BenchmarkXPRT Blog banner

Category: TensorRT

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

A first look at the upcoming AIXPRT learning tool

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

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

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

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!

Justin

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.

Check out the other XPRTs:

Forgot your password?