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'Microsoft becomes maintainer of the Windows version of PyTorch'
 author: Maxim Lukiyanov - Principal PM at Microsoft, Emad Barsoum - Group EM at Microsoft, Guoliang Hua - Principal EM at Microsoft, Nikita Shulga - Tech Lead at Facebook, Geeta Chauhan - PE Lead at Facebook, Chris Gottbrath - Technical PM at Facebook, Jiachen Pu - Engineer at Facebook
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 Along with the PyTorch 1.6 release, we are excited to announce that Microsoft has expanded its participation in the PyTorch community and is taking ownership of the development and maintenance of the PyTorch build for Windows.
diff --git a/_posts/2020-07-28-pytorch-feature-classification-changes.md b/_posts/2020-07-28-pytorch-feature-classification-changes.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch feature classification changes'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [six]
 ---
 
 Traditionally features in PyTorch were classified as either stable or experimental with an implicit third option of testing bleeding edge features by building master or through installing nightly builds (available via prebuilt whls). This has, in a few cases, caused some confusion around the level of readiness, commitment to the feature and backward compatibility that can be expected from a user perspective. Moving forward, we’d like to better classify the 3 types of features as well as define explicitly here what each mean from a user perspective.
diff --git a/_posts/2020-08-11-efficient-pytorch-io-library-for-large-datasets-many-files-many-gpus.md b/_posts/2020-08-11-efficient-pytorch-io-library-for-large-datasets-many-files-many-gpus.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'Efficient PyTorch I/O library for Large Datasets, Many Files, Many GPUs'
 author: Alex Aizman, Gavin Maltby, Thomas Breuel
 image: /assets/images/bert2.png
+tags: [six]
 ---
 
 Data sets are growing bigger every day and GPUs are getting faster. This means there are more data sets for deep learning researchers and engineers to train and validate their models.
diff --git a/_posts/2020-08-18-pytorch-1.6-now-includes-stochastic-weight-averaging.md b/_posts/2020-08-18-pytorch-1.6-now-includes-stochastic-weight-averaging.md
@@ -3,9 +3,12 @@ layout: blog_detail
 title: 'PyTorch 1.6 now includes Stochastic Weight Averaging'
 author: Pavel Izmailov, Andrew Gordon Wilson and Vincent Queneneville-Belair
 image: /assets/images/bert2.png
+tags: [six]
 ---
 
-Do you use stochastic gradient descent (SGD) or Adam? Regardless of the procedure you use to train your neural network, you can likely achieve significantly better generalization at virtually no additional cost with a simple new technique now natively supported in PyTorch 1.6, Stochastic Weight Averaging (SWA) [1]. Even if you have already trained your model, it’s easy to realize the benefits of SWA by running SWA for a small number of epochs starting with a pre-trained model. [Again](https://twitter.com/MilesCranmer/status/1282140440892932096) and [again](https://twitter.com/leopd/status/1285969855062192129), researchers are  discovering that SWA improves the performance of well-tuned models in a wide array of practical applications with little cost or effort!
+Do you use stochastic gradient descent (SGD) or Adam? Regardless of the procedure you use to train your neural network, you can likely achieve significantly better generalization at virtually no additional cost with a simple new technique now natively supported in PyTorch 1.6, Stochastic Weight Averaging (SWA) [1]. Even if you have already trained your model, it’s easy to realize the benefits of SWA by running SWA for a small number of epochs starting with a pre-trained model.
+
+[Again](https://twitter.com/MilesCranmer/status/1282140440892932096) and [again](https://twitter.com/leopd/status/1285969855062192129), researchers are  discovering that SWA improves the performance of well-tuned models in a wide array of practical applications with little cost or effort!
 
