diff --git a/about.html b/about.html index 0c722098c5..64562c58b1 100644 --- a/about.html +++ b/about.html @@ -69,7 +69,7 @@ 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. It enables acceleration of machine learning inferencing across all of your deployment targets using a single set of API. ONNX Runtime automatically parses through your model to identify optimization opportunities and provides access to the best hardware acceleration available.

- ONNX Runtime also offers training acceleration (in preview), which incorporates innovations from Microsoft Research and is proven across production workloads like Office 365, Bing and Visual Studio. + ONNX Runtime also offers training acceleration, which incorporates innovations from Microsoft Research and is proven across production workloads like Office 365, Bing and Visual Studio.

Join us on Github @@ -88,18 +88,18 @@

Optimization and acceleration

- Run any ONNX model using a single set of inference APIs that provide access to the best hardware acceleration available. Built-in optimization features trim and consolidate nodes without impacting model accuracy. Additionally, full backwards compatibility for ONNX and ONNX-ML ensures all ONNX models can be inferenced. + Run any ONNX model using a single set of inference APIs that provide access to the best hardware acceleration available. Built-in optimization features trim and consolidate nodes without impacting model accuracy. Additionally, full backwards compatibility for ONNX and ONNX-ML ensures all ONNX models can be inferenced.

- Illustration of blank boxes conveying the breadth of API and paltform support + Illustration of blank boxes conveying the breadth of API and platform support

API and platform support

- Take advantage of the benefits of ONNX Runtime without changing your technology stack. Access ONNX Runtime using your preferred API — C#, C++, C, Python, or Java. Support for Linux, Windows and Mac allows you to build and deploy applications without worry. + Take advantage of the benefits of ONNX Runtime without changing your technology stack. Access ONNX Runtime using your preferred API — C#, C++, C, Python, or Java. Support for Linux, Windows and Mac allows you to build and deploy applications without worry.

@@ -200,4 +200,4 @@ - \ No newline at end of file + diff --git a/css/custom.css b/css/custom.css index eca13af40b..ac380e9e4d 100644 --- a/css/custom.css +++ b/css/custom.css @@ -204,7 +204,7 @@ a.link:active .link-arrow{ } .sponsor-logo img{ max-width: 100%; - height: 100px; + max-height: 100px; } .hw-logo img{ max-width:150px; @@ -664,7 +664,7 @@ a .abbr[data-original-title], a abbr[title]{ font-size: 24px; } .blue-title-columns h3.quote{ - font-size: 18px; + font-size: 16px; text-align: center; } .blue-title-columns h3.hardware{ diff --git a/images/logos/adobe-logo.png b/images/logos/adobe-logo.png new file mode 100644 index 0000000000..d0e31e571a Binary files /dev/null and b/images/logos/adobe-logo.png differ diff --git a/index.html b/index.html index ddcc17a249..57fd89e28b 100644 --- a/index.html +++ b/index.html @@ -381,69 +381,28 @@
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“We use ONNX Runtime to easily deploy thousands of open-source state-of-the-art models in the Hugging Face model hub and accelerate private models for customers of the Accelerated Inference API on CPU and GPU.”

– Morgan Funtowicz, Machine Learning Engineer, Hugging Face

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- -

“The unique combination of ONNX Runtime and SAS Event Stream Processing changes the game for developers and systems integrators by supporting flexible pipelines and enabling them to target multiple hardware platforms for the same AI models without bundling and packaging changes. This is crucial considering the additional build and test effort saved on an ongoing basis.”

- – Saurabh Mishra, Senior Manager, Product Management, Internet of Things, SAS

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“The ONNX Runtime API for Java enables Java developers and Oracle customers to seamlessly consume and execute ONNX machine-learning models, while taking advantage of the expressive power, high performance, and scalability of Java.”

– Stephen Green, Director of Machine Learning Research Group, Oracle

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“ONNX Runtime has vastly increased Vespa.ai’s capacity for evaluating large models, both in performance and model types we support.”

- – Lester Solbakken, Principal Engineer, Vespa.ai, Verizon Media

+

“With ONNX Runtime, Adobe Target got flexibility and standardization in one package: flexibility for our customers to train ML models in the frameworks of their choice, and standardization to robustly deploy those models at scale for fast inference, to deliver true, real-time personalized experiences.”

+ – Georgiana Copil, Senior Computer Scientist, Adobe

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- -

“We use ONNX Runtime to accelerate model training for a 300M+ parameters model that powers code autocompletion in Visual Studio IntelliCode.”

- – Neel Sundaresan, Director SW Engineering, Data & AI, Developer Division, Microsoft

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“Using a common model and code base, the ONNX Runtime allows Peakspeed to easily flip between platforms to help our customers choose the most cost-effective solution based on their infrastructure and requirements.”

- – Oscar Kramer, Chief Geospatial Scientist, Peakspeed

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“We needed a runtime engine to handle the transition from data science land to a high-performance production runtime system. ONNX Runtime (ORT) simply ‘just worked’. Having no previous experience with ORT, I was able to easily convert my models, and had prototypes running inference in multiple languages within just a few hours. ORT will be my go-to runtime engine for the foreseeable future.”

- – Bill McCrary, Application Architect, Samtec

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“At CERN in the ATLAS experiment, we have integrated the C++ API of ONNX Runtime into our software framework: Athena. We are currently performing inferences using ONNX models especially in the reconstruction of electrons and muons. We are benefiting from its C++ compatibility, platform*-to-ONNX converters (* Keras, TensorFlow, PyTorch, etc) and its thread safety.”

- – ATLAS Experiment team, CERN (European Organization for Nuclear Research)

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@@ -451,7 +410,54 @@ – Jason Coverston, Product Director, Navitaire
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“ONNX Runtime has vastly increased Vespa.ai’s capacity for evaluating large models, both in performance and model types we support.”

+ – Lester Solbakken, Principal Engineer, Vespa.ai, Verizon Media

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+ +

“Using a common model and code base, the ONNX Runtime allows Peakspeed to easily flip between platforms to help our customers choose the most cost-effective solution based on their infrastructure and requirements.”

+ – Oscar Kramer, Chief Geospatial Scientist, Peakspeed

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+ +

“The unique combination of ONNX Runtime and SAS Event Stream Processing changes the game for developers and systems integrators by supporting flexible pipelines and enabling them to target multiple hardware platforms for the same AI models without bundling and packaging changes. This is crucial considering the additional build and test effort saved on an ongoing basis.”

+ – Saurabh Mishra, Senior Manager, Product Management, Internet of Things, SAS

+
+
+ +

“We use ONNX Runtime to accelerate model training for a 300M+ parameters model that powers code autocompletion in Visual Studio IntelliCode.”

+ – Neel Sundaresan, Director SW Engineering, Data & AI, Developer Division, Microsoft

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+
+
+ +
+ +

“At CERN in the ATLAS experiment, we have integrated the C++ API of ONNX Runtime into our software framework: Athena. We are currently performing inferences using ONNX models especially in the reconstruction of electrons and muons. We are benefiting from its C++ compatibility, platform*-to-ONNX converters (* Keras, TensorFlow, PyTorch, etc) and its thread safety.”

+ – ATLAS Experiment team, CERN (European Organization for Nuclear Research)

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+ +

“We needed a runtime engine to handle the transition from data science land to a high-performance production runtime system. ONNX Runtime (ORT) simply ‘just worked’. Having no previous experience with ORT, I was able to easily convert my models, and had prototypes running inference in multiple languages within just a few hours. ORT will be my go-to runtime engine for the foreseeable future.”

+ – Bill McCrary, Application Architect, Samtec

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+