Run R scripts from the command line on Ubuntu

Running R scripts from the command line can be a powerful way to:

  • Automate your R scripts
  • Integrate R into production
  • Call R through other tools or systems
There are basically two Linux commands that are used:
  1. RScript (preferred)
  2. The older command is  R CMD BATCH.

A better way to run R scripts in batch mode is Rscript, and its comes with R.

See the example below to see the difference between using RScript and R CMD BATCH.

Save 

print("hello world")

as a r script file and name it helloworld.r, and then run it in your terminal:

(Be sure to first cd to the path where you saved the helloworld.r file.)

then type the commands (the lines below in bold) to your temrinal

$ Rscript helloworld.r
[1] "hello world"
$ R CMD BATCH helloworld.r
$

We can see that Rscript directly  output to the terminal , and R CMD BATCH has done nothing. But actually, R CMD BATCH has written its output to a file called helloworld.r.Rout(it is located at the same place where you put helloworld.r), and that output includes both the commands and output, just like in interactive mode, along with some runtime stats:

> print("hello world")
[1] "hello world"
> 
> 
> proc.time()
   user  system elapsed 
  0.080   0.004   0.113

You can call these directly from the command line or integrate them into a bash script. You can also call these from any job scheduler.

Note, these are R related tools. The RStudio IDE does not currently come with tools that enhance or manage the RScript and R CMD BATCH functions. However, there is a shell built into the IDE and you could conceivably call these commands from there.

 The alternative to the using the Linux command line is to use the source() function inside of R. The source function will also call a script, but you have to be inside an R session to use it.

References:

How to run R scripts from the command line (Nathan Stephens on January 02, 2017)

Running R batch mode on Linux (pdf)

RScript man page

Setup R environment on Ubuntu 16.04 (R-Base and RStudio)

This post provides instructions for installing R-Base and RStudio on Ubuntu 16.04.

  • Install R-Base

You can find R-Base in the Software Center; this would be the easy way to do it. However, the Software Center versions are often out of date, which can be a pain moving foward when your packages are based on the most current version of R Base. The easy fix is to download and install R Base directly from the Cran servers.

1. Add R repository

First, we’ve got to add a line to our /etc/apt/sources.list file. This can be accomplished with the following. Note the “xenial” in the line, indicating Ubuntu 16.04. If you have a different version, just change that.

sudo echo "deb http://cran.rstudio.com/bin/linux/ubuntu xenial/" | sudo tee -a /etc/apt/sources.list

2. Add R to Ubuntu Keyring

First:

 gpg --keyserver keyserver.ubuntu.com --recv-key E084DAB9

Then:

 gpg -a --export E084DAB9 | sudo apt-key add -

3. Install R-Base

sudo apt-get update
sudo apt-get install r-base r-base-dev

 

If you would like to use R in IDE like RStudio, See below for the instructions.

  • Installing RStudio

Use CTRL + ALT + T to open your terminal, then use the commands below. If you would like to install the latest version, just change the link info after the wget command. (Note that you can get latest RStudio download link at here. See the picture below the install commands to see how to get the latest version of RStudio for you. Be sure to revise the command part associated with the version you would like to install accordingly, which I highlight in red and italic below.)

# Download and Install RStudio
sudo apt-get install gdebi-core
wget https://download1.rstudio.org/rstudio-1.0.136-amd64.deb
sudo gdebi rstudio-1.0.136-amd64.deb
rm rstudio-1.0.136-amd64.deb

References:

How to Install R on Linux Ubuntu 16.04 Xenial Xerus (April 26, 2016 By Kris Eberwein)

Install R and RStudio on Ubuntu 12.04/14.04/16.04 (Michael Galarnyk on Dec 17, 2016 )

 

Parallel programming on Ubuntu using OpenMP with C/C++

This post introduces the basic of parallel programming on Ubuntu using OpenMP with C/C++. (MP in OpenMP refers to as Multi-processing.)

OpenMP, is an an API that enables direct multi-threaded, shared memory parallelism. See here for a nice OpenMP tutorial. A good introduction of OpenMP can be found here.

See here for wiki intro to OpenMP.

OpenMP uses a portable, scalable model that gives programmers a simple and flexible interface for developing parallel applications for platforms ranging from the standard desktop computer to the supercomputer.

An application built with the hybrid model of parallel programming can run on a computer cluster using both OpenMP and Message Passing Interface (MPI), such that OpenMP is used for parallelism within a (multi-core) node while MPI is used for parallelism between nodes. There have also been efforts to run OpenMP on software distributed shared memory systems, to translate OpenMP into MPI and to extend OpenMP for non-shared memory systems.

1: Create a text file and paste the follow code into it

Writing the hello world code in C is the same everywhere. There are only minor differences in the code.

