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Category: Machine learning

Coming soon: An interactive AIXPRT selector tool

Justin Greene
By Justin Greene
on October 31, 2019

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

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT development community, image classification, IoT, Machine learning, MXNet, object detection, OpenVINO, Performance benchmarking, recommender system, TensorFlow, TensorRT, Wide and Deep | Tagged , , , , , , , , |

Progress updates: HDXPRT 4 and AIXPRT

Justin Greene
By Justin Greene
on October 24, 2019

Over the next few weeks, we’re expecting to publish both an updated HDXPRT 4 build and the AIXPRT public release (GA). Timelines may change as a result of development or testing issues, but we want to provide a brief update on where both projects stand.

HDXPRT 4

As we discussed last week, Adobe removed Photoshop Elements 2018, the application that HDXPRT 4 uses for the Edit Photos scenario, from their public download page. This means that new HDXPRT 4 testers are currently unable to successfully complete the benchmark installation process.

To fix the problem, we adapted HDXPRT 4’s Edit Photos scripts to use PSE 2020, and we hope to begin testing by the end of this week. We appreciate everyone’s patience as we put a solution in place, and we’ll publish the new build as soon as possible.

AIXPRT

We’re now in the third week of the AIXPRT Community Preview 3 (CP3) period, and we’re working on finalizing the AIXPRT GA installation packages for release. Because several of AIXPRT’s component toolkits release updates on a regular basis, it’s likely that we’ll need to update AIXPRT’s installation packages more frequently than we have with previous XPRT benchmarks. At the moment, we’re working to integrate and test recent updates to OpenVINO and TensorRT before GA.

As usual, we’ll keep you informed here in the blog. If you have any questions or comments about HDXPRT or AIXPRT, please let us know. We do value your feedback.

Justin

Posted in Adobe, AI, AIXPRT, Application-based benchmarks, benchmark, Benchmarking, BenchmarkXPRT, BenchmarkXPRT development community, Community Preview, HDXPRT, HDXPRT 4, HDXPRT development process, Machine learning, OpenVINO, Performance benchmarking, TensorRT | Tagged , , , , , , , |

How to use alternate configuration files with AIXPRT

Justin Greene
By Justin Greene
on October 10, 2019

In last week’s AIXPRT Community Preview 3 announcement, we mentioned the new public GitHub repository that we’re using to publish AIXPRT-related information and resources. In addition to the installation readmes for each AIXPRT installation package, the repository contains a selection of alternative test config files that testers can use to quickly and easily change a test’s parameters.

As we discussed in previous blog entries about batch size, levels of precision, and number of concurrent instances, AIXPRT testers can adjust each of these key variables by editing the JSON file in the AIXPRT/Config directory. While the process is straightforward, editing each of the variables in a config file can take some time, and testers don’t always know the appropriate values for their system. To address both of these issues, we are offering a selection of alternative config files that testers can download and drop into the AIXPRT/Config directory.

In the GitHub repository, we’ve organized the available config files first by operating system (Linux_Ubuntu and Windows) and then by vendor (All, Intel, and NVIDIA). Within each section, testers will find preconfigured JSON files set up for several scenarios, such as running with multiple concurrent instances on a system’s CPU or GPU, running with FP32 precision instead of FP16, etc. The picture below shows the preconfigured files that are currently available for systems running Ubuntu on Intel hardware.

AIXPRT public repository snip 2

Because potential AIXPRT use cases cut across a wide range of hardware segments, including desktops, edge devices, and servers, not all AIXPRT workloads and configs will be applicable to each segment. As we move towards the AIXPRT GA, we’re working to find the best way to parse out these distinctions and communicate them to end users. In many cases, the ideal combination of test configuration variables remains an open question for ongoing research. However, we hope the alternative configuration files will help by giving testers a starting place.

If you experiment with an alternative test configuration file, please note that it should replace the existing default config file. If more than one config file is present, AIXPRT will run all the configurations and generate a separate result for each. More information about the config files and detailed instructions for how to handle the files are available in the EditConfig.md document in the public repository.

We’ll continue to keep everyone up to date with AIXPRT news here in the blog. If you have any questions or comments, please let us know.

Justin

Posted in AI, AIXPRT, benchmark, Benchmark metrics, Collaborative benchmark development, Community Preview, GitHub, Intel, Linux, Machine learning, NVIDIA, Performance benchmarking, Ubuntu | Tagged , , , , , , , , , , |

AIXPRT Community Preview 3 is here!

Justin Greene
By Justin Greene
on October 3, 2019

We’re happy to announce that the AIXPRT Community Preview 3 (CP3) is now available! As we discussed in last week’s blog, testers can expect three significant changes in AIXPRT CP3:

  • We updated support for the Ubuntu test packages from Ubuntu version 16.04 LTS to version 18.04 LTS.
  • We added TensorRT test packages for Windows and Ubuntu. Previously, AIXPRT testers could test only the TensorFlow variant of TensorRT. Now, they can use TensorRT to test systems with NVIDIA GPUs.
  • We added the Wide and Deep recommender system workload with the MXNet toolkit for Ubuntu systems.


