SETTING UP THE RASPBERRY PI

Getting the pi set up for GCP:

Create an environment variable for the correct distribution

export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)"

Add the Cloud SDK distribution URI as a package

echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list

Import the Google Cloud Platform public key

curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

Update the package list and install the Cloud SDK

sudo apt-get update && sudo apt-get install google-cloud-sdk

Docker tip: If installing the Cloud SDK inside a Docker image, use a single RUN step instead:

RUN export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" && \
    echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add - && \
    sudo apt-get update && sudo apt-get install google-cloud-sdk

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Authenticating PI to GCP

Now initialize your Raspberry PI and authenticate it with your GCP project

gcloud init --console-only

You will get a link to run in your browser on your PC. Go to your PC - enter the link and your GCP credentials.

It returns a key. Paste the key back into your PI.

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Accessing GCP Projects from PI

Your Raspberry PI is now authenticated to your GCP project

if you want to copy files up to GCP storage

gsutil cp filename gs://yourBucketName

Additional python libraries

pip install opencv-python

Info on accessing camera from flask

https://blog.miguelgrinberg.com/post/flask-video-streaming-revisited

Set up tensor flow:

https://github.com/samjabrahams/tensorflow-on-raspberry-pi

Setup Python libraries

For Python 2.7

sudo apt-get install python-pip python-dev

For Python 3.3+

sudo apt-get install python3-pip python3-dev Next, download the wheel file from this repository and install it:

For Python 2.7

wget https://github.com/samjabrahams/tensorflow-on-raspberry-pi/releases/download/v1.1.0/tensorflow-1.1.0-cp27-none-linux_armv7l.whl sudo pip install tensorflow-1.1.0-cp27-none-linux_armv7l.whl

For Python 3.4

wget https://github.com/samjabrahams/tensorflow-on-raspberry-pi/releases/download/v1.1.0/tensorflow-1.1.0-cp34-cp34m-linux_armv7l.whl sudo pip3 install tensorflow-1.1.0-cp34-cp34m-linux_armv7l.whl

Finally, we need to reinstall the mock library to keep it from throwing an error when we import TensorFlow:

For Python 2.7

sudo pip uninstall mock sudo pip install mock

For Python 3.3+

sudo pip3 uninstall mock sudo pip3 install mock

Docker image Instructions on setting up a Docker image to run on Raspberry Pi are being maintained by @romilly here, and a pre-built image is hosted on DockerHub here. Woot!

Troubleshooting This section will attempt to maintain a list of remedies for problems that may occur while installing from pip

"tensorflow-1.1.0-cp27-none-linux_armv7l.whl is not a supported wheel on this platform." This wheel was built with Python 2.7, and can't be installed with a version of pip that uses Python 3. If you get the above message, try running the following command instead:

sudo pip2 install tensorflow-1.1.0-cp27-none-linux_armv7l.whl Vice-versa for trying to install the Python 3 wheel. If you get the error "tensorflow-1.1.0-cp34-cp34m-any.whl is not a supported wheel on this platform.", try this command:

sudo pip3 install tensorflow-1.1.0-cp34-cp34m-linux_armv7l.whl Note: the provided binaries are for Python 2.7 and 3.4 only. If you've installed Python 3.5/3.6 from source on your machine, you'll need to either explicitly install these wheels for 3.4, or you'll need to build TensorFlow from source. Once there's an officially supported installation of Python 3.5+, this repo will start including wheels for those versions.

DOCKER SETUP:

Running the image This run instruction expects a directory called myNotebooks within your shome directory.

docker pull romilly/rpi-docker-tensorflow

If you save an IPython notebook to the myNotebooks sub-directory while running your container, it will get saved to the myNotebooks directory on your Pi.

Notebooks saved to that directory will be persistent - in other words, they will still be there when the container is stopped and restarted.

Execute this command in a terminal:

docker run -it -p 8888:8888 -v ~/myNotebooks:/notebooks/myNotebooks romilly/rpi-docker-tensorflow

Previously there were some spurious warnings when the container started but the latest resin image fixes these. (Thanks, resinators!)

You can probably ignore the warnings about the insecurity of the IPython server configuration so long as you do not store any sensitive data or code in your notebooks.

Connecting to the notebooks Open a browser on http://raspberrypi:8888 where raspberrypi is the hostname of the Pi on which the docker image is running, or on http://localhost:8888 on the Pi itself.

Stopping the image In the terminal session where you ran the container, type Ctr-C twice in quick succession. The container will stop.

Sources Docker: https://github.com/umiddelb/armhf/wiki/Get-Docker-up-and-running-on-the-RaspberryPi-(ARMv6)-in-four-steps-(Wheezy) Base image: from a post by the chaps at resin.io Pi tensorflow whl file from Sam Abrahm's Github project Notebooks and notebook config from The Tensorflow Docker Build on Github

**** THOSE DOcKER INSTALL INSTRUcTIONS DO NOT WORK

This installs docker on the PI 3 Jesse stretch

Installing Docker Installing Docker CE on Raspbian (Jessie or Stretch) for Raspberry Pi 2 and 3 is straightforward, and it’s fully supported by Docker.

We’re going to install Docker from the official Docker repositories. While there are Docker packages on the Raspbian repos too, those are not kept up to date, which is something of an issue with a fast-evolving software like Docker.

To install Docker CE on Raspbian Jessie/Stretch:

Install some required packages first

sudo apt update sudo apt install -y
apt-transport-https
ca-certificates
curl
gnupg2
software-properties-common

Get the Docker signing key for packages

curl -fsSL https://download.docker.com/linux/$(. /etc/os-release; echo "$ID")/gpg | sudo apt-key add -

Add the Docker official repos

echo "deb [arch=armhf] https://download.docker.com/linux/$(. /etc/os-release; echo "$ID")
$(lsb_release -cs) stable" |
sudo tee /etc/apt/sources.list.d/docker.list

Install Docker

sudo apt update sudo apt install docker-ce That’s it! The next step is about starting Docker and enabling it at boot:

sudo systemctl enable docker sudo systemctl start docker Now that we have Docker running, we can test it by running the “hello world” image:

sudo docker run --rm arm32v7/hello-world

Install required packages

apt update apt install -y python python-pip

Install Docker Compose from pip

pip install docker-compose

For the GOOGLE API's you need to have credentials

Create a key for a service account on GCP. It will then download a keyfile in json format.

You will need to have this environment variable set to the key. I renamed mine and put it in a gcpkey directory.

export GOOGLE_APPLICATION_CREDENTIALS="/home/pi/gcpkey/gcp.json"