- Git Jupyter Notebook
- Jupyter Notebook Install
- Download Jupyter Notebook From Github Free
- Upload Jupyter Notebook To Github
- Jupyter Github Integration
- Jupyter Notebook Online
Display Jupyter notebook in GitHub. Ask Question 1. I have some Jupyter notebooks that I created using SAS University Edition. They run fine locally. Download a single folder or directory from a GitHub repo. How do I update a GitHub forked repository? Add images to README.md on GitHub. Python Data Science Handbook: full text in Jupyter Notebooks - jakevdp/PythonDataScienceHandbook. Download GitHub Desktop and try again. Launching Xcode. If nothing happens. Python Data Science Handbook. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Oct 31, 2015 - As we mentioned in that post, we just put together a collaboration feature to bring Jupyter notebooks under the fold of our existing GitHub.
You can test pySpark or Scala code that you want to run on the Cloudera Cluster on a local machine before you take up the Cluster resources.
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Install Java
Git Jupyter Notebook
If nothing happens, download GitHub Desktop and try again. The base container is jupyter/minimal-notebook and this Community Stack is setup via the guide. Oct 31, 2015 - Within the container, a Jupyter notebook server is running, which. I guess learners could always save and download their notebooks to the.
Currently, this will only work with Java version 8, not version 9.
Check Java version with
java -version
from the command prompt. java version '1.8.0_151'
is Version 8.If you do not have the correct version, download and install it.You will need the 'jdk' not the 'jre'https://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html
Install Python
One of the easiest ways is to install python with Anaconda.https://www.anaconda.com/download/
I installed for all users, which installs in
C:ProgramDataAnaconda3
.Install Spark
I downloaded the latest version pre-built for the latest version of Hadoop ('Apache Hadoop 2.7 and later' as of this writing.).You don't actually install Spark, but just extract the compressed files to a folder.I put all of the files in the compressed 'spark-2.2.0-bin-hadoop2.7' folder in
C:sparkspark
.Install Scala
Direct link to Windows binaries.https://downloads.lightbend.com/scala/2.12.4/scala-2.12.4.msi
Which is on: http://www.scala-lang.org/download/
I installed Scala in
C:sparkscala
Install Windows Hadoop binaries
You will need the Windows Hadoop binaries that match the version of Spark you installed.https://github.com/steveloughran/winutils
You can download the whole repository with git.Assuming you have git installed, open a command prompt from the folder you want to download the git repo into a folder.(I chose
Then run
C:sparkhadoop
).Then run
git clone https://github.com/steveloughran/winutils.git
from the command prompt.Or you can use the following link to download the whole repo as a zip file.https://github.com/steveloughran/winutils/archive/master.zip
If you downloaded to the same location I did, thenthe
WinUtils.exe
we will use is in C:Sparkhadoopbinhadoop-2.8.1
.This is because the build of Spark we downloaded is for 'Apache Hadoop 2.7 and later.'Note: A few tutorials have you download the Hadoop binaries directly from https://hadoop.apache.org/releases.html.
I did not do that.
I did not do that.
Change the Spark Log Properties
One tutorial recommended the following, not sure if it is necessary, but it did not break anything.
- Go into your spark/conf folder and rename log4j.properties.template to log4j.properties
- Open log4j.properties in a text editor and change log4j.rootCategory to WARN from INFO
Create Local Python Environment Identical to Cluster
In order to develop and test your pyspark code locally and ensure that it will run on the cluster,you will need to create a python environment that is identical to the python environment on the cluster.
You may think that the cluster should conform to your environment, and this can be done if you are the cluster manager.
However, you likely share the cluster with many other people and it can be difficult to make the cluster conform to everyone's python environment.Therefore, it is easier to create a local python environment that is identical to the cluster's python environment.
However, you likely share the cluster with many other people and it can be difficult to make the cluster conform to everyone's python environment.Therefore, it is easier to create a local python environment that is identical to the cluster's python environment.
