| <?xml version="1.0" encoding="UTF-8"?> | |
| <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> | |
| <plist version="1.0"> | |
| <dict> | |
| <key>AllowClipboardAccess</key> | |
| <true/> | |
| <key>AppleAntiAliasingThreshold</key> | |
| <integer>1</integer> | |
| <key>ApplePressAndHoldEnabled</key> | |
| <false/> |
There are a number of additional dependencies required for getting things installed on OS X. Starting with a blank slate OS X machine, this is the process it takes:
# Install Xcode Command Line Tools
# Install Homebrew
ruby -e "$(curl -fsSkL raw.github.com/mxcl/homebrew/go)"
| Installing CUDA Toolkit 5.5 on Ubuntu 12.10 Linux | |
| The following explains how to install CUDA Toolkit 5.5 on 64-bit Ubuntu 12.10 Linux. I have tested it on a self-assembled desktop with AMD Phenom II X4 CPU, 4GB RAM, 500GB hard drive, 650W power supply, and NVIDIA GeForce GTX 550 Ti graphics card. The instruction assumes you have the necessary CUDA compatible hardware support. Depending on your system configuration, your mileage may vary. | |
| CUDA Repository | |
| Retrieve the CUDA repository package for Ubuntu 12.10 from the CUDA download site and install it in a terminal. | |
| $ sudo dpkg -i cuda-repo-ubuntu1210_5.5-0_amd64.deb | |
| $ sudo apt-get update | |
| Linux Kernel Header | |
| Then you need to install the necessary Linux kernel headers. |
| Install Pylearn2 on a Ubuntu box | |
| https://github.com/lisa-lab/pylearn2 | |
| Assume that the default version of Python is 2.7.3 | |
| Tested on a machine with | |
| - Linux 3.2.0-26-generic x86_64 GNU/Linux | |
| - Ubuntu 12.04.2 LTS (Precise Pangolin) |
| Installing CUDA 5 on Ubuntu 12.04 | |
| copied from: http://sn0v.wordpress.com/2012/12/07/installing-cuda-5-on-ubuntu-12-04/ | |
| This is a follow up to my previous post which was aimed at CUDA 4.2 on Ubuntu 12.04. Although 12.10 is out, it’s not as stable as I would like it to be – I’d recommend sticking with Ubuntu 12.04 for development unless you have specific reasons to upgrade. With CUDA 5, Nvidia has greatly simplified the installation process for Linux, packaging the CUDA toolkit, the SDK and the development drivers all in a neat little package. | |
| A few points before we begin though - | |
| As always, a working CUDA installation requires a CUDA capable card. |
| # remgit.sh | |
| # Creates a remote git repository from the current local directory | |
| # Configuration | |
| # Replace SSH_USERNAME, SSH_HOST, SSH_GIT_PATH with your details | |
| USER=SSH_USERNAME | |
| HOST=SSH_HOST | |
| GIT_PATH=SSH_GIT_PATH | |
| REPO=${PWD##*/} |
| /** | |
| The following example uses this function to calculate the matrix-vector product using | |
| a blas/lapack routine: | |
| / 3 1 3 \ / -1 \ | |
| | 1 5 9 | * | -1 |. | |
| \ 2 6 5 / \ 1 / | |
| D2 version based on the C version at http://www.seehuhn.de/pages/linear |
| """ | |
| cymex.pyx | |
| ========= | |
| This file shows how to bootstrap a C extension for MATLAB with just Cython, | |
| and no C stubs. This strategy probably would work for extending other | |
| languages as well, with C/Python. | |
| It is necessary to redirect MATLAB's libgfortran link as found in | |
| $MATLAB/sys/os/glnxa64/libgfortran.so.3 to the one to which your NumPy's |