TL;DR: Learn more about increasing performance for MobileNetV2_SSD models, via pruning and decreasing post-production time. Read time: 3 minutes, 15 seconds In many object detection scenarios, there’s not a moment to lose. A fraction of a second can mean the difference between a self-driving car hitting a dog crossing the street or narrowly missing it.… Read More Speeding Up Memory-Bound Object Detection Models: MobileNetV2_SSD
Neural Magic delivers best-in-class deep learning performance on commodity CPUs. We do this via: Model optimization techniques like pruning and quantization Smart algorithms that utilize CPU memory more effectively. To help visualize the power of Neural Magic, we recorded three short end-to-end video guides on how to install our software, prepare and run a model… Read More Neural Magic End-to-End Demo Videos
TL;DR: In addition to the general hyperparameters described in the previous post, the sparsity to target per layer is arguably the most critical hyperparameter you can set. Below we give you the reason why, and show you how. Reading time: 10 minutes, 47 seconds Welcome to Part 4 in Neural Magic’s five-part blog series on… Read More Part 4: Sparsity per Layer Hyperparameter
TL;DR: To facilitate the GMP process when pruning a network, several hyperparameters must be defined. These include general hyperparameters such as learning rate, pruning update frequency, and pruning schedule function in addition to the sparsity per layer. All hyperparameters affect end level recovery, loss, and performance. Reading time: 5 minutes, 5 seconds Welcome to Part… Read More Part 3: Gradual Magnitude Pruning (GMP) Hyperparameters
In our new blog series, we’re interviewing data scientists and machine learning engineers about their career paths, areas of interest and thoughts on the future of AI. We kick off this week with a 20-year veteran and jack-of-all-trades when it comes to machine learning and data science: Mani Sarkar. Mani is a strategic machine learning… Read More Machine Learning Engineer Spotlight: Mani Sarkar
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?