Create Environment
To get a list of all of the packages installed on the cluster run `the following from the Cloudera Data Science Workbench (CDSW)or other python console interface to the cluster.
!pip freeze > requirements.txt
import os
os.system('conda-env export > freeze.yml')
Then download the
requirements.txt
and freeze.yml
files to your local machine.Now create a Python environment the same as the cluster.
Replace
Open a command prompt and run the following:
Replace
<name>
with an environment name of your choice. I named my environment cluster_env
.Open a command prompt and run the following:
conda env create -f freeze.yml -n <name>
To install all of the packages with the same versions that were on the cluster, run the following commands from the command prompt.
The first command activates the cloned environment that has whatever you chose above.
The first command activates the cloned environment that has whatever you chose above.
activate <name>
The second command installs each package with conda, and if conda fails, with pip.
FOR /F 'delims=~' %f in (requirements.txt) DO conda install --yes '%f' || pip install '%f'
If you want to run pyspark in a jupyter notebook, you will also have to install jupyter in this new python environment.From the activated new python environment, run the following:
conda install jupyter
If you want to run pyspark in a spyder, you will also have to install spyder in this new python environment.From the activated new python environment, run the following:
conda install spyder
Create Windows Batch Files
Instead of changing all of your system variables permenantly, you can just change the system variables for a particular session.To do this, you will need to create a Windows batch file that:
- sets the following system variables to the correct path
HADOOP_HOME
,JAVA_HOME
,SCALA_HOME
,SPARK_HOME
, - sets the system
PATH
variable to include the Anaconda folders and thebin
folders of the variables set above, - sets the
PYSPARK_PYTHON
system variable to the excecutable pyspark will use, - sets the
PYSPARK_DRIVER_PYTHON
system variable which determines which program pyspark will run in, - sets the
PYSPARK_DRIVER_PYTHON_OPTS
system variable which determines the options of the program pyspark will run in, - activates the python environment that will be used which also sets the
PYTHONPATH
system variable, - calls
pyspark
orspark-shell
from where they are installed.
If you installed the same program versions in the same folders as I did, and you named your files and environments the same,then the following will work for you.If not, then change the appropriate paths, files, and names accordingly.
Jupyter
To run pyspark in a jupyter notebook, save the following to a windows batch file, which is just a text file with a
Name the file whatever you want. I named mine
.bat
extention.Name the file whatever you want. I named mine
pysp.bat
.Note: Even though the
the build of Spark we installed is 'Apache Hadoop 2.7 and later'.
HADOOP_HOME
variable is set to hadoop-2.8.1
,the build of Spark we installed is 'Apache Hadoop 2.7 and later'.
Spyder
To run pyspark in Spyder, save the following to a windows batch file.
Name the file whatever you want. I named mine
Name the file whatever you want. I named mine
pyspspy.bat
.The only difference from the above batch file is we start spark-submit and point to the spyder startup file.
Jupyter Notebook Install
Pyspark console
To run pyspark in the console, save the following to a windows batch file.
Name the file whatever you want. I named mine
Name the file whatever you want. I named mine
pyspsh.bat
.The only difference from the above batch file is the
PYSPARK_DRIVER_PYTHON
, and PYSPARK_DRIVER_PYTHON_OPTS
variables are not set.Spark (Scala) Console
To run spark in the console where you will need to program in native Scala, save the following to a windows batch file.
Name the file whatever you want. I named mine
Name the file whatever you want. I named mine
spsh.bat
.The only differences from the above batch files are we don't set any python system variables and we call
spark-shell
instead of pyspark
.Start Spark
To run Spark in a Jupyter notebook, simply run your pyspark batch file.
(Assuming you installed in the same locations.)
Type
(Assuming you installed in the same locations.)
Type
import sys; sys.version
in one code cell and sc
in another code cell and you should get something similar to the following..
To run Spark in the console, run your spark-shell batch file.
You should get a console window like the one shown below.
