+ 
@@ -147,6 +156,14 @@
+ 
diff --git a/css/custom.css b/css/custom.css
index 1fbe2c8b2c..3bc9a089fc 100644
--- a/css/custom.css
+++ b/css/custom.css
@@ -229,10 +229,17 @@ h3.blue-text{
color: #000000;
}
+.article-date{
+ color: #666666;
+ text-transform: uppercase;
+ font-size:14px;
+}
+
.news{
background-color: #e8e8e8;
- padding:3rem;
+ padding:2rem;
font-size:18px;
+ margin: 2rem;
}
.alert{
@@ -651,6 +658,7 @@ a .abbr[data-original-title], a abbr[title]{
}
.news-img{
padding-left: 3rem;
+ padding-top: 3rem;
}
.resources-img {
position: relative;
diff --git a/images/BERT-ORTTRT.png b/images/BERT-ORTTRT.png
new file mode 100644
index 0000000000..e93b3bf218
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diff --git a/images/huggingface-spaces.png b/images/huggingface-spaces.png
new file mode 100644
index 0000000000..3d7b7b8185
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diff --git a/images/hypefactors-ort.png b/images/hypefactors-ort.png
new file mode 100644
index 0000000000..5063a93204
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diff --git a/images/logos/clearblade-logo.png b/images/logos/clearblade-logo.png
new file mode 100644
index 0000000000..b7344eb2e1
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diff --git a/images/logos/opennlp-logo.png b/images/logos/opennlp-logo.png
new file mode 100644
index 0000000000..99777e1535
Binary files /dev/null and b/images/logos/opennlp-logo.png differ
diff --git a/images/ort-web.png b/images/ort-web.png
new file mode 100644
index 0000000000..d8a542bde6
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diff --git a/images/xamarin-ort.png b/images/xamarin-ort.png
new file mode 100644
index 0000000000..22286e80a7
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diff --git a/index.html b/index.html
index aa61826a9a..6b77660e9d 100644
--- a/index.html
+++ b/index.html
@@ -46,7 +46,7 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+ News & Announcements
+
+
+
+
+
+ + Choosing which machine learning model to use, sharing a model with a colleague, and quickly trying out a model are all reasons why you may find yourself wanting to quickly run inference on a model. You can configure your environment and download Jupyter notebooks, but it would be nicer if there was a way to run a model with even less effort... +
Read + more +
+
+ Live demos of machine learning models with ONNX and Hugging Face Spaces
+ June 6, 2022+ + Choosing which machine learning model to use, sharing a model with a colleague, and quickly trying out a model are all reasons why you may find yourself wanting to quickly run inference on a model. You can configure your environment and download Jupyter notebooks, but it would be nicer if there was a way to run a model with even less effort... +
Read + more +
+ 
+
+
+
+
+
+
+ + Transformer-based models have revolutionized the natural language processing (NLP) domain. Ever since its inception, transformer architecture has been integrated into models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) for performing tasks such as text generation or summarization and question and answering to name a few... +
Read + more +
+
+ Optimizing and deploying transformer INT8 inference with ONNX Runtime-TensorRT on NVIDIA GPUs
+ May 2, 2022+ + Transformer-based models have revolutionized the natural language processing (NLP) domain. Ever since its inception, transformer architecture has been integrated into models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) for performing tasks such as text generation or summarization and question and answering to name a few... +
Read + more +
+ 
+
+
+
+
+
+
+ + Scale, performance, and efficient deployment of state-of-the-art Deep Learning models are ubiquitous challenges as applied machine learning grows across the industry. We’re happy to see that the ONNX Runtime Machine Learning model inferencing solution we’ve built and use in high-volume Microsoft products and services also resonates with our open source community, enabling new capabilities that drive content relevance and productivity... +
Read + more +
+
+ Scaling-up PyTorch inference: Serving billions of daily NLP inferences with ONNX Runtime
+ April 19, 2022+ + Scale, performance, and efficient deployment of state-of-the-art Deep Learning models are ubiquitous challenges as applied machine learning grows across the industry. We’re happy to see that the ONNX Runtime Machine Learning model inferencing solution we’ve built and use in high-volume Microsoft products and services also resonates with our open source community, enabling new capabilities that drive content relevance and productivity... +
Read + more +
+ 
+
+
+
+
+
+
+ + ONNX Runtime now supports building mobile applications in C# with Xamarin. Support for Android and iOS is included in the ONNX Runtime release 1.10 NuGet package. This enables C# developers to build AI applications for Android and iOS to execute ONNX models on mobile devices with ONNX Runtime... +
Read + more +
+
+ Add AI to mobile applications with Xamarin and ONNX Runtime
