Organizations and products using ONNX Runtime
“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
“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)
“At GhostWriter.AI, we integrate NLP models in different international markets and regulated industries. Our customers use many technology stacks and frameworks, which change over time. With ONNX Runtime, we can provide maximum performance combined with the total flexibility of making inferences using the technology our customers prefer, from Python to C#, deploying where they choose, from cloud to embedded systems.”
–Mauro Bennici, CTO, Ghostwriter.AI
“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
“TWe are excited to support ONNX Runtime on the Intel® Distribution of OpenVINO™. This accelerates machine learning inference across Intel hardware and gives developers the flexibility to choose the combination of Intel hardware that best meets their needs from CPU to VPU or FPGA.”
–Jonathan Ballon, Vice President and General Manager, Intel Internet of Things Group
“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
“With customers around the globe, we’re seeing increased interest in deploying more effective models to power pricing solutions via ONNX Runtime. ONNX Runtime’s performance has given us the confidence to use this solution with our customers with more extreme transaction volume requirements.”
–Jason Coverston, Product Director, Navitaire
“ONNX Runtime enables our customers to easily apply NVIDIA TensorRT’s powerful optimizations to machine learning models, irrespective of the training framework, and deploy across NVIDIA GPUs and edge devices.”
– Kari Ann Briski, Sr. Director, Accelerated Computing Software and AI Product, NVIDIA
“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
“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
“The mission of PTW is to guarantee radiation therapy safely. Bringing an AI model from research into the clinic can be a challenge, however. These are very different software and hardware environments. ONNX Runtime bridges the gap and allows us to choose the best possible tools for research and be sure deployment into any environment will just work.”
–Jan Weidner, Research Software Engineer, PTW Dosimetry
“With support for ONNX Runtime, our customers and developers can cross the boundaries of the model training framework, easily deploy ML models in Rockchip NPU powered devices.”
–Feng Chen, Senior Vice President, Rockchip
“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
“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
“ONNX Runtime’s simple C API with DirectML provider enabled Topaz Labs to add support for AMD GPUs and NVIDIA Tensor Cores in just a couple of days. Furthermore, our models load many times faster on GPU than any other frameworks. Even our larger models with about 100 million parameters load within seconds.”
–Suraj Raghuraman, Head of AI Engine, Topaz Labs
“At the USDA we use ONNX Runtime in GuideMaker, a program we developed to design pools of guide RNAs needed for large-scale gene editing experiments with CRISPR-Cas. ONNX allowed us to make an existing model more interoperable and ONNX Runtime speeds up predictions of guide RNA binding.”
–Adam Rivers, Computational Biologist, United States Department of Agriculture, Agricultural Research Service
“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
“Xilinx is excited that Microsoft has announced Vitis™ AI interoperability and runtime support for ONNX Runtime, enabling developers to deploy machine learning models for inference to FPGA IaaS such as Azure NP series VMs and Xilinx edge devices.”
–Sudip Nag, Corporate Vice President, Software & AI Products, Xilinx