Here’s a quick tutorial on how to install, setup and test the Tensorflow 2.0 implementation of OpenPose on the macOS. Dec 18, 2018.
Introduction
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I want to write Machine Learning (ML) applications and so I need to select tools that will help me do this. I have very little practical experience and so the purpose of this post is to show my discovery process in the hope that it also helps others on the same path.
TensorFlow
I have a strong desire to one day take advantage of Google’s Tensor Processing Unit (TPU). Clearly Google is aggressively re-positioning itself as a Artificial Intelligence (AI) company; they are hiring a large number of top resources, they are leading certain types of research (such as with DeepMind), they are creating cloud-based services such as Google Cloud ML, they are incorporating ML into all their products, and with TensorFlow they have created an open-source software library for Machine Intelligence. To me, the attraction of using TensorFlow and the TPU includes:
Tensorflow Download Ans Setup Machine Learning
![]() Alternatives to TensorFlow
Major alternatives to this includes Torch and Theano. Torch is framework written in Lua and is extensively used at Facebook’s AI Research Laboratory (FAIR), Twitter Cortex and NVIDIA. Facebooks’s Yann LeCun is an important AI pioneer and his research papers, such as the latest on Tracking The World State with Recurrent Entity Networks, are very important in the development of AI. NVIDIA are key in providing GPUs and supercomputers for AI projects (such as the NVIDIA DGX-1 recently given to OpenAI). Torch and NVIDIA are a very strong combination and should seriously be considered for ML development. If following this path, it would be advisable to use Ubuntu as most of the NVIDIA tools, such as DIGITS and their Deep Learning Software, use Ubuntu as the preferred operating environment.
Theano is a Python library and is used by the Montreal Institute for Learning Algorithms (University of Montreal), one of the leading AI research universities. Theano is one of the oldest complete libraries available; TensorFlow is considered the next generation improvement over Theano.
These libraries have their attractions, with Torch being the most interesting. So, although I will initially gravitate towards TensorFlow, it will also be important to continuously monitor developments in Torch and NVIDIA and no doubt this will become part of my toolkit.
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Installing TensorFlow
My software development environment includes a MacBook Pro (Retina, 15-inch, Mid 2015) running macOS Sierra version 10.12.3 and homebrew. This version of the MacBook Pro does not have a NVIDIA GPU therefore I cannot install a version of TensorFlow with GPU support.
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In the longer term I plan to use Google’s Go Programming Language for software development. There is a Go binding to TensorFlow but this is currently experimental; the TensorFlow Python API is the most complete therefore I will use that. The TensorFlow Python API supports Python 2.7 and Python 3.3+ so it is assumed you have already installed Python and pip via homebrew. The steps to install TensorFlow (without GPU support) are:
Once this is completed then you should be able to test the TensorFlow implementation by running:
If you choose to install from source you will need to:
Bazel is an open source tool that allows for the automation of building and testing of software. Google uses Blaze as its internal build tool and released and open-sourced part of the Blaze tool as Bazel. To install Bazel and the Python dependencies, run the following:
Next you will need to clone and configure the repository as follows:
When configuring I would activate Google Cloud Platform support and of course there is no GPU on my system. Once configuration is complete you can build TensorFlow (without GPU support) as follows:
You will need to ensure that the name of the .whl file matches the current version. You can then test the installation as follows:
You can then test this implementation by running:
A full description on how to download and setup TensorFlow for your platform can be found here.
Installing Torch
There are many suggestions for installing Torch but on macOS it is best to follow the standard installation process provided on the Torch site here.
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TensorFlow provides a C API that can be used to buildbindings for other languages. The API is defined in
c_api.h and designed for simplicity and uniformity rather than convenience.
Nightly Libtensorflow C packages
Libtensorflow packages are built nightly and uploaded to GCS for all supportedplatforms. They are uploaded to thelibtensorflow-nightly GCS bucketand are indexed by operating system and date built. For MacOS and Linux sharedobjects, we have ascriptthat renames the .so files versioned to the current date copied into thedirectory with the artifacts.
Supported Platforms
TensorFlow for C is supported on the following systems:
SetupDownloadTensorflow Install On Mac
Extract
Extract the downloaded archive, which contains the header files to include inyour C program and the shared libraries to link against.
On Linux and macOS, you may want to extract to
/usr/local/lib :
Linker
On Linux/macOS, if you extract the TensorFlow C library to a system directory,such as
/usr/local , configure the linker with ldconfig :
Free download mac os 9. If you extract the TensorFlow C library to a non-system directory, such as
~/mydir , then configure the linker environmental variables:
BuildExample program
With the TensorFlow C library installed, create an example program with thefollowing source code (
hello_tf.c ):
Compile
Compile the example program to create an executable, then run:
The command outputs: Success:
Hello from TensorFlow C library version number
Tensorflow Download Ans Setup Mac OsThe TensorFlow C library is configured.
If the program doesn't build, make sure that
gcc can access the TensorFlow Clibrary. If extracted to /usr/local , explicitly pass the library location tothe compiler:
Build from sourceTensorflow Download Ans Setup Macro
TensorFlow is open source. Readthe instructions to build TensorFlow's C library from source code.
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