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Category: Servers

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.


XPRTs in the datacenter

The XPRTs have been very successful on desktops, notebooks, tablets, and phones. People have run WebXPRT over 295,000 times. It and other benchmarks such as MobileXPRT, HDXPRT, and CrXPRT are important tools globally for evaluating device performance on various consumer and business client platforms.

We’ve begun branching out with tests for edge devices with AIXPRT, our new artificial intelligence benchmark. While typical consumers won’t be able to run AIXPRT on their devices initially, we feel that it is important for the XPRTs to play an active role in a critical emerging market. (We’ll have some updates on the AIXPRT front in the next few weeks.)

Recently, both community members and others have asked about the possibility of the XPRTs moving into the datacenter. Folks face challenges in evaluating the performance and suitability to task of such datacenter mainstays as servers, storage, networking infrastructure, clusters, and converged solutions. These challenges include the lack of easy-to-run benchmarks, the complexity and cost of the equipment (multi-tier servers, large amounts of storage, and fast networks) necessary to run tests, and confusion about best testing practices.

PT has a lot of expertise in measuring datacenter performance, as you can tell from the hundreds of datacenter-focused test reports on our website. We see great potential in our working with the BenchmarkXPRT Development Community to help in this area. It is very possible that, as with AIXPRT, our approach to datacenter benchmarks would differ from the approach we’ve taken with previous benchmarks. While we have ideas for useful benchmarks we might develop down the road, more immediate steps could be drafting white papers, developing testing guidelines, or working with vendors to set up a lab.

Right now, we’re trying to gauge the level of interest in having such tools and in helping us carry out these initiatives. What are the biggest challenges you face in datacenter-focused performance and suitability to task evaluations? Would you be willing to work with us in this area? We’d love to hear from you and will be reaching out to members of the community over the coming weeks.

As always, thanks for your help!


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