How to Deploy a Django Application on a Server Running Ubuntu

This post provides tutorials on how to deploy a Django application on a server running Ubuntu.

Softwares needed to set up a server for Django application:

  • Virtualenv & Virtualenvwrapper / Conda
  • Django
  • Gunicorn
  • Nginx
  • Supervisor

Concept

Nginx will face the outside world. It will serve media files (images, CSS, etc.) directly from the file system. However, it can’t talk directly to Django applications; it needs something that will run the application, feed it requests from the web, and return responses.

That’s Gunicorn‘s job. Gunicorn will create a Unix socket, and serve responses to Nginx via the wsgi protocol – the socket passes data in both directions:

The outside world <-> Nginx <-> The socket <-> Gunicorn

All this family will live into a Virtualenv. Already wondered why Virtualenv is so useful when you develop Pythons’s applications? Continue to read and you will understand.

Before you start

References

I assume you have a server available on which you have root privileges. I am using a server running Debian 7, so everything here should also work on an Ubuntu server or other Debian-based distribution. If you’re using an RPM-based distro (such as CentOS), you will need to replace the aptitude commands by their yum counterparts and if you’re using FreeBSD you can install the components from ports.

Apache and Nginx together on Ubuntu

Is it possible to run  Nginx and Apache at the same time on the same machine?

The answer is YES.

This post provides instructions on how to configure Apache and Nginx  to work together on the same machine running Ubuntu.

Overview

Nginx and Apache are great and powerful web servers. However, they both have drawbacks; Apache uses up server memory while Nginx (best used for static files) require the help of php-fpm to process dynamic content.

Nginx is an excellent lightweight web server designed to serve high traffic while Apache is another popular web server serving more than half of all active websites in the world. One can combine the two web servers to significant effect, with Nginx serving as static web server front and while Apache is processing the back end. So let’s look into how to configure your Nginx to work with Apache side by side.

Configuring Apache and Nginx

Apache and Nginx can definitely run simultaneously. The default config will not allow them to start at the same time because they will both try to listen on the same port and the same IP.However, you can easily either change the ports or ports and IPs, or IPs. There are various ways to make them run either one behind the other(usually Apache behind Nginx because Nginx will be the first entry point in the chain since it’s faster for static resources, and Apache will then only be triggered for some advanced dynamic rendering/processing) or just side by side.

Set different ports for each server. That means you can leave port 80 for Nginx and assign Apache a different port.

Nginx

Install Nginx

Install and configure Nginx that will serve as the front end of your site.

$ sudo apt-get install nginx

Once it has downloaded, configure the virtual host to run on the front end. However, a few changes are required on the configuration file.

Configure Nginx

Open up the nginx configuration file

$ sudo nano /etc/nginx/sites-available/example

For example, you could tell apache to listen on 127.0.0.1:8080 and instruct Nginx to reverse –proxy traffic to Apache while still serving static content.

Those text in blue is where you should edit according to your server info.

 server {
listen 127.0.0.1:80;
server_name some.name another.dname;
access_log /var/log/nginx/something-access.log;
location / {
proxy_pass http://localhost:8080;
proxy_redirect off;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
}
location ~* ^.+\.(jpg|js|jpeg|png)$ {
root /some/where/on/your/disks;
}
# put your static hosting config here.
}

Activate the virtual host.

$ sudo ln -s /etc/nginx/sites-available/example /etc/nginx/sites-enabled/example

Delete the default Nginx server block.

$ sudo rm /etc/nginx/sites-enabled/default

Apache

Install Apache

Install the backend which is Apache

$ sudo apt-get install apache2

Apache starts running on port 80 as Nginx is not started.Let’s make Apache listen on a different port so that they can work together.

Configuring Apache

Open Apache ports.conf file using the below command

$ sudo nano /etc/apache2/ports.conf

Look for the following line;

Listen 127.0.0.1:80

and  change it to

Listen 127.0.0.1:8080

Save and Exit.

Next, edit the default virtual host file in Apache.

The <VirtualHost> in this file is set to serve sites only on port 80

$ sudo nano /etc/apache2/sites-available/000-default.conf

Look for the following line,

<VirtualHost 127.0.0.1:80>

then, change it to;

<VirtualHost 127.0.0.1:8080>

Save the file and reload Apache.

$ sudo service apache2 reload

Verify that Apache is now listening on 8080.

$ sudo netstat -tlpn

The output is shown below, with apache2 listening on :::8080.

Active Internet connections (only servers)
Proto Recv-Q Send-Q Local Address Foreign Address State PID/Program name
tcp 0 0 0.0.0.0:22 0.0.0.0:* LISTEN 1086/sshd
tcp6 0 0 :::8080 :::* LISTEN 4678/apache2

Nginx is configured and running as the front web server on port while Apache is configured to run on the backend on port 8080. Nginx redirects proxy traffic as it still serves static content.

Conclusion

The most important thing we take from this simple configuration is that Apache and Nginx can and do work together. A problem may arise when they both listen to the same ports. By giving them different ports to listen to, your system functionality is assured.

