Bohumír Zámečník bzamecnik

Let's compile TensorFlow ourselves with AVX vector instruction to speed up inference on a CPU.

OSX:

https://www.tensorflow.org/install/install_sources#prepare_environment_for_mac_os tensorflow/tensorflow#12979

fix checksum in tensorflow/workspace.bzl
e5fdeee6b28cf6c38d61243adff06628baa434a22b5ebb7432d2a7fbabbdb13d but wanted 6d43b9d223ce09e5d4ce8b0060cb8a7513577a35a64c7e3dad10f0703bf3ad93
comment the sha256 checksums tensorflow/tensorflow#6729

Installing CUDA 8.0 on PC with with GTX 980 Ti and Ubuntu 16.04

State as of 2017-07-31.

Recently I was installing CUDA on a Azure NC6 VM with Tesla K80 and later the same day I also upgraded my personal machine with GTX 980 Ti from Ubuntu 15.10 to 16.04 and completely reinstalled CUDA.

I had NVIDIA driver 361, CUDA 7.5 and cuDNN 4 or 5 and wanted CUDA 8.0

Installing NVIDIA CUDA on Azure NC with Tesla K80 and Ubuntu 16.04

State as of 2017-07-31.

You can also check a guide to upgrade CUDA on a [PC with with GTX 980 Ti and Ubuntu 16.04](https://gist.github.com/bzamecnik/61b293a3891e166797491f38d579d060.

Target versions

NVIDIA driver 375.66

	=== Track 0
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	package com.github.bzamecnik.regex

	import scala.concurrent.duration._

	/**
	* Wraps String to make its usage time limited.
	*
	* After given time duration charAt() operation will start throwing a
	* TimeoutException.
	*

	_ _ ___
	___ ___\| \|_ ___ _ __ (_) .-' `'.
	/ _ \ / __\| __/ _ \\| '_ \\| \| / \
	\| (_) \| (__\| \|\| (_) \| \|_) \| \| \| ;
	\___/ \___\|\__\___/\| .__/\|_\| \| \| ___.--,
	Raspbian \|_\| _.._ \|0) ~ (0) \| _.---'`__.-( (_.
	GNU/Linux 7 __.--'`_.. '.__.\ '--. \_.-' ,.--'` `""`
	( ,.--'` ',__ /./; ;, '.__.'` __
	_`) ) .---.__.' / \| \|\ \__..--"" """--.,_
	`---' .'.''-._.-'`_./ /\ '. \ _.-~~~````~~~-._`-.__.'

	# Question: can we replicate https://github.com/rossumai/keras-multi-gpu/issues/2
	# InvalidArgumentError: Shape [-1] has negative dimensions
	# [[Node: replica_0_1/model_1_sample_weights = Placeholder[dtype=DT_FLOAT, shape=[?], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

	import tensorflow as tf

	a = tf.placeholder(dtype=tf.int8, shape=(None, None))
	b = tf.placeholder(dtype=tf.int8, shape=(None, None))
	c = tf.add(a, b)

	'''Trains a simple deep NN on the MNIST dataset.

	Gets to 98.40% test accuracy after 20 epochs
	(there is a lot of margin for parameter tuning).
	2 seconds per epoch on a K520 GPU.
	'''

	from __future__ import print_function

	import keras

	import keras.backend as K
	from keras.layers import Lambda
	from keras.layers.merge import concatenate
	from keras.models import Model
	import keras.optimizers
	from keras.optimizers import clip_norm, Optimizer
	import tensorflow as tf

	# this should be fairly ready
	class DataParallelOptimizer(Optimizer):

	_ _
	_ __ ___ \| \| ___ \| \| __
	\| '_ ` _ \\| \|/ _ \\| \|/ /
	\| \| \| \| \| \| \| (_) \| < 0--0^^^^^^^^^^^^\________
	\|_\| \|_\| \|_\|_\|\___/\|_\|\_\ \__/\|\|-------\|\|---------~
	`` ``