Git Product home page Git Product logo

nasa / ml-airport-configuration Goto Github PK

View Code? Open in Web Editor NEW
26.0 5.0 3.0 9.87 MB

The ML-airport-configuration software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for predicting airport configuration as a time series. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models.

License: Other

Python 100.00%
usg-artificial-intelligence airport configuration usg-ai-training-data airport-simulation

ml-airport-configuration's Introduction

https://nasa.gov/ nasa | Twitter nasa | LinkedIn



Hi ๐Ÿ‘‹, welcome to the NASA org on github.com!

Github.com/nasa has one of the largest collections of NASA open-source code repositories. Members of the NASA org can find instructions for github.com/nasa in http://nasa.github.io/.

๐Ÿ”ญ Additional open-source code repositories resides in a variety of locations other than github.com/nasa. To discover code across all of these locations, we suggest you use code.nasa.gov & software.nasa.gov. These are two different sites holding metadata that describe code projects. Any code released through the NASA Software Release Authority process should be cataloged on those sites.

Is a page with short descriptions of all of NASA's open-source code. Code.nasa.gov feeds into code.gov, which covers open-source and government-source code from many different U.S. governmental agencies. To assist in discovery, code projects described on code.nasa.gov have both human and A.I.-generated tags. These can be useful for finding related code projects.

Contains metadata descriptions for all code projects in code.nasa.gov as well as government-source code projects only sharable with other government agencies. It is part of the large https://technology.nasa.gov/ that also includes patents and spinoffs. To help discoverability, software.nasa.gov puts each code project into one fo the following categories: Business Systems and Project Management, System Testing, Operations, Design and Integration Tools, Vehicle Management (Space/Air/Ground), Data Servers Processing and Handling, Propulsion, Structures and Mechanisms, Crew and Life Support, Data and Image Processing, Materials and Processes, Electronics and Electrical Power, Environmental Science (Earth, Air, Space, Exoplanet), Autonomous Systems, and Aeronautics.



NOTE - PROFILE READMES CURRENTLY DON'T WORK FOR ORG PROFILES ONLY USER PROFILES :(

https://github.community/t/readme-for-organization-front-page/2920

ml-airport-configuration's People

Contributors

aamblard avatar amchurchill avatar justingosses avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar

ml-airport-configuration's Issues

Install fails: cannot find `data_services`

The installation steps from the readme fail:

$ conda env create -f conda.yaml
Collecting package metadata (repodata.json): done
Solving environment: done

Downloading and Extracting Packages
python-json-logger-0 | 9 KB      | #################################################################################################################################################################################################################################### | 100%
... more ...
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Installing pip dependencies: \ Ran pip subprocess with arguments:
['/home/robert/anaconda3/envs/airport-config-prediction/bin/python', '-m', 'pip', 'install', '-U', '-r', '/home/robert/projects/ml-airport-configuration/condaenv.pj0entq0.requirements.txt']
Pip subprocess output:
Collecting mlflow==1.19
  Downloading mlflow-1.19.0-py3-none-any.whl (14.4 MB)

Pip subprocess error:
ERROR: Could not find a version that satisfies the requirement data_services<1.0.2,>=1.0.1dev86 (from versions: none)
ERROR: No matching distribution found for data_services<1.0.2,>=1.0.1dev86

failed

CondaEnvException: Pip failed

It can't seem to locate the data_services module referred to in:

Despite being in the pip section of conda.yaml, pip install data_services fails with ERROR: No matching distribution found for data_services. It doesn't appear to be in PyPI.

Could you help me understand where that code comes from? Thank you and looking forward to learning from your code!

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.