References

 

 

 

Install Keras with GPU TensorFlow as backend on Ubuntu 16.04

This post introduces how to install Keras with TensorFlow as backend on Ubuntu Server 16.04 LTS with CUDA 8 and a NVIDIA TITAN X (Pascal) GPU, but it should work for Ubuntu Desktop 16.04 LTS.

We gratefully acknowledge the support of NVIDIA Corporation with awarding one Titan X Pascal GPU used for our machine learning and deep learning based research.

Keras is a great choice to learn machine learning and deep learning. Keras has easy syntax and can use Google TensorFlow or Microsoft CNTK or Theano as its backend.  Keras is simply a wrapper around more complex numerical computation engines such as TensorFlow and Theano.

Keras abstracts away much of the complexity of building a deep neural network, leaving us with a very simple, nice, and easy to use interface to rapidly build, test, and deploy deep learning architectures.

TensorFlow is extremely flexible, allowing you to deploy network computation to multiple CPUs, GPUs, servers, or even mobile systems without having to change a single line of code.

This makes TensorFlow an excellent choice for training distributed deep learning networks in an architecture agnostic way.

Now Let’s start on the installation of Keras with TensorFlow as its backend.

1: Setup Python virtual environment

Check my post about more details about how to setup python virtual environment and why it is better to install python libraries in Python virtual environment.

  • Install pip and Virtualenv for python and python 3:
$ sudo apt-get update
$ sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev
  • Create a Virtualenv environment in the directory for python and python 3:
#for python 2
virtualenv --system-site-packages -p python ~/keras-tf-venv

# for python 3 
virtualenv --system-site-packages -p python3 ~/keras-tf-venv3

(Note: To delete a virtual environment, just delete its folder.  For example, In our cases, it would be rm -rf keras-tf-venv or rm -rf keras-tf-venv3.)

2: Update & Install NVIDIA Drivers (skip this if you do not need to TensorFlow GPU version)

Check another post I wrote(steps 1-4 in that post) for detailed instructions about how to update and install NVIDIA Drive and CUDA 8.0 and cuDNN for the requirements of GPU TensorFlow installation.

Notes: If you have old version of NVIDIA driver installed used the following to remove it first before installation of new driver.

Step 1: Remove older version of NVIDIA
sudo apt-get purge nvidia*

Step 2: Reboot the system

test whesther it is removed

$ sudo nvidia-smi 
$ sudo: nvidia-smi: command not found  # this means the old driver was uninstalled.

(Note: If you have older version of CUDA and cuDNN installed, check the post for uninstallation.  How to uninstall CUDA Toolkit and cuDNN under Linux? (02/16/2017) (pdf). Actually to uninstall (older version) of CUDA, it tells you how to uninstall it when you install, see the Install cuda 8.0 below.)

(Note: I tried to install the latest Nvidia drive, latest cuda and latest cudnn (i.e., v6.0), but it did not work for me when I installed TensorFlow. After a few testing, I found when I install Nvidia drive 375.82,  cuda_8.0.61_375.26_linux.run, cudnn-8.0-linux-x64-v5.1.tgz. it works.)

Install cuda 8.0:

Toolkit: Installed in /usr/local/cuda-8.0
Samples: Installed in /home/liping, but missing recommended libraries

Please make sure that
– PATH includes /usr/local/cuda-8.0/bin
– LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root

To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin

Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.

***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
sudo <CudaInstaller>.run -silent -driver

Logfile is /tmp/cuda_install_3813.log

 

3: Install TensorFlow

Before installing TensorFlow and Keras, be sure to activate your python virtual environment first.

# for python 2
$ source ~/keras-tf-venv/bin/activate  # If using bash
(keras-tf-venv)$  # Your prompt should change

# for python 3
$ source ~/keras-tf-venv3/bin/activate  # If using bash
(keras-tf-venv3)$  # Your prompt should change

 (keras-tf-venv)$ pip install --upgrade tensorflow   # Python 2.7; CPU support (no GPU support)
 (keras-tf-venv3)$ pip3 install --upgrade tensorflow   # Python 3.n; CPU support (no GPU support)
 (keras-tf-venv)$ pip install --upgrade tensorflow-gpu  # Python 2.7;  GPU support
 (keras-tf-venv3)$ pip3 install --upgrade tensorflow-gpu # Python 3.n; GPU support

Note: If the commands for installing TensorFlow given above failed (typically because you invoked a pip version lower than 8.1), install TensorFlow in the active virtualenv environment by issuing a command of the following format:

 (keras-tf-venv)$ pip install --upgrade TF_PYTHON_URL   # Python 2.7
 (keras-tf-venv3)$ pip3 install --upgrade TF_PYTHON_URL  # Python 3.N

where TF_PYTHON_URL identifies the URL of the TensorFlow Python package. The appropriate value of TF_PYTHON_URLdepends on the operating system, Python version, and GPU support. Find the appropriate value for TF_PYTHON_URL for your system here. For example, if you are installing TensorFlow for Linux, Python 2.7, and CPU-only support, issue the following command to install TensorFlow in the active virtualenv environment: (see below for examples. Note that check here to get the latest version for your system.)

