Category: TensorFlow

An update on AIXPRT development

on May 2, 2019

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

Posted in AI, AIXPRT, Benchmarking, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Cross-platform benchmarks, Future of performance evaluation, Linux, Machine learning, OpenVINO, Performance benchmarking, TensorFlow, TensorRT, Ubuntu | Tagged AI, AIXPRT, Community Preview, Ubuntu, Windows |

All about the AIXPRT Community Preview

By Justin Greene

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 | Tagged AI, AIXPRT, artificial intelligence, benchmark, Community Preview, machine learning, OpenVINO, TensorFlow, TensorRT |

Engaging AI

By Justin Greene

on February 7, 2019

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.

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

Posted in AI, AIXPRT, Benchmark metrics, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Education, Engaging AI, Future of performance evaluation, North Carolina State University, TensorFlow |

An update on the AIXPRT Request for Comments preview

By Justin Greene

on January 24, 2019

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

Posted in AI, AIXPRT, Benchmark metrics, BenchmarkXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Cross-platform benchmarks, Future of performance evaluation, OpenVINO, TensorFlow, TensorRT | Tagged AI, AIXPRT, BenchmarkXPRT, OpenVINO, TensorFlow, TensorRT |

The AIXPRT Request for Comments preview build

By Bill Catchings

on December 20, 2018

In the next few days, we’ll be publishing the first AIXPRT tool as a Request for Comments (RFC) preview build, an early version of one of the AIXPRT tools we’re developing to help evaluate machine learning performance.

We’re inviting folks to run the workload and send in their thoughts and suggestions. Only BenchmarkXPRT Development Community members have access to our RFCs and the opportunity to provide feedback. However, because we’re seeking broad input from experts in this field, we’ll gladly make anyone interested in participating a member.

This AIXPRT RFC preview build includes support for the Intel OpenVINO computer vision toolkit to run image classification workloads with ResNet-50 and SSD-MobileNet v1 networks. The test reports FP32 and FP16 levels of precision. The system requirements are:

Operating system = Ubuntu 16.04
CPU = 6th to 8th generation Intel Core or Xeon processors, or Intel Pentium processors N4200/5, N3350/5, N3450/5 with Intel HD Graphics

We welcome input on all aspects of the benchmark, including scope, workloads, metrics and scores, user experience, and reporting. We will add support for TensorFlow and TensorRT to the AIXPRT RFC preview build during the preview period. We are accepting feedback through January 25th, 2019, after which we’ll collect and evaluate responses before publishing the next build. Because this is an RFC release, we ask that testers do not publish scores or use the results for comparison purposes.

We’ll send out a community announcement when the RFC preview build is officially available, and we’ll also post an announcement and RFC preview build user guide on AIXPRT.com. We’re hosting the AIXPRT RFC preview build in a dedicated GitHub repository, so please contact us at BenchmarkXPRTsupport@principledtechnologies.com to gain access.

This is just the next step for AIXPRT. With your help, we hope to add more workloads and other frameworks in the coming months. We look forward to receiving your feedback!

Bill

Posted in AI, AIXPRT, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Future of performance evaluation, Machine learning, Open development, OpenVINO, Performance benchmarking, TensorFlow, TensorRT, Ubuntu, What makes a good benchmark? |

Category: TensorFlow

An update on AIXPRT development

All about the AIXPRT Community Preview

Engaging AI

An update on the AIXPRT Request for Comments preview

The AIXPRT Request for Comments preview build

Check out the other XPRTs: