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Category: AI

An update on AIXPRT development

It’s been almost two months since the AIXPRT Community Preview went live, and we want to provide folks with a quick update. Community Preview periods for the XPRTs generally last about a month. Because of the complexity of AIXPRT and some of the feedback we’ve received, we plan to release a second AIXPRT Community Preview (CP2) later this month.

One of the biggest additions in CP2 will be the ability to run AIXPRT on Windows. AIXPRT currently requires test systems to run Ubuntu 16.04 LTS. This is fine for testers accustomed to Linux environments, but presents obstacles for those who want to test in a traditional Windows environment. We will not be changing the tests themselves, so this update will not influence existing results from Ubuntu. We plan to make CP2 available for download from the BenchmarkXPRT website for people who don’t wish to deal with GitHub.

Also, after speaking with testers and learning more about the kinds of data points people are looking for in AIXPRT results, we’ve decided to make significant adjustments to the AIXPRT results viewer. To make it easier for visitors to find what they’re looking for, we’ll add filters for key categories such as batch size, toolkit, and latency percentile (e.g., 50th, 90th, 99th), among others. We’ll also allow users to set desired ranges for metrics such as throughput and latency.

Finally, we’re adding a demo mode that displays some images and other information on the screen while a test is running to give users a better idea what is happening. While we haven’t seen results change while running in demo mode, users should not publish demo results or use them for comparison.

We hope to release CP2 in the second half of May and a GA version in mid-June. However, this project has more uncertainties than we usually encounter with the XPRTs, so that timeline could easily change.

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

Bill

More, faster, better: The future according to Mobile World Congress 2019

More is more data, which the trillions of devices in the coming Internet of Things will be pumping through our air into our (computing) clouds in hitherto unseen quantities.

Faster is the speed at which tomorrow’s 5G networks will carry this data—and the responses and actions from our automated assistants (and possibly overlords).

Better is the quality of the data analysis and recommendations, thanks primarily to the vast army of AI-powered analytics engines that will be poring over everything digital the planet has to say.

Swimming through this perpetual data tsunami will be we humans and our many devices, our laptops and tablets and smartphones and smart watches and, ultimately, implants. If we are to believe the promise of this year’s Mobile World Congress in Barcelona—and of course I do want to believe it, who wouldn’t?—the result of all of this will be a better world for all humanity, no person left behind. As I walked the show floor, I could not help but feel and want to embrace its optimism.

The catch, of course, is that we have a tremendous amount of work to do between where we are today and this fabulous future.

We must, for example, make sure that every computing node that will contribute to these powerful AI programs is up to the task. From the smartphone to the datacenter, AI will end up being a very distributed and very demanding workload. That’s one of the reasons we’ve been developing AIXPRT. Without tools that let us accurately compare different devices, the industry won’t be able to keep delivering the levels of performance improvements that we need to realize these dreams.

We must also think a lot about how to accurately measure all other aspects of our devices’ performance, because the demands this future will place on them are going to be significant. Fortunately, the always evolving XPRT family of tools is up to the task.

The coming 5G revolution, like all tech leaps forward before it, will not come evenly. Different 5G devices will end up behaving differently, some better and some worse. That fact, plus our constant and growing reliance on bandwidth, suggests that maybe the XPRT community should turn its attention to the task of measuring bandwidth. What do you think?

One thing is certain: we at the Benchmark XPRT Development Community have a role to play in building the tools necessary to test the tech the world will need to deliver on the promise of this exciting trade show. We look forward to that work.

All about the AIXPRT Community Preview

Last week, Bill discussed our plans for the AIXPRT Community Preview (CP). I’m happy to report that, despite some last-minute tweaks and testing, we’re close to being on schedule. We expect to take the CP build live in the coming days, and will send a message to community members to let them know when the build is available in the AIXPRT GitHub repository.

As we mentioned last week, the AIXPRT CP build includes support for the Intel OpenVINO, TensorFlow (CPU and GPU), and TensorFlow with NVIDIA TensorRT toolkits to run image-classification workloads with ResNet-50 and SSD-MobileNet v1 networks. The test reports FP32, FP16, and INT8 levels of precision. Although the minimum CPU and GPU requirements vary by toolkit, the test systems must be running Ubuntu 16.04 LTS. You’ll be able to find more detail on those requirements in the installation instructions that we’ll post on AIXPRT.com.

We’re making the AIXPRT CP available to anyone interested in participating, but you must have a GitHub account. To gain access to the CP, please contact us and let us know your GitHub username. Once we receive it, we’ll send you an invitation to join the repository as a collaborator.

We’re allowing folks to quote test results during the CP period, and we’ll publish results from our lab and other members of the community at AIXPRT.com. Because this testing involves so many complex variables, we may contact testers if we see published results that seem to be significantly different than those from comparable systems. During the CP period, On the AIXPRT results page, we’ll provide detailed instructions on how to send in your results for publication on our site. For each set of results we receive , we’ll disclose all of the detailed test, software, and hardware information that the tester provides. In doing so, our goal is to make it possible for others to reproduce the test and confirm that they get similar numbers.

If you make changes to the code during testing, we ask that you email us and describe those changes. We’ll evaluate if those changes should become part of AIXPRT. We also require that users do not publish results from modified versions of the code during the CP period.

We expect the AIXPRT CP period to last about four to six weeks, placing the public release around the end of March or beginning of April. In the meantime, we welcome your thoughts and suggestions about all aspects of the benchmark.

Please let us know if you have any questions. Stay tuned to AIXPRT.com and the blog for more developments, and we look forward to seeing your results!

JNG

Engaging AI

In December, we wrote about our recent collaboration with students from North Carolina State University’s Department of Computer Science. We challenged the students to create a software console that includes an intuitive user interface, computes a performance metric, and uploads results to our database. The specific objective was to make it easy for testers to configure and run an implementation of the TensorFlow framework. In general, we hoped that the end product would model some of the same basic functions we plan to implement with AIXPRT, our machine-learning performance evaluation tool, currently under development.

The students did an outstanding job, and we hope to incorporate some of their work into AIXPRT in the future. We’ve been calling the overall project “Engaging AI” because it produced a functional tool that can help users interact with TensorFlow, and it was the first time that the students had an opportunity to work with AI tools. You can read more details on the Engaging AI page. We also have a new video that describes the project, including the new skillsets our students acquired to achieve success.

engaging-ai-vid

Finally, interested BenchmarkXPRT Development Community members can access to the project’s source code and additional documentation on our XPRT Experiments page. We hope you’ll check it out!

Justin

An update on the AIXPRT Request for Comments preview

As we approach the end of the original feedback window for the AIXPRT Request for Comments preview build, we want to update folks on the status of the project and what to expect in the coming weeks.

First, thanks to those who’ve downloaded the AIXPRT OpenVINO package and sent in their questions and comments. We value your feedback, and it’s instrumental in making AIXPRT a better tool. We’re currently working through some issues with the TensorFlow and TensorRT packages, and hope to add support for those to the RFC preview build repository very soon.

We’re also hoping to have a full-fledged community preview (CP) ready in mid to late February. Like our other community previews, the AIXPRT CP would be solid enough to allow folks to start quoting numbers. We typically make our benchmarks available to the general public four to six weeks after the community preview period begins, so if that schedule holds, it would place the public AIXPRT release around the end of March.

In light of the schedule described above, you still have time to gain access to the AIXPRT RFC preview build and give your feedback, so let us know if you’d like to check it out. The installation and testing process can take less than an hour, but getting everything properly set up can take a few tries. We are hard at work trying to make that process more straightforward. We welcome your input on all aspects of the benchmark, including workloads, ease of use, metrics, scores, and reporting.

Thanks for your help!

Justin

The HDXPRT 4 Community Preview is now available!

Today we’re releasing the HDXPRT 4 Community Preview (CP). Just like previous versions of HDXPRT, HDXPRT 4 uses trial versions of commercial applications to complete workload tasks. For some of those programs, such as Audacity and HandBrake, HDXPRT 4 includes installers in the HDXPRT installation package. For other programs, such as Adobe Photoshop Elements 2018 and CyberLink Media Espresso 7.5, users need to download the necessary installers prior to testing by using the links and instructions in the HDXPRT 4 User Manual.

In addition to the editing photos, editing music, and converting videos workloads from prior versions of the benchmark, HDXPRT 4 includes two new Photoshop Elements scenarios. The first utilizes an AI tool that corrects closed eyes in photos, and the second creates a single panoramic photo from seven separate photos.

HDXPRT 4 is compatible with systems running Windows 10, and the installation package is slightly smaller than previous versions at just over 4.7 GB.

Because this is a community preview, it is available only to community members, who may download the preview from the HDXPRT tab in the Members’ Area. Because we expect results from CP testing to be comparable to results from the general release, members may publish their CP test results.

After you try the CP, please send us your comments. If you send information that’s relevant to the entire community, we may post an anonymous version of your comments to the forum. Thanks for your participation!

Justin

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