#for python 2.7 -- CPU only
(keras-tf-venv)$pip install --upgrade \ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.1.0-cp27-none-linux_x86_64.whl

#for python 2.7 -- GPU support
(keras-tf-venv)$pip install --upgrade \ https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.1.0-cp27-none-linux_x86_64.whl

# for python 3.5 -- CPU only
(keras-tf-venv3)$ pip3 install --upgrade \
https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.1.0-cp35-cp35m-linux_x86_64.whl

# for python 3.5 -- GPU support
(keras-tf-venv3)$ pip3 install --upgrade \ 
https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-linux_x86_64.whl
  • Validate your TensorFlow installation. (as I just installed GPU tensorflow, so if you install CPU TensorFlow, the output might be slightly different.)

#For Python 2.7

(keras-tf-venv) :~$ python
Python 2.7.12 (default, Nov 19 2016, 06:48:10) 
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2017-08-01 14:28:31.257054: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 14:28:31.257090: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 14:28:31.257103: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 14:28:31.257114: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 14:28:31.257128: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 14:28:32.253475: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties: 
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:03:00.0
Total memory: 11.90GiB
Free memory: 11.75GiB
2017-08-01 14:28:32.253512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 
2017-08-01 14:28:32.253519: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y 
2017-08-01 14:28:32.253533: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:03:00.0)
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> exit()
(keras-tf-venv) :~$ 

#for python 3

(keras-tf-venv3) :~$ python3
Python 3.5.2 (default, Nov 17 2016, 17:05:23) 
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2017-08-01 13:54:30.458376: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 13:54:30.458413: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 13:54:30.458425: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 13:54:30.458436: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 13:54:30.458448: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-08-01 13:54:31.420661: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties: 
name: TITAN X (Pascal)
major: 6 minor: 1 memoryClockRate (GHz) 1.531
pciBusID 0000:03:00.0
Total memory: 11.90GiB
Free memory: 11.75GiB
2017-08-01 13:54:31.420692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 
2017-08-01 13:54:31.420699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y 
2017-08-01 13:54:31.420712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: TITAN X (Pascal), pci bus id: 0000:03:00.0)
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
>>> exit() 
(keras-tf-venv3) :~$

If you see the output as below, it indicates your TensorFlow was installed correctly.

Hello, TensorFlow!

3: Install Keras

(Note: Be sure that you activated your python virtual environment before you install Keras.)

Installing Keras is even easier than installing TensorFlow.

First, let’s install a few dependencies:

#for python 2
$ pip install numpy scipy
$ pip install scikit-learn
$ pip install pillow
$ pip install h5py

#for python 3
$ pip3 install numpy scipy
$ pip3 install scikit-learn
$ pip3 install pillow
$ pip3 install h5py

4: Verify that your keras.json file is configured correctly

Let’s now check the contents of our keras.json  configuration file. You can find this file at ~/.keras/keras.json .

use nano to open and edit the file.

$ nano ~/.keras/keras.json

The default values should be something like this:

{
 "epsilon": 1e-07,
 "backend": "tensorflow",
 "floatx": "float32",
 "image_data_format": "channels_last"
}

Can’t find your keras.json file?

On most systems the keras.json  file (and associated subdirectories) will not be created until you open up a Python shell and directly import the keras  package itself.

If you find that the ~/.keras/keras.json  file does not exist on your system, simply open up a shell, (optionally) access your Python virtual environment (if you are using virtual environments), and then import Keras:

#for python 2
$ python
>>> import keras
>>> quit()

#for python 3
$ python3
>>> import keras
>>> quit()

From there, you should see that your keras.json  file now exists on your local disk.

If you see any errors when importing keras  go back to the top of step 4 and ensure your keras.json  configuration file has been properly updated.

5: Test Keras + TensorFlow installation

To verify that Keras + TensorFlow have been installed, simply access the keras_tf  environment using the workon  command, open up a Python shell, and import keras :

Specifically, you can see the text Using TensorFlow backend  display when importing Keras — this successfully demonstrates that Keras has been installed with the TensorFlow backend.

Note: each time you would like to use Keras, you need to activate the virtual environment into which it installed, and when you are done using Keras, deactivate the environment.

# for python 2
(keras-tf-venv)$ deactivate
$  # Your prompt should change back

#for python 3
(keras-tf-venv3)$ deactivate
$  # Your prompt should change back

Note: To delete a virtual environment, just delete its folder. (In this case, it would be rm -rf keras-tf-venv or rm -rf keras-tf-venv3.)

