History of cluster building
Before 2014, the company’s applications were all deployed in the physical machine. In the age of physical machines, we need to wait an average of one week for the allocation to application on-line. Due to the lack of isolation, applications would affected each other, resulting in a lot of potential risks. At that time, the average number of tomcat instances on each physical machine was no more than nine. The resource of physical machine was seriously wasted and the scheduling was inflexible. The time of application migration took hours due to the breakdown of physical machines. And the auto-scaling cannot be achieved. To enhance the efficiency of application deployment, the company has developed such as compilation-packaging, automatic deployment, log collection, resource monitoring and some other systems.
编者按：Pete Warden是TensorFlow移动团队的技术负责人。曾在Jetpac担任首次技术官。Jetpac的深度学习技术经过优化，可在移动和嵌入式设备上运行。该公司已于2014年被谷歌收购。Pete还曾在苹果公司从事GPU优化领域的图像处理工作，并为O’Reilly撰写多本数据处理方面的书籍。本文为Pete Warden为一般大众撰写的如何用TensorFlow构建图片分类器（TensorFlow for poets，How to build your own image classifier with no coding），希望让不太懂机器学习专业知识的人也能享受到机器学习的益处。