Replace Lines with Given Strings in Shell

Let's learn how to replace given lines by given string. Consider, for example,

$ cat some_file

abcdefg

hijklmn

opqrstu

vwxyz

12345

67890

Let's replace line 2 with string THIS_IS_NEW_LINE2

$ sed '2s/.*/THIS_IS_NEW_LINE2/g' some_file

abcdefg

THIS_IS_NEW_LINE2

opqrstu

vwxyz

12345

67890

You can replace multiple lines with multiple -e options

$ sed -e '2s/.*/THIS_IS_NEW_LINE2/g' -e '3s/.*/THIS_IS_NEW_LINE3/g' some_file

abcdefg

THIS_IS_NEW_LINE2

THIS_IS_NEW_LINE3

vwxyz

12345

67890

Of course, you can always use -i option to modify file on the fly

$ sed -i '2s/.*/THIS_IS_NEW_LINE2/g' some_file

$ cat some_file

abcdefg

THIS_IS_NEW_LINE2

opqrstu

vwxyz

12345

67890

Print Selected Lines in Shell

Let's assume you want to print certain lines in given text. For example, consider

$ cat some_file

1

2

3

4

5

6

7

8

9

Let's say you want to print lines 3-5. This is very easy using sed:

$ sed -n '3,5p' some_file

3

4

5

Let's say you want to print all lines, except 3-5. This can be done with d option:

$ sed '3,5d' some_file

1

2

6

7

8

9

Note that by default sed prints all lines, so -n option asks sed to print only 3 to 5 lines and suppresses the default behavior. On the other hand, the d letter in the single quotes asks sed to delete the lines 3-5 from printing by default, so it prints only the rest of the lines instead. 

Find Line Number Matching Given Expression in Shell

Say you want to find the line number that matches a given search string. For instance,
$ cat some_file.txt
this is some file line 1
it was written in Vim line 2
let's learn unix commands line 3

To find an expression, say unix, we can use grep
$ grep unix some_file
let's learn unix commands line 3

To show the line number of the expression, use -n option
$ grep -n unix some_file
3:let's learn unix commands line 3

To output the line number only, pipe with cut command
$ grep -n unix some_file | cut -d : -f 1
3

Here, -d option specifies delimiter, and -f option specifies the position separated by the delimiter.

If there are multiple lines matching the given expression, you can always use -m option of grep to print just first N of them
$ grep -n -m 2 unix some_file | cut -d : -f 1
1
2

Happy hacking!

Build HTK on macOS with Minimum Effort

In the last post, I presented instructions to build HTK on Ubuntu. In this post, I will show you how to compile HTK on macOS.

As before, download HTK latest stable version source code, 3.4.1 at the time of writing, from here. Extract the files as usual.

$ tar xfz HTK-3.4.1.tar.gz

$ cd htk

Next, you need some required software tools from Apple.

$ xcode-select --install

Now, let's configure and make

$ ./configure --prefix=$(pwd)

$ make

You will probably encounter an error complaining

(cd HTKLib && /Applications/Xcode.app/Contents/Developer/usr/bin/make HTKLib.a) \

 || case "" in *k*) fail=yes;; *) exit 1;; esac;

gcc  -ansi -g -O2 -DNO_AUDIO -D'ARCH="darwin"' -I/usr/include/malloc -Wall -Wno-switch -g -O2 -I. -DPHNALG   -c -o HGraf.o HGraf.c

HGraf.c:73:10: fatal error: 'X11/Xlib.h' file not found

There are two options here. The first is to simply skip HSLab package that requires X11, thereby building without X11.

$ ./configure --prefix=$(pwd) --disable-hslab

$ make

$ make install

You will see all binary files in the bin folder.

The other option is to install X11 library. Download XQuartz from here and install it. This will create /opt/X11/ folder with necessary library and header files. We can now manually compile HTKLib package by specifying the header files
$ cd HTKLib
$ gcc  -ansi -g -O2 -DNO_AUDIO -D'ARCH="darwin"' -I/usr/include/malloc -Wall -Wno-switch -g -O2 -I. -DPHNALG   -c -o HGraf.o HGraf.c -I /opt/X11/include
$ cd ..