To run Spark in the console, run your spark-shell batch file.
You should get a console window like the one shown below.
Create Shortcuts to Windows Batch Files
I find it useful to write Windows batch files and keep them in a folder.For me, this is
I then add the path to that folder to the system
C:UsersUser.NameDocumentsBatFiles
.I then add the path to that folder to the system
%PATH%
variable.In that folder I create text files with a
.bat
file extention for each command, or series of commands, I want a shortcut to so I don't have to type so much all the time.For instance, in that folder I have a batch file pysp.bat
that only has the following line C:Sparksparkbinpyspark
.So when I want to start pySpark in a Jupyter notebook,all I have to do is type
The benefit of calling from the Windows file browser path box is that whatever folder the file browser is curently in will be the location where the notebook will start in.
pysp
from any command prompt, run window, or even from the Windows file browser path box.The benefit of calling from the Windows file browser path box is that whatever folder the file browser is curently in will be the location where the notebook will start in.
With GitHub we can store our code online, and with Jupyter notebook we can execute only a segment of our Python code. I want to use them together. I am able to edit code with Jupyter notebook that is stored on my computer. But, I am unable to find a way to run a code that stored on GitHub. So, do you know a way to do that.
Here are some examples:https://github.com/biolab/ipynb/blob/master/2015-bi/lcs.ipynbhttps://github.com/julienr/ipynb_playground/blob/master/misc_ml/curse_dimensionality.ipynbhttps://github.com/rvuduc/cse6040-ipynbs/blob/master/01--intro-py.ipynb
user6867490
2 Answers
1. If you just want to run Python code hosted on Github or in a Gist:
Download Jupyter Notebook From Github Free
The IPython Magic command
%load
, as described in tip# 8 here, will replace the contents of the Jupyter notebook cell with an external script. The source can either be a file on your computer or a URL.
The trick with a Github or Gist-hosted script is to direct it at the URL for raw code. You can easily get that URL by browsing the script on GitHub and pressing
The trick with a Github or Gist-hosted script is to direct it at the URL for raw code. You can easily get that URL by browsing the script on GitHub and pressing
Raw
in the toolbar just above the code. Combine what you extract from the address bar to get something along the lines of this:That will pull in the code to the notebook's namespace when you execute it in a Jupyter notebook cell.
More about using raw code via GitHub or Gists here and here. More on other magic commands can be found here.
More about using raw code via GitHub or Gists here and here. More on other magic commands can be found here.
Similarly, if you want to bring the script in as a file you can call in the notebook using
%run
(or from the command line equivalent), use curl
in the notebook cell and the script will be added to the current directory.2. If you want to run a notebook placed on GitHub:
Or if you want others to be able to easily run that notebook.
Check out MyBinder.org highlighted in this Nature article here. More information on the service can be found here, here, and here.
Check out MyBinder.org highlighted in this Nature article here. More information on the service can be found here, here, and here.
At the MyBinder.org page you can point the service at any Github repository. The caveat though is that unless it is fairly vanilla python in the notebook, you'll hit dependency issues. You can set it up to address that as guided by here and here.
That was done to produce this launchable repo after I forked one that had not been set up to use the Binder system initially. Another example, this one R code, based on a gist shared on a twitter exchange can be seen here.
That was done to produce this launchable repo after I forked one that had not been set up to use the Binder system initially. Another example, this one R code, based on a gist shared on a twitter exchange can be seen here.
Using that, you can get a
Launch Binder
badge that you can add to your repository and launch it any time. See an example that you can launch here.Upload Jupyter Notebook To Github
WayneWayne
Github is a tool for version and source control, you will need to get a copy of the code to a local environment.
Jupyter Github Integration
There is a beginner's tutorial here
Once that you have set up a github account and define your local and remote repositories, you will be able to retrieve the code with
git checkout
. Further explanation is in the tutorial Jupyter Notebook Online
Carlos Monroy NieblasCarlos Monroy Nieblas