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# https://help.ubuntu.com/community/AptGet/Howto
# run after changing /etc/apt/sources.list or /etc/apt/preferences
apt-get update

# upgrade all installed packages
apt-get upgrade

# upgrade all installed packages with "smart" conflict resolution
apt-get dist-upgrade

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%env CUDA_VISIBLE_DEVICES=0

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writer = tf.summary.FileWriter(logdir='checkpoints', graph=sess.graph)
writer.flush()

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pip install -U pip setuptools twine

Edit ~/.pypirc and comment out or remove repository:

[pypi]
#repository:https://pypi.python.org/pypi

Use twine to upload your module to pypi from within the folder containing the module source, setup.py, and other files:

python setup.py sdist

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# cuda 9
wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda-repo-ubuntu1704-9-0-local_9.0.176-1_amd64-deb
sudo dpkg -i cuda-repo-ubuntu1704-9-0-local_9.0.176-1_amd64-deb
sudo apt-key adv --fetch-keys \
     http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
sudo apt-get update
sudo apt-get install cuda-9-0


# cuDNN 7.05

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gcloud compute ssh instance-0 --ssh-flag="-L 8888:localhost:8888"

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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

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import tensorflow as tf

train_dataset = tf.data.Dataset.range(10)
train_iterator = train_dataset.make_one_shot_iterator()
train_next = train_iterator.get_next()

test_dataset = tf.data.Dataset.range(10, 20)
test_iterator = test_dataset.make_one_shot_iterator()
test_next = test_iterator.get_next()

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# from https://stackoverflow.com/questions/42463172/how-to-perform-max-mean-pooling-on-a-2d-array-using-numpy
import numpy as np
def asStride(arr,sub_shape,stride):
    '''Get a strided sub-matrices view of an ndarray.
    See also skimage.util.shape.view_as_windows()
    '''
    s0,s1=arr.strides[:2]
    m1,n1=arr.shape[:2]
    m2,n2=sub_shape
    view_shape=(1+(m1-m2)//stride[0],1+(n1-n2)//stride[1],m2,n2)+arr.shape[2:]

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!pip3 install datalab

from google.datalab.ml import TensorBoard
TensorBoard().start('./model_dir/')

for pid in TensorBoard.list()['pid']:
  TensorBoard().stop(pid)
  print('Stopped TensorBoard with pid {}'.format(pid))