We also need to set library path:
$ export LIBRARY_PATH=/opt/X11/lib

Now, we are ready to resume the make process

$ make

$ make install

Viola! You should see bin directory with all HTK binary files!

Build HTK in Ubuntu 16.04 with Minimum Effort

This post will walk through compilation of HTK on Ubuntu 16.04 64-bit. For macOS, take a look at this post.

First, you will need to download HTK from here. In this post, I will assume you download the latest stable version, 3.4.1. Extract the source files and change the directory
$ tar xfz HTK-3.4.1.tar.gz && cd htk

Because HTK was designed for 32-bit OS, we need to set the environment as such for 64-bit OS. Enter 32-bit environment by running
$ linux32 bash

Now, we do the usual things, starting off with configure
$ ./configure --prefix=$(pwd) && make

You will see an error:
HGraf.c:73:77: fatal error: X11/Xlib.h: No such file or directory
compilation terminated.

To find out which library you need, you can run the following
$ sudo apt-get install apt-file -y && apt-file update
$ apt-file search Xlib.h

You will see some lines of search result, one of which should read
libx11-dev: /usr/include/X11/Xlib.h

From this, you know that you need to install libx11-dev package.
$ sudo apt-get install libx11-dev -y

Let's resume make
$ make

You will encounter yet another error, complaining
Makefile:77: *** missing separator (did you mean TAB instead of 8 spaces?).  Stop.

It turns out that there is an error in HLMTools/Makefile line 77. In the very beginning of the line, there are a number of spaces, which should be replaced by a single tab.
<INSERT TAB HERE>if [ ! -d $(bindir) -a X_ = X_yes ] ; then mkdir -p $(bindir) ; fi

OK, let's continue.
$ make

You should see the compilation succeeds without any problem. Let's install this and you should find HTK binary files inside bin folder in the current directory!
$ make install
$ bin/HCopy

How to SSH into VirtualBox Guest from Host with NAT

Suppose you want to install Linux on your Macbook with VirtualBox. Since GUI is so heavy and resources-consuming, you may want to run Linux guest system in the headless mode and ssh into the guest system from your Macbook. The following shall let you do so.

1. Select Guest system in VirtualBox menu open up settings.

2. In the Network tab, make sure you have enabled an adapter with NAT.

3. Open up Advanced menu and click Port Forwarding.

4. Create a rule where Protocol = TCP, Host IP = 127.0.0.1, Host Port = 2222, Guest IP = 10.0.2.15, and Guest Port = 22.

5. Now, install openssh-server on your guest system, and you should be good to go by running the command from your host:
$ ssh -p 2222 guest_user@127.0.0.1

Setting up Your Web App with Flask

With Python Flask, you can easily setup a web application that is interactive. In this tutorial, we will build a simple deep neural network server that will classify the given image into one of the 1000 categories. When done, your app will look like

Let's create a folder where all your web app files will reside.
$ mkdir ~/web_app
$ cd ~/web_app

Install Flas, Keras and OpenCV modules
$ pip install flask keras opencv-python

Next, create two more folders as below
$ mkdir templates uploads

Create server.py and copy the code below:

Create templates/index.html file and copy the code below:


Create templates/predict.html file and copy the code below:


That's it! Your web app will classify a given image---either uploaded directly from the client or using the web url---using ResNet50 pre-trained network.

To run the server, run
$ python server.py

While the server is running, you can browse to http://SERVER_IP:8888 to view the web app, where of course SERVER_IP must be replaced with the server's actual IP address.

Running Keras Deep Neural Network Inference Web Server using WebDNN

Web app is a great tool for simple demo across any platforms. I find it very useful because I can easily show my trained model's output to anyone like a breeze. The catch, however, would be setting up the server, but once the server is up and running, it cannot be any better to boast in your resume and show off to your boss with your trained model.

Here is a tutorial for setting up a simple neural network inference server using WebDNN library. Although its Github page and official documentations are very clear, I still had to spend some time to get it to work. In addition, the official documentation instructs users to compile and install emscripten from source, which will consume quite some time; I will show you how to get around this with install pre-compiled version.