#include<stdio.h>

int main( int ac, char **av)

{

#pragma omp parallel // specify the code between the curly brackets is part of an OpenMP parallel section.

{

printf("Hello World!!!\n");

}

return 0;

}

Save the code as anyname.c.  Now let’s compile the code.

2:  Open a terminal (CTRL + ALT + T)

3: Compile the code

In your terminal, cd to the path where the c script you just created is located.

To compile your code, simply type this in the terminal

gcc anyname.c -o anyname.out

If there is any problem in the code, you must fix it and compile again. Now to next (big) step .

4: Number of threads and running in OpenMp

When the code is compiled correctly, we can run it parallel form using OpenMP. firstly we determine number of threads we are going to use. type this in your terminal:

export OMP_NUM_THREADS=4

you can try changing the number of threads into numbers you like. (Note that how many maximum threads you can use depends on your machine’s hardware.)

Then, we are going to compile it using the OpenMP. It is similar to normal compiling but with addition of a few words. The format is like this:

gcc -fopenmp anyname.c -o anyname.out

after that, you can run the program. To run the program, type this into your terminal:

./anyname.out

you will find out that the hello world prints out as many times as your thread number.

 

Notes for c++ with OpenMP :

Here I will just talk about how to compile cpp code, the other part with OpenMP is the same as c code, except for: need to change .c to .cpp and gcc to g++.

If you don’t have C/C++ compiler then first install it by  (Normally this comes with an installed Ubuntu OS):
sudo apt-get build-essential
Then open Gedit or any editor , write your code and save it as
“myprogram.cpp”. (See below for a simple example of c++ code)

#include <iostream>
int main()
{
  #pragma omp parallel 
  {
      std::cout << "hello world \n";
    }
    return 0;
}

Next step is to compile:
Make sure your terminal is cd to the folder where you have saved your .cpp file.
cd /path to/the cpp file
Once done you can compile it using G++
g++ myprogram.cpp
This will output a.out (by default) which is your executable code that can be run as
 ./a.out
If you want to name the output instead of a.out, you can pass a parameter to G++
g++ myprogram.cpp -o myprogram
This time it will output a file “myprogram” which again can be run in the same way
./myprogram

 

Notes for OpenMP:

You don’t install OpenMP. OpenMP is a feature of the compiler. Check that the compiler you are using implements OpenMP, which is an API for parallel programming in C/C++/Fortran.

Check with the OpenMP website, openmp.org for a list of compilers and platforms that support OpenMP programming.

OpenMP Directives:

  1. Directive Format
  2. C/C++ Directive Format
  3. Directive Scoping
  4. PARALLEL Construct

(Thanks Weiming for introducing this cool feature of OpenMP to the lab.)

Posts referenced:

Read file from line 2 or skip header row in Python

This post introduces how to read file from line 2 in Python.

  • method 1:
with open(fname) as f:
  next(f)
  for line in f:
    #do something

Note: If you need the header later, instead of next(f) use f.readline() and store it as a variable.
Or use header_line = next(f).

  • method 2
f = open(fname,'r')
lines = f.readlines()[1:]
f.close()

This will skip 1 line. for example, [‘a’, ‘b’, ‘c’][1:] => [‘b’, ‘c’]

 

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.

  • 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

Find which version of TensorFlow was installed

This depends on how you installed TensorFlow.

Pip installation

Run:

python -c 'import tensorflow as tf; print(tf.__version__)'  # for Python 2
python3 -c 'import tensorflow as tf; print(tf.__version__)'  # for Python 3

Note that python is symlinked to /usr/bin/python3 in some Linux distributions, so use pythoninstead of python3 in these cases.

pip list | grep tensorflow for Python 2 or pip3 list | grep tensorflow for Python 3 will also show the version of Tensorflow installed.

Virtualenv installation

Run:

python -c 'import tensorflow as tf; print(tf.__version__)'  # for both Python 2 and Python 3

pip list | grep tensorflow will also show the version of Tensorflow installed.

Examples

For example, I have installed TensorFlow 0.12.1 in a virtualenv for Python 3. So, I get:

and installed TensorFlow from source for Python 2 (not in a virtualenv).

Referenced:

Jupyter and JupyterHub

This post provides an introduction to Jupyter and the comparison between  Jupyter and JupyterHub.

Jupyter is a browser-based interactive notebook for programming, mathematics, and data science. It supports a number of languages via plugins (“kernels”), such as Python, Ruby, Haskell, R, Scala and Julia.

JupyterHub: a multi-user server, manages and proxies multiple instances of the single-user Jupyter notebook server.

Pros of Jupyter:

  • convenient for debugging – does not need to rerun the whole script when debugging a small fragment of the code.
  • convenient for reporting

 

Cons of Jupyter:

  • It messes with your version control.