+ December 14, 2021+ + ONNX Runtime now supports building mobile applications in C# with Xamarin. Support for Android and iOS is included in the ONNX Runtime release 1.10 NuGet package. This enables C# developers to build AI applications for Android and iOS to execute ONNX models on mobile devices with ONNX Runtime... +
Read + more +
+ 
+
+
+
+
+
+
+ + We are introducing ONNX Runtime Web (ORT Web), a new feature in ONNX Runtime to enable JavaScript developers to run and deploy machine learning models in browsers. It also helps enable new classes of on-device computation. ORT Web will be replacing the soon to be deprecated onnx.js... +
Read + more +
+
+ ONNX Runtime Web—running your machine learning model in browser
+ September 2, 2021+ + We are introducing ONNX Runtime Web (ORT Web), a new feature in ONNX Runtime to enable JavaScript developers to run and deploy machine learning models in browsers. It also helps enable new classes of on-device computation. ORT Web will be replacing the soon to be deprecated onnx.js... +
Read + more +
+ 
+
+
+
+
+
+
+ + ONNX Runtime (ORT) for PyTorch accelerates training large scale models across multiple GPUs with up to 37% increase in training throughput over PyTorch and up to 86% speed up when combined with DeepSpeed... +
Read + more +
+
+ Accelerate PyTorch transformer model training with ONNX Runtime – a deep dive
+ July 13, 2021+ + ONNX Runtime (ORT) for PyTorch accelerates training large scale models across multiple GPUs with up to 37% increase in training throughput over PyTorch and up to 86% speed up when combined with DeepSpeed... +
Read + more +
+ 
+
+
+
+
+
+
+ + With a simple change to your PyTorch training script, you can now speed up training large language models with torch_ort.ORTModule, running on the target hardware of your choice.
Read + more +
+ Accelerate PyTorch training with torch-ort
+ July 13, 2021+ + With a simple change to your PyTorch training script, you can now speed up training large language models with torch_ort.ORTModule, running on the target hardware of your choice.
Training deep learning models requires ever-increasing compute and memory resources. Today we release torch_ort.ORTModule, to accelerate distributed training of PyTorch models, reducing the time and resources needed for training...
+Read + more +
+ 
+
+
+
+
+
+
+ + ONNX Runtime is an open-source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. Today, we are excited to announce a preview version of ONNX Runtime in release 1.8.1 featuring support for AMD Instinct™ GPUs facilitated by the AMD ROCm™ open software platform... +
Read + more +
+ ONNX Runtime release 1.8.1 previews support for accelerated training on AMD GPUs with the AMD ROCm™ Open Software Platform
+ July 13, 2021+ + ONNX Runtime is an open-source project that is designed to accelerate machine learning across a wide range of frameworks, operating systems, and hardware platforms. Today, we are excited to announce a preview version of ONNX Runtime in release 1.8.1 featuring support for AMD Instinct™ GPUs facilitated by the AMD ROCm™ open software platform... +
Read + more +
+ 
+
+
+
+
+
+
+ + Large-scale transformer models, such as GPT-2 and GPT-3, are among the most useful self-supervised transformer language models for natural language processing tasks such as language translation, question answering, passage summarization, text generation, and so on...
+
Read + more +
+ Journey to optimize large scale transformer model inference with ONNX Runtime
+ June 30, 2021+ + Large-scale transformer models, such as GPT-2 and GPT-3, are among the most useful self-supervised transformer language models for natural language processing tasks such as language translation, question answering, passage summarization, text generation, and so on...
+
Read + more +
+ 
+
+
+
+
+
+
+ + Artificial Intelligence (AI) developers enjoy the flexibility of choosing a model training framework of their choice. This includes both open-source frameworks as well as vendor-specific ones. While this is great for innovation, it does introduce the challenge of operationalization across different hardware platforms... +
Read + more +
+ SAS and Microsoft collaborate to democratize the use of Deep Learning Models
+ March 30, 2021+ + Artificial Intelligence (AI) developers enjoy the flexibility of choosing a model training framework of their choice. This includes both open-source frameworks as well as vendor-specific ones. While this is great for innovation, it does introduce the challenge of operationalization across different hardware platforms... +
Read + more +
+ 
+
+
+
+
+
+
+
+
+ + The performance improvements provided by ONNX Runtime powered by Intel® Deep Learning Boost: Vector Neural Network Instructions (Intel® DL Boost: VNNI) greatly improves performance of machine learning model execution for developers... +
Read + more +
+
+ Optimizing BERT model for Intel CPU Cores using ONNX runtime default execution provider
+ March 1, 2021+ + The performance improvements provided by ONNX Runtime powered by Intel® Deep Learning Boost: Vector Neural Network Instructions (Intel® DL Boost: VNNI) greatly improves performance of machine learning model execution for developers... +
Read + more +
+ 
+
+