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Category: OpenVINO

AIXPRT is here!

We’re happy to announce that AIXPRT is now available to the public! AIXPRT includes support for the Intel OpenVINO, TensorFlow, and NVIDIA TensorRT toolkits to run image-classification and object-detection workloads with the ResNet-50 and SSD-MobileNet v1networks, as well as a Wide and Deep recommender system workload with the Apache MXNet toolkit. The test reports FP32, FP16, and INT8 levels of precision.

To access AIXPRT, visit the AIXPRT download page. There, a download table displays the AIXPRT test packages. Locate the operating system and toolkit you wish to test and click the corresponding Download link. For detailed installation instructions and information on hardware and software requirements for each package, click the package’s Readme link. If you’re not sure which AIXPRT package to choose, the AIXPRT package selector tool will help to guide you through the selection process.

In addition, the Helpful Info box on contains links to a repository of AIXPRT resources, as well links to XPRT blog discussions about key AIXPRT test configuration settings such as batch size and precision.

We hope AIXPRT will prove to be a valuable tool for you, and we’re thankful for all the input we received during the preview period! If you have any questions about AIXPRT, please let us know.

Coming soon: An interactive AIXPRT selector tool

AI workloads are now relevant to all types of hardware, from servers to laptops to IOT devices, so we intentionally designed AIXPRT to support a wide range of potential hardware, toolkit, and workload configurations. This approach provides AIXPRT testers with a tool that is flexible enough to adapt to a variety of environments. The downside is that the number of options makes it fairly complicated to figure out which AIXPRT download package suits your needs.

To help testers navigate this complexity, we’ve been working on a new interactive selector tool. The tool is not yet live, but the screenshots and descriptions below provide a preview of what’s to come.

The tool will include drop-down menus for the key factors that go into determining the correct AIXPRT download package, along with a description of the options. Users can proceed in any order but will need to make a selection for each category. Since not all combinations work together, each selection the user makes will eliminate some of the options in the remaining categories.

AIXPRT user guide snip 1

After a user selects an option, a check mark appears on the category icon, and the selection for that category appears in the category box (e.g., TensorFlow in the Toolkit category). This shows users which categories they’ve completed and the selections they’ve made. After a user selects options in more than one category, a Start over button appears in the lower-left corner. Clicking this button clears all existing selections and provides users with a clean slate.

Once every category is complete, a Download button appears in the lower-right corner. When you click this, a popup appears that provides a link for the correct download package and associated readme file.

AIXPRT user guide snip 2

We hope the selector tool will help make the AIXPRT download and installation process easier for those who are unfamiliar with the benchmark. Testers who already know exactly which package they need will be able to bypass the tool and go directly to a download table.

The tool will debut with the AIXPRT 1.0 GA in the next few days, and we’ll let everyone know when that happens! If you have any questions or comments about AIXPRT, please let us know.


Progress updates: HDXPRT 4 and AIXPRT

Over the next few weeks, we’re expecting to publish both an updated HDXPRT 4 build and the AIXPRT public release (GA). Timelines may change as a result of development or testing issues, but we want to provide a brief update on where both projects stand.


As we discussed last week, Adobe removed Photoshop Elements 2018, the application that HDXPRT 4 uses for the Edit Photos scenario, from their public download page. This means that new HDXPRT 4 testers are currently unable to successfully complete the benchmark installation process.

To fix the problem, we adapted HDXPRT 4’s Edit Photos scripts to use PSE 2020, and we hope to begin testing by the end of this week. We appreciate everyone’s patience as we put a solution in place, and we’ll publish the new build as soon as possible.


We’re now in the third week of the AIXPRT Community Preview 3 (CP3) period, and we’re working on finalizing the AIXPRT GA installation packages for release. Because several of AIXPRT’s component toolkits release updates on a regular basis, it’s likely that we’ll need to update AIXPRT’s installation packages more frequently than we have with previous XPRT benchmarks. At the moment, we’re working to integrate and test recent updates to OpenVINO and TensorRT before GA.

As usual, we’ll keep you informed here in the blog. If you have any questions or comments about HDXPRT or AIXPRT, please let us know. We do value your feedback.


AIXPRT Community Preview 3 is here!

We’re happy to announce that the AIXPRT Community Preview 3 (CP3) is now available! As we discussed in last week’s blog, testers can expect three significant changes in AIXPRT CP3:

  • We updated support for the Ubuntu test packages from Ubuntu version 16.04 LTS to version 18.04 LTS.
  • We added TensorRT test packages for Windows and Ubuntu. Previously, AIXPRT testers could test only the TensorFlow variant of TensorRT. Now, they can use TensorRT to test systems with NVIDIA GPUs.
  • We added the Wide and Deep recommender system workload with the MXNet toolkit for Ubuntu systems.

