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Category: object detection

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

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

Potential web technology additions for WebXPRT 4

A few months ago, we invited readers to send in their thoughts and ideas about web technologies and workload scenarios that may be a good fit for the next WebXPRT. We’d like to share a few of those ideas today, and we invite you to continue to send your feedback. We’re approaching the time when we need to begin firming up plans for a WebXPRT 4 development cycle in 2021, but there’s still plenty of time for you to help shape the future of the benchmark.

One of the most promising ideas for WebXPRT 4 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.

In addition, the Chrome team is dropping Portable Native Client (PNaCL) support in favor of WASM, which is why we had to remove a PNaCL workload when updating CrXPRT 2015 to CrXPRT 2. We generally model CrXPRT workloads on existing WebXPRT workloads, so familiarizing ourselves with WASM could ultimately benefit more than one XPRT benchmark.

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 variety of tasks including image classification, object detection, sentence encoding, natural language processing, and more. 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 investigating ways to incorporate a battery life test. What do you think? Let us know!

Justin

Make confident choices about your company’s future tech with the XPRTs

Durham, NC, April 23, 2020 — Principled Technologies and the BenchmarkXPRT Development Community have released a video on the benefits of consulting the XPRTs before committing to new technology purchases.

AIXPRT, one of the battery of XPRT benchmark tools, runs image-classification and object-detection workloads to determine how well tech handles AI and machine learning.

CloudXPRT, another XPRT tool, accurately measures the end-to-end performance of modern, cloud-first applications deployed on infrastructure as a service (IaaS) platforms – allowing corporate decision-makers to select the best configuration for every objective.

All of the XPRTs give companies the real-world information necessary to determine which prospective future tech p – and which will disappoint

According to the video, “The XPRTs don’t just look at specs and features; they gauge a technology solution’s real-world performance and capabilities. So you know whether switching environments is worth the investment. How well solutions support machine learning and other AI capabilities. If next-gen releases beat their rivals or fall behind the curve.”

Watch the video at facts.pt/pyt88k5. To learn more about how AIXPRT, CloudXPRT, WebXPRT, MobileXPRT, TouchXPRT, CrXPRT, and HDXPRT can help IT decision-makers can make confident choices about future purchases, go to www.BenchmarkXPRT.com.

About Principled Technologies, Inc.
Principled Technologies, Inc. is the leading provider of technology marketing and learning & development services. It administers the BenchmarkXPRT Development Community.

Principled Technologies, Inc. is located in Durham, North Carolina, USA. For more information, please visit www.principledtechnologies.com.

Company Contact
Justin Greene
BenchmarkXPRT Development Community
Principled Technologies, Inc.
1007 Slater Road, Suite #300
Durham, NC 27703
BenchmarkXPRTsupport@PrincipledTechnologies.com

Understanding AIXPRT’s default number of requests

A few weeks ago, we discussed how AIXPRT testers can adjust the key variables of batch size, levels of precision, and number of concurrent instances by editing the JSON test configuration file in the AIXPRT/Config directory. In addition to those key variables, there is another variable in the config file called “total_requests” that has a different default setting depending on the AIXPRT test package you choose. This setting can significantly affect a test run, so it’s important for testers to know how it works.

The total_requests variable specifies how many inference requests AIXPRT will send to a network (e.g., ResNet-50) during one test iteration at a given batch size (e.g., Batch 1, 2, 4, etc.). This simulates the inference demand that the end users place on the system. Because we designed AIXPRT to run on different types of hardware, it makes sense to set the default number of requests for each test package to suit the most likely hardware environment for that package.

For example, testing with OpenVINO on Windows aligns more closely with a consumer (i.e., desktop or laptop) scenario than testing with OpenVINO on Ubuntu, which is more typical of server/datacenter testing. Desktop testers require a much lower inference demand than server testers, so the default total_requests settings for the two packages reflect that. The default for the OpenVINO/Windows package is 500, while the default for the OpenVINO/Ubuntu package is 5,000.

Also, setting the number of requests so low that a system finishes each workload in less than 1 second can produce high run-to-run variation, so our default settings represent a lower boundary that will work well for common test scenarios.

Below, we provide the current default total_requests setting for each AIXPRT test package:

  • MXNet: 1,000
  • OpenVINO Ubuntu: 5,000
  • OpenVINO Windows: 500
  • TensorFlow Ubuntu: 100
  • TensorFlow Windows: 10
  • TensorRT Ubuntu: 5,000
  • TensorRT Windows: 500


Testers can adjust these variables in the config file according to their own needs. Finding the optimal combination of machine learning variables for each scenario is often a matter of trial and error, and the default settings represent what we think is a reasonable starting point for each test package.

To adjust the total_requests setting, start by locating and opening the JSON test configuration file in the AIXPRT/Config directory. Below, we show a section of the default config file (CPU_INT8.json) for the OpenVINO-Windows test package (AIXPRT_1.0_OpenVINO_Windows.zip). For each batch size, the total_requests setting appears at the bottom of the list of configurable variables. In this case, the default setting Is 500. Change the total_requests numerical value for each batch size in the config file, save your changes, and close the file.

Total requests snip

Note that if you are running multiple concurrent instances, OpenVINO and TensorRT automatically distribute the number of requests among the instances. MXNet and TensorFlow users must manually allocate the instances in the config file. You can find an example of how to structure manual allocation here. We hope to make this process automatic for all toolkits in a future update.

We hope this information helps you understand the total_requests setting, and why the default values differ from one test package to another. If you have any questions or comments about this or other aspects of AIXPRT, please let us know.

Justin

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 AIXPRT.com 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.

Justin

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