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I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.
This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea
Tensorflow 1.4 Mac OS High Sierra 10.13 GPU Support
Tensorflow
System information
OS - High Sierra 10.13
Tensorflow - 1.4
Xcode command line tools - 8.2 (Download from here: Xcode - Support - Apple Developer & Switch to different clang version: sudo xcode-select --switch/Library/Developer/CommandLineTools & check version: clang -v)
How to serve a custom HTTPS domain on GitHub Pages with CloudFlare: *FREE*, secure and performant by default
Instructions
CloudFlare is an awesome reverse cache proxy and CDN that provides DNS, free HTTPS (TLS) support, best-in-class performance settings (gzip, SDCH, HTTP/2, sane Cache-Control and E-Tag headers, etc.), minification, etc.
Make sure you have registered a domain name.
Sign up for CloudFlare and create an account for your domain.
From the CloudFlare settings for that domain, enable HTTPS/SSL and set up a Page Rule to force HTTPS redirects. (If you want to get fancy, you can also enable automatic minification for text-based assets [HTML/CSS/JS/SVG/etc.], which is a pretty cool feature if you don't want already have a build step for minification.)
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
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