TL;DR: Gradual Magnitude Pruning (GMP) is one of the best pruning approaches to use due to its simplicity, ease of use, and performance on a wide variety of models. There are three general stages to GMP: stabilization, pruning, and fine-tuning. Reading time: 5 minutes, 6 seconds Welcome to Part 2 in Neural Magic’s five-part blog… Read More Part 2: An Intro to Gradual Magnitude Pruning (GMP)
We are excited to announce the Neural Magic 1.1 product release. This product milestone contains new feature updates, an improved user experience, and stability enhancements that will simplify the ability for our clients to achieve GPU-class performance on commodity CPUs. Neural Magic Inference Engine Enables clients to run mission critical deep learning models on commodity… Read More Neural Magic 1.1 Product Release
TL;DR: Pruning is an important concept in machine learning. When done right, it can significantly speed up neural network deployments, while reducing model storage size. In this blog series, we’ll explore pruning in-depth, and give you some strategies for effectively pruning your own networks. We’ll start part 1 with a general overview, the algorithms typically… Read More Part 1: What is Pruning in Machine Learning?
TL;DR: Learn more about use cases for lightweight MobileNetV2 models, and how Neural Magic’s Inference Engine exploits its architecture to run them even faster on commodity CPUs. Read Time: 4 minutes, 32 seconds Ever wonder what’s the machine learning model that powers “Portrait Mode” on your iPhone or the ability to swap out backgrounds on… Read More How to Get Faster MobileNetV2 Performance on CPUs
Last updated: November 17, 2020 The Neural Magic Model Repo includes pre-trained, performance-optimized models ready to use for your machine learning projects The Model Repo features models sparsified with the latest pruning techniques to deliver exceptional performance on CPUs, and accelerates the process of deploying those models in production. Currently, teams can choose from a… Read More What deep learning models are in the Neural Magic Model Repo?
Run computer vision models at lower cost with a suite of new tools that simplify model performance. Today, Neural Magic is announcing the release of its Inference Engine software, the NM Model Repo, and our ML Tooling. Now, data science teams can run computer vision models in production on commodity CPUs – at a fraction… Read More Neural Magic Launches High-Performance Inference Engine and Tool Suite for CPUs
With greater speeds and accuracy. Does your data science team use ResNet? Neural Magic found a novel way to run ResNet models on commodity CPUs with GPU-class performance, at a fraction of the cost. By making ResNet models achieve best-in-class performance on everyday CPUs, teams can experience drastic cost savings. In this blog post, we’ll… Read More How to Run ResNet at a Fraction of the Cost
Read Time: 2 min We are excited to announce a joint collaboration between Neural Magic, Cisco, and Intel to accelerate deep learning performance. Today, Enterprises struggle with the process of getting trained machine learning models into production in support of their mission critical business applications and subsequent inference needs. Too often, sacrifices and trade-offs are… Read More Neural Magic, Cisco, and Intel Collaborate to Accelerate Deep Learning Performance
Neural Magic is expanding what’s possible with machine learning. It levels the machine learning playing field by turning everyday CPUs into high performance machine learning compute resources. With Neural Magic, you can now achieve machine learning performance breakthroughs, at scale, with all the flexibility and cost efficiency of software. How is this possible? Let us… Read More Who is Neural Magic? How does it work?
Earlier this year, Nir Shavit, professor of EECS at MIT and CEO of Neural Magic, joined Ben Lorica for an open discussion on the Data Exchange Podcast. The conversation spanned multicore software, neurobiology and deep learning. The full episode can be downloaded from iTunes, Android, Spotify, Stitcher, Google, and RSS. The full transcript follows, lightly… Read More The combination of the right software and commodity hardware will prove capable of handling most machine learning tasks