yaroslavvb

def sessrun(fetches):
  values = tf.get_default_session().run(fetches)
  return {fetches[i]: values[i] for i in range(len(values))}

a = tf.constant(1)
b = tf.constant(2)
c = tf.constant(3)

sess = tf.InteractiveSession()
result1 = sessrun([a, b])

yaroslavvb

import tensorflow as tf
from numpy.testing.utils import nulp_diff
import time

n = 1024
v1 = tf.Variable(tf.ones_initializer((n, n)))
v2 = tf.Variable(tf.ones_initializer((n, n)))
op = v1.assign(tf.matmul(v1, v2)/n).op
norm = tf.reduce_sum(v1)

yaroslavvb

"""Example of launching distributed service and then bringint it down."""

import subprocess
import tensorflow as tf
import time
import sys

flags = tf.flags
flags.DEFINE_string("port1", "12222", "port of worker1")
flags.DEFINE_string("port2", "12223", "port of worker2")

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import tensorflow as tf
fn = "/tmp/efs/yaroslav/g/train_1/events.out.tfevents.1476852642.g-w-1-vblgv"
for summary in tf.train.summary_iterator():
  print(summary)

yaroslavvb

import tensorflow as tf
from tensorflow.python.client import timeline

n = 1024
with tf.device("cpu:0"):
    a1 = tf.ones((n, n))
    a2 = tf.ones((n, n))
with tf.device("cpu:1"):
    a3 = tf.matmul(a1, a2)
with tf.device("cpu:2"):

yaroslavvb

# testing variable order init

import tensorflow as tf

def initialize_all_variables(sess=None):
  """Initializes all uninitialized variables in correct order. Initializers
  are only run for uninitialized variables, so it's safe to run this multiple
  times.

  Args:

yaroslavvb

a = tf.constant([[1,2],[3,4]])
row_to_add = tf.constant([[1, 1]])
updated_row = a[0]+row_to_add
updated_a = tf.concat(0, [updated_row, a[1:]])
sess.run(updated_a)

yaroslavvb

a = tf.constant([[1,2],[3,4]])
row_to_add = tf.constant([1, 1])
original_row = a[0]
updated_row = original_row + row_to_add
unchanged_indices = tf.range(tf.size(a))
changed_indices = tf.range(a.get_shape()[0])
a_flat = tf.reshape(a, [-1])
updated_a_flat = tf.dynamic_stitch([unchanged_indices, changed_indices], [a_flat, updated_row])
updated_a = tf.reshape(updated_a_flat, a.get_shape())
print sess.run(updated_a)

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"""Benchmark tensorflow distributed by adding vector of ones on worker2
to variable on worker1 as fast as possible.

On 2014 macbook, TensorFlow 0.10 this shows

Local rate:       2175.28 MB per second
Distributed rate: 107.13 MB per second

"""

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# try running cpu intensive test on two devices

import tensorflow as tf
import time

def matmul_op():
  """Multiply two matrices together"""
  
  n = 2000
  a = tf.ones((n, n), dtype=tf.float32)