Data science platform
Anaconda
Anaconda is a data science platform that supports Python and R. It comes with many packages that are needed to perform good data science.
Multi-user installation
Note the differences below, but this mostly follows [Anaconda’s Installing on Linux for multiple users] (https://docs.anaconda.com/anaconda/install/multi-user/#multi-user-anaconda-installation-on-linux).
- Login as root,
sudo -i
. - Follow instructions but change the install directory to /opt/anaconda3.
- Create a new groups,
addgroup conda-grp
. - Set the conda-grp as owner of the anaconda directory,
chgrp -R conda-grp /opt/anaconda3
. - Change read/write permsissions on the directory,
chmod 770 -R /opt/anaconda3
. - Add users to the group,
usermod -a -G conda-grp youruser
. - Logout root,
exit
. - Exit your shell session
exit
. - Start a new session.
- Connect your user to the base environment
source /opt/anaconda3/bin/activate
conda init
- Restart your session again.
- Upon login, you should note (base) appears by your prompt.
- Verify the installation,
conda list
which lists all of the installed packages. - Test python is from Anaconda,
python
. This starts the python shell with a heading that indicates the version and the source as Anaconda. - Quit the python shell,
exit()
. - Update the conda package manager,
conda update conda
. - Update all packages,
conda update --all
.
Your Anaconda installation is complete and has fully functioning python environment. Now view the User guide. If you are working in the gui, start Anaconda Navigator, anaconda-navigator
.
Jupyter
Jupyter is a web-based interactive development environment for notebooks, code, and data. JupyterLab is the next-gen version of Jupyter Notebook. JupyterLab is great to run on a standalone computer but I wish my Linux system to serve multi-user sessions of JupyterLab. This is most efficiently done using JupyterHub.
Install JupyterHub
This mostly follows the JupyterHub guide, instructions for using conda.
- Install JupyterHub,
conda install -c conda-forge jupyterhub
. - Install Jupyter Notebook to run locally,
conda install notebook
. - Install JupyterLab to run locally and on the hub,
conda install jupyterlab
.
Test the installation by launching JupyterHub and then connect to it via a browser in the server’s GUI.
- Launch JupyterLab,
jupyterhub
. - Open your browser and connect to localhost:8000.
- Enter your server login credentials.
- You are logged into a standard Jupyter Notebook.
- Check JupyterLab by navigating to /lab instead of /tree.
- When done, close the browser. Then stop JupyterHub with CTRL + C.
Configuring JupyterHub
This configuration is based upon the JupyterHub documentation.
- Create a default configuration,
jupyterhub --generate-config
. - Edit the configuration file.
nano jupyterhub_config.py
by adding,
c.JupyterHub.bind_url = 'http://0.0.0.0:8000'
c.JupyterHub.concurrent_spawn_limit = 20
c.JupyterHub.port = 8000
c.JupyterHub.authenticator_class = 'jupyterhub.auth.PAMAuthenticator'
- Move the configuration,
mv jupyterhub_config.py /etc/jupyterhub/jupyterhub_config.py
.
JupyterHub as a service
from Run jupyterhub as a system service
- Create and edit the configuration file,
sudo nano /etc/systemd/system/jupyterhub.service
.
- Assuming Anaconda 3 was installed multi-user into /opt/anaconda3. If you’re not sure, you may check location with,
which jupyterhub
. And assuming that the jupyterhub configuration file is /etc/jupyterhub/jupyterhub_config.py. Add the following to the configuration:
[Unit]
Description=Jupyterhub
After=syslog.target network.target
[Service]
User=root
Environment="PATH=/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/opt/anaconda3/bin"
ExecStart=/opt/anaconda3/bin/jupyterhub -f /etc/jupyterhub/jupyterhub_config.py
[Install]
WantedBy=multi-user.target
- Reload daemon,
sudo systemctl daemon-reload
. - The availbale commands for the service are start | stop | status.
- Start the jupyterhub service,
sudo systemctl start jupyterhub
. - Check the status,
sudo systemctl status jupyterhub
. - Verify by connecting with a browser.
- Stop the service,
sudo systemclt stop jupyterhub
. - Enable the service to start upon boot,
sudo systemctl enable jupyterhub
.
R language
First, we are going to install RStudio. This will be used for running beta and production code. Next, we will install and R environment in Anaconda for use with Jupyter.
RStudio Installation
Install the desktop application RStudio along with the proper R binaries.
- Install prerequisites,
sudo apt install gdebi-core r-base
.- If you get a message about a broken install, try
sudo apt --fix-broken install
and try again.
- If you get a message about a broken install, try
- [Download RStudio] (https://rstudio.com/products/rstudio/download/).
- Run the install package,
sudo dpkg -i ~/Downloads/rstudio-1.2.5033-amd64.deb
.
- Test the installation by running RStudio from the GUI desktop.
Locate the R binary and make a note of it for future use.
which R
- Mine is located at /usr/bin/R.
- Determine the version of R.
- From the command line,
R --version| grep -Eo 'R version [0-9.]+ \([0-9]{4}-[0-9]{2}-[0-9]{2}\)';
. - From RStudio,
R.version.string
.
