Welcome back to the AI practitioners
guide for beginners.
I'm Beenish Zia.
And in this episode, you will get a quick overview
of what's covered in the guide for training and testing
the TensorFlow framework in a single node,
as well as the multi-node Intel Xeon scalable
platform in a bare-metal environment.
First, let's talk about bare-metal deployment.
One of the ways smaller businesses
and curious developers are deploying
artificial intelligence models are workloads on the Intel Xeon
scalable-based platform is via bare metal.
What bare metal means here is you
acquire an Intel Xeon scalable-based system
with just the base hardware and need
to assemble the hardware components together, and then
configure all the software pieces including the OS
and all AI packages yourself.
When it comes to bare metal, you could either
be deploying on a single node or on a cluster
with multiple nodes.
Let's first look at the steps for single-node deployment.
Single node means you have one server
system with the latest Intel Xeon scalable
processors installed and all necessary hardware
configurations done.
The hardware configuration can include
selecting the right processor skew,
installing the right memory DIMMs, applicable SSDs,
and as necessary, an ethernet or InfiniBand connection.
In addition, the BIOS has to be up to date
before you can start.
The guide gives you a sample configuration
for the hardware and software stack
if you need some inspiration.
The next step is to install an operating system.
In our example, we are using CentOS and detail
the steps on operating system installation
is also included in the guide for your reference.
Once you've installed the OS, you
will need to configure YUM, install EPEL, which
provides 100% high-quality add-on software packages
for Linux distribution.
And lastly, install GCC if it's not already
part of your OS installation.
Once the OS installation with basic add-on software packages
is done, next you will start installing TensorFlow.
Installation of the framework can
be done using various methods.
In the guide, I've used virtual environment for installation.
This includes installing the necessary dependencies.
Once the dependencies are installed,
install the virtual environment and activate it.
And make sure all the dependencies are up to date
before installing the latest version of Intel
Optimization for TensorFlow.
Now that you've installed TensorFlow,
you should test your environment.
In the guide, I've done this using CFR 10 training data.
Training and testing your trained model
will require you to run a couple of Python scripts
that are already available as part of the TensorFlow package.
The expected results with details on each step
is covered in the guide.
Now that we have covered single-node deployment,
let's cover steps for multiple-node deployment.
When it comes to multiple nodes, you have a cluster of systems.
For example, you have two or more
of the latest Intel Xeon scalable-based server systems
connected and managed by a host node.
The initial steps for multiple-node deployment
are very similar to single node.
Where the major change happens is
after you've done your compiler installation.
You will need to install OpenMPI followed by Python 3.6
installation, and then install Horovod.
Horovod helps run TensorFlow in a distributed fashion.
Once that is done, the rest of the steps
from training and testing your environment
is similar to single note, with complete details
mentioned in the guide.
The choice of using single node versus multiple node
depends highly on the user and the requirements
for their specific applications.
Please check out the written "AI Practitioners Guide
for Beginners" for complete details on deploying TensorFlow
on a bare-metal environment.
Thanks for watching and keep developing.
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