To access AIXPRT CP3, click this access link and submit the brief information form unless you’ve already done so for CP2. You will then gain access to the AIXPRT community preview page. (If you’re not already a BenchmarkXPRT Development Community member, we’ll contact you with more information about your membership.)

On the community preview page, a download table displays the currently available AIXPRT CP3 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 corresponding Readme link. Instead of providing installation guide PDFs as we did for CP2, we are now directing testers to a public GitHub repository. The repository contains the installation readmes for all the test packages, as well as a selection of alternative test configuration files. We’ll discuss the alternative configuration files in more detail in a future blog post.

Note: Those who have access to the existing AIXPRT GitHub repository will be able to access CP3 in the same way as previous versions.

We’ll continue to keep everyone up to date with AIXPRT news here in the blog. If you have any questions or comments, please let us know.

Justin

Posted in AI, AIXPRT, benchmark, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Future of performance evaluation, image classification, Machine learning, MXNet, NVIDIA, object detection, OpenVINO, Performance benchmarking, recommender system, ResNet-50, SSD-MobileNet v1, TensorFlow, TensorRT, Ubuntu, Wide and Deep | Tagged , , , , , , , , , , , , , |

An update on AIXPRT development

Justin Greene
By Justin Greene
on September 26, 2019

It’s been a while since we last discussed the AIXPRT Community Preview 3 (CP3) release schedule, so we want to let everyone know where things stand. Testing for CP3 has taken longer than we predicted, but we believe we’re nearly ready for the release.

Testers can expect three significant changes in AIXPRT CP3. First, we updated support for the Ubuntu test packages. During the initial development phase of AIXPRT, Ubuntu version 16.04 LTS (Long Term Support) was the most current LTS version, but version 18.04 is now available.

Second, we have added TensorRT test packages for Windows and Ubuntu. Previously, AIXPRT testers could test only the TensorFlow variant of TensorRT. Now, they can use TensorRT to test systems with NVIDIA GPUs.

Third, we have added the Wide and Deep recommender system workload with the MXNet toolkit. Recommender systems are AI-based information-filtering tools that learn from end user input and behavior patterns and try to present them with optimized outputs that suit their needs and preferences. If you’ve used Netflix, YouTube, or Amazon accounts, you’ve encountered recommender systems that learn from your behavior.

Currently, the recommender system workload in AIXPRT CP3 is available for Ubuntu testing, but not for Windows. Recommender system inference workloads typically run on datacenter hardware, which tends to be Linux based. If enough community members are interested in running the MXNet/Wide and Deep test package on Windows, we can investigate what that would entail. If you’d like to see that option, please let us know.

As always, if you have any questions about the AIXPRT development process, feel free to ask!

Justin

Posted in AI, AIXPRT, benchmark, Benchmarking, BenchmarkXPRT development community, Collaborative benchmark development, Community Preview, Linux, Machine learning, MXNet, Performance benchmarking, Ubuntu, What makes a good benchmark?, Wide and Deep, Windows |

Understanding concurrent instances in AIXPRT

Justin Greene
By Justin Greene
on September 12, 2019

Over the past few weeks, we’ve discussed several of the key configuration variables in AIXPRT, such as batch size and level of precision. Today, we’re discussing another key variable: number of concurrent instances. In the context of machine learning inference, this refers to how many instances of the network model (ResNet-50, SSD-MobileNet, etc.) the benchmark runs simultaneously.

By default, the toolkits in AIXPRT run one instance at a time and distribute the compute load according to the characteristics of the CPU or GPU under test, as well as any relevant optimizations or accelerators in the toolkit’s reference library. By setting the number of concurrent instances to a number greater than one, a tester can use multiple CPUs or GPUs to run multiple instances of a model at the same time, usually to increase throughput.

With multiple concurrent instances, a tester can leverage additional compute resources to potentially achieve higher throughput without sacrificing latency goals.

In the current version of AIXPRT, testers can run multiple concurrent instances in the OpenVINO, TensorFlow, and TensorRT toolkits. When AIXPRT Community Preview 3 becomes available, this option will extend to the MXNet toolkit. OpenVINO and TensorRT automatically allocate hardware for each instance and don’t let users make manual adjustments. TensorFlow and MXNet require users to manually bind instances to specific hardware. (Manual hardware allocation for multiple instances is more complicated than we can cover today, so we may devote a future blog entry to that topic.)

Setting the number of concurrent instances in AIXPRT

The screenshot below shows part of a sample config file (the same one we used when we discussed batch size and precision). The value in the “concurrent instances” row indicates how many concurrent instances will be operating during the test. In this example, the number is one. To change that value, a tester simply replaces it with the desired number and saves the changes.

Config_snip

If you have any questions or comments (about concurrent instances or anything else), please feel free to contact us.

Justin

Posted in AI, AIXPRT, Benchmarking, Benchmarking computing devices, Community Preview, Cross-platform benchmarks, Machine learning, MXNet, OpenVINO, Performance benchmarking, ResNet-50, SSD-MobileNet v1, TensorFlow, TensorRT | Tagged , , , , , , , , , , |

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