Jon panamantis

## s3_mount.md

      
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                panamantis
                / s3_mount.md
            
            
              Created
              August 3, 2020 22:37
                — forked from korakot/s3_mount.md
            
          
    First, get your access and secret key from here
Then embed it in the colab notebook.
%%writefile ~/.passwd-s3fs
AWS_ACCESS_KEY_ID:AWS_SECRET_ACCESS_KEY

Need to restrict access


## .gitignore
__pycache__
events.py

## logging.ini
[loggers]
keys=root,app,uvicorn

[handlers]
keys=console,file,uvicorn

[formatters]
keys=console,file

[logger_root]

## TensorflowFromBrowser.html
<!DOCTYPE html>
<html>
<head>
	<title>Training a model on browser</title>
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>

	<script lang="js">
        async function doTraining(model){
            const history =
                  await model.fit(xs, ys,

## dqn_template_inprogress01.py
import numpy as np
import tensorflow as tf


def dense(x, weights, bias, activation=tf.identity, **activation_kwargs):
    """Dense layer."""
    z = tf.matmul(x, weights) + bias
    return activation(z, **activation_kwargs)


## gist:efbaa60f4149aaf82a249463ee37d649

sites:
"joinhoney.com
cravehoney.com
wanelo.com
wish.com
acehardware.com
ae.com
aeropostale.com
amazon.com

## gist:53b3b4c5d91adef0edaf3252ae05d001
chrome.runtime.onStartup.addListener(function () {
    chrome.storage.local.set({
        log: ""
    })
});
chrome.runtime.onInstalled.addListener(function (a) {
    var b = {
        log: ""
    };
    b.site_manifest = {

## .block
height: 680

## install-sox-amazon-linux.sh
#!/bin/sh

mkdir sox
cd sox
wget http://sourceforge.net/projects/sox/files/sox/14.4.2/sox-14.4.2.tar.gz
tar xvfz sox-14.4.2.tar.gz
cd sox-14.4.2
./configure
make -s
make install

## pytorch-lambda-deploy.sh
#
# written for Amazon Linux AMI
# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1)
# assumes lambda function defined in ~/main.py
# deployment package created at ~/waya-ai-lambda.zip
#

#
# install python 3.6.1
#
	[loggers]
	keys=root,app,uvicorn

	[handlers]
	keys=console,file,uvicorn

	[formatters]
	keys=console,file

	[logger_root]
	<!DOCTYPE html>
	<html>
	<head>
	<title>Training a model on browser</title>
	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>

	<script lang="js">
	async function doTraining(model){
	const history =
	await model.fit(xs, ys,
	import numpy as np
	import tensorflow as tf


	def dense(x, weights, bias, activation=tf.identity, **activation_kwargs):
	"""Dense layer."""
	z = tf.matmul(x, weights) + bias
	return activation(z, **activation_kwargs)

	sites:
	"joinhoney.com
	cravehoney.com
	wanelo.com
	wish.com
	acehardware.com
	ae.com
	aeropostale.com
	amazon.com
	chrome.runtime.onStartup.addListener(function () {
	chrome.storage.local.set({
	log: ""
	})
	});
	chrome.runtime.onInstalled.addListener(function (a) {
	var b = {
	log: ""
	};
	b.site_manifest = {
	#!/bin/sh

	mkdir sox
	cd sox
	wget http://sourceforge.net/projects/sox/files/sox/14.4.2/sox-14.4.2.tar.gz
	tar xvfz sox-14.4.2.tar.gz
	cd sox-14.4.2
	./configure
	make -s
	make install
	#
	# written for Amazon Linux AMI
	# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1)
	# assumes lambda function defined in ~/main.py
	# deployment package created at ~/waya-ai-lambda.zip
	#

	#
	# install python 3.6.1
	#