rookiepig

#include "caffeclassifier.h"

CaffeClassifier::CaffeClassifier(const string& model_file,
                       const string& trained_file,
                       const string& mean_file,
                       const string& label_file,
                       const bool use_GPU,
                       const int batch_size) {
   if (use_GPU)
       Caffe::set_mode(Caffe::GPU);

rookiepig

# Try to copy "a" value to "c" while simultaneously adding vector of 1's to a.
# If the copy is started before the first assign_add, the copied value will be inconsistent.
#
# Running it on macbook my "c" ends up with a mix of values between 1 and 6
#
#
# 16.017478 copy 1 (0) starting
# 17.006894 write 1 (0) starting
# 28.431654 write 1 ending (11.4247 sec)
# 29.436692 write 1 (1) starting

rookiepig

# Dependencies:
# portpicker (pip install portpicker)
# tcmalloc4 (sudo apt-get install google-perftools)
# TF 0.12
#
#
# Benchmarks on Xeon E5-2630 v3 @ 2.40GHz
#
# export LD_PRELOAD=/usr/lib/libtcmalloc.so.4
# python benchmark_grpc_recv.py --data_mb=128

rookiepig

#!/usr/bin/env python
# Benchmark transferring data, part of troubleshooting https://github.com/tensorflow/tensorflow/issues/6116
#
# Take a independent workers communicating with b parameter shards
# Each worker tries to add to variables stored on parameter server as fast as
# possible.
# 
# macbook
# ps=1: 1.6 GB/s
# ps=2: 2.6 GB/s

rookiepig

# 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

rookiepig

# 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)

rookiepig

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"):

rookiepig

Table Of Content

Skip to the relevant sections if needed.

• 2-min tutorial to do it the quick-and-dirty-way
• Concepts for resolving Git conflicts
• Setting up different editors / tool for using git mergetool
  • Finding out what mergetool editors are supported
• mergetool simple code example for vimdiff
  • Resolving conflict from a Git pull
• Other great references and tutorials

rookiepig

vimdiff cheat sheet

##git mergetool

In the middle file (future merged file), you can navigate between conflicts with ]c and [c.

Choose which version you want to keep with :diffget //2 or :diffget //3 (the //2 and //3 are unique identifiers for the target/master copy and the merge/branch copy file names).

:diffupdate (to remove leftover spacing issues)

:only (once you’re done reviewing all conflicts, this shows only the middle/merged file)