Showing posts from 2017

Scheduling machine learning jobs to run in sequence

This might save you a few minutes of research time: I sometimes need to set up a number of Keras (or TensorFlow) runs to occur in sequence to run overnight, while I am away from work, etc. I don't want the processing to stop if any single job fails. I use Task Spooler that is in Ubuntu and other Linux distros and can be installed on MacOS using " brew install task-spooler ". Note, on Ubuntu, the command is tsp You can schedule any shell command command to run by prepending "ts". Examples: cd run1 ts python cd ../run2 ts python ts # get a list of all queued, running, and finished processes ts -c 3 # get the stdout for process 3 ts -t 3 # get the tail output for process 3 ts -C # clear the list of finished jobs This simple setup is not really appropriate for doing hyper parameter tuning but is useful to set up a series of runs.

Forget the whole world and focus on your world

Some people dream of “making it big,” dreaming of starting the next Facebook or Google. Poor people fantasize about becoming very wealthy. I think this is misplaced focus. I prefer to live in and think about a much smaller world: Being of service to family and friends and enjoying their company  Being of value to coworkers and customers Providing value and entertainment to people who read my books Getting a lot of exercise and eating great food, in small portions Enjoy reading great books and watching great movies Yes, that is enough for me. I leave changing the world for other people.

Easy Jupyter notebook setup on AWS GPU EC2 with machine learning AMI

The Amazon  machine learning AMI  (link may change in the future) is set up for CUDA/GPU support and preinstalled: TensorFlow, Keras, MXNet, Caffe, Caffe2, PyTorch, Theano, CNTK, and Torch. I chose the least expensive  g2.2xlarge  EC2 instance type with a GPU and used the One Click Launch option (you will need to specify a key file pem file for the AWS region where you are starting the instance). to have an instance running and available in about a minute. This GPU instance costs $0.65/hour so remember to either stop it (if you want to reuse it later and don't mind paying a small cost of persistent local storage) or terminate it if you don't want to be charged for the 60GB of SSD storage space associated with the EC2. I am very comfortable working in SSH shells using Emacs, screen, etc. When an instance boots up, the Actions -> Connect menu shows you the temporary public address which you can use to SSH in: ssh -i "~/.ssh/key-pair.pem" ec2-user@ec2-54-201

My blockchain side project to 'give something back'

I am very busy with my new machine learning job but I always like to try to split off some of my own free time for occasional side projects that I think will help me learn new technologies. My latest side interest is in blockchain technologies  and specifically I am interested in blockchain as a platform and environment for AI agents. I liked  Tim O’Reilley’s call for action for corporations and people to take a longer term view of working for things of long term value to society in his recent keynote speech: Our Skynet Moment  While I consider myself to be a talented practitioner for building machine learning and general AI applications since 1982, I don't feel like I work at the level of creating any groundbreaking technologies myself. So, as far as 'giving something back' to society, it seems like my best bet is in putting some energy into distributed systems that push back against centralized control by corporations and governments, things that enpower people. Alt

New job and two deep dives into tech

I haven't written a public blog post in four months because I have been busy moving to another state and starting a new job at Capital One (Master Software Engineer, role is tech lead and manager of a small machine learning team). Life has been really good: excitement of new job challenges and Carol and I have been enjoying the university town of Urbana/Champaign Illinois. I am also finishing up two course specializations at Coursera: Probabilistic Graph Models and Deep Learning. The deep learning class series is just a review for me, and in contrast I find the PGM class series very challenging (I am taking these PGM classes at a slow and relaxed pace - Coursera lets you split classes to multiple sessions). I am reading two books that I can highly recommend: Fran├žois Chollet's book "Deep Learning with Python" that serves as an idea book for advance use of deep learning using Keras. Fran├žois is the creator of the Keras framework and his new book is a treasure store

I updated my Natural Language Processing (NLP) library for Pharo Smalltalk

I have recently spent some time playing around in Pharo Smalltalk and in the process made some improvements to my NLP library: I changed the license to MIT and added summarization and sentence segmentation. Older code provides functionality for part of speech tagging and categorization. Code, data, and directions are in my github repository nlp_smalltalk . My first experience with Smalltalk was early 1983. The year before my company had bought a Xerox 1108 Lisp Machine for me and a Xerox technical sales person offered me a one month trial license for their Smalltalk system. Pharo Smalltalk very much impresses me both for its interactive programming environment and also for the many fine libraries written for it. I don't spend much time programming in the Pharo environment so I am very far from being an expert. That said, I find it a great programming environment for getting code working quickly.

I am using Lisp more, and big changes to my consulting business

I haven't been using Lisp languages much in the last five or six years since I started using Ruby or Haskell more often to experiment with new ideas and projects. Now that I am winding down my remote consulting business (more detail later) I want to spend more time writing: I have three book projects that I am currently working on: "Practical Scheme Programming (Using Chez Scheme and Chicken Scheme)" , "Ruby Programming Playbook" ,  and a fourth edition update to " Loving Common Lisp, or the Savvy Programmer's Secret Weapon" . All three of these books will be released using a Creative Commons no commercial reuse, no modifications, share with attribution license, so copies of these eBooks can be shared with your friends. I was very excited when the fantastic Chez Scheme system was open sourced but some of the excitement was soon tempered by the time required to get much of my existing Scheme code running under Chez Scheme and  R6RS. To be hon

Technology, antifragile businesses, and workflow

I have been enjoying Nassim Taleb's book 'Antifragile' in which I have learned (or better understood) how difficult to impossible it is to predict the future, especially events with a low probability. Taleb does convince that it is possible and desirable to rate personal habits, health issues, business, governments, etc. as to how fragile <--> robust <--> antifragile they are. Robust is good, antifragile is even better. It is fragile, for example, to depend on the salary from one company to support your family while investing heavily in that company's stock. It is more robust having a side business to earn extra money and to broadly distribute long term investments. It is antifragile to own multiple businesses. Taleb argues, and I agree, that it is better to earn less but have safer more distributed income streams. Personally, I have three businesses: software development consulting, writing books, and I am a landlord for income properties. I am in the pro

Happy New Year 2017

Happy New Year everyone! We live in interesting times. We are witnessing exponential growth in technologies and social and economic change. I am going to share my personal views on these two topics and then conclude with my plans for 2017 for leading a free and inspired life. It is difficult for us humans to really understand exponential growth, as we are seeing in artificial intelligence and other technologies like genetic engineering. One personal way to come to grips with exponential growth is to conduct a thought experiment: compare the technological changes in the world between the times you were ten and twenty years old and the changes in technology in the last ten years. Even a few years ago my cellphone did a fairly poor job at understanding my spoken speech and now it understands me almost perfectly and speech input is now the way that many of us interact with our mobile devices. In my field of machine learning and artificial intelligence, deep learning neural networks hav