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Particle Boron Setup Notes

Notes for using Particle.io Boron IoT via USB.

Flashing OTA has been a major paint for me as Boron 2G/3G can take forever before connecting to network. During development, it is best to use USB instead of Cell connection to Flash code.

Install Particle CLI

brew install dfu-util

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GCP VPC tunnel

Notes from Qwiklabs: Optimizing networks on GCP

Creating VPC tunnels manually. This is not how you connect VPC's in GCP, but this shows what all the pieces are.

Create two VPCs with a subnet and firewall rules

gcloud compute networks create cloud —subnet-mode custom
gcloud compute firewall-rules create cloud-fw —network cloud —allow tcp:22,tcp:5001,udp:5001,icmp

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sudo systemctl mask sleep.target suspend.target hibernate.target hybrid-sleep.target
sudo systemctl set-default multi-user.target

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Debug Horizontal Scroll

(function (d) {
    var w = d.documentElement.offsetWidth,
        t = d.createTreeWalker(d.body, NodeFilter.SHOW_ELEMENT),
        b;
    while (t.nextNode()) {
        b = t.currentNode.getBoundingClientRect();
 if (b.right > w || b.left < 0) {

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This is how I set up GCP VM and my Deep Learning PC for ML and Geo work.

On GCP, I normally use a n1-highmem-4 (4xvCPU, 26GB) with Tesla T4 GPU ($0.4/hr)

My own Deep Learning PC is a Ryzen 3900 (12 cores) 64GB memory and RTX2070 Super ($3K)

Install conda

Install MiniConda.

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# The Dockerfile used in https://coral.ai/docs/edgetpu/retrain-classification/ is a bit old and does not work

# Below is the updated Dockerfile to retrain image classifier for use in Coral EdgeTPU

FROM tensorflow/tensorflow:1.11.0-devel-py3

# Install wget (to make life easier below) and editors (to allow people to edit
# the files inside the container)
RUN apt-get update && \
    apt-get install -y wget vim emacs nano git && \

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# Sometimes EarlyStopping is too much hassle

class myCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if (logs.get('acc') >= 0.998):
            print("\nReached 99.8% accuracy so cancelling training!")
            self.model.stop_training = True
cb = myCallback()

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ENV APP_HOME /app ENV PORT 8080 CMD exec gunicorn --bind 0.0.0.0:$PORT \
                         --workers 1 --chdir $APP_HOME --threads 1 app:app

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#!/usr/bin/env bash
gcloud services enable cloudkms.googleapis.com

KEYRING=keyring
KEY=secrets

gcloud kms keys list --location global --keyring $KEYRING

if [[ $? -ne 0 ]]; then
    gcloud kms keyrings create ${KEYRING} --location global

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import os
import uuid
from google.cloud import storage, kms_v1


def load(app):
    config_file_name = "secrets-%s.cfg" % str(uuid.uuid4())

    storage_client = storage.Client()
    bucket = storage_client.get_bucket('vault')