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.
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!
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.
If you have any questions or comments (about concurrent instances or anything else), please feel free to contact us.
AIXPRT Community Preview 2 (CP2) has been generating quite a bit of interest among the BenchmarkXPRT Development Community and members of the tech press. We’re excited that the tool has piqued curiosity and that folks are recognizing its value for technical analysis. When talking with folks about test setup and configuration, we keep hearing the same questions:
- How do I find the exact toolkit or package that I need?
- How do I find the instructions for a specific toolkit?
- What test configuration variables are most important for producing consistent, relevant results?
- How do I know which values to choose when configuring options such as iterations, concurrent instances, and batch size?
In the coming weeks, we’ll be working to provide detailed answers to questions about test configuration. In response to the confusion about finding specific packages and instructions, we’ve redesigned the CP2 download page to make it easier for you to find what you need. Below, we show a snapshot from the new CP2 download table. Instead of having to download the entire CP2 package that includes the OpenVINO, TensorFlow, and TensorRT in TensorFlow test packages, you can now download one package at a time. In the Documentation column, we’ve posted package-specific instructions, so you won’t have to wade through the entire installation guide to find the instructions you need.
We hope these changes make it easier for people to experiment with AIXPRT. As always, please feel free to contact us with any questions or comments you may have.
We’re glad to see so much interest in the AIXPRT CP2 build. Over the past few days, we’ve received two questions about the setup process: 1) where to find instructions for setting up AIXPRT on Windows, and 2) whether we could make it easier to install Intel OpenVINO on test systems.
In response to the first question, testers can find the relevant instructions for each framework in the readme files included in the AIXPRT install package. Instructions for Windows installation are in section 3 of the OpenVINO and TensorFlow readmes. Please note that whether you’re running AIXPRT on Ubuntu or Windows, be sure to read the “Known Issues” section in the readme, as there may be issues relevant to your specific configuration.
The readme files for each respective framework in the CP2 package are located here:
We’re also working on consolidating the instructions into a central document that will make it easier for everyone to find the instructions they need.
In response to the question about OpenVINO installation, we’re working on an AIXPRT CP2 package that includes a precompiled version of OpenVINO R5.0.1 for easy installation on Windows via a few quick commands, and a script that installs the necessary OpenVINO dependencies. We’re currently testing the build, and we’ll make it available to testers as soon as possible.
The tests themselves will not change, so the new build will not influence existing results from Ubuntu or Windows. We hope it will simply facilitate the setup and testing process for many users.
We appreciate each bit of feedback that we receive, so if you have any suggestions for AIXPRT, please let us know!
Over the last few weeks, we’ve gotten great feedback about the kinds of data points people are looking for in AIXPRT results, as well as suggestions for how to improve the AIXPRT results viewer. To make it easier for visitors to find what they’re looking for, we’ve made a number of changes:
- You can now filter results in categories such as framework, target hardware, batch size, and precision, and can designate minimum throughput and maximum latency scores. When you select a value from a drop-down menu or enter text, the results change immediately to reflect the filter.
- You can search for variables such as processor vendor or processor speed.
- The viewer displays eight results per page by default and lets you change this to 16, 48, or Show all.
The following features of the viewer, which have been present previously, can help you to navigate more efficiently:
- Click the tabs at the top of the table to switch from ResNet-50 network results to SSD-MobileNet network results.
- Click the header of any column to sort the data on that variable. One click sorts A-Z and two clicks sort Z-A.
- Click the link in the Source column to visit a detailed page on that result. The page contains additional test configuration and system hardware information and lets you download results files.
We hope these changes will improve the utility of the results table. We’ll continue to add features to improve the experience. If you have any suggestions, please let us know!