ebenolson

import numpy as np
import theano
import theano.tensor as T

X = T.tensor3()
W = theano.shared(np.ones((1,1,1,3)).astype('float32'))
W_shape = (1, 1, 1, 3)

input = X

ebenolson

Subtensor{::, ::, int64:int64:} [@A] ''   
 |InplaceDimShuffle{0,1,3} [@B] ''   
 | |ConvOp{('imshp', (1, 1, 8)),('kshp', (1, 3)),('nkern', 1),('bsize', 1),('dx', 1),('dy', 1),('out_mode', 'full'),('unroll_batch', None),('unroll_kern', None),('unroll_patch', True),('imshp_logical', (1, 1, 8)),('kshp_logical', (1, 3)),('kshp_logical_top_aligned', True)} [@C] ''   
 |   |InplaceDimShuffle{0,1,x,2} [@D] ''   
 |   | |Subtensor{::, ::, int64:int64:} [@E] ''   
 |   |   |InplaceDimShuffle{0,1,3} [@F] ''   
 |   |   | |ConvOp{('imshp', (1, 1, 8)),('kshp', (1, 3)),('nkern', 1),('bsize', 1),('dx', 1),('dy', 1),('out_mode', 'full'),('unroll_batch', None),('unroll_kern', None),('unroll_patch', True),('imshp_logical', (1, 1, 8)),('kshp_logical', (1, 3)),('kshp_logical_top_aligned', True)} [@G] ''   
 |   |   |   |InplaceDimShuffle{0,1,x,2} [@H] ''   
 |   |   |   | |Subtensor{::, ::, int64:int64:} [@I] ''   

ebenolson

choose-mirror-bin mirror/http/proxy string
d-i base-installer/kernel/override-image string linux-server
d-i clock-setup/utc boolean true
d-i clock-setup/utc-auto boolean true
d-i finish-install/reboot_in_progress note
d-i grub-installer/only_debian boolean true
d-i grub-installer/with_other_os boolean true
d-i partman-auto-lvm/guided_size string max
d-i partman-auto/choose_recipe select atomic
d-i partman-auto/method string lvm

ebenolson

#!/bin/bash
cd ~/pydata2015
git pull

source ~/miniconda2/bin/activate py27
# register with landing page
curl --form ip="`curl http://169.254.169.254/latest/meta-data/public-ipv4`" http://pydata.ebenolson.com/addip

# start notebook server
ipython notebook --ip="*"

ebenolson

FROM ubuntu:trusty

########################################
#
# openslide 3.4.1 image based on Ubuntu:trusty
#
#######################################

# Set Locale

ebenolson

import theano, numpy
from theano import tensor as T
import lasagne

#fake data, same shapes
train_in = numpy.zeros((100,46,30)).astype('float32')
train_out = numpy.zeros((100,46)).astype('int32')
mask_train = numpy.zeros((100,46)).astype('float32')

input_var = T.tensor3('input_var')

ebenolson

ebenolson

# Context manager to generate batches in the background via a process pool
# Usage:
#
# def batch(seed):
#    .... # generate minibatch
#    return minibatch
#
# with BatchGenCM(batch) as bg:
#    minibatch = next(bg)
#    .... # do something with minibatch

ebenolson

ebenolson