To access AIXPRT CP3, click this access link and submit the brief information form unless you’ve already done so for CP2. You will then gain access to the AIXPRT community preview page. (If you’re not already a BenchmarkXPRT Development Community member, we’ll contact you with more information about your membership.)

On the community preview page, a download table displays the currently available AIXPRT CP3 test packages. Locate the operating system and toolkit you wish to test, and click the corresponding Download link. For detailed installation instructions and information on hardware and software requirements for each package, click the corresponding Readme link. Instead of providing installation guide PDFs as we did for CP2, we are now directing testers to a public GitHub repository. The repository contains the installation readmes for all the test packages, as well as a selection of alternative test configuration files. We’ll discuss the alternative configuration files in more detail in a future blog post.

Note: Those who have access to the existing AIXPRT GitHub repository will be able to access CP3 in the same way as previous versions.

We’ll continue to keep everyone up to date with AIXPRT news here in the blog. If you have any questions or comments, please let us know.


Understanding concurrent instances in AIXPRT

Over the past few weeks, we’ve discussed several of the key configuration variables in AIXPRT, such as batch size and level of precision. Today, we’re discussing another key variable: number of concurrent instances. In the context of machine learning inference, this refers to how many instances of the network model (ResNet-50, SSD-MobileNet, etc.) the benchmark runs simultaneously.

By default, the toolkits in AIXPRT run one instance at a time and distribute the compute load according to the characteristics of the CPU or GPU under test, as well as any relevant optimizations or accelerators in the toolkit’s reference library. By setting the number of concurrent instances to a number greater than one, a tester can use multiple CPUs or GPUs to run multiple instances of a model at the same time, usually to increase throughput.

With multiple concurrent instances, a tester can leverage additional compute resources to potentially achieve higher throughput without sacrificing latency goals.

In the current version of AIXPRT, testers can run multiple concurrent instances in the OpenVINO, TensorFlow, and TensorRT toolkits. When AIXPRT Community Preview 3 becomes available, this option will extend to the MXNet toolkit. OpenVINO and TensorRT automatically allocate hardware for each instance and don’t let users make manual adjustments. TensorFlow and MXNet require users to manually bind instances to specific hardware. (Manual hardware allocation for multiple instances is more complicated than we can cover today, so we may devote a future blog entry to that topic.)

Setting the number of concurrent instances in AIXPRT

The screenshot below shows part of a sample config file (the same one we used when we discussed batch size and precision). The value in the “concurrent instances” row indicates how many concurrent instances will be operating during the test. In this example, the number is one. To change that value, a tester simply replaces it with the desired number and saves the changes.


If you have any questions or comments (about concurrent instances or anything else), please feel free to contact us.


Understanding AIXPRT results

Last week, we discussed the changes we made to the AIXPRT Community Preview 2 (CP2) download page as part of our ongoing effort to make AIXPRT easier to use. This week, we want to discuss the basics of understanding AIXPRT results by talking about the numbers that really matter and how to access and read the actual results files.

To understand AIXPRT results at a high level, it’s important to revisit the core purpose of the benchmark. AIXPRT’s bundled toolkits measure inference latency (the speed of image processing) and throughput (the number of images processed in a given time period) for image recognition (ResNet-50) and object detection (SSD-MobileNet v1) tasks. Testers have the option of adjusting variables such as batch size (the number of input samples to process simultaneously) to try and achieve higher levels of throughput, but higher throughput can come at the expense of increased latency per task. In real-time or near real-time use cases such as performing image recognition on individual photos being captured by a camera, lower latency is important because it improves the user experience. In other cases, such as performing image recognition on a large library of photos, achieving higher throughput might be preferable; designating larger batch sizes or running concurrent instances might allow the overall workload to complete more quickly.

The dynamics of these performance tradeoffs ensure that there is no single good score for all machine learning scenarios. Some testers might prefer lower latency, while others would sacrifice latency to achieve the higher level of throughput that their use case demands.

Testers can find latency and throughput numbers for each completed run in a JSON results file in the AIXPRT/Results folder. The test also generates CSV results files that are in the same folder. The raw results files report values for each AI task configuration (e.g., ResNet-50, Batch1, on CPU). Parsing and consolidating the raw data can take some time, so we’re developing a results file parsing tool to make the job much easier.

The results parsing tool is currently available in the AIXPRT CP2 OpenVINO – Windows package, and we hope to make it available for more packages soon. Using the tool is as simple as running a single command, and detailed instructions for how to do so are in the AIXPRT OpenVINO on Windows user guide. The tool produces a summary (example below) that makes it easier to quickly identify relevant comparison points such as maximum throughput and minimum latency.

AIXPRT results summary

In addition to the summary, the tool displays the throughput and latency results for each AI task configuration tested by the benchmark. AIXPRT runs each AI task multiple times and reports the average inference throughput and corresponding latency percentiles.

AIXPRT results details

We hope that this information helps to make it easier to understand AIXPRT results. If you have any questions or comments, please feel free to contact us.


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