Getting started with Python in Galileo
Let’s have a look at our files
Our python_example folder contains three files named python_example.py, mtcars.csv, and Dockerfile. The example_python.py script conducts a linear regression, makes two simple plots, and then runs a Monte Carlo simulation. The Monte Carlo simulates tossing a die 10 million times and calculates the ratio of rolls that equal six.
Understanding the user interface
When you log into Galileo, the first thing you’ll see is your Dashboard:
To run the python_example.py file, start by navigating to the Missions tab using the side menu.
Drag and drop the entire python_example folder you downloaded from our GitHub to the “Add a mission” staging area. Once the folder has been uploaded, click on the “Run mission” button in the newly-created “Galileo-examples-python” mission below the staging area. You will be asked to select a station on which to run the mission.
Choose the “Linux” station to begin and click “Run mission”. After the mission has been launched, you’ll be able to see the job running in the Jobs tab. The job runs quickly in Galileo – try running it locally and comparing.
When the example job completes, hit the Download button under Action to download the results.
The results folder will be downloaded as a .zip that contains an output.log file returning the results of the analysis and a folder called results where plots and other files that were created by the analysis are stored.
Let’s take a look at the output.log file first, which returns the results of the regression we ran:
Next, if we look in the results folder, we can see the plot we made:
Using the Configuration Wizard to create your own project
When you drag and drop a custom Python job’s folder to start a mission on Galileo, the Configuration Wizard will appear to help you create a computing environment that is perfectly-suited for the custom job:
Once you have selected the mission type (Python), given the job a name, and selected the specific version of Python to use, you will also need to provide the wizard with the name of the .py file that initiates the job. Next, specify the dependencies the job requires, either by selecting them from the provided list or by manually entering the names, separated by a single space, and then clicking the “Add Dependencies Manually” button.
Finally, you will be asked to set two advanced settings, though you may leave them as the default settings if you are not relevant to your custom job.
Hit submit, and the job will launch and can be monitored and interacted with just like the example job by navigating to the Jobs panel.