- From the command line,
R environment in Anaconda
Here we install an R environment for easy use in JupyterHub. This will be managed separately from the RStudio we previously installed.
- In the base environment, run
conda install nb_conda_kernels
. This allows Jupyter launched from base to access kernels from other environments. See nb_conda_kernels. - Create the R environment,
conda create -n r-env -c r r-essentials r-base r-irkernel
. - Activate r-env,
conda activate r-env
. Then deactivate,conda deactivate
. - Test availability of r-env in JupyterHub. You might have to restart JupyterHub.
Verify which R is being used
Previously, we determined that R resided at /usr/bin/R. Now verify that the R being used from your base environment and by RStudio is the same as installed by RStudio.
- Verify that the location used by base matches previous,
which R
. - Verify that location used by RStudio matches previous,
- Open RStudio, click ther Terminal tab to get console.
echo $RSTUDIO_WHICH_R
.- If needed, set it,
export RSTUDIO_WHICH_R=/usr/bin/R
. - Reopen RStudio to verify functioning.
- Save the environment variable system-wide by creating or editing the file /etc/profile.d/myenvvars.sh.
sudo nano /etc/profile.d/myenvvars.sh
- Add the line export RSTUDIO_WHICH_R=/usr/bin/R. Reboot the computer to verify that changes are permanent.
from RStudio support, StackOverflow, and Ubuntu Environment Variables
Julia language
Julia is a high level scientific programming language. I have found it very good at handling various maths and machine learning.
Install Julia
This is a straightforward download, extract and point to binaries operation.
- Download the current release from Julia.
cd Downloads
.wget "https://julialang-s3.julialang.org/bin/linux/x64/1.4/julia-1.4.0-linux-x86_64.tar.gz"
.
- Extract,
tar -xzvf julia-1.4.0-linux-x86_64.tar.gz
. - Move the folder,
sudo mv julia-1.4.0 /opt/
. - Create a symbolic link to the Julia binary,
sudo ln -s /opt/julia-1.4.0/bin/julia /usr/local/bin/julia
.
- Test by entering,
julia
. The Julia REPL terminal should start. exit()
when done.
Upgrading Julia
Just follow the same process as for installing, just update the symbolic links.
- Move the old link,
sudo mv /usr/local/bin/julia /usr/local/bin/julia-old-ver-number
.
- Create a new link,
sudo ln -s /opt/julia-new-ver-num/bin/julia /usr/local/bin/julia
.
If you remove the old installation directory, remember to also remove the old link. You will also still need to reinstall your packages on the new version.
Install Juno
Juno is an IDE for the Julia environment. It is built upon Atom.
To install Atom using the Ubuntu package manager, follow their Flight Manual.
Next, we log into the desktop, open the Atom editor and add Juno.
- Atom is a GUI IDE, so open it from the navigation menu.
- Open settings with ** CTRL + ,**
- Open the Install pane.
- Search for the package uber-juno.
- Install uber-juno.
When this is complete, the Julia REPL shuold try to start. It might spend some time pre-compiling. Try a command, such as cos(1)
to verify functioning. You will also notice a new menu bar entry for Juno.
Add Julia to JupyterHub
Let’s get Julia working with JupyterHub.
- Start Julia REPL,
julia
. - Enter package mode.
]
. add IJulia
.build IJulia
.- Exit package mode,
Backspace
. - Exit Julia,
exit()
.
Test in Jupyter.
- Restart Jupyter,
sudo systemctl start jupyterhub
andsud systemctl stop jupyterhub
. - Log in to JupyterHub.
- View available environments in the New pulldown or view the Lab interface by changing tree to lab in the URL.
Add packages
I like functionality and Julia packages add great functionality. For the ability to just use it, I add all of the packages that are included in Julia Pro’s curated packages list.
- Add the general programming packages,
add DataStructures LightGraphs JuliaWebAPI IJulia Nettle DSP NearestNeighbors Parameters ParserCombinator Libz BenchmarkTools Rebugger Debugger
- Since I work with graphs regularly, add additional LightGraphs functionality,
add LightGraphsExtras MetaGraphs SimpleWeightedGraphs GraphIO
- Add math packages,
add Calculus DataFrames StatsBase Distributions HypothesisTests GLM OnlineStats DifferentialEquations SymPy KernelDensity Zygote
.
- Optimization and databases,
add Optim Roots JDBC
.
- User interfaces and visualizations,
add PyPlot Interact LaTeXStrings Formatting Images Plots GR UnicodePlots ImageMagick StatPlots PGFPlots
.
- Machine learning,
add Knet Clustering DecisionTree MLBase Flux Metalhead ScikitLearn
.
- Interoperability,
add RCall PyCall Conda
.
- File and data formats,
add JSON JLD2 CSV LightXML StaticArrays ProtoBuf CuArrays
.
- Economics and finance,
add QuantEcon BusinessDays Miletus
.
- Others,
add JuMP Turing JuliaDB
.
Interact
To use interactive plots, a bit more work needs to be done.
conda install nodejs
TensorFlow is not yet working.