Of course, if you are doing anything more than tinkering, it may be best to avoid experimental BLAS implementations unless you know what you’re doing. I have not been able to find information about how reliably this happens or how impactful it can be. My aim with this blog post was to document the performance of R using the standard vs. an optimized BLAS in three different operating systems.Īttempting to replicate the following results should be done cautiously, with the caveat that alternative BLAS reportedly have the potential to impact the stability or precision of the calculations. This is done in part through better use – or any use – of the multiple processor cores present in most modern computers. Using an alternate BLAS can result in improved speed in several basic operations that underlie many of the higher-level statistics and data science operations that are common in academic and industry research. The default BLAS that comes with R is optimized for stability, but not for speed. If it is installed in a different location, write the path to it instead of ~/anaconda/.To efficiently execute many of its low-level computations, R utilizes a BLAS (Basic Linear Algebra Subprograms/Subroutines). Previously in this guide we used Anaconda to install this library, so, if you did it, the path after ~/anaconda/ should be the same for you, but you need to check whether ~/anaconda/ is where your Anaconda is installed. This means that there is no guarantee the second argument will work for you as specified here, since your hdf5 library may reside in a different location. The first argument of the commands is the path of the library to be relinked as shown in the otool output, the second is the actual location of the library and the third is the executable file the linking information of which we are editing. Install_name_tool -change ~/anaconda/lib/libhdf5.10.dylib. Install_name_tool -change ~/anaconda/lib/libhdf5_hl.10.dylib. This way PYTHON_LIB will point to Anaconda's lib folder. Since we are using Anaconda, we should uncomment the second line and comment the first one. By default the first line is not commented and the second one is. In the next block, we need to specify the correct path to the Python libraries. Do ls ~/anaconda to see whether this folder exists, and if it is not, you should locate where you installed Anaconda and specify that path instead. In principle, the second PYTHON_INCLUDE will override the first one, but just to make sure and not ask for the troubles let's comment the first one.ĪNACONDA_HOME specifies where your Anaconda is installed - make sure the path is correct. PYTHON_INCLUDE : = $(ANACONDA_HOME )/include \ $(ANACONDA_HOME )/include/python2.7 \ $(ANACONDA_HOME )/lib/python2.7/site-packages/numpy/core/include \ Include path: # Verify anaconda location, sometimes it's in root. # PYTHON_INCLUDE := /usr/include/python2.7 \ # /usr/lib/python2.7/dist-packages/numpy/core/include # Anaconda Python distribution is quite popular. # We need to be able to find Python.h and numpy/arrayobject.h. # NOTE: this is required only if you will compile the python interface. Here's which dependencies we'll download using Homebrew:Įxecute the following commands to download and install the dependencies: Here's which dependencies we'll download using Anaconda: They are listed in the Prerequisites section of the official guide. You need to download all the dependencies of Caffe. Read carefully the guide so that you have a general idea of what needs to be done from the official point of view. Compile and test the Python module for Caffeįirst of all, clone the Caffe repository from GitHub to some directory and head to the official installation guide.Configure the Caffe compilation by editing nfig.Here is a big picture of what we will do: If something is not present on Anaconda, we will use Homebrew to install it. We will use Anaconda as our primary platform, so if something we need is available on Anaconda, we'll tend to use Anaconda to get it. We will compile a very basic working setup without the CUDA support. Homebrew: supplies some dependencies of Caffe.Anaconda: supplies most of the dependencies of Caffe.Here is the setup this guide was tested for: Caffe is a work in progress, so its installation is not that trivial. In this post, I will describe how to install the Caffe neural network framework on Mac OS X as a Python library.
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