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Category: TensorFlow

A note about AIXPRT

Justin Greene
By Justin Greene
on June 30, 2022

Recently, a member of the tech press asked us about the status of AIXPRT, our benchmark that measures machine learning inference performance. We want to share our answer here in the blog for the benefit of other readers. The writer said it seemed like we had not updated AIXPRT in a long time, and wondered whether we had any immediate plans to do so.

It’s true that we haven’t updated AIXPRT in quite some time. Unfortunately, while a few tech press publications and OEM labs began experimenting with AIXPRT testing, the benchmark never got the traction we hoped for, and we’ve decided to invest our resources elsewhere for the time being. The AIXPRT installation packages are still available for people to use or reference as they wish, but we have not updated the benchmark to work with the latest platform versions (OpenVINO, TensorFlow, etc.). It’s likely that several components in each package are out of date.

If you are interested in AIXPRT and would like us to bring it up to date, please let us know. We can’t promise that we’ll revive the benchmark, but your feedback could be a valuable contribution as we try to gauge the benchmarking community’s interest.

Justin

Posted in AI, AIXPRT, benchmark, Machine learning, OpenVINO, Performance benchmarking, TensorFlow | Tagged , , , , , , |

Considering WebAssembly for WebXPRT 4

Justin Greene
By Justin Greene
on March 25, 2021

Earlier this month, we discussed a few of our ideas for possible changes in WebXPRT 4, including new web technologies that may work well in a browser benchmark. Today, we’re going to focus on one of those technologies, WebAssembly, in more detail.

WebAssembly (WASM) is a binary instruction format that works across all modern browsers. WASM provides a sandboxed environment that operates at native speeds and takes advantage of common hardware specs across platforms. WASM’s capabilities offer web developers a great deal of flexibility for running complex client applications in the browser. That level of flexibility may enable workload scenario options for WebXPRT 4 such as gaming, video editing, VR, virtual machines, and image recognition. We’re excited about those possibilities, but it remains to be seen which WASM use cases will meet the criteria we look for when considering new WebXPRT workloads, such as relevancy to real life, consistency and replicability, and the broadest-possible level of cross-browser support.

One WASM workload that we’re investigating is a web-based machine learning workload with TensorFlow for JavaScript (TensorFlow.js). TensorFlow.js offers pre-trained models for a wide variety of tasks, including image classification, object detection, sentence encoding, and natural language processing. TensorFlow.js originally used WebGL technology on the back end, but now it’s possible to run the workload using WASM. We could also use this technology to enhance one of WebXPRT’s existing AI-themed workloads, such as Organize Album using AI or Encrypt Notes and OCR Scan.

We’re can’t yet say that a WASM workload will definitely appear in WebXPRT 4, but the technology is promising. Do you have any experience with WASM, or ideas for WASM workloads? There’s still time for you to help shape the future of WebXPRT 4, so let us know what you think!

Justin

Posted in benchmark, BenchmarkXPRT, Browser-based benchmarks, Collaborative benchmark development, Cross-platform benchmarks, image processing, Machine learning, Performance benchmarking, TensorFlow, Virtual reality, Web-based testing, WebAssembly, WebGL, WebXPRT, WebXPRT 3 | Tagged , , , , , , , , , |

Moving forward with WebXPRT 4

Justin Greene
By Justin Greene
on March 4, 2021

In the coming months, we’ll be moving forward with the first stages of the WebXPRT 4 development process. It’s been a while since we last asked readers to send their thoughts about web technologies and workloads that may be a good fit for WebXPRT 4, but we’re still very much open to ideas. If you missed our previous posts about possible changes for WebXPRT 4, we recap the most prominent ideas below. We also request specific feedback regarding a potential battery life component.

  • Community members have asked about a WebXPRT 4 battery life test. Any such test would likely be very similar to the performance-weighted battery life test in CrXPRT 2 (as opposed to a simple rundown test). While WebXPRT runs in almost any browser, cross-browser compatibility issues could cause a WebXPRT battery life test to run in only one browser. If this turned out to be the case, would you still be interested in using the battery life test? Please let us know.
  • One of the most promising ideas is the potential addition of one or more WebAssembly (WASM) workloads. WASM is a low-level, binary instruction format that works across all modern browsers. It offers web developers a great deal of flexibility and provides the speed and efficiency necessary for running complex client applications in the browser. WASM enables a variety of workload scenario options, including gaming, video editing, VR, virtual machines, image recognition, and interactive educational content.
  • We are also considering adding a web-based machine learning workload with TensorFlow for JavaScript (TensorFlow.js). TensorFlow.js offers pre-trained models for a wide range of tasks including image classification, object detection, sentence encoding, and natural language processing. We could also use this technology to enhance one of WebXPRT’s existing AI-themed workloads, such as Organize Album using AI or Encrypt Notes and OCR Scan.
  • Other ideas include using a WebGL-based workload to target GPUs, and simulating common web applications.

