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Returning user. Before exploring AI, it is important to understand that implementing AI in your organization will be a journey. Picture a 2x2 chart with increasing business value on the y-axis and decreasing the cost to solve on the x-axis; naturally, the most impactful challenges to tackle first are in the upper-right quadrant.
The next steps are to determine which AI or other approach is best-suited to each problem, and then assess whether you have the expertise required to implement the solution. Additionally, you should know whether those experts embrace a fail-fast continuous improvement philosophy, since AI projects typically involve more uncertainty, trial and error, and exploration than more traditional and deterministic software development projects. Once the human element is in place, the next step is to source data and prepare it for analysis, as well as to stand up whatever technology infrastructure is required to tackle the problem.
A classic example is an initial resistance to data analytics in sports, where general managers and scouts scoffed at the idea of computer algorithms outsmarting their years of experience and tribal knowledge.
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In several of his works, he explores the idea of humans and machines working collectively to change the world. Understanding how this collective intelligence of humans and AI can affect your business strategy is critical. This section details on the three ways to deploy a TensorFlow framework for deep learning training and inference for an Intel Xeon platform-based infrastructure. This document used a virtual environment for installing TensorFlow.
In this section, CentOS7. Download an updated version of the software from the CentOS website. Disable updates and extras. Certain procedures in this document require packages to be built against the kernel. A future kernel update may break the compatibility of these built packages with the new kernel, so disabling repository updates and extras is recommended to provide further longevity to this document.
To use this document after such an update, it is necessary to redefine repository paths to point to CentOS 7. To disable repository updates and extras: Yum-config-manager --disable updates --disable extras. To install EPEL latest version for all packages required :. It should be part of the Development Tools install in OS installation. Look in the Appendix.
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Check by typing:. If not installed, find the latest installation here. There are various ways to install TensorFlow. This document uses virtualenv, a tool to create isolated Python environments 9. This document was deployed and tested using TensorFlow 0. Google releases an updated version of TensorFlow on a regular cadence, so using the latest available version of TensorFlow wheel is recommended.
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However, these may not be optimized for CPUs. After installing a version of TensorFlow wheel, you have the option to upgrade to the latest TensorFlow, but be advised that the upgraded version might not be CPU optimized. Download a clone of the TensorFlow repository from GitHub. For this document, tests were done for both K steps and 60K steps, for a batch size of , and logging frequency of To learn more about the differences between epoch, batch size, and iterations, read the Performance Guide for TensorFlow.
Please update before running. It can be run while the training script is still running toward the end of the number of steps, or it can be run after the training script has finished. A similar-looking result below was achieved with the system described in the Hardware and Software Bill of Materials Section of this document.
Note that these numbers are only for educational purposes and no specific CPU optimizations were performed. Their blog has been the source of the content for this section. Many complex deep learning models are required to be trained on multi-node. Therefore, Intel has also performed scaling studies on multi-node clusters of Intel Xeon Scalable processors.
This section will provide steps to deploy TensorFlow on clusters of Intel Xeon processors using Horovod, a distributed training framework for TensorFlow. It uses MPI concepts such as allgather and allreduce to handle the cross-replicas communication and weight updates. Horovod is installed as a separate Python package. This white paper assumes that a multiple node cluster has been set up and there is communication between the head node and compute nodes.
You can check by typing:. Some existing clusters already have available OpenMPI. In this section, we will use OpenMPI 3. OpenMPI can be installed following instructions in this link. As part of the OS installation, necessary packages must have been installed. Update all necessary packages as follows:. Proceed with installing Python. This section provides steps to install Python 3. Uber Horovod supports running TensorFlow in a distributed fashion. Install Horovod as a standalone Python package as follows:.
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Please check the following link to install Horovod from this source :. The current TensorFlow benchmarks are recently modified to use Horovod. Obtain the benchmark code from GitHub:. This section discusses run commands needed to run distributed TensorFlow using Horovod framework.
For 1 MPI process per node, the configuration will be as follows; other environment variables will remain the same. You may also need to change other hyper-parameters. For running models on a multi-node cluster, use a similar run script as the one above. For example, to run on node 2 MPI per node , where each node is an Intel Xeon Gold processor, the distributed training can be launched as shown below.
All the export lists will be the same as above. All of these technologies are incorporated within TensorFlow codebase. Uber Horovod is one distributed TensorFlow technology that was able to harness the power of Intel Xeon processors. It uses MPI underneath, and uses Ring based reduction and gather for deep learning parameters. In other words, time to train a DL network can be accelerated by as much as 57x resnet 50 and 58x inception V3 using 64 Intel Xeon processor nodes compared to a single such node.
Install Anaconda by using following command. Note: You will need to open a new terminal to for the Anaconda installation to become active. Besides the install method described above, Intel Optimization for TensorFlow is distributed as wheels, docker images and conda package on the web page Intel channel. This section will cover installing Intel Optimization for TensorFlow using docker images. Install Docker on CentOS. After the Docker package has been installed, start the daemon. Enable it system-wide and check its status by using the following commands:.
Finally, run a container test image to verify that Docker works properly, using the following command:. Note: In case of issues with Docker connection timeout, and you are behind a proxy server for example in a corporate setting , you may need to add certain configurations in the Docker system service file. Available container configurations and tags can be found here. For example:. Note: The container is a light image; you will have to install basic Linux packages like yum, wget, vi, etc.
The authors ran the following steps before cloning the benchmark. The container image is based on Ubuntu. However, published documents such as Horovod distributed training on Kubernetes using MLT are recommended reading for distributed training on Kubernetes. Sign in to your AWS Management console with your username and password. Then select EC2 instance. Choose the ideal fit for your application. Choose the instance type for deep learning and deployment needs.
Then click Review and Launch. Create a private key file by selecting Create New Key Pair, and download it to a safe location. Then launch the instance. You will see screen shot as follows:. You can retry to launch the instance after 30 minutes.