First, one needs to clone WebDNN repository from Github:
$ git clone https://github.com/mil-tokyo/webdnn.git && cd webdnn

Note that WebDNN only supports Python3.6+, so you need to install this unless you already have it on the system. To check the your Python3 version, run
$ python3 --version

Make sure that it is 3.6+. There are plenty of resources that you can search on Google on how to install Python 3.6+. For instance, on Mac OS X, using homebrew is probably the easiest way:
$ brew install python3

Once you have Python 3.6+, it is a good idea to create virtual environment for what you will need to do.
$ virtualenv -p `which python3` python3

Now, activate the environment and install necessary packages
$ source python3/bin/activate
$ pip install tensorflow-gpu keras h5py

The Python environment is now complete. You now need to setup emscripten environment.
$ git clone https://github.com/juj/emsdk.git && cd emsdk
$ ./emsdk install latest
$ ./emsdk activate latest
$ source ./emsdk_env.sh

Now, we need to install eigen library.
$ wget http://bitbucket.org/eigen/eigen/get/3.3.3.tar.bz2
$ tar jxf 3.3.3.tar.bz2
$ export CPLUS_INCLUDE_PATH=$PWD/eigen-eigen-67e894c6cd8f
$ cd ..

Finally, we are ready. Let's first create pre-trained ResNet Keras model that we will use. Run Python and run the following lines
$ python
>>> from keras.applications import resnet50
>>> model = resnet50.ResNet50(include_top=True, weights='imagenet')
>>> model.save("resnet50.h5")

Exit Python and run the following
$ python ./bin/convert_keras.py resnet50.h5 --input_shape '(1,224,224,3)' --out output

After some time, it will generates files in output directory. To run the server, we first need to modify the example/resnet/script.js file. We must make sure to point to the correct directory by changing the weight path. For the version I have, I modified line 39 to point to output directory.
let runner = await WebDNN.load(`/output`, {backendOrder: backend_name});

Finally, we are ready to run the server. To start the server, run the following in webdnn directory
$ python -m http.server

On your web browser, go to address localhost:8000/example/resnet/index.html

You should be able to test ResNet50 model!

Using RAM Disk for Expediting Neural Net Training

These days I am constantly experimenting models with different architecture / hyper parameters. Because I am working with lots of images, I realized that it takes quite a bit of time for loading training or validation images every epoch from the disk. Yes, I am using a solid state drive, but it is still slow compared to RAM. To speed up the training, I have been saving images into the RAM in my Python code, which definitely expedites the training.

However, there are two main issues I see. The first is that raw training files are usually in JPEG images, which do not take up much space. However, when I am saving the image data in the Python code, I save the images in the numpy array of bitmap format, which significantly expands the storage space.

The second issue is that I have two GPUs in the server, thus sharing the CPU and RAM. When I am running different models on each GPU, but they share the same set of training data, I would have two copies of the exact same dataset in memory.

So, I was looking for a solution, and I found one that is very easy to implement. On Linux, there is a way to create a RAM disk, which is basically chunk of data saved in RAM but the system treats it as if it is a disk. Basically, I would mount a RAM disk and have the programs access the data from this RAM disk mount location.

Here is how to do it. The instructions below are based on this excellent article. First, create a folder to which you will mount the RAM disk.
$ sudo mkdir /mnt/ramdisk

Next, mount the RAM disk with specified space. For example,
$ sudo mount -t tmpfs -o size=1024m tmpfs /mnt/ramdisk

Finally, copy your training data into this folder and make sure to have your training code point to the new location.
$ cp -r /your/training/data /mnt/ramdisk

Happy training!

** Something to keep in mind **
- This is RAM, so every time your system reboots, the content will be gone
- Make sure that you have enough free RAM so that your data can fit in

Thread-Safe Generators in Python

In this post, we will go over how to create thread-safe generators in Python. This post is heavily based on this excellent article.

Generator makes life so much easier when coding in Python, but there is a catch; raw generators are not thread-safe. Consider the example below:

We see that the generator does not produce correct output when multiple threads are accessing this at the same time.

One easy way to make it thread-safe is by creating a wrapper class that simply lets only one thread to execute the generator's next method at any given time with threading lock. This is shown below:

Note that the generator now is thread-safe but doesn't execute its next method in parallel. You can also use Python's decorator to make it look even easier, although it basically does the same thing.