We’ll start work on WebXPRT 4 soon, but there’s still time to send your comments and ideas, so please do so as quickly as possible!

Justin

Posted in battery life, benchmark, BenchmarkXPRT, Browser-based benchmarks, Collaborative benchmark development, Future of performance evaluation, Performance benchmarking, TensorFlow, WebAssembly, WebGL, WebXPRT, WebXPRT 3 | Tagged , , , , , , , , , |

The AIXPRT learning tool is now live (and a CloudXPRT version is on the way)!

Justin Greene
By Justin Greene
on December 10, 2020

We’re happy to announce that the AIXPRT learning tool is now live! We designed the tool to serve as an information hub for common AIXPRT topics and questions, and to help tech journalists, OEM lab engineers, and everyone who is interested in AIXPRT find the answers they need in as little time as possible.

The tool features four primary areas of content:

  • The Q&A section provides quick answers to the questions we receive most from testers and the tech press.
  • The AIXPRT: the basics section describes specific topics such as the benchmark’s toolkits, networks, workloads, and hardware and software requirements.
  • The testing and results section covers the testing process, metrics, and how to publish results.
  • The AI/ML primer provides brief, easy-to-understand definitions of key AI and ML terms and concepts for those who want to learn more about the subject.

The first screenshot below shows the home screen. To show how some of the popup information sections appear, the second screenshot shows the Inference tasks (workloads) entry in the AI/ML Primer section. 

We’re excited about the new AIXPRT learning tool, and we’re also happy to report that we’re working on a version of the tool for CloudXPRT. We hope to make the CloudXPRT tool available early next year, and we’ll post more information in the blog as we get closer to taking it live.

If you have any questions about the tool, please let us know!

Justin

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT, Cloud, CloudXPRT, computer vision, Cross-platform benchmarks, data analytics, image classification, Machine learning, object detection, OpenVINO, Performance benchmarking, TensorFlow, TensorRT | Tagged , , , , , , |

A first look at the upcoming AIXPRT learning tool

Justin Greene
By Justin Greene
on October 29, 2020

Last month, we announced that we’re working on a new AIXPRT learning tool. Because we want tech journalists, OEM lab engineers, and everyone who is interested in AIXPRT to be able to find the answers they need in as little time as possible, we’re designing this tool to serve as an information hub for common AIXPRT topics and questions.

We’re still finalizing aspects of the tool’s content and design, so some details may change, but we can now share a sneak peak of the main landing page. In the screenshot below, you can see that the tool will feature four primary areas of content:

  • The FAQ section will provide quick answers to the questions we receive most from testers and the tech press.
  • The AIXPRT basics section will describe specific topics such as the benchmark’s toolkits, networks, workloads, and hardware and software requirements.
  • The testing and results section will cover the testing process, the metrics the benchmark produces, and how to publish results.
  • The AI/ML primer will provide brief, easy-to-understand definitions of key AI and ML terms and concepts for those who want to learn more about the subject.

We’re excited about the new AIXPRT learning tool, and will share more information here in the blog as we get closer to a release date. If you have any questions about the tool, please let us know!

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, BenchmarkXPRT, Cross-platform benchmarks, Interactive tools, Machine learning, OpenVINO, Performance benchmarking, results, TensorFlow, TensorRT |

We’re working on an update for the AIXPRT OpenVINO workload

Justin Greene
By Justin Greene
on July 23, 2020

Shortly after the initial AIXPRT release, we noted that each of the toolkits AIXPRT uses (Intel OpenVINO, TensorFlow, NVIDIA TensorRT, and Apache MXNet) is on its own development schedule, and new versions will sometimes appear with little warning. When this happens, we’ll have to respond by updating specific AIXPRT installation packages, giving AIXPRT testers relatively short notice.

This is one of those times! Intel recently released OpenVINO 2020.3 Long-Term Support (LTS), and we’re planning to update the AIXPRT OpenVINO packages with the LTS version. The LTS version targets environments that benefit from maximum stability, and don’t require a constant stream of new tools and feature changes. In other words, it’s well suited for a benchmark, and we think it’s a good fit for AIXPRT moving forward.

We don’t yet know what impact the new version will have on AIXPRT OpenVINO test results. A substantial part of the development process will involve testing the new packages on a variety of platforms to see how performance changes. We’ll communicate our findings here in the blog, so AIXPRT testers will know what to expect.

Thankfully, the modular nature of the AIXPRT installation packages ensures that we don’t need to revise the entire AIXPRT suite every time a toolkit update goes live. If you test with only TensorFlow, TensorRT, or MXNet, or a combination of those toolkits, this update won’t affect your testing.

We’re not ready to commit to a release date for the new build, but anticipate it will be in September.

If you have any questions about AIXPRT or OpenVINO, please let us know!

Justin

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT, BenchmarkXPRT development community, MXNet, OpenVINO, TensorFlow, TensorRT | Tagged , , , , , |

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