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ONNX Community Day! Streamed live on Jun 24, 2022
This event is being hosted in-person at the brand-new Microsoft Silicon Valley Campus on Friday, June 24th.
The event will cover ONNX Community updates, partner and user stories, and plenty of community networking.
ONNX Community Day!
Brief summary:
The detaled timeline summary:
ONNX Community Day! Streamed live on Jun 24, 2022
This event is being hosted in-person at the brand-new Microsoft Silicon Valley Campus on Friday, June 24th.
The event will cover ONNX Community updates, partner and user stories, and plenty of community networking.
ONNX Community Day!
Brief summary:
The detaled timeline summary:
ONNX: Past, Present, and Future - Jim Spohrer, IBM & Prasanth Pulavarthi, Microsoft
ONNX: Past, Present, and Future - Jim Spohrer, IBM & Prasanth Pulavarthi, Microsoft
The "ONNX: Past, Present, and Future" video features IBM's Jim Spohrer and Microsoft's Prasanth Pulavarthi discussing the growth and future of the open-source AI framework ONNX. They highlight the importance of standardizing AI models' deployment through the interchanging format provided by ONNX, enabling seamless optimization across different deep learning frameworks. Additionally, they discuss the recent developments in ONNX runtime's ability to work with various hardware accelerators and offer tips and resources for getting started with ONNX. The speakers answer audience questions regarding ONNX's capabilities, commercial deployment, and upcoming certification plans while urging viewers to get involved in the ONNX community.
Onnx-mlir: an MLIR-based Compiler for ONNX Models - The Latest Status
Onnx-mlir: an MLIR-based Compiler for ONNX Models - The Latest Status
Onnx-mlir is a compiler for ONNX models that uses MLIR and LLVM for optimization and code generation, supporting CPUs and custom accelerators. Dong Lin from IBM Research emphasizes the importance of thorough testing and highlights the framework's use in online scoring services and model serving frameworks. Onnx-mlir has multiple dialects for CPU and accelerator, with optimizations at various levels, and has been shown to speed up a credit card fraud detection model by 11 times using an IBM accelerator. The project welcomes community contributions to optimize important operators and support niche ML operators and other accelerators such as GPUs.
PFVM - A Neural Network Compiler that uses ONNX as its intermediate representation
PFVM - A Neural Network Compiler that uses ONNX as its intermediate representation
In this video, Zijian Xu from Preferred Networks introduces PFVM, a neural network compiler that uses ONNX as its intermediate representation for module optimization. He discusses how PFVM takes exported ONNX as input, optimizes it, and executes the model with specified backends using third-party APIs. Genji describes the importance of optimization, including extending ONNX with customer operators, shape inference, and graph simplification. He also addresses the limitations of current ONNX compilers, including the need for more support in the dynamic case, and suggests implementing more inference functions. Zijian Xu emphasizes the importance of reducing kernel range overhead and memory usage for faster computation and suggests utilizing static information available on machines for scheduling and shaping inference.
YVR18-332 TVM compiler stack and ONNX support
YVR18-332 TVM compiler stack and ONNX support
The YVR18-332 video discusses the TVM compiler stack, which is a community-led deep learning stack that supports a range of hardware and front-ends, including ONNX. The speaker discusses how TVM can optimize models at the stereo level, allowing developers to explore the search space and find the best configuration. They also discuss the automatic optimizations TVM offers, including loop transformations and GPU acceleration. The speaker talks about the TVM roadmap which includes enabling 8-bit support and automated tuning on the graph level. Additionally, they discuss the ONNX TV interface and the need to unify the standard interface for all ecosystems. Finally, the video pauses for lunch.
designed to explore the search space and find the best configuration.
.NET MAUI Community Standup - ONNX Runtime with Mike Parker
.NET MAUI Community Standup - ONNX Runtime with Mike Parker
In this video the guest speaker Mike Parker introduces the ONNX runtime, an open-source and cross-platform tool that enables machine learning optimization and acceleration across multiple hardware platforms. Parker explains the importance of using the ONNX runtime and showcases how it can be used in .NET MAUI projects to classify images using the MobileNet object classification model. The hosts and Parker discuss the benefits of running machine learning models on a device and the ability to avoid backend infrastructure costs. Additionally, the team shares helpful resources, including Parker's blog on this subject and their partnership with Al Blount for .NET MAUI and Xamarin support.
[Virtual meetup] Interoperable AI: ONNX e ONNXRuntime in C++ (M. Arena, M. Verasani)
[Virtual meetup] Interoperable AI: ONNX e ONNXRuntime in C++ (M. Arena, M. Verasani)
The video discusses the challenges of using different frameworks to train machine learning algorithms, leading to a lack of interoperability, and introduces ONNX and ONNXRuntime that aim to create a universal format for deep learning models. ONNX converts neural networks into static computational graphs, allowing for optimized performance during inference. ONNXRuntime allows for the conversion of any framework into ONNX format and provides acceleration libraries that can be used to target any hardware platform. The video showcases examples of using ONNX and ONNXRuntime, as well as discussing their use in C++ and providing advice for better understanding the project and its documentation.
Marco Arena and Matteo Verasani also discuss the benefits of using ONNX and ONNXRuntime in C++ for machine learning models, highlighting the flexibility of the framework and its ability to easily convert models from different frameworks without sacrificing performance. They provide examples of converting models to ONNX format and demonstrate the use of the ONNXRuntime for inference mode, showcasing improvements in performance with a classic Python model. Additionally, they discuss their work with embedded systems and the potential benefits of benchmarking ONNXRuntime on GPUs. The speakers also mention future virtual meetups and express hope for incorporating more networking opportunities for attendees.
[CppDay20] Interoperable AI: ONNX & ONNXRuntime in C++ (M. Arena, M.Verasani)
[CppDay20] Interoperable AI: ONNX & ONNXRuntime in C++ (M. Arena, M.Verasani)
The use of machine learning and deep learning algorithms is increasing, and there is a need for tools that can deploy these algorithms on different platforms. The ONNX tool provides interoperability between different frameworks and platforms, allowing developers to convert their algorithms from one framework to another and deploy them on different devices, even if they are not familiar with the specific framework or platform. ONNX Runtime is an inference engine that can leverage custom accelerators to accelerate models during the inference stage and can target a variety of hardware platforms. The speakers demonstrate the use of ONNX and ONNX Runtime in C++ programming, with examples of linear regression and neural network models. They also discuss the benefits of using ONNX and ONNX Runtime in fine-tuning a network's execution, optimizing loading time, and executing sequential images.
Accelerating Machine Learning with ONNX Runtime and Hugging Face
Accelerating Machine Learning with ONNX Runtime and Hugging Face
The video "Accelerating Machine Learning with ONNX Runtime and Hugging Face" discusses the creation of Hugging Face's Optimum library, which focuses on accelerating transformer models from training to inference by easily applying ONNX runtime. The library simplifies the bridge between the transformer library and hardware acceleration, creating an easy-to-use toolkit for production performance. By applying the optimizations provided by ONNX Runtime, users can benefit from all hardware acceleration, resulting in faster inference pipelines. A collaboration within the Hugging Face community is enabling sequence-to-sequence model optimization using these accelerated inference pipeline classes, and an end-to-end example showed that using the Optimum Library can result in a 44% throughput increase or latency decrease while conserving 99.6% of the original model accuracy.