Danny's tech notebook | 丹尼技術手札

Monday, August 7, 2017

[Caffe] How to use Caffe to solve the regression problem?

There is a question coming up to my mind recently. How to use Caffe to solve the regression problem? We used to see a bunch of examples related to image recognition with labels and they are classification problem. In my experience, I have done this problem using TensorFlow, not Caffe. But, I think in theory they are both the same. The key point is using EuclideanLossLayer as the final Loss Layer and it's the detail from the official web site:

[Raspberry Pi] Use Wireless and Ethernet together

The following content is my Raspberry Pi 3's setting in /etc/network/interface as follows. In my case, I both use wireless and ethernet device at the same time.

# Include files from /etc/network/interfaces.d:
source-directory /etc/network/interfaces.d

auto lo
iface lo inet loopback

auto wlan0
allow-hotplug wlan0
iface wlan0 inet manual
    Wpa-conf /etc/wpa_supplicant/wpa_supplicant.conf

allow-hotplug eth0
iface eth0 inet static
    address 140.96.29.224
    netmask 255.255.255.0
    up ip route add 100.85.0.0/24 via 140.96.29.254 dev eth0
    up ip route add 140.96.29.0/24 via 140.96.29.254 dev eth0
    up ip route add 140.96.98.0/24 via 140.96.29.254 dev eth0

[Debug] Debugging Python and C++ exposed by boost together

During the studying of Caffe, I was curious about how Caffe provides Python interface and what kind of tool uses for wrapping. Then, the answer is Boost.Python. I think for C++ developer, it is worth time to learn and I will study it sooner. In this post, I want to introduce the debugging skill which I found in this post and I believe these are very useful such as debugging Caffe with Python Layer. Here is the link:
https://stackoverflow.com/questions/38898459/debugging-python-and-c-exposed-by-boost-together

[PCIe] lspci command and the PCIe devices in my server

The following content is about my PCIe devices/drivers and the lspci command results.

$ cd /sys/bus/pci_express/drivers

$ ls -al

drwxr-xr-x 2 root root 0 7月 6 15:33 aer/

drwxr-xr-x 2 root root 0 7月 6 15:33 pciehp/

drwxr-xr-x 2 root root 0 7月 6 15:33 pcie_pme/

[Caffe] Install Caffe and the depended packages

This article is just for me to quickly record the all the steps to install the depended packages for Caffe. So, be careful that it maybe is not good for you to walk through them in your environment. ^_^

# Install CCMAKE

$ sudo apt-get install cmake-curses-gui

[NCCL] Build and run the test of NCCL

NCCL requires at least CUDA 7.0 and Kepler or newer GPUs. Best performance is achieved when all GPUs are located on a common PCIe root complex, but multi-socket configurations are also supported.

Note: NCCL may also work with CUDA 6.5, but this is an untested configuration.

Build & run

To build the library and tests.

$ cd nccl
$ make CUDA_HOME=<cuda install path> test
Test binaries are located in the subdirectories nccl/build/test/{single,mpi}.

$ ~/git/nccl$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./build/lib
$ ~/git/nccl$ ./build/test/single/all_reduce_test 100000000
# Using devices
# Rank 0 uses device 0 [0x04] GeForce GTX 1080 Ti
# Rank 1 uses device 1 [0x05] GeForce GTX 1080 Ti
# Rank 2 uses device 2 [0x08] GeForce GTX 1080 Ti
# Rank 3 uses device 3 [0x09] GeForce GTX 1080 Ti
# Rank 4 uses device 4 [0x83] GeForce GTX 1080 Ti
# Rank 5 uses device 5 [0x84] GeForce GTX 1080 Ti
# Rank 6 uses device 6 [0x87] GeForce GTX 1080 Ti
# Rank 7 uses device 7 [0x88] GeForce GTX 1080 Ti

# out-of-place in-place
# bytes N type op time algbw busbw res time algbw busbw res
100000000 100000000 char sum 30.244 3.31 5.79 0e+00 29.892 3.35 5.85 0e+00
100000000 100000000 char prod 30.493 3.28 5.74 0e+00 30.524 3.28 5.73 0e+00
100000000 100000000 char max 29.745 3.36 5.88 0e+00 29.877 3.35 5.86 0e+00
100000000 100000000 char min 29.744 3.36 5.88 0e+00 29.868 3.35 5.86 0e+00
100000000 25000000 int sum 29.692 3.37 5.89 0e+00 29.754 3.36 5.88 0e+00
100000000 25000000 int prod 30.733 3.25 5.69 0e+00 30.697 3.26 5.70 0e+00
100000000 25000000 int max 29.871 3.35 5.86 0e+00 29.700 3.37 5.89 0e+00
100000000 25000000 int min 29.809 3.35 5.87 0e+00 29.852 3.35 5.86 0e+00
100000000 50000000 half sum 28.590 3.50 6.12 1e-02 27.545 3.63 6.35 1e-02
100000000 50000000 half prod 27.416 3.65 6.38 1e-03 27.375 3.65 6.39 1e-03
100000000 50000000 half max 30.811 3.25 5.68 0e+00 30.670 3.26 5.71 0e+00
100000000 50000000 half min 30.818 3.24 5.68 0e+00 30.931 3.23 5.66 0e+00
100000000 25000000 float sum 29.719 3.36 5.89 1e-06 29.750 3.36 5.88 1e-06
100000000 25000000 float prod 29.741 3.36 5.88 1e-07 30.029 3.33 5.83 1e-07
100000000 25000000 float max 28.400 3.52 6.16 0e+00 28.400 3.52 6.16 0e+00
100000000 25000000 float min 28.364 3.53 6.17 0e+00 28.434 3.52 6.15 0e+00
100000000 12500000 double sum 33.989 2.94 5.15 0e+00 34.104 2.93 5.13 0e+00
100000000 12500000 double prod 33.895 2.95 5.16 2e-16 33.833 2.96 5.17 2e-16
100000000 12500000 double max 30.228 3.31 5.79 0e+00 30.273 3.30 5.78 0e+00
100000000 12500000 double min 30.324 3.30 5.77 0e+00 30.341 3.30 5.77 0e+00
100000000 12500000 int64 sum 29.914 3.34 5.85 0e+00 30.036 3.33 5.83 0e+00
100000000 12500000 int64 prod 30.975 3.23 5.65 0e+00 31.083 3.22 5.63 0e+00
100000000 12500000 int64 max 29.954 3.34 5.84 0e+00 29.949 3.34 5.84 0e+00
100000000 12500000 int64 min 29.946 3.34 5.84 0e+00 29.952 3.34 5.84 0e+00
100000000 12500000 uint64 sum 29.981 3.34 5.84 0e+00 30.100 3.32 5.81 0e+00
100000000 12500000 uint64 prod 30.911 3.24 5.66 0e+00 30.800 3.25 5.68 0e+00
100000000 12500000 uint64 max 29.890 3.35 5.85 0e+00 29.947 3.34 5.84 0e+00
100000000 12500000 uint64 min 29.929 3.34 5.85 0e+00 29.964 3.34 5.84 0e+00

Out of bounds values : 0 OK
Avg bus bandwidth : 5.81761

[Mpld3] Render Matplotlib chart to web using Mpld3

The following example is about rendering a matplotlib chart on web, which is based on Django framework to build up. I encountered some problems before, such as, not able to see chart on the web page or having a run-time error after reloading the page. But, all the problems are solved.

<< demo/views.py>>

import matplotlib.pyplot as plt
import numpy as np
import mpld3

def plot_test1(request):
context = {}
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
N = 100

"""
Demo about using matplotlib and mpld3 to rendor charts
"""
scatter = ax.scatter(np.random.normal(size=N),
np.random.normal(size=N),
c=np.random.random(size=N),
s=1000 * np.random.random(size=N),
alpha=0.3,
cmap=plt.cm.jet)
ax.grid(color='white', linestyle='solid')

ax.set_title("Scatter Plot (with tooltips!)", size=20)

labels = ['point {0}'.format(i + 1) for i in range(N)]
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)
#figure = mpld3.fig_to_html(fig)
figure = json.dumps(mpld3.fig_to_dict(fig))
context.update({ 'figure' : figure })

"""
Demo about using tensorflow to predict the result
"""
num = np.random.randint(100)
prediction = predict_service.predict(num)
context.update({ 'num' : num })
context.update({ 'prediction' : prediction })

return render(request, 'demo/demo.html', context)

<<demo/demo.html>>

So, we can see the result as follows:

[Hadoop] To build a Hadoop environment (a single node cluster)

For the purpose of studying Hadoop, I have to build a testing environment to do. I found some resource links are good enough to build a single node cluster of Hadoop MapReduce as follows. And there are additional changes from my environment that I want to add some comments for my reference.

http://www.thebigdata.cn/Hadoop/15184.html
http://www.powerxing.com/install-hadoop/

Login the user "hadoop"

$ sudo su - hadoop

Go to the location of Hadoop

$ /usr/local/hadoop

Add the variables in ~/.bashrc

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64 export HADOOP_HOME=/usr/local/hadoop export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop export HADOOP_INSTALL=/usr/local/hadoop export PATH=$PATH:$HADOOP_INSTALL/bin export PATH=$PATH:$HADOOP_INSTALL/sbin export HADOOP_MAPRED_HOME=$HADOOP_INSTALL export HADOOP_COMMON_HOME=$HADOOP_INSTALL export HADOOP_HDFS_HOME=$HADOOP_INSTALL export YARN_HOME=$HADOOP_INSTALL

Modify $JAVA_HOME in etc/hadoop/hadoop-env.sh

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64

Start dfs and yarn

$ sbin/start-dfs.sh
$ sbin/start-yarn.sh

Finally, we can try the Hadoop MapReduce example as follows:
$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar grep input output 'dfs[a-z.]+'