yaroslavvb

import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from tensorflow.contrib import graph_editor as ge


def make_conditional_initializer(v):
    """Makes initializer of variable var lazy, returns new conditional init
    op."""

    cond = tf.is_variable_initialized(v)

yaroslavvb

1. Making  Pull Request

First, click "Fork" on github tensorflow page

Then in Terminal:

export user=yaroslavvb
export branch_name
git clone https://github.com/$user/tensorflow.git
cd tensorflow

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)

yaroslavvb

import tensorflow as tf
from tensorflow.contrib.eager.python import tfe
tfe.enable_eager_execution()
context = tf.device('/gpu:0')
context.__enter__()

# download resnet_model
import sys, os, urllib.request
resnet_model_url="https://raw.githubusercontent.com/tensorflow/models/master/official/resnet/resnet_model.py"
response = urllib.request.urlopen(resnet_model_url)

yaroslavvb

import torch
import numpy as np
import time

def benchmark():
  
  SIZE_GB = 1
  num_iters = 100
  def compute():
    a = torch.ones(int(SIZE_GB*1000*250000)).cuda()

yaroslavvb

import util as u

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np

# todo: make images global

yaroslavvb

# Example of profiling session.run overhead
# for python profiling
# python -m cProfile -o session-run-benchmark-feed.prof session-run-benchmark.py feed_dict
# python -m cProfile -o session-run-benchmark-variable.prof session-run-benchmark.py variable
# pip install snakeviz
# snakeviz  session-run-benchmark-feed.prof
# snakeviz  session-run-benchmark.prof
#
#
# Feed_dict: 147 usec, no feed dict, 71 usec

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# make things wide
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

from IPython.display import clear_output, Image, display, HTML

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:

yaroslavvb

from google.protobuf import text_format
from tensorflow.core.framework import op_def_pb2
ops = op_def_pb2.OpList()

ops_text = open("/local_home/yaroslav/tensorflow.git/tensorflow/tensorflow/core/ops/ops.pbtxt").read()
text_format.Merge(ops_text, ops)
print(len(ops.op))

yaroslavvb

# Benchmark transferring data from TF into Python runtime
#
## Dependencies:
# portpicker (pip install portpicker)
# tcmalloc4 (sudo apt-get install google-perftools)
# TF 0.12  (for var.read_value(), ones_initializer())
#
# On Linux default malloc is slow
# sudo apt-get install google-perftools
# export LD_PRELOAD="/usr/lib/libtcmalloc.so.4"