Yao-Ching Huang huang06

package name	package version	python	numpy	keras	protobuf	scipy	url
Tensorflow1	1.15.0	27,35,36,37	>=1.16.0,<2.0	-	>= 3.6.1	-	https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/tools/pip_package/setup.py
	1.15.2	35,36,37	>=1.16.0,<2.0	-	>= 3.6.1	-	https://github.com/tensorflow/tensorflow/blob/v1.15.2/tensorflow/tools/pip_package/setup.py
	1.15.3	35,36,37	>=1.16.0,<2.0	-	>= 3.6.1	-	https://github.com/tensorflow/tensorflow/blob/v1.15.3/tensorflow/tools/pip_package/setup.py
	1.15.4	35,36,37	>=1.16.0,<1.19.0	-	>= 3.6.1	-	https://github.com/tensorflow/tensorflow/blob/v1.15.4/tensorflow/tools/pip_package/setup.py
	1.15.5	36,37	>=1.16.0,<1.19.0	-	>= 3.6.1	-	https://github.com/tensorflow/tensorflow/blob/v1.15.5/tensorflow/tools/pip_package/setup.py

Tensorflow2	2.6.0	36,37,38,39	~= 1.19.2	~= 2.6	>= 3.9.2	-	https://github.com/tensorflow/tensorflow/blob/v2.6.0/tensorflow/tools/pip_package/setup.py
	2.6.1	36,37

	# https://stackoverflow.com/questions/50824943/how-to-efficiently-split-a-large-dataframe-into-many-parquet-files
	# https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_to_dataset.html

	import pandas as pd
	import pyarrow.parquet as pq
	import pyarrow as pa

	df = pd.read_csv("CSV_LOCATION")
	table = pa.Table.from_pandas(df)
	pq.write_to_dataset(

	echo "This is coming from .bash_profile"

	if [ -f /Users/tom/.bashrc ]; then
	. /Users/tom/.bashrc
	fi

	" =======================
	" Plugin Configuration
	" =======================
	call plug#begin()
	" The default plugin directory will be as follows:
	" - Vim (Linux/macOS): '~/.vim/plugged'
	" - Vim (Windows): '~/vimfiles/plugged'
	" - Neovim (Linux/macOS/Windows): stdpath('data') . '/plugged'
	" You can specify a custom plugin directory by passing it as the argument
	" - e.g. `call plug#begin('~/.vim/plugged')`

	---
	apiVersion: v1
	kind: Namespace
	metadata:
	name: helloworld
	---
	apiVersion: networking.k8s.io/v1beta1
	kind: Ingress
	metadata:
	name: helloworld

	# Forward the IngressController
	# kubectl port-forward ingress-nginx-controller 8080:80

	# Access our service via Ingress:
	# curl localhost:8080 -d Sysdig -H "Host: openfaas-figlet.local"
	---
	apiVersion: v1
	kind: Namespace
	metadata:
	name: openfaas-figlet