Blog redesign

_config.yml

@@ -64,6 +64,9 @@ collections:
     output: true
   news:
     output: true
+  blog:
+    output: true
+    permalink: /blog/:path/
 
 pagination:
   enabled: true

_includes/blog_jumbotron.html

@@ -0,0 +1,16 @@
+<div class="jumbotron jumbotron-fluid">
+  <div class="container">
+    {% for post in include.posts limit:1 %}
+    <p class="featured-post">Featured Post</p>
+    <h1 class="blog-index-title">
+      <a href="{{ site.baseurl }}{{ post.url }}">{{ post.title }}</a>
+    </h1>
+    <p class="featured-blog-preview">{{ post.preview | truncate: 150 }}</p>
+
+    <a href="{{ site.baseurl }}{{ post.url }}" class="btn btn-lg with-right-arrow">
+      Read More
+    </a>
+
+    {% endfor %}
+  </div>
+</div>

_layouts/blog.html

@@ -7,73 +7,7 @@
 
   <div class="main-background blog-background"></div>
 
-  {% assign posts = paginator.posts %}
-  {% assign display_post_categories = site.posts | map: 'categories' | join: ',' | replace: '-', ' ' | split: ',' | uniq | sort %}
-  {% assign current_page = page.url | downcase | remove: ".html" | split: '/'  %}
-  {% assign post_categories = site.posts | map: 'categories' | join: ',' | split: ',' | uniq | sort %}
-
-  <div class="jumbotron jumbotron-fluid">
-    <div class="container">
-      {% for post in posts limit:1 %}
-      <p class="featured-post">Featured Post</p>
-      <h1 class="blog-index-title">
-        <a href="{{ site.baseurl }}{{ post.url }}">{{ post.title }}</a>
-      </h1>
-      <p class="featured-blog-preview">{{ post.excerpt | remove: '<p>' | remove: '</p>' | truncate: 100 }}</p>
-
-      <a href="{{ site.baseurl }}{{ post.url }}" class="btn btn-lg with-right-arrow">
-        Read More
-      </a>
-
-      {% endfor %}
-    </div>
-  </div>
-
-  <div class="main-content-wrapper">
-    <div class="main-content">
-      <div class="container">
-<!-- Temporarily hidden sub menu until there are more blog posts and categories -->
-<!--         <nav class="navbar navbar-expand-lg navbar-light main-content-menu">
-          <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#blogPostFilter" aria-controls="navbarNavDropdown"
-            aria-expanded="false" aria-label="Toggle navigation">
-            <span class="navbar-toggler-icon"></span>
-          </button>
-
-          <div class="collapse navbar-collapse" id="blogPostFilter">
-            <ul class="navbar-nav">
-              <li class="nav-item">
-                <a class="nav-link {% if current_page[3] == null %} selected {% endif %}" data-category="all" href="{{ site.baseurl }}/blog">All</a>
-              </li>
-              {% for category in display_post_categories %}
-              <li class="nav-item">
-                <a class="nav-link {% if current_page[3] == post_categories[forloop.index0] %} selected {% endif %}" data-category="{{ category | slugify }}" href="{{ site.baseurl }}/blog/categories/{{ category | slugify }}">{{ category }}</a>
-              </li>
-              {% endfor %}
-              <li class="nav-item">
-                  <a class="nav-link">Search</a>
-                </li>
-            </ul>
-          </div>
-        </nav> -->
-<!-- End submenu -->
-
-        <div class="row blog-index">
-          {% for post in posts %}
-          <div class="col-md-4">
-            <p class="blog-date">{{ post.date | date: '%B %d, %Y' }}</p>
-            <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">
-          </div>
-          {% endfor %}
-        </div>
-
-        {% include pagination_buttons.html %}
-      </div>
-    </div>
-  </div>
+  {{ content }}
 
   {% include quick_start_module.html %}
 

_posts/2017-5-11-a-tour-of-pytorch-internals-1.md

@@ -4,6 +4,9 @@ title: "A Tour of PyTorch Internals (Part I)"
 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]
+preview: '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.'
 ---
 
 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:

_posts/2017-6-27-a-tour-of-pytorch-internals-2.md

@@ -4,6 +4,9 @@ title: "PyTorch Internals Part II - The Build System"
 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]
+preview: 'In the first 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.'
 ---
 
 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:

_posts/2018-01-19-a-year-in.md

@@ -4,6 +4,9 @@ title: "PyTorch, a year in...."
 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]
+preview: 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.
 ---
 
 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.