  • Code can only be run in chunks

  • It’s difficult to keep track

  • Code often ends up very fragmented

  • The output is incomplete

  • Potential security risks?

    The notebook opens a http port. Pray to lord it will not land on 0.0.0.0 host. In that case the whole universe has access to your notebook and thus to your system.

 

References:

http://opiateforthemass.es/articles/why-i-dont-like-jupyter-fka-ipython-notebook/

Install TensorFlow for Python 2.7 and Python 3.5 on the same machine (Ubuntu 16.04)

I already installed GPU TensorFlow from source for Python 2 (see this post), and now I would like to also install GPU TensorFlow for Python 3 on the same machine using Virtualenv.

Virtualenv is a tool to keep the dependencies required by different Python projects in separate places. The Virtualenv installation of TensorFlow will not override pre-existing version of the Python packages needed by TensorFlow. See here for a detailed introduction of how virtualenv works and some basic usage.

With Virtualenv the installation is as follows:

  • Install pip and Virtualenv:
$ sudo apt-get update
$ sudo apt-get install python-pip python-dev python-virtualenv
  • Create a Virtualenv environment in the directory for python 3 ~/tensorflow-venv3:
$ virtualenv --system-site-packages -p python3 ~/tensorflow-venv3 

#for python 2
$ virtualenv --system-site-packages -p python ~/tensorflow-venv 

The --system-site-packages Option

If you build with virtualenv --system-site-packages ENV, your virtual environment will inherit packages from /usr/lib/python2.7/site-packages (or wherever your global site-packages directory is).

This can be used if you have control over the global site-packages directory, and you want to depend on the packages there. If you want isolation from the global system, do not use this flag.

  • Activate the virtual environment:
$ source ~/tensorflow-venv3/bin/activate  # If using bash
(tensorflow-venv3)$  # Your prompt should change
  • Install TensorFlow in the virtualenv for python 3:

Now, install TensorFlow just as you would for a regular Pip installation. First select the correct binary to install (from this page):

# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow-venv3)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-linux_x86_64.whl
# Python 2
(tensorflow-venv2)$ pip install --upgrade $TF_BINARY_URL

# Python 3
(tensorflow-venv3)$ pip3 install --upgrade $TF_BINARY_URL

Or you can choose the .whl file you built from source by yourself. Like the one I built in the post GPU tensorflow installation from source

# Python 2 pip install /path to/the .whl file you built from source/tensorflow-0.12.1-cp27-cp27mu-linux_x86_64.whl # Python 3 pip3 install /path to/the .whl file you built from source/tensorflow-0.12.1-cp27-cp27mu-linux_x86_64.whl

Note that when I used the .whl file I built to intall tensorflow into the virtualenv, I met this error. So I ended up installing the binary file from this page.

pip3 install ~/tensorflow_pkg/tensorflow-0.12.1-cp27-cp27mu-linux_x86_64.whl 
tensorflow-0.12.1-cp27-cp27mu-linux_x86_64.whl is not a supported wheel on this platform.
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow-venv3)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-linux_x86_64.whl
  • After the install you will activate the Virtualenv environment each time you want to use TensorFlow.
  • With the Virtualenv environment activated, you can now test your TensorFlow installation.

In your virtualenv, open a python session and type import tensorflow as tf.

If all went well, you should see the following output:

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
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so locally
>>>
  •  When you are done using TensorFlow, deactivate the environment.
    (tensorflow-venv3)$ deactivate
    
    $  # Your prompt should change back
    

To use TensorFlow later you will have to activate the Virtualenv environment again:

$ source ~/tensorflow-venv3/bin/activate  # If using bash.

(tensorflow-venv3)$  # Your prompt should change.
# Run Python programs that use TensorFlow.
...
# When you are done using TensorFlow, deactivate the environment.
(tensorflow-venv3)$ deactivate
  • To delete a virtual environment, just delete its folder. (In this case, it would be rm -rf tensorflow-venv3.)

You can test whether both TensorFlow installed in python 2 and python 3 works. See below for my example.

 

Posts I referenced:

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):

 

 

 

 

 

Open multiple files using “open ” and “with open” in Python

This post introduces two ways to open multiple files in Python.

  • “with open” # do not need to bother to close the file(s) if use “with open”
with open("datafile.csv" , "r") as f:
  f.read()
with open("datafile2.csv" , "r") as f2:
  f2.read()

Or

try:
  with open('file.csv', 'w') as f, open('file2.csv', 'w') as f2:
    do_something()
except IOError as e:
  print 'Operation failed: %s' % e.strerror
  • “open”  # need to close the file when use open.
f = open("datafile.csv" , "r")
f2 = open("datafile2.csv" , "r")
f.read()
f2.read()
f.close()
f2.close()

 

References: