Lab environment for data science applications.
Examples and exercises in this lab are presented as Jupyter Notebooks. Get started with this lab simply by cloning this repository within your own Jupyter environment or by launching an environment in the cloud services listed below.
Details about how to clone this environment in your machine can is presented here.
Java and SQL Notebooks run over BeakerX. The links below run the Jupyter platform with BearkerX. There are two versions:
- Latest tested and stable version
- should always work since runs over a release already mounted and tested
- Latest version available
- mounts the latest commit - has the latest repository updates, but sometimes still needs a test
You can mount a Jupyter BeakerX kernels yourself in a Docker image. See instructions at BeakerX. If you already have Docker installed on your machine, this statement will run BeakerX in Docker:
docker run -p 8888:8888 beakerx/beakerx
You can map your local lab2learn drive with the mapping:
docker run -p 8888:8888 -v /home/your_home/git/lab2learn/:/home/beakerx/lab2learn beakerx/beakerx
The local path in the example (first path) considers the home of a Linux OS. Replace with a proper path in your OS. In Linux, replace your_home with your home directory.
In the console, the Jupyter will show a Web address to run
http://7daa79d4ba87:8888/?token=b4831e2674e915aaqf3231aa8810f9d3c8ujjaf917cnx7594&token=b4831e2674e915aaqf3231aa8810f9d3c8ujjaf917cnx7594
Replace the first part by localhost as:
http://localhost:8888/?token=b4831e2674e915aaqf3231aa8810f9d3c8ujjaf917cnx7594&token=b4831e2674e915aaqf3231aa8810f9d3c8ujjaf917cnx7594
Via MyBinder.org (instance of the project jupyterhub/binderhub)
Sala e Laboratório BD em 20/09/2019
Este roteiro apresenta um problema central de gerenciamento de estradas e trajetos que ligam cidades. A partir do problema, são lançados desafios de: