On-the-cheap Machine Learning, revisited

A short update on my On the Cheap Machine Learning blog post. After almost a year of use, I’m still pretty happy with the setup. The hardware has worked well. I haven’t done as much independent ML research as I had hoped, but I have contributed many hours of night-time GPU cycles to the Leela Zero open-source Go-game-AI project. I don’t think I would change anything about the build, and there’s nothing about it I want to upgrade yet.

However, in the past year a new option has appeared for on-the-cheap machine learning: Google’s Colaboratory project. Colaboratory is a free web-based IDE for writing machine learning applications. What’s especially cool about it is that comes with access to a cloud-based GPU. The GPU they provide is the NVIDIA K80, which is not the fastest GPU, but it’s still plenty fast for experimenting with machine learning. [Disclosure: I work for Google, but not in any groups related to Google Colaboratory.]

Colaboratory puts machine learning within the reach of anyone with a modern web browser, even if that browser is on a device (like a laptop, tablet, or even phone) that doesn’t have a high-end GPU.

I find myself using Colaboratory instead of my own desktop computer simply because my son’s often using my desktop computer for schoolwork, YouTube and playing games.

Comments

Post a Comment

Popular posts from this blog

GLES Quake - a port of Quake to the Android platform

I've just published the source code to a port of Quake to the Android platform . This is something I did a while ago as a internal test application. It's not very useful as a game, because there hasn't been any attempt to optimize the controls for the mobile phone. It's also awkward to install because the end user has to supply the Quake data files on their own. Still, I thought people might enjoy seeing a full-sized example of how to write a native code video game for the Android platform. So there it is, in all its retro glory. (Porting Quake II or Quake III is left as an exercise for the reader. :-) What's different about this particular port of Quake? Converted the original application into a DLL Android applications are written in Java, but they are allowed to call native languge DLLs, and the native language DLLs are allowed to call a limited selection of OS APIs, which include OpenGL ES and Linux File I/O. I was able to make the game work by using Java for: T
Read more

A Taipei-Torrent postmortem: Writing a BitTorrent client in Go

This is a BitTorrent client. There are many like it, but this one is mine. -- the BitTorrent Implementer's Creed For fun I've started writing a command-line BitTorrent client in Google's go programming language. The program, Taipei-Torrent , is about 70% done. It can successfully download a torrent, but there are still lots of edge cases to implement. Go routines and channels are a good base for writing multithreaded network code. My design uses goroutines as follows: a single main goroutine contains most of the BitTorrent client logic. each BitTorrent peer is serviced by two goroutines: one to read data from the peer, the other to write data to the peer. a goroutine is used to communicate with the tracker some "time.Tick" goroutines are used to wake the main goroutine up periodically to perform housekeeping duties. All the complicated data structures are owned by the main goroutine. The other goroutines just perform potentially blocking network I/O using channel
Read more

A Multi-threaded Go Raytracer

Image
Above is the output of the raytracer. Below is a diagnostic mode showing which goroutines raytraced which section of the screen. Each goroutine has its own color to outline the pixels it traces: I wrote a simple multi-threaded ray tracer in Google's new "go" language. It's an adaptation of Flying Frog Consultancy's Raytracer . It runs single-threaded about 1/2 the speed of a comparable C++ version. I guess the C++ version benefits from a more optimizing compiler and the ability to inline small functions. Compared to ordinary C/C++, the Go version was easier to multithread. On my dual-core Macbook Pro I get an 1.80x speedup when running with GOMAXPROCS > 1: $ GOMAXPROCS=1 time ./gotrace 1.52 real 1.50 user 0.01 sys $ GOMAXPROCS=2 time ./gotrace 0.82 real 1.50 user 0.01 sys $ GOMAXPROCS=3 time ./gotrace 0 .81 real 1.50 user 0.01 sys On an eight-core, 16 Hyperthread HP Z600 running Ubuntu 9
Read more