 
 SWA has a wide range of applications and features:
diff --git a/_posts/2020-1-15-pytorch-1-dot-4-released-and-domain-libraries-updated.md b/_posts/2020-1-15-pytorch-1-dot-4-released-and-domain-libraries-updated.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch 1.4 released, domain libraries updated'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 Today, we’re announcing the availability of PyTorch 1.4, along with updates to the PyTorch domain libraries. These releases build on top of the announcements from [NeurIPS 2019](https://pytorch.org/blog/pytorch-adds-new-tools-and-libraries-welcomes-preferred-networks-to-its-community/), where we shared the availability of PyTorch Elastic, a new classification framework for image and video, and the addition of Preferred Networks to the PyTorch community. For those that attended the workshops at NeurIPS, the content can be found [here](https://research.fb.com/neurips-2019-expo-workshops/).
diff --git a/_posts/2020-3-26-introduction-to-quantization-on-pytorch.md b/_posts/2020-3-26-introduction-to-quantization-on-pytorch.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'Introduction to Quantization on PyTorch'
 author: Raghuraman Krishnamoorthi, James Reed, Min Ni, Chris Gottbrath, and Seth Weidman
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 It’s important to make efficient use of both server-side and on-device compute resources when developing machine learning applications. To support more efficient deployment on servers and edge devices, PyTorch added a support for model quantization using the familiar eager mode Python API.
diff --git a/_posts/2020-4-21-pytorch-1-dot-5-released-with-new-and-updated-apis.md b/_posts/2020-4-21-pytorch-1-dot-5-released-with-new-and-updated-apis.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch 1.5 released, new and updated APIs including C++ frontend API parity with Python'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 
diff --git a/_posts/2020-4-21-pytorch-library-updates-new-model-serving-library.md b/_posts/2020-4-21-pytorch-library-updates-new-model-serving-library.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch library updates including new model serving library '
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 
diff --git a/_posts/2020-5-5-updates-improvements-to-pytorch-tutorials.md b/_posts/2020-5-5-updates-improvements-to-pytorch-tutorials.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'Updates & Improvements to PyTorch Tutorials'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 PyTorch.org provides researchers and developers with documentation, installation instructions, latest news, community projects, tutorials, and more. Today, we are introducing usability and content improvements including tutorials in additional categories, a new recipe format for quickly referencing common topics, sorting using tags, and an updated homepage.
diff --git a/_posts/2020-7-28-pytorch-1.6-released.md b/_posts/2020-7-28-pytorch-1.6-released.md
@@ -3,6 +3,7 @@ layout: blog_detail
 title: 'PyTorch 1.6 released w/ Native AMP Support, Microsoft joins as maintainers for Windows'
 author: Team PyTorch
 image: /assets/images/bert2.png
+tags: [five]
 ---
 
 Today, we’re announcing the availability of PyTorch 1.6, along with updated domain libraries. We are also excited to announce the team at [Microsoft is now maintaining Windows builds and binaries](https://pytorch.org/blog/microsoft-becomes-maintainer-of-the-windows-version-of-pytorch) and will also be supporting the community on GitHub as well as the PyTorch Windows discussion forums.
diff --git a/blog/all-posts.html b/blog/all-posts.html
@@ -16,12 +16,12 @@
 
         <div class="list row blog-index">
           {% for post in posts %}
-            <div class="col-md-4">
+            <div class="col-md-4" data-tags="{{ post.tags | join: ',' }}">
               <img src="{{ post.image }}" class="img-fluid blog-img">
               <h4>
                 <a href="{{ site.baseurl }}{{ post.url }}">{{ post.title }}</a>
               </h4>
-              <p>{{ post.excerpt | remove: '<p>' | remove: '</p>' | truncate: 500}}</p>
+              <p>{{ post.excerpt | remove: '<p>' | remove: '</p>' | truncate: 500 | strip_html }}</p>
               <p class="blog-date">{{ post.date | date: '%B %d, %Y' }}</p>
             </div>
           {% endfor %}
@@ -35,4 +35,4 @@ <h4>
 </div>
 
 <script src="//cdnjs.cloudflare.com/ajax/libs/list.js/1.5.0/list.min.js"></script>
-<script id="filter-script" list-id="all-blog-posts" display-count="200" pagination="false" src="{{ site.baseurl }}/assets/filter-hub-tags.js"></script>
+<script id="filter-script" list-id="all-blog-posts" display-count="6" pagination="true" src="{{ site.baseurl }}/assets/filter-hub-tags.js"></script>
diff --git a/blog/landing-page.html b/blog/landing-page.html
@@ -23,8 +23,7 @@
             <h4>
               <a href="{{ site.baseurl }}{{ post.url }}">{{ post.title }}</a>
             </h4>
-            <p>{{ post.excerpt | remove: '<p>' | remove: '</p>' | truncate: 500}}</p>
-            <img src="{{ post.image }}" class="img-fluid">
+            <p>{{ post.excerpt | remove: '<p>' | remove: '</p>' | truncate: 500 | strip_html }}</p>
           </div>
         {% endfor %}
       </div>
