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Moving forward with WebXPRT 4

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!


The Introduction to AIXPRT white paper is now available!

Today, we published the Introduction to AIXPRT white paper. The paper serves as an overview of the benchmark and a consolidation of AIXPRT-related information that we’ve published in the XPRT blog over the past several months. For folks who are completely new to AIXPRT and veteran testers who need to brush up on pre-test configuration procedures, we hope this paper will be a quick, one-stop reference that helps reduce the learning curve.

The paper describes the AIXPRT toolkits and workloads, adjusting key test parameters (batch size, level of precision, number of concurrent instances, and default number of requests), using alternate test configuration files, understanding and submitting results, and accessing the source code.

We hope that Introduction to AIXPRT will prove to be a valuable resource. Moving forward, readers will be able to access the paper from the Helpful Info box on and the AIXPRT section of our XPRT white papers page. If you have any questions about AIXPRT, please let us know!


AIXPRT’s unique development path

With four separate machine learning toolkits on their own development schedules, three workloads, and a wide range of possible configurations and use cases, AIXPRT has more moving parts than any of the XPRT benchmark tools to date. Because there are so many different components, and because we want AIXPRT to provide consistently relevant evaluation data in the rapidly evolving AI and machine learning spaces, we anticipate a cadence of AIXPRT updates in the future that will be more frequent than the schedules we’ve used for other XPRTs in the past. With that expectation in mind, we want to let AIXPRT testers know that when we release an AIXPRT update, they can expect minimized disruption, consideration for their testing needs, and clear communication.

Minimized disruption

Each AIXPRT toolkit (Intel OpenVINO, TensorFlow, NVIDIA TensorRT, and Apache MXNet) is on its own development schedule, and we won’t always have a lot of advance notice when new versions are on the way. Hypothetically, a new version of OpenVINO could release one month, and a new version of TensorRT just two months later. Thankfully, the modular nature of AIXPRT’s installation packages ensures that we won’t need to revise the entire AIXPRT suite every time a toolkit update goes live. Instead, we’ll update each package individually when necessary. This means that if you only test with a single AIXPRT package, updates to the other packages won’t affect your testing. For us to maintain AIXPRT’s relevance, there’s unfortunately no way to avoid all disruption, but we’ll work to keep it to a minimum.

Consideration for testers

As we move forward, when software compatibility issues force us to update an AIXPRT package, we may discover that the update has a significant effect on results. If we find that results from the new package are no longer comparable to those from previous tests, we’ll share the differences that we’re seeing in our lab. As always, we will use documentation and versioning to make sure that testers know what to expect and  that there’s no confusion about which package to use.

Clear communication

When we update any package, we’ll make sure to communicate any updates in the new build as clearly as possible. We’ll document all changes thoroughly in the package readmes, and we’ll talk through significant updates here in the blog. We’re also available to answer questions about AIXPRT and any other XPRT-related topic, so feel free to ask!


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 ( 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.


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 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.


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