jaassoon

## meraki_strongswan_notes.md

      
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                jaassoon
                / meraki_strongswan_notes.md
            
            
              Created
              December 1, 2018 11:20
                — forked from psanford/meraki_strongswan_notes.md
            
              
                connect to meraki client vpn from strongswan (ubuntu 16.04 edition)
              
          
    These are my notes for connecting to a meraki client vpn from ubuntu 16.04. This configuration assumes you are using a psk for the ipsec auth.
Install the following packages:
apt-get install -y strongswan xl2tpd

Configure strong swan


## train_on_batch_with_tensorboard.py
# This example shows how to use keras TensorBoard callback
# with model.train_on_batch

import tensorflow.keras as keras

# Setup the model
model = keras.models.Sequential()
model.add(...) # Add your layers
model.compile(...) # Compile as usual

## demo.py
import numpy as np
import tensorflow as tf
from keras.callbacks import TensorBoard
from keras.layers import Input, Dense
from keras.models import Model


def write_log(callback, names, logs, batch_no):
    for name, value in zip(names, logs):
        summary = tf.Summary()

## cuda-with-cudnn.sh
# cuda.sh
# check if GPU ready
lspci | grep -i nvidia
# If NVIDIA driver was installed via the *.run file:
# sudo nvidia-uninstall
yum install -y gcc gcc-c++ kernel-devel kernel-headers
yum install -y libXmu libXmu-devel mesa-libGLU mesa-libGLU-devel \
libX11-common.noarch libX11-devel libX11 \
libXi libXi-devel
yum -y update

## buildcv2.sh
# buildcv2.sh
#!/bin/bash
yum -y install gcc gcc-c++ freetype-devel glib2-devel cairo-devel libtool
basePath=/mnt/Data/jason
python36Path=/mnt/Data/jason/env36

#install colm
cd {basePath}/jp-ocr/3rdparty/{colm}
./autogen.sh
./configure

## squeezeNet-by-deeplearnjs.html
<!DOCTYPE html>
<html>
<script src="https://cdn.jsdelivr.net/npm/deeplearn"></script>
<script src='https://unpkg.com/deeplearn-squeezenet'></script>
<div id='target' ondrop='dropit(event);' ondragover='dragoverit(event);'>
  Drag another image onto this image to predict (make sure browser window is focused).
  <br><br>
  If you have cross origin problems, download the image to your computer
  first, then drag it here.
  <div id='img'><img id='cat' src='https://storage.googleapis.com/learnjs-data/images/cat.jpeg' crossorigin></div>

## docker_ubuntu17.1_tensorflow_build_from_src.Dockerfile
FROM ubuntu:17.10
ENV container docker
WORKDIR /opt

# ##### Configure the build for configuration.
ENV TF_NEED_GCP 0
ENV TF_NEED_HDFS 0
ENV TF_ENABLE_XLA 0
ENV TF_NEED_CUDA 0
ENV TF_NEED_OPENCL 0

## tf_serving.sh
sudo su

# Java
yum -y install java-1.8.0-openjdk-devel

# Build Esentials (minimal)
yum -y install gcc gcc-c++ kernel-devel make automake autoconf swig git unzip libtool binutils

# Extra Packages for Enterprise Linux (EPEL) (for pip, zeromq3)
yum -y install epel-release
	# This example shows how to use keras TensorBoard callback
	# with model.train_on_batch

	import tensorflow.keras as keras

	# Setup the model
	model = keras.models.Sequential()
	model.add(...) # Add your layers
	model.compile(...) # Compile as usual
	import numpy as np
	import tensorflow as tf
	from keras.callbacks import TensorBoard
	from keras.layers import Input, Dense
	from keras.models import Model


	def write_log(callback, names, logs, batch_no):
	for name, value in zip(names, logs):
	summary = tf.Summary()
	# cuda.sh
	# check if GPU ready
	lspci \| grep -i nvidia
	# If NVIDIA driver was installed via the *.run file:
	# sudo nvidia-uninstall
	yum install -y gcc gcc-c++ kernel-devel kernel-headers
	yum install -y libXmu libXmu-devel mesa-libGLU mesa-libGLU-devel \
	libX11-common.noarch libX11-devel libX11 \
	libXi libXi-devel
	yum -y update
	# buildcv2.sh
	#!/bin/bash
	yum -y install gcc gcc-c++ freetype-devel glib2-devel cairo-devel libtool
	basePath=/mnt/Data/jason
	python36Path=/mnt/Data/jason/env36

	#install colm
	cd {basePath}/jp-ocr/3rdparty/{colm}
	./autogen.sh
	./configure
	<!DOCTYPE html>
	<html>
	<script src="https://cdn.jsdelivr.net/npm/deeplearn"></script>
	<script src='https://unpkg.com/deeplearn-squeezenet'></script>
	<div id='target' ondrop='dropit(event);' ondragover='dragoverit(event);'>
	Drag another image onto this image to predict (make sure browser window is focused).
	<br><br>
	If you have cross origin problems, download the image to your computer
	first, then drag it here.
	<div id='img'><img id='cat' src='https://storage.googleapis.com/learnjs-data/images/cat.jpeg' crossorigin></div>
	FROM ubuntu:17.10
	ENV container docker
	WORKDIR /opt

	# ##### Configure the build for configuration.
	ENV TF_NEED_GCP 0
	ENV TF_NEED_HDFS 0
	ENV TF_ENABLE_XLA 0
	ENV TF_NEED_CUDA 0
	ENV TF_NEED_OPENCL 0
	sudo su

	# Java
	yum -y install java-1.8.0-openjdk-devel

	# Build Esentials (minimal)
	yum -y install gcc gcc-c++ kernel-devel make automake autoconf swig git unzip libtool binutils

	# Extra Packages for Enterprise Linux (EPEL) (for pip, zeromq3)
	yum -y install epel-release