Introduction
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:
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.
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.
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: Microsoft word for mac trial 2012.
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:
Mathematica 11.2 mac download. 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 2 packages are available
Older versions of TensorFlow
For TensorFlow 1.x, CPU and GPU packages are separate:
System requirements
pip .
Hardware requirements
1. Install the Python development environment on your system
Check if your Python environment is already configured:
Requires Python 3.5–3.8, pip and venv >= 19.0
If these packages are already installed, skip to the next step.
Otherwise, install Python, the pip package manager, and venv: UbuntumacOS
Install using the Homebrew package manager:
Windows
Install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, and 2019. Starting with the TensorFlow 2.1.0 version, the
msvcp140_1.dll file is required from this package (which may not be provided from older redistributable packages). The redistributable comes with Visual Studio 2019 but can be installed separately: https://yellowbat.weebly.com/blog/download-audio-hijack-for-mac.
Make sure long paths are enabled on Windows.
Install the 64-bitPython 3 release for Windows (select
pip as an optional feature).
Raspberry Pi
Requirements for the Raspbian operating system:
OtherIf not in a virtual environment, use python3 -m pip for the commands below. This ensures that you upgrade and use the Python pip instead of the system pip.
2. Create a virtual environment (recommended)
Python virtual environments are used to isolate package installation from the system.
Ubuntu / macOS
Create a new virtual environment by choosing a Python interpreter and making a
./venv directory to hold it: Sims 2 collection mac download.
Tensorflow Download Ans Setup Macro
Activate the virtual environment using a shell-specific command:
https://tincrifec.hatenablog.com/entry/2020/10/14/223132. When the virtual environment is active, your shell prompt is prefixed with
(venv) .
Install packages within a virtual environment without affecting the host system setup. Start by upgrading
pip : Indie flower font download mac.
And to exit the virtual environment later:
Windows
Create a new virtual environment by choosing a Python interpreter and making a
.venv directory to hold it:
Activate the virtual environment:
Install packages within a virtual environment without affecting the host system setup. Start by upgrading
pip :
And to exit the virtual environment later:
Conda
While the TensorFlow provided pip package is recommended, acommunity-supportedAnaconda packageis available. To install, read the Anaconda TensorFlow guide.
Tensorflow Download Ans Setup Machine Learning3. Install the TensorFlow pip package
Choose one of the following TensorFlow packages to install from PyPI:
setup.py file under REQUIRED_PACKAGES .
Virtual environment install
Verify the install:
System install
Verify the install: https://renewalien341.weebly.com/blog/free-powerpoint-download-mac-2007.
Success: TensorFlow is now installed. Read the tutorials to get started.
Package location
A few installation mechanisms require the URL of the TensorFlow Python package. The value you specify depends on your Python version.
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