Home / Tech / Using AI Containers | AI Practitioners Guide for Beginners | Episode 3 | Intel Software

Using AI Containers | AI Practitioners Guide for Beginners | Episode 3 | Intel Software

[youtube https://www.youtube.com/watch?v=CT1rdSLLUaE&w=560&h=315]

Welcome back to the AI Practitioners Guide for Beginners I’m Beenish Zia, and on this episode, you’re going to get a fast overview of deploying the TensorFlow framework on an Intel Xeon scalable platform using the Intel optimization for TensorFlow Docker images

In-the end video, you learned about deploying on bare metal, doing all installations from scratch That's a outstanding strategy to memorize the steps involved and provides you independence to tweak base environment settings for your specific use case However, a whole lot of developers would somewhat have the bottom optimizations if to them as a part of a package, to allow them to accelerate their workload training and testing For folk on this category, using an optimized TensorFlow container can be accurate There are various containers existing on various open-source communities

The one I've used in-the guide is the Intel optimization for TensorFlow Docker images for single-node deployment The opening step for deploying the container on an Intel Xeon scalable platform is similar to those followed in bare metal deployment Upon getting configured the hardware, you would install an OS In our example, we are using CentOS Detailed steps on OS installation is besides included in-the guide for your reference

Once you've installed the OS, you’ll need to configure YUM; install EPEL, that gives 100% high-quality add-on software packages for Linux distribution; and lastly, install the GCC whether it's not already a part of your OS installation Once the OS installation with basic add-on software packages is completed, you’ll commence with downloading and installing Anaconda Anaconda is an open-source distribution of Python that makes it simple to make use of Python and machine learning packages on Linux and other operating systems Once Anaconda installation is finish, you would wish to Accessible a modern terminal, and at Anaconda timely, install the most recent Intel optimization for TensorFlow from Anaconda Now, the following step is to install Docker, unless you have already got it installed


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You will have Docker because we shall be using the Intel optimization for TensorFlow Docker images Once Docker has been installed appropriately, all you would like now’s to tug the Docker images discussed using the Docker pull command Following pulling the Docker container image, you possibly can run it as a Jupyter Notebook or run it as an interactive terminal In-the running Docker image, you possibly can download the most recent benchmarks for deep learning training and testing One thing you’ll need to notice is the container image is lightweight, which implies you’ll have to install basic Linux packages inside the running Docker image– like YUM, WGET, [? WE, ?] et cetera– prior to you possibly can run the TensorFlow benchmarks

On the time the guide was developed, no Docker containers optimized for Intel technology were available for multiple nodes, so no tested directions for that were included However, published documents, reminiscent of "Horovod distributed training on Kubernetes using MLT," are beneficial reading Moreover, to deploy TensorFlow via containers on multiple nodes, review the whitepaper in-the links in addition to Nauta The opening document provides steps to create singularity containers using a multi-node environment after which deploy those singularity containers Don't forget to take a look at the finish written guide on the Intel Developer Zone for more details on deploying TensorFlow on an Intel Xeon scalable platform using containers

Thanks for watching, and protect developing

Source: Youtube

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