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This is the second entry in our AWS-centric blog series leading up to the AWS Startup Showcase on Thursday, March 9th. We are excited to be a part of this event with other selected visionary AI startups to talk about the future of deploying AI into production at scale. Sign up here to register for this… Read More Build Scalable NLP and Computer Vision Pipelines With DeepSparse - Now Available From the AWS Marketplace (Part 2 of 3-Blog Series)
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Neural Magic’s DeepSparse Inference Runtime can now be deployed directly from the AWS Marketplace. DeepSparse supports more than 60 different EC2 instance types and sizes, allowing you to quickly deploy the infrastructure that works best for your use case, based on cost and performance. In this blog post, we will illustrate how easy it is… Read More Neural Magic’s DeepSparse Inference Runtime Now Available in the AWS Marketplace (Part 1 of 3-Blog Series)
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Here are highlights of the 1.4 product release of our DeepSparse, SparseML, and SparseZoo libraries. The full technical release notes are always available within our GitHub release indexes linked from the specific Neural Magic repository. If you have any questions, need assistance, or simply want to say hello to our vibrant ML performance community, join… Read More Neural Magic 1.4 Product Release
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Simplify Pre-processing Pipelines with Sequence Bucketing to Decrease Memory Utilization and Inference Time For Efficient ML DeepSparse is an inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. DeepSparse has built-in performance features, like sequence bucketing, to lower latency and increase the throughput of deep learning pipelines. These features… Read More Process Text Faster Through Sequence Bucketing and DeepSparse
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According to a recent poll from Ultralytics, the creators of YOLO object detection models, 22% of ML experts experience difficulty deploying their vision AI models. Getting into production successfully is hard, and scaling while in production is even harder. To improve this step in the ML pipeline, Ultralytics partnered with Neural Magic, whose DeepSparse runtime… Read More Accelerating Object Detection Deployments With Ultralytics & Neural Magic
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Object detection is a crucial task in computer vision. With applications in fields such as image and video analysis, robotics, and autonomous vehicles, object detection involves identifying and locating objects within an image or video. Traditionally, it has been tackled using various techniques, including edge and corner detection, template matching, and machine learning-based approaches. In… Read More Object Detection: Your Ultimate Guide to Easy Deployment and Fast Inferencing
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Introducing YOLOv8—the latest object detection, segmentation, and classification architecture to hit the computer vision scene! Developed by Ultralytics, the authors behind the wildly popular YOLOv3 and YOLOv5 models, YOLOv8 takes object detection to the next level with its anchor-free design. But it's not just about cutting-edge accuracy. YOLOv8 is designed for real-world deployment, with a… Read More YOLOv8 Detection 10x Faster With DeepSparse—Over 500 FPS on a CPU
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The field of biology is constantly advancing as researchers around the world work to uncover new insights into the mechanisms of life. With the vast amount of information being published on a daily basis, it can be a daunting task for biologists to stay up-to-date and extract relevant data for their research. This is where… Read More Revolutionizing Biology Research With Lightning-Fast NLP: Introducing Sparse BioBERT
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We’ve partnered with Ultralytics to optimize and simplify your YOLOv5 deployment. Want to accelerate the deployment of your YOLOv5 models? We’ve got you covered! Introducing our newest partner, Ultralytics, who makes artificial intelligence easy. While Ultralytics YOLOv5 object detection architectures and pre-trained models offer popular vision AI, Neural Magic provides software tools that emphasize peak… Read More Deploy YOLOv5 With Neural Magic’s DeepSparse for GPU-Class Performance on CPUs
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In the Sparse Real-time Instance Segmentation post, you saw how to perform real-time segmentation on a laptop using YOLACT (You Only Look At CoefficienTs). You learned that image segmentation is applied in areas such as detecting a fruit, picking it up, and placing it in a bin. Image segmentation models can also: Image segmentation—also referred… Read More Image Segmentation: Your Ultimate Guide to Easy Deployment and Fast Inferencing