Tag Archives: machine learning

All about the AIXPRT Community Preview

on February 28, 2019

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 We also require that users do not publish results from modified versions of the code during the CP period.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

Posted in AI, AIXPRT, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Future of performance evaluation, Machine learning, OpenVINO, Performance benchmarking, TensorFlow, TensorRT, Ubuntu | Also tagged AI, AIXPRT, artificial intelligence, benchmark, Community Preview, OpenVINO, TensorFlow, TensorRT

Preparing for the AIXPRT Community Preview

By Bill Catchings

on February 21, 2019

Thanks to everyone who downloaded the AIXPRT Request for Comments (RFC) preview build. Next week, we’re planning to publish the AIXPRT Community Preview (CP). 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. As with the RFC build, the test systems must be running Ubuntu 16.04 LTS. The minimum CPU and GPU requirements vary according to the toolkit being used, and we will publish more details about the hardware minimums next week.

As with our other community previews, we think the AIXPRT CP candidate is solid enough to allow folks to start quoting test results. During CP periods, we generally allow members to publish their own results, but wait until the build is available to the public before we post results on our site. Because community feedback is especially important for AIXPRT, we will handle things a bit differently. During the CP period, we’ll publish results that we produce as well as those from other members of the community, which you’ll be able to view at AIXPRT.com.

We’ll also provide detailed instructions for publishing results and sending them to us. Because of the high number of variables in each potential test configuration, we’ll ask testers to disclose more test, software, and hardware information than in the past. We will make this information available along with the results on AIXPRT.com. Our goal is that others can reproduce these numbers and confirm that they get similar results.

Our CP periods typically last four to six weeks before we make the benchmark available to the general public. If that schedule holds, it would place the public AIXPRT release around the end of March. During the CP period, we welcome your thoughts and suggestions about all aspects of the benchmark.

Also, we normally restrict access to our CPs to BenchmarkXPRT Development Community members. However, because we’re seeking broad input from experts in this field, we’ll gladly make the CP available to anyone interested in participating who has a GitHub account. To gain access, 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.

Please let us know if you have any questions. We look forward to hearing your feedback.

Bill

Posted in Uncategorized | Also tagged AI, AIXPRT, BenchmarkXPRT, BenchmarkXPRT Development Community, Community Preview, GitHub, Intel, MobileNet, NVIDIA, OpenVINO, ResNet, TensorFlow, TensorRT, test

XPRT collaborations: North Carolina State University

By Justin Greene

on December 6, 2018

For those of us who work on the BenchmarkXPRT tools, a core goal is involving new contributors and interested parties in the benchmark development process. Adding voices to the discussion fosters the collaboration and innovation that lead to powerful benchmark tools with lasting relevance.

One vehicle for outreach that we especially enjoy is sponsoring a student project through North Carolina State University. Each semester, the Senior Design Center in the university’s Department of Computer Science partners with external companies and organizations to provide student teams with an opportunity to work on real-world programming projects. If you’ve followed the XPRTs for a while, you may remember previous student projects such as Nebula Wolf, a mini-game that shows how well different devices handle games, and VR Demo, a virtual reality prototype workload based on a room escape scenario.

This fall, a team of NC State students is developing a software console for automating machine learning tests. Ideally, the tool will let future testers specify custom workload combinations, compute a performance metric, and upload results to our database. The project will also assess the impact of the framework on performance scores. In fact, the console will perform many of the same functions we plan to implement with AIXPRT.

The students have worked very hard on the project, and have learned quite a bit about benchmarking practices and several new software tools. The project will wrap up in the next couple of weeks, and we’ll share additional details as soon as possible. Early next year, we’ll publish a video about the experience.

If you’d like to join the NC State students and hundreds of other XPRT community members in the future of benchmark development, please let us know!

Justin

Posted in AI, AIXPRT, Benchmarking, BenchmarkXPRT development community, Collaborative benchmark development, Education, Future of performance evaluation, Nebula Wolf, North Carolina State University, Virtual reality, VR Demo | Also tagged AI, benchmarks, computer science, gaming, NC State, NCSU, North Carolina, North Carolina State University, university, virtual reality

Machine learning everywhere!

By Bill Catchings

on August 10, 2017

I usually think of machine learning as an emerging technology that will have a big impact on our lives in the not too distant future through applications like autonomous driving. Everywhere I look, however, I see areas where machine learning will affect our lives much sooner in a myriad of smaller ways.

A recent article in Wired described one such example. It told about the work some MIT and Google researchers have done using machine learning to retouch photos. I would do this by using a photo editing program to do something like adjust the color saturation of a whole photo. Instead, their algorithm applies different filters to different parts of a photo. So, faces in the foreground might get different treatment than the sunset in the background.

The researchers train the neural network using professionally retouched photos. I love the idea of a program that automatically improves the look of my less-than-professional personal photos.

What I found more exciting, however, is that the researchers could make their software efficient enough to run on a smartphone in a fraction of a second. That makes it significantly more useful.

This technology is not yet available, but it seems like something that could show up in existing photo or camera apps before long. I hope to see it soon on a smartphone in my hand!

All of that made me think about how we might incorporate such an algorithm in the XPRTs. When I started reading the article, I was thinking it might fit well in our upcoming machine-learning XPRT. By the time I finished it, however, I realized it might belong in a future version of one of the other XPRTs, like MobileXPRT. What do you think?