References:

Installing Keras with TensorFlow backend (by on November 14, 2016 in Deep Learning, Libraries, Tutorials)

Installing keras makes tensorflow can’t find GPU

Installing Nvidia, Cuda, CuDNN, TensorFlow and Keras

https://www.tensorflow.org/install/install_linux

Keras as a simplified interface to TensorFlow: tutorial

I: Calling Keras layers on TensorFlow tensors

II: Using Keras models with TensorFlow

III: Multi-GPU and distributed training

IV: Exporting a model with TensorFlow-serving

Get the number of CPUs/cores in Linux from the command line

This post introduces how to find out the number of CPUs/cores on Linux machines from command line.

  • method 1:

$ nproc –all

20   # this means there are 20 cores on the linux machine.

  • method 2:

$ lscpu

CPU(s):                20

On-line CPU(s) list:   0-19

Thread(s) per core:    2

Core(s) per socket:    10

 

 

Commonly used Linux commands (Ubuntu)

This page lists commonly used Linux commands to help those who are not very familiar with Linux command environment. I have been collecting and recording those from my experience.

I was once a beginner, so I can understand the pain for Linux beginners. I have not yet seen any post that has done comprehensive collection of commands on Linux, so I thought I could help this out. That is why you see this post. Here you go. Happy Learning!

You can see further reading list at the end of this post.

Note: Do not contain space in your filename or directory name, use underscore instead of space.

======Basic commands:

  • cd 

this command will goes back to the home directory of your account, no matter where your current directory is located in your terminal.

  • cd ../

this command will go to the parent directory of your current directory in terminal

  • rmdir 

remove/delete an empty folder.

example:

first cd into the parent folder of the empty folder to remove

rmdir test

  • rm -rf ./*

    first cd to a directory, and this command will empty all of things under the current directory

  • ls  

list all the files and folders under current path

  • ls -l  

list all the files and folders with details like dates.

  • ls -l -t

list files and folders ordered by time.

  • ls -ltrh 

list all the csv files under the current directory in long format by time and in reverse order, the file size in human readable format (e.g., in mb, or gb, instead of byte size)

  • -l List in long format. If the output is to a terminal, a total sum for all the file sizes is output on a line before the long listing.
  • -r Reverse the order of the sort to get reverse lexicographical order or the oldest entries first (or largest files last, if combined with sort by size.
  • -t Sort by time modified (most recently modified first) before sorting the operands by lexicographical order.
  • ls -ltrh *.csv

list all the csv files under the current directory in long format by time and in reverse order, the file size in human readable format (e.g., in mb, or gb, instead of byte size)

  • find

find -name ‘*.jpg’ -exec cp {} ./test/ \;

Find all jpg files  and then copy the found files to the folder test which is subfolder of current path.
Note that: the current path should be the path where the files to search are located. (i.e., use cd to locate to the directory where the files are in before type in the cmd below into terminal.)

  • rm -r -f

-r means recursive, it will remove folders and subfolders and files within the folders and subfolders

-f means force

  • mkdir [folder name]

create new folder

example:

mkdir image

  • cp [filename] [new filename]

copy and rename file

  • cp [filename] [path/to/new/lotcation/filename]

copy the file to another location

if you use this commond to copy a directory, you would meet this error:

cp: omitting directory ...

The error notice means you told cp to copy files and not directories. The warning is about cp finding a directory and informing you it will be skipped.

  • cp -r [directory] [path/to/new/location/directory]

copy a directory to another location.

cp -r means recursive and this option will make cp also include sub-directories.

If you meet permission denied error, add sudo before the command, and it will ask your password.

  • mv [directory] [path/to/new/location/directory]

If you meet permission denied error, add sudo before the command, and it will ask your password.

  • nano [new file name or /path/to/new file/new file name]

example:

nano  myexample   #it will create a new empty file named “myexample” under the current directory

  • nano [file name]

If the file name already exists, it will open the file and you can edit it.

Note: Ctrl+O to save the file, and then hit Enter, and then Ctrl +X to close the file.

 

 

======More advanced commands:

  • cd into directory without having permission

When cd into a directory and the following error occurs

bash: cd: your-dirctory: Permission denied

The solution is:

Enter super user mode, and cd into the directory that you are not permissioned to go into. Sudo requires administrator password.

sudo su  
cd directory  # you will notice that your prompt changes after your enter your root password. now you can cd to the directory.

# to exit "super user" mode, type exit.
  • lspci

check GPU information on Ubuntu

look for “VGA compatible controller:”…

  • sudo nvidia-smi

check GPU info and GPU usage.

  • sudo reboot -h now

reboot a server from terminal

  • sudo shutdown -h now

shut down a server from terminal

Note: If your Ubuntu Server 16.o4 LTS has Black Screen after reboot or shut down, try pressing (simultaneously) Ctrl + Alt + F2 to see whether you can switch to different console

  • vncserver -kill :1  

This is a vncserver command. It is used to kill a port of a GUI by VNC server, where 1 is the port you would like to kill.

  • vncviewer -via username@yourserver_hostname :1

connect to a server via vncviewer from a linux-based client. you need to change the port number 1 to yours.

echo is a built-in command in the bash and C shells that writes its arguments to standard output.

See here, and here, and herefor example usage of it.

  • cat

See here for example usage of cat command.

  • chmod

see here for example usage of chmod command.