_posts/2018-03-5-tensor-comprehensions.md

@@ -3,6 +3,9 @@ layout: blog_detail
 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]
+preview: '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.'
 ---
 
 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.

_posts/2018-04-22-pytorch-0_4_0-migration-guide.md

@@ -2,6 +2,9 @@
 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]
+preview: '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.'
 ---
 
 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:

_posts/2018-05-2-the-road-to-1_0.md

@@ -3,6 +3,9 @@ layout: blog_detail
 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]
+preview: 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.
 ---
 
 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.

_posts/2019-05-08-model-serving-in-pyorch.md

@@ -3,6 +3,9 @@ layout: blog_detail
 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]
+preview: '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. Usually when people talk about taking a model “to production,” they usually mean performing **inference**, sometimes called model evaluation or prediction or serving. At the level of a function call, in PyTorch, inference looks something like this:'
 ---
 
 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.
@@ -41,18 +44,18 @@ The above is a somewhat arbitrary breakdown of different approaches based on a s
 
 So, if you're a PyTorch user, what should you use if you want to take your models to production?
 
-If you're on mobile or working on an embedded system like a robot, direct embedding in your application is often the right choice. 
+If you're on mobile or working on an embedded system like a robot, direct embedding in your application is often the right choice.
 For mobile specifically, your use case might be served by the ONNX export functionality.
 Note that ONNX, by its very nature, has limitations and doesn't support all of the functionality provided by the larger PyTorch project.
-You can check out [this tutorial](https://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html) on deploying PyTorch models to mobile using ONNX to see if this path might suit your use case. 
+You can check out [this tutorial](https://pytorch.org/tutorials/advanced/super_resolution_with_caffe2.html) on deploying PyTorch models to mobile using ONNX to see if this path might suit your use case.
 That said, we've heard that there's a lot more that PyTorch users want to do on mobile, so look for more mobile-specific functionality in PyTorch in the future.
 For other embedded systems, like robots, running [inference on a PyTorch model from the C++ API](https://pytorch.org/tutorials/advanced/cpp_export.html) could be the right solution.
 
 If you can't use the cloud or prefer to manage all services using the same technology, you can follow [this example](https://medium.com/datadriveninvestor/deploy-your-pytorch-model-to-production-f69460192217) to build a simple model microservice using the Flask web framework.
 
 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](https://course.fast.ai/deployment_amzn_sagemaker.html) 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.
 

_posts/2019-05-1-pytorch-adds-new-dev-tools.md

@@ -2,6 +2,9 @@
 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]
+preview: Since its release just a few months ago, PyTorch 1.0 has been rapidly adopted as a powerful, flexible deep learning platform that enables engineers and researchers to move quickly from research to production. We are highlighting some of the ways the AI engineering and research community is using PyTorch 1.0. We’re also sharing new details about the latest release, PyTorch 1.1, and showcasing some of the new development tools created by the community.
 ---
 
 _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/)_

_posts/2019-06-10-towards-reproducible-research-with-pytorch-hub.md

@@ -3,6 +3,9 @@ layout: blog_detail
 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]
+preview: '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.'
 ---
 
 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.

_posts/2019-07-18-pytorch-ecosystem.md

@@ -2,6 +2,9 @@
 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]
+preview: '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.'
 ---
 
 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.

_posts/2019-07-23-mapillary-research.md

@@ -3,6 +3,9 @@ layout: blog_detail
 title: 'Mapillary Research: Seamless Scene Segmentation and In-Place Activated BatchNorm'
 author: Lorenzo Porzi, Mapillary
 redirect_from: /2019/07/23/mapillary-research.html
+image: /assets/images/bert2.png
+tags: [four]
+preview: 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.
 ---
 
 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.

_posts/2019-08-08-pytorch-1.2-and-domain-api-release.md

@@ -3,6 +3,9 @@ layout: blog_detail
 title: 'New Releases: PyTorch 1.2, torchtext 0.4, torchaudio 0.3, and torchvision 0.4'
 author: Team PyTorch
 redirect_from: /2019/08/06/pytorch_aug2019_releases.html
+image: /assets/images/bert2.png
+tags: [four]
+preveiw: '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.'
 ---
 
 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.