pangyuteng

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

Module autoreload

To auto-reload modules in jupyter notebook (so that changes in files *.py doesn't require manual reloading):

# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2

	version: '2'
	services:
	myapp:
	build: .
	container_name: "myapp"
	image: debian/latest
	environment:
	- NODE_ENV=development
	- FOO=bar
	volumes:

	#!/bin/bash
	#====================================================================
	# aptのアップデート
	#====================================================================
	yes \| sudo apt-get update
	yes \| sudo apt-get upgrade

	#====================================================================
	# 『デスクトップ』『音楽』などの日本語フォルダー名を英語表記にする
	#====================================================================

	# Note – this is not a bash script (some of the steps require reboot)
	# I named it .sh just so Github does correct syntax highlighting.
	#
	# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
	#
	# The CUDA part is mostly based on this excellent blog post:
	# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

	# Install various packages
	sudo apt-get update

	"""
	A weighted version of categorical_crossentropy for keras (2.0.6). This lets you apply a weight to unbalanced classes.
	@url: https://gist.github.com/wassname/ce364fddfc8a025bfab4348cf5de852d
	@author: wassname
	"""
	from keras import backend as K
	def weighted_categorical_crossentropy(weights):
	"""
	A weighted version of keras.objectives.categorical_crossentropy

	############################################################################
	#
	# Condor job that runs a TensorFlow Docker container.
	# Based on http://chtc.cs.wisc.edu/helloworld.shtml and
	# http://research.cs.wisc.edu/htcondor/HTCondorWeek2016/presentations/FriThain_Docker.pdf
	# and https://www.tensorflow.org/get_started/os_setup#docker_installation
	#
	# Assumes the job is submitted from the WID pool so
	# allow flocking to the CHTC pool.
	#

	#!/bin/bash

	TENSORFLOW_COMMIT=9e76bf324f6bac63137a02bb6e6ec9120703ea9b # August 16, 2017
	TENSORFLOW_SERVING_COMMIT=267d682bf43df1c8e87332d3712c411baf162fe9 # August 18, 2017
	MODELS_COMMIT=78007443138108abf5170b296b4d703b49454487 # July 25, 2017

	if [ -z $TENSORFLOW_SERVING_REPO_PATH ]; then
	TENSORFLOW_SERVING_REPO_PATH="serving"
	fi
	INITIAL_PATH=$(pwd)