 

  • check supercomputing Cluster’s Linux distribution and version

$ lsb_release -a
LSB Version:    :base-4.0-amd64:base-4.0-noarch:core-4.0-amd64:core-4.0-noarch:graphics-4.0-amd64:graphics-4.0-noarch:printing-4.0-amd64:printing-4.0-noarch
Distributor ID:    RedHatEnterpriseServer
Description:    Red Hat Enterprise Linux Server release 6.4 (Santiago)
Release:    6.4
Codename:    Santiago

  • show the list of top processes ordered by RAM and CPU  use in descendant form

(remove the pipeline and head if you want to see the full list):

$ ps -eo pid,ppid,cmd,%mem,%cpu --sort=-%mem | head

Brief explanation of the options used in the command above:

— The -o (or –format) option of ps allows us to specify the output format.

— the processes’ PIDs (pid), PPIDs (pid)

— the name of the executable file associated with the process (cmd), and

— the RAM and CPU utilization (%mem and %cpu, respectively).

We can use --sort to sort by either %mem or %cpu. By default, the output will be sorted in ascendant form, but usually we prefer to reverse that order by adding a minus sign in front of the sort criteria to make it list in descendant.

To add other fields to the output, or change the sort criteria, refer to the OUTPUT FORMAT CONTROL section in the man page of ps command.


======File Transfer: getting files to/from your account on a server

  • On Linux generally the command line scp command.
 Examples of using the command line are:

scp -p file_name username@yourserver_hostname:destination/directory

  • or for a full directory tree:

scp -pr dir_name username@yourserver_hostname:destination/directory

 

Note that if you want to transfer files from server to your client computer, just reverse the directory.

e.g., scp -pr username@yourserver_hostname:source/directory dir_name_on_your_client

 

======download files

  • wget (tool for downloading files)  (pdf)
  • See Linux wget command (pdf), which provides detailed and comprehensive different tags (options) to use with wget command.

======Save terminal output to a file

  • sudo command -option | tee logThis command will show output on terminal and save to a file at the same time.
  • Save terminal output to a fileredirect the output to a file: someCommand > someFile.txt Or if you want to append data: someCommand >> someFile.txt If you want stderr too use this: someCommand &> someFile.txt or this to append:  someCommand &>> someFile.txt
  • Tail -f log.txt

Python related commands:

  • enter python environment

type python in terminal, and it will show python 2.7 version info and also enter into python 2 environment

  • enter python 3 environment

python3 

and it will show python 3 version info (e.g., python 3.5.2) and also enter into python 3 environment.

 

======Git related commands

  • git clone the url to gitclone

for example:

first cd into the folder you want the models to be cloned to in your terminal, and then issue this command. it will clone the model foder from https://github.com/tensorflow/models under your current folder in your terminal.

git clone https://github.com/tensorflow/models

 

======Some useful shortcuts on linux

  • you can open multiple terminals

open each terminal by pressing Ctrl + Alt + T.

  • Shortcut to bring all open terminals to the front

After you bring one terminal window in the front, press Alt+~ to bring all other terminal windows in the front one by one:

  • CTRL + C − terminate the current command. 

======References and further reading list:

Linux and Unix top 10 command pages  (See here for links to more commands intro)

Below is a listing of the top 10 Unix command pages by the amount of times they have been accessed on the Computer Hope server.

  1. Linux and Unix tar command help
  2. Linux and Unix chmod command help
  3. Linux and Unix ls command help
  4. Linux and Unix find command help
  5. Information about the Linux and Unix grep command
  6. Linux and Unix cp command help
  7. Linux and Unix vi command help
  8. Linux and Unix ifconfig command help
  9. Linux and Unix date command help
  10. Linux and Unix kill command help

======TOC of the nice tutorial: Linux Shell Commands: A Tutorial Quick Reference for Desktop Users

Table of Contents

1. A Short Intro to the Command Line

This chapter will acquaint you with the basics of the command line. To maximize your learning, you should follow along by typing in the example commands given. Every major Linux distribution has a menu item called “shell”, “console”, “terminal” or the like, which will give you a window with a command line interface. In this book, I assume that readers work in a graphical desktop environment and use the Bash shell in a terminal window. Bash is the default shell in all major Linux distributions.

2. Getting Information

The commands presented in this chapter provide valuable information on the state and configuration of your system.

3. Managing Files and Directories

The command line offers you great flexibility in creating, copying, moving and editing files and directories, as this chapter shows.

  • cd (change directory)  (pdf)
  • chgrp (change group ownership)  (pdf)
  • chmod (change file permissions)  (pdf)
  • chown (change file ownership)  (pdf)
  • cp (copy files and directories)  (pdf)
  • dd (write data to devices)  (pdf)
  • find (search for files)  (pdf)
  • ln (make links between files)  (pdf)
  • locate (find files by name)  (pdf)
  • mkdir (create a directory)  (pdf)
  • mount (mount file systems)  (pdf)
  • mv (rename files)  (pdf)
  • rm (remove files or directories)  (pdf)
  • rmdir (remove empty directories)  (pdf)
  • shred (delete a file securely)  (pdf)
  • touch (change file timestamps)  (pdf)
  • umount (unmount file systems)  (pdf)

4. Managing Processes

Linux provides powerful tools for controlling the execution of your programs. Some of the most important tools are presented in this chapter.

  • disown (detach a job from the shell)   (pdf)
  • kill (terminate a process)  (pdf)
  • ps (list running processes)  (pdf)
  • pstree (display a tree of processes)  (pdf)
  • shutdown (halt or reboot the system)  (pdf)
  • sudo (execute a command as root)  (pdf)

5. Working with Text

Processing plain text files is a big strength of Linux. The commands presented in this chapter allow you to display particular parts of files (e.g. head, tail), reorder their contents (e.g. sort), carry out search/replace operations (e.g. grep, sed), and much more.

  • cat (concatenate and output files)  (pdf)
  • cut (output columns from files)  (pdf)
  • diff (show differences between files)  (pdf)
  • grep (print lines matching a pattern)  (pdf)
  • head (output the first part of files)  (pdf)
  • less (view file by pages)  (pdf)
  • pdftk (manipulate PDF files)  (pdf)
  • sed (search and replace text)  (pdf)
  • sort (sort lines of text files)  (pdf)
  • tail (output the last part of files)  (pdf)
  • wc (count lines, words and characters)  (pdf)

6. Being Productive

This chapter collects some commands that can help you accomplish everyday tasks quickly and efficiently. Many of the commands are faster or more reliable replacements for popular graphical applications. For example, wget can replace a graphical download manager.

  • alias (define command shortcuts)  (pdf)
  • alsamixer (audio mixer)  (pdf)
  • bc (command line calculator)  (pdf)
  • history (display command history)  (pdf)
  • rsync (fast, versatile file copying tool)  (pdf)
  • tar (Linux archiving utility)  (pdf)
  • unrar (extract files from RAR archives)  (pdf)
  • unzip (extract files from ZIP archives)  (pdf)
  • wget (tool for downloading files)  (pdf)
  • xmodmap (change key bindings)  (pdf)

======The end of the TOC of the nice tutorialLinux Shell Commands: A Tutorial Quick Reference for Desktop Users

======apt-get usages

======curl command examples

cURL can be used in many different and useful ways. Using cURL, we can download, upload and manage files, check email address, or even update status on some of the social media websites or check the weather outside.

cURL is very useful command line tool to transfer data from / to a server. cURL supports various protocols, including FILE, HTTP, HTTPS, IMAP, IMAPS, LDAP, DICT, LDAPS, TELNET, FTP, FTPS, GOPHER, RTMP, RTSP, SCP, SFTP, POP3, POP3S, SMB, SMBS, SMTP, SMTPS, and TFTP.

This tutorial covers five of the most useful and basic uses of cURL tool:

–Check URL

One of the most common and simplest uses of cURL is typing the command itself, followed by the URL you want to check

curl https://example.com
#This command will display the content of the URL on your terminal

–Save the output of the URL to a file

The output of the cURL command can be easily saved to a file by adding the -o option to the command, as shown below

curl -o website https://example.com
#the output will be save to a file named ‘website’ in the current working directory

–Download files with cURL

curl -O https://example.com/file.zip

# the -O option used for saving files to current working directory without renaming
# e.g.,  the ‘file.zip’ zip archive will be downloaded to the current working directory.
curl -o archive.zip https://domain.com/file.zip

# the ‘file.zip’ archive will be downloaded and saved as ‘archive.zip’.
curl -O https://domain.com/file.zip -O https://domain.com/file2.zip

# cURL can be also used to download multiple files simultaneously
#cURL can be also used to download files securely via SSH

curl -u user sftp://server.domain.com/path/to/file

# Note that the full path of the file to be downloaded is required

–Get HTTP header information from a website

You can easily get HTTP header information from any website you want by adding the -I option (capital ‘i’) to cURL.

curl -I http://example.com

–Access an FTP server

#  access your FTP server with cURL 
curl ftp://ftp.domain.com --user username:password

# cURL will connect to the FTP server and list all files and directories in user’s home directory
curl ftp://ftp.domain.com/file.zip --user username:password
# download a file via FTP using curl
curl -T file.zip ftp://ftp.domain.com/ --user username:password
# upload a file to  the FTP server

–check cURL manual page to see all available cURL options and functionalities

man curl

This post covers detailed and comprehensive explanation of different options to use with curl command.

  • $ free -m

Linux has the habit of caching lots of things for faster performance, so that memory can be freed and used if needed.

  • $ cat /proc/meminfo
  • $ vmstat -s

Install GPU TensorFlow from Source on Ubuntu Server 16.04 LTS

I installed GPU TensorFlow from source on Ubuntu Server 16.04 LTS with CUDA 8 and a GeForce GTX 1080 GPU, but it should work for Ubuntu Desktop 16.04 LTS.

