Ravi Panchumarthy ravi9

ravi9 / notebook.sh

Last active February 13, 2017 21:12 — forked from yangj1e/notebook.sh

Deploy Jupyter Notebook server on Amazon EC2 Instance

	#!/usr/bin/env bash

	cd ~
	wget http://repo.continuum.io/archive/Anaconda2-4.0.0-Linux-x86_64.sh
	bash Anaconda2-4.0.0-Linux-x86_64.sh -b
	echo 'PATH="/home/ubuntu/anaconda2/bin:$PATH"' >> .bashrc
	. .bashrc

	jupyter notebook --generate-config

ravi9 / ubuntu_smartupdate.sh

Created August 4, 2017 16:14

ubuntu_smartupdate to resolve any license issue when trying to apt-get update.

	wget https://bitbucket.org/maaaks/mscripts/raw/794bf751497987802a31e6466a99995efc17f213/smartupdate.sh
	chmod 755 smartupdate.sh
	./smartupdate.sh

ravi9 / mount_s3_on_EC2_ubuntu.sh

Created August 4, 2017 16:19

Mount AWS S3 bucket on Ubuntu EC2 Instance

	sudo apt-get update
	sudo apt-get install automake autotools-dev fuse g++ git libcurl4-gnutls-dev libfuse-dev libssl-dev libxml2-dev make pkg-config

	git clone https://github.com/s3fs-fuse/s3fs-fuse.git
	cd s3fs-fuse
	./autogen.sh
	./configure
	make
	sudo make install

ravi9 / intelcaffe_install_rhel_ec2.sh

Last active August 31, 2017 22:27

Install script for installing Intel-Caffe on RHEL 7.x

	#This script is tested on AWS instance RHEL - Red Hat Enterprise Linux (RHEL) 7 (HVM) https://aws.amazon.com/marketplace/pp/B00KWBZVK6
	#This installs Intel Caffe with python dependencies.
	#This script is suited for SSD example. Uncomment Line#49 for general use. See https://github.com/intel/caffe/issues/65

	sudo yum group install -y "Development Tools"

	wget https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
	sudo rpm -ivh epel-release-latest-7.noarch.rpm

	sudo yum -y install python-devel python-pip tkinter gtk2-2.24.28-8.el7.x86_64 gtk2-devel

ravi9 / install_intel_tf1.5.sh

Created February 21, 2018 22:26

Build and Install instructions for Intel Tensorflow 1.5

	##
	#Preferred GCC version > 5
	##

	#Install Bazel
	# Bazel install intructions on CentOS
	#Get the latest Bazel: https://github.com/bazelbuild/bazel/releases
	wget https://github.com/bazelbuild/bazel/releases/download/0.9.0/bazel-0.9.0-installer-linux-x86_64.sh
	chmod 755 bazel-0.9.0-installer-linux-x86_64.sh
	sudo ./bazel-0.9.0-installer-linux-x86_64.sh

ravi9 / install_intel_tensorflow_from_wheel.sh

Last active May 22, 2018 17:40

Install Intel tensorflow with MKL from python wheel

	wget https://repo.continuum.io/archive/Anaconda2-5.0.1-Linux-x86_64.sh
	bash Anaconda2-5.0.1-Linux-x86_64.sh

	conda config --add channels intel

	#For Python 2.7
	conda create -n tf_intel_py27 -c intel python=2 pip numpy
	source activate tf_intel_py27
	pip install -i https://pypi.anaconda.org/intel/simple tensorflow

ravi9 / nmt_infer_batchsize.sh

Created April 4, 2018 17:16

Tensorflow NMT inference varying batchsize

	#!/bin/bash
	#nmt_infer_batchsize.sh

	###
	#Prereq:
	#sudo apt install -y moreutils jq
	###

	echo -e "\nBZ WPS \n"
	#set -x

ravi9 / tf_nmt_bench.sh

Created April 4, 2018 17:41

Tensorflow NMT benchmark setup

	#Git clone NMT
	git clone https://github.com/tensorflow/nmt.git

	#Download Dataset
	cd nmt
	nmt/scripts/download_iwslt15.sh /home/ubuntu/nmt_data

	#Run training for few steps to get a model, so that we can use it for inference benchmarking.
	python -m nmt.nmt \
	--src=vi --tgt=en \

ravi9 / keras_tracing.sh

Last active May 4, 2018 16:48

Simple mnist example for using keras to save tracing info with TF

	#Install requirements.
	pip install virtualenv
	virtualenv --python=/usr/bin/python2.7 keras_tracing
	source keras_tracing/bin/activate
	cd keras_tracing/
	pip install keras
	pip install -i https://pypi.anaconda.org/intel/simple tensorflow

	#Clone mnist sample code
	git clone https://github.com/ravi9/keras-trace-example.git

ravi9 / quick_tfcnnbenchmarks.sh

Last active May 4, 2018 19:55

Quick benchmarking resnet50 with MKL and without MKL on TF

	#Quick benchmarking resnet50 with MKL and NO MKL on TF

	#Following are steps:
	# Create MKL virtual envirnoment
	# pip install a TF1.8 MKL Wheel
	# git clone TF_CNN benchmarks.
	# run resnet50 inference benchmark.
	#
	# Create NO-MKL virtual envirnoment
	# pip install a TF1.8 Wheel this will install eigen based TF