Deep Learning

3 月，跳不动了？>>> ONNX Models to be Runnable Natively on 100s of Millions of Windows Devices March 7, 2018 by ML Blog Team This post was authored by Eric Boyd, CVP, AI Data & Infrastructure. Today Microsoft is announcing the next major update to Windows will include the ability to run Open Neural Network Exchange (ONNX) models natively with hardware acceleration. This brings 100s of millions of Windows devices, ranging from IoT edge devices to HoloLens to 2-in-1s and desktop PCs, into the ONNX ecosystem. Data scientists and developers creating AI models will be able to deploy their innovations to

Deploying Deep Learning Models on Kubernetes with GPUs April 19, 2018 by ML Blog Team This post is authored by Mathew Salvaris and Fidan Boylu Uz, Senior Data Scientists at Microsoft. One of the major challenges that data scientists often face is closing the gap between training a deep learning model and deploying it at production scale. Training of these models is a resource intensive task that requires a lot of computational power and is typically done using GPUs. The resource requirement is less of a problem for deployment since inference tends not to pose as heavy a computational burden as

区别于其他入门教程的“手把手式”，本文更强调“因”而非“果”。我之所以加上“通用”字样，是因为在你了解了这个开发环境之后，那些很low的错误你就不会犯了。 大家都知道深度学习涉及到大量的模型、算法，看着那些乱糟糟的公式符号，心中一定是“WTF”。我想说的是，这些你都不要管，所谓车到山前必有路。 所需安装包 通常以我的习惯是以最简单的方式来接触一门新的技术，并且尽量抛弃新的（边缘）技术的介入，如果因为一些其他因素来导致学习树的不断扩大，会变得很低效，所以我们直击核心。以最常用的windows环境为例。 这里以 windows7+TensorFlow-gpu1.5+cuda8+cudnn6+anaconda5+python3.6 为例。这里强烈推荐GPU版本，因为深度学习动辄几小时、几天、几周的运行市场，GPU加速会节省你很多时间（甚至电费）。 cuda_8.0.61_windows.exe http://developer2.download.nvidia.com/compute/cuda/8.0/secure/Prod2/local_installers/cuda_8.0.61_windows.exe ： 从NIVDIA官网下载需要找到历史版本 Legacy Releases 。 tensorflow代码引用的cuda库必须 绝对匹配 ，比如tensorflow1.3-1