A Utility Library that assists in Geospatial Machine Learning by:

  • supporting creation of a project with boilerplate code for model training

  • exporting annotations from Europa

  • populating template project with configurable components for model

  • fetching samples from dataset shards available at AIStore

  • orchestrating model training and validation

  • deploying project to Arche for efficient training in a node cluster

Flow

docs/phobos.png

Features

  • Polyaxon auto-param capture

  • Configuration enforcement and management for translation into Dione environment

  • Precomposed loss functions and metrics

  • Get annotations from Europa

TODO

  • ETL datasets via CLI on AIStore

  • Multi Input and Multi Output models

  • Static analysis code

  • Dataset abstraction

  • Standard dataset loaders

  • Pretrained models

Build Details

  • packages are managed using poetry

  • packages poetry maintains pyproject.toml

  • PRs and commits to develop branch trigger github actions

Tests

>>> make install
>>> make test-light

A GPU machine is requried for test-heavy

>>> make install
>>> make test-heavy

Installation

`pip install phobos`

Usage

Get all the annotation tasks available in Europa

`phobos get --all --email <email> --passwd <password>`

Download one particular annotation task from Europa

`phobos get --task <task ID> --path <directory to save anntoations> --email <email> --passwd <password>`

Create a project boilerplate code

`phobos init --project_name <project name> --project_description <project description>`

Run an experiment

`phobos run`

Run associated tensorboard

`phobos tensorboard --uuid <project id>`

License

GPLv3

Documentation

View documentation here

Image

Use gcr.io/granular-ai/phobos:latest

Project links

Key dates

PyPI data
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  • Released:
Latest release

3 maintainers

PyPI data
Data sourced directly from PyPI's database.
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Credits

Author: Sagar Verma

License

MIT License (MIT)

Requires

Python >=3.7.0, <4.0.0

Classifiers

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