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Documentation 0.0.4 Help

Getting Started

Installing the Dev. Environment

  1. Ensure that you have the right version of Python. The required Python version can be seen in pyproject.toml

    + [tool.poetry.dependencies] + python = "..." +

  2. Start by cloning our repository.

    + git clone https://github.com/Forest-Recovery-Digital-Companion/FRDC-ML.git +

  3. Then, create a Python Virtual Env pyvenv

    python -m venv venv/
    python3 -m venv venv/

  4. Install Poetry Then check if it's installed with

    poetry --version

  5. Activate the virtual environment

    + cd venv/Scripts + activate + cd ../.. +
    + source venv/bin/activate +

  6. Install the dependencies. You should be in the same directory as pyproject.toml

    + poetry install --with dev +

  7. Install Pre-Commit Hooks

    + pre-commit install +

Setting Up Google Cloud

  1. We use Google Cloud to store our datasets. To set up Google Cloud, install the Google Cloud CLI

  2. Then, you need to authenticate your account.

  3. To make sure everything is working, run the following command:

    + gsutil ls +

Pre-commit Hooks

  • + pre-commit install +

Running the Tests

  1. Run the tests to make sure everything is working

    + pytest +

  2. In case of errors:

    google.auth.exceptions.DefaultCredentialsError

    If you get this error, it means that you haven't authenticated your Google Cloud account. See Setting Up Google Cloud

    ModuleNotFoundError

    If you get this error, it means that you haven't installed the dependencies. See Installing the Dev. Environment

Our Repository Structure

Before starting development, take a look at our repository structure. This will help you understand where to put your code.

Core Dependencies
Resources
Pipeline
Tests
Repo Dependencies
Dataset Loaders
Preprocessing Fn.
Train Deps
Model Architectures
Datasets ...
Model Training Pipeline
FRDC
src/frdc/
rsc/
pipeline/
tests/
pyproject.toml,poetry.lock
./load/
./preprocess/
./train/
./models/
./dataset_name/
./model_tests/
src/frdc/

Source Code for our package. These are the unit components of our pipeline.

rsc/

Resources. These are usually cached datasets

pipeline/

Pipeline code. These are the full ML tests of our pipeline.

tests/

PyTest tests. These are unit tests & integration tests.

Unit, Integration, and Pipeline Tests

We have 3 types of tests:

  • Unit Tests are usually small, single function tests.

  • Integration Tests are larger tests that tests a mock pipeline.

  • Pipeline Tests are the true production pipeline tests that will generate a model.

Where Should I contribute?

Changing a small component

If you're changing a small component, such as a argument for preprocessing, a new model architecture, or a new configuration for a dataset, take a look at the src/frdc/ directory.

Adding a test

By adding a new component, you'll need to add a new test. Take a look at the tests/ directory.

Changing the pipeline

If you're a ML Researcher, you'll probably be changing the pipeline. Take a look at the pipeline/ directory.

Adding a dependency

If you're adding a new dependency, use poetry add PACKAGE and commit the changes to pyproject.toml and poetry.lock.

Last modified: 23 October 2023
Overview
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Documentation 0.0.4 Help

Overview

Forest Recovery Digital Companion (FRDC) is a ML-assisted companion for ecologists to automatically classify surveyed trees via an Unmanned Aerial Vehicle (UAV).

This package, FRDC-ML is the Machine Learning backbone of this project, a centralized repository of tools and model architectures to be used in the FRDC pipeline.

Get started here

Other Projects

FRDC-UI

The User Interface Repository for FRDC, a WebApp GUI for ecologists to adjust annotations.

Last modified: 23 October 2023
Getting Started
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