In this tutorial I will be going through the process of building the latest TensorFlow from sources for Ubuntu Server 16.04.  TensorFlow now supports using Cuda 8.0 & CuDNN 5.1 so you can use the pip’s from their website for a much easier install.

In order to use TensorFlow with GPU support you must have a NVIDIA graphic card with a minimum compute capability of 3.0.

Getting started I am going to assume you know some of the basics of using a terminal in Linux. (Check this post for commonly used Linux commands.)

1: Install Required Packages

Open a terminal by pressing Ctrl + Alt + T.

(Because it is Ubuntu Server 16.04, need to install those required packages below, if you are on Ubuntu Desktop 16.04, most of the libraries below already come with the OS installation.)

Paste each line one at a time (without the $) using Shift + Ctrl + V

$ sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev

2: Update & Install NVIDIA Drivers

Note that if you have a monitor connected to your server, be sure to disconnect it before you start to install the NVIDIA drivers. Otherwise, it may cause trouble when you reboot your server after you install your NVIDIA drivers. You can reconnect your monitor after you successfully install the NVIDIA drivers.

You must also have the 367 (or later) NVidia drivers installed, this can easily be done from Ubuntu’s built in additional drivers after you update your driver packages. (you can check the latest drivers version according to your GPU info from The NVIDIA downloads page, for example, mine is 375.)

$ sudo add-apt-repository ppa:graphics-drivers/ppa
$ sudo apt update
$ sudo apt-get install nvidia-375  

(Note: use the following command if you encounter this error “sudo: add-apt-repository: command not found”)

$ sudo apt-get install software-properties-common

Once installed the driver restart your computer. You can use the command below to reboot the server from command line.

$ sudo reboot -h now

If you experience any troubles booting linux or logging in: try disabling fast & safe boot in your bios and modifying your grub boot options to enable nomodeset.

You can use the following command to get various diagnostics of the GTX 1080.

$ sudo nvidia-smi

 

3: Install NVIDIA CUDA Toolkit 8.0 

Skip if not installing with GPU support

(Note: If you have older version of CUDA and cuDNN installed, check the post for uninstallation.  How to uninstall CUDA Toolkit and cuDNN under Linux? (02/16/2017) (pdf))

(If you need to use command line to transfer files from your clienet computer to your server. refer to the following scp command)

File Transfer: getting files to/from  your Ubuntu server

copy file:

scp -p file_name username@yourserver_hostname:destination/directory

for a full directory tree:

scp -pr dir_name username@yourserver_hostname:destination/directory

 

To install the Nvidia Toolkit  download base installation .run file from Nvidia website (download the .run file. NOT THE DEB FILE!!).

 

$ cd ~/Downloads # or directory to where you downloaded file
$ sudo sh cuda_8.0.44_linux.run  # hold s to skip

This will install cuda into: /usr/local/cuda-8.0

MAKE SURE YOU SAY NO TO INSTALLING NVIDIA DRIVERS! (Very important, If you answer yes, the GTX 1080 375 driver will be overwritten.

Also make sure you select yes to creating a symbolic link to your cuda directory.

(FYI, the following is the questions to be asked.)

The following contains specific license terms and conditions
for four separate NVIDIA products. By accepting this
agreement, you agree to comply with all the terms and
conditions applicable to the specific product(s) included
herein.

Do you accept the previously read EULA?
accept/decline/quit: accept

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
(y)es/(n)o/(q)uit: n

Install the CUDA 8.0 Toolkit?
(y)es/(n)o/(q)uit: y

Enter Toolkit Location
[ default is /usr/local/cuda-8.0 ]:

Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y

Install the CUDA 8.0 Samples?
(y)es/(n)o/(q)uit: y

Enter CUDA Samples Location
[ default is /home/liping ]:

Installing the CUDA Toolkit in /usr/local/cuda-8.0 …
Installing the CUDA Samples in /home/liping …
Copying samples to /home/liping/NVIDIA_CUDA-8.0_Samples now…
Finished copying samples.

 

= Summary =
===========

Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-8.0
Samples:  Installed in /home/liping, but missing recommended libraries

Please make sure that
 –   PATH includes /usr/local/cuda-8.0/bin
 –   LD_LIBRARY_PATH includes /usr/local/cuda-8.0/lib64, or, add /usr/local/cuda-8.0/lib64 to /etc/ld.so.conf and run ldconfig as root

To uninstall the CUDA Toolkit, run the uninstall script in /usr/local/cuda-8.0/bin

Please see CUDA_Installation_Guide_Linux.pdf in /usr/local/cuda-8.0/doc/pdf for detailed information on setting up CUDA.

