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Tag Archives: machine learning

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 [...]

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 [...]

AIXPRT Community Preview 3 is here!

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 [...]

Understanding the basics of AIXPRT precision settings

A few weeks ago, we discussed one of AIXPRT’s key configuration variables, batch size. Today, we’re discussing another key variable: the level of precision. In the context of machine learning (ML) inference, the level of precision refers to the computer number format (FP32, FP16, or INT8) representing the weights (parameters) a network model uses when [...]

Understanding AIXPRT batch size

Last week, we wrote about the basics of understanding AIXPRT results. This week, we’re discussing one of the benchmark’s key test configuration variables: batch size. Talking about batch size can be confusing, because the phrase can refer to different concepts depending on the machine learning (ML) context in which it’s used. AIXPRT tests inference, so [...]

Understanding AIXPRT results

Last week, we discussed the changes we made to the AIXPRT Community Preview 2 (CP2) download page as part of our ongoing effort to make AIXPRT easier to use. This week, we want to discuss the basics of understanding AIXPRT results by talking about the numbers that really matter and how to access and read [...]

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