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Author Archives: Justin Greene

A necessary update for HDXPRT 4

If you tried to install HDXPRT 4 over the past few days, you likely noticed that Adobe Photoshop Elements 2018, the version the Edit Photos scenario uses, is no longer available on the Adobe Photoshop Elements download page. In the past, Adobe has provided access to multiple older versions of their software for some time [...]

How to use alternate configuration files with AIXPRT

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 [...]

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 [...]

An update on AIXPRT development

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 [...]

An updated HDXPRT 4 v1.1 installer package

Today, we published an updated HDXPRT 4 v1.1 installer package that addresses an issue brought to light by HDXPRT testers and our own follow-up testing. We’ve also encountered an issue caused by anti-virus program interference during the HDXPRT installation process, so we’re providing steps for a workaround below. Neither the updated build nor the workaround [...]

Understanding concurrent instances in AIXPRT

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 [...]

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