***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
    sudo <CudaInstaller>.run -silent -driver

Logfile is /tmp/cuda_install_7169.log

 

4: Install NVIDIA cuDNN

Once the CUDA Toolkit is installed, download cuDNN v5.1 for Cuda 8.0 from NVIDIA website (Note that you will be asked to register an NVIDIA developer account in order to download) and extract into /usr/local/cuda via:

$ sudo tar -xzvf cudnn-8.0-linux-x64-v5.1.tgz
$ sudo cp cuda/include/cudnn.h /usr/local/cuda/include
$ sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
$ sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

Then update your bash file:

$ nano ~/.bashrc

This will open your bash file in a text editor which you will scroll to the bottom and add these lines:

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda

Once you save and close the text file you can return to your original terminal and type this command to reload your .bashrc file:

$ source ~/.bashrc

5: Install Bazel

Instructions also on Bazel website

$ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
$ curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add -
$ sudo apt-get update
$ sudo apt-get install bazel
$ sudo apt-get upgrade bazel

6: Clone TensorFlow

$ cd ~
$ git clone https://github.com/tensorflow/tensorflow

7: Configure TensorFlow Installation

$ cd ~/tensorflow
$ ./configure

Use defaults by pressing enter for all except:

Please specify the location of python. [Default is /usr/bin/python]:

For Python 2 use default or If you wish to build for Python 3 enter:

$ /usr/bin/python3.5

Please input the desired Python library path to use. Default is [/usr/local/lib/python2.7/dist-packages]:

For Python 2 use default or If you wish to build for Python 3 enter:

$ /usr/local/lib/python3.5/dist-packages

Unless you have a Radeon graphic card you can say no to OpenCL support. (has anyone tested this? ping me if so!)

Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]:

$ 8.0

Please specify the Cudnn version you want to use. [Leave empty to use system default]:

$ 5

Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: “3.5,5.2”]: 5.2,6.1
……….
INFO: Starting clean (this may take a while). Consider using –expunge_async if the clean takes more than several minutes.
………
INFO: All external dependencies fetched successfully.
Configuration finished

If all was done correctly you should see:

INFO: All external dependencies fetched successfully.
Configuration finished.

8: Build TensorFlow

Warning Resource Intensive I recommend having at least 8GB of computer memory.

(Note that you current path in terminal is ~/tensorflow) 

If you want to build TensorFlow with GPU support enter (Note that the command should be one line):

$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

For CPU only enter:

$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package

9:Build & Install Pip Package

(Note that you current path in terminal is ~/tensorflow) 

This will build the pip package required for installing TensorFlow in your ~/tensorflow_pkg [you can change this directory as the one you like]

$ bazel-bin/tensorflow/tools/pip_package/build_pip_package ~/tensorflow_pkg

Remember that, at any time, you can manually force the project to be reconfigured (run the ./configure file in step 7 above to reconfigure) and built from scratch by emptying the directory ~/tensorflow_pkg  with:

rm -rf ./*

Now you can cd into the directory where you build your tensorflow, for example my case is  ~/tensorflow_pkg

then issue the following command according to you are using python or python 3.

To Install Using Python 3 (remove sudo if using a virtualenv)

$ sudo pip3 install tensorflow-0.12.1-cp27-cp27mu-linux_x86_64.whl

# tip: after you type tensorflow, you can hit Tab on your keyboard to autofill the name of the .whl file you just built

For Python 2 (remove sudo if using a virtualenv)

$ sudo pip install tensorflow-0.12.1-cp27-cp27mu-linux_x86_64.whl

# tip: after you type tensorflow, you can hit Tab on your keyboard to autofill the name of the .whl file you just built

Note that if you meet this error:

The directory ‘/home/youraccountname/.cache/pip/http’ or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo’s -H flag. 

Change the command above to

 sudo -H pip install tensorflow-0.12.1-cp27-cp27mu-linux_x86_64.whl

If you meet this warning

You are using pip version 8.1.1, however version 9.0.1 is available.
You should consider upgrading via the ‘pip install –upgrade pip’ command.

I would suggest just ignore this – sometimes after doing upgrade there might appear some trouble because of dependencies.

10: Test Your Installation

Finally, time to test our installation.

To test the installation, open an interactive Python shell and import the TensorFlow module:

$ cd # this will return to your home root directory ~
$ python  # or python3
… 
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcurand.so.8.0 locally

With the TensorFlow module imported, the next step to test the installation is to create a TensorFlow Session, which will initialize the available computing devices and provide a means of executing computation graphs:

>>> sess = tf.Session()
>>> sess = tf.Session() 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:03:00.0
Total memory: 7.92GiB
Free memory: 7.81GiB
…

To manually control which devices are visible to TensorFlow, set the CUDA_VISIBLE_DEVICES environment variable when launching Python. For example, to force the use of only GPU 0:

$ CUDA_VISIBLE_DEVICES=0 python

You should now be able to run a Hello World application:

>>> hello_world = tf.constant("Hello, TensorFlow!") 
>>> print sess.run(hello_world) 
Hello, TensorFlow! 
>>> print sess.run(tf.constant(12)*tf.constant(3)) 
36 

TensorFlow also has instructions on how to do a basic test and a list of common installation problems.

You should now have TensorFlow installed on your computer. This tutorial was tested on a fresh install of Ubuntu Server 16.04 with a GeForce GTX 1080.

 

Referenced posts (See this page for more TensorFlow setup links I collected):