While working on a Raspberry Pi image that had been used prior by an electrical engineer to setup all of the dependencies for the hardware, there was an error when trying to upgrade to use Tensorflow. Tensorflow was needed to run a model trained with Cognitive Services: Custom Vision Service. The error was when the script imported Numpy. That caused the following error:
numpy/core/_multiarray_umath.cpython-35m-arm-linux-gnueabihf.so: undefined symbol: cblas_sgemm
To remedy this, all of the installations of Numpy had to be uninstalled. The following commands were run:
- apt-get remove python-numpy
- apt-get remove python3-numpy
- pip3 uninstall numpy
After all three of those commands complete, Numpy was reinstalled using the package provided for raspian:
apt-get install python3-numpy
Designing an Intelligent Edge in Microsoft Azure was just published on Pluralsight! Check it out. Here is a synopsis of what’s in it:
This course targets software developers that are looking to integrate AI solutions in edge scenarios ranging from an edge data center down to secure microcontrollers. This course will showcase how to design solutions using Microsoft Azure.
Cloud computing has moved more and more out of the cloud and onto the edge. In this course, Designing an Intelligent Edge in Microsoft Azure, you will learn foundational knowledge of edge computing, its intersection with AI, and how to utilize both with Microsoft Azure. First, you will learn the concepts of edge computing. Next, you will discover how to create an edge solution utilizing Azure Stack, Azure Data Box Edge, and Azure IoT Edge. Finally, you will explore how to utilize off the shelf AI and build your own for Azure IoT Edge. When you are finished with this course, you will have the skills and knowledge of AI on the edge needed to architect your next edge solution. Software required: Microsoft Azure, .NET
For one project, there was a need for multiple models within the same Python application. These models were trained using the Cognitive Services: Custom Vision Service. There are two steps to using an exported model:
- Prepare the image
- Classify the image
Prepare an image for prediction
Classify the image
To run multiple models in Python was fairly simple. Simply call tf.reset_default_graph() after saving the loaded session into memory.
After the CustomVisionCategorizer is create, just call score and it will score with the labels in the map.
I look forward to speaking on AI on the Edge at DotNetSouth.Tech. This year is the conference’s first year so check it out.
AI on the Edge
The next evolution in cloud computing is a smarter application not in the cloud. As the cloud has continued to evolve, the applications that utilize it have had more and more capabilities of the cloud. This presentation will show how to push logic and machine learning from the cloud to an edge application. Afterward, creating edge applications which utilize the intelligence of the cloud should become effortless.