Setting up macbook for updates from seedutil

  As part of a discussion of how to build test VMs, a colleague mentioned how they were using the seedutil tool to help configure Macs to access Apple’s beta updates. I hadn’t run across this tool before, so I decided to do some research and see if I could make it work for my own testing needs. For more details, see below the jump. seedutil is available at the following location in macOS 10.13.2: 1 /System/Library/PrivateFrameworks/Seeding.framework/Versions/A/Resources/seedutil There is no manpage for it, but if you call the  seedutil  tool by itself it will display the following options: computername:~ username$ sudo /System/Library/PrivateFrameworks/Seeding.framework/Resources/seedutil -help Password: usage: seedutil enroll SEED_PROGRAM seedutil unenroll seedutil current seedutil migrate OLD_VERSION NEW_VERSION seedutil fixup computername:~ username$ view raw gistfile1.txt hosted with

Freezing models in Tensorflow

Not my original post. However made some updates minor in the posts with latest version Freeze Tensorflow models and serve on web In this tutorial, we shall learn how to freeze a trained Tensorflow Model and serve it on a webserver. You can do this for any network you have trained but we shall use the trained model for dog/cat classification in this earlier tutorial  and serve it on a python Flask webserver.  So you trained a new model using Tensorflow and now you want to show it off to your friends and colleagues on a website for a hackathon or a new startup idea. Let’s see how to achieve that. 1. What is Freezing Tensorflow models? Neural networks are computationally very expensive. Let’s look at the architecture of alexnet which is relatively simple neural network:  Let’s calculate the number of variables required for prediction:           conv1 layer: ( 11*11)*3*96 (weights) + 96 (biases)            = 34944           conv2 layer: ( 5*5)*96*256