Set Up Your Device
You will need to install packages on your edge device so that your device can
make inferences. You will also need to either install
AWS IoT Greengrass core or
Deep Learning Runtime (DLR)coco_ssd_mobilenet
object detection algorithm and you
will use DLR.
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Install additional packages
In addition to Boto3, you must install certain libraries on your edge device. What libraries you install depends on your use case.
For example, for the
coco_ssd_mobilenet
object detection algorithm you downloaded earlier, you need to install NumPyfor data manipulation and statistics, PIL to load images, and Matplotlib to generate plots. You also need a copy of TensorFlow if you want to gauge the impact of compiling with Neo versus a baseline. !pip3 install numpy pillow tensorflow matplotlib
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Install inference engine on your device
To run your Neo-compiled model, install the Deep Learning Runtime (DLR)
on your device. DLR is a compact, common runtime for deep learning models and decision tree models. On x86_64 CPU targets running Linux, you can install the latest release of the DLR package using the following pip
command:!pip install dlr
For installation of DLR on GPU targets or non-x86 edge devices, refer to Releases
for prebuilt binaries, or Installing DLR for building DLR from source. For example, to install DLR for Raspberry Pi 3, you can use: !pip install https://neo-ai-dlr-release.s3-us-west-2.amazonaws.com/v1.3.0/pi-armv7l-raspbian4.14.71-glibc2_24-libstdcpp3_4/dlr-1.3.0-py3-none-any.whl