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GCP IoT Setup

GCP SDK Setup

Device: Camera Setup


0. Prerequisites


1. Install the dependencies for the Apache MXNet framework

# Temporarily increase the swap size for installing scipi
sudo vim /etc/dphys-swapfile
# Change CONF_SWAPSIZE = 1000
/etc/init.d/dphys-swapfile restart

# Activate environment
source activate pinenuts

# Save the file armv7l.sh
cd Iot_EdgeComputing/src/conda_env/conda_aws/aws_greegrass_image_connector
sudo bash armv71.sh


2. Install the Apache MXNet Framework

Download ggc-mxnet-v1.2.1-python-raspi.tar.gz

# Install the MXNet framework
scp ggc-mxnet-v1.2.1-python-raspi.tar.gz pi@192.168.178.29:/home/pi

# Install the MXNet framework
cd /home/pi
tar -xzf ggc-mxnet-v1.2.1-python-raspi.tar.gz
cd ggc-mxnet-v1.2.1-python-raspi
./mxnet_installer.sh

# Copy file to my project repo
scp greengrassObjectClassification.zip ~/Documents/Iot_EdgeComputing/src/conda_env/conda_aws/ml_interface


3. Create an MXNet Model Package

# 1) Download three files
mkdir ml_interface
cd ml_interface
curl -O https://s3.amazonaws.com/model-server/model_archive_1.0/examples/squeezenet_v1.1/squeezenet_v1.1-symbol.json

curl -O https://s3.amazonaws.com/model-server/model_archive_1.0/examples/squeezenet_v1.1/squeezenet_v1.1-0000.params

curl -O https://s3.amazonaws.com/model-server/model_archive_1.0/examples/squeezenet_v1.1/synset.txt

# 2) Zip files
sudo zip -r squeezenet.zip squeezenet_v1.1-symbol.json squeezenet_v1.1-0000.params synset.txt

4. Create and Publish a Lambda Function

5: Add the Lambda Function to the Greengrass Group

6. Add Resources to the Greengrass Group

drawing


7. Add a Subscription to the Greengrass Group

8. Deploy the Greengrass Group

9. Configuring an NVIDIA Jetson TX2

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