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Configuration Reference

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The SolarHub system is configured through a centralized YAML file located at configs/system_config.yaml. This file defines everything from our task types to our machine learning platforms.


1. System Metadata

system:
name: SolarHub
codename: Aurora
version: 1.0
  • name: The primary name of the platform.
  • codename: The project's internal name (Aurora).

2. Platform Integrations

Machine Learning

ml:
training_platform: kaggle
dataset_platform: huggingface
  • training_platform: Defines where our ML kernels run. Currently, we use Kaggle for its GPU acceleration and free compute hours.
  • dataset_platform: Where our long-term datasets are stored (HuggingFace).

3. HuggingFace Settings

huggingface:
dataset_repo_prefix: SpaceGen/solarhub-
model_repo_prefix: SpaceGen/solarhub-model-
token_secret: HF_TOKEN
  • dataset_repo_prefix: The common prefix for all dataset repositories.
  • model_repo_prefix: The common prefix for all model weight repositories.
  • token_secret: The name of the GitHub Actions secret that contains the HuggingFace write token.

4. Data Task Types

The data.task_types section defines which solar features we currently support.

data:
task_types:
- sunspot
- solar_flare
- magnetogram
- coronal_hole
- prominence
- active_region
- cme

These task types are used by:

  1. pull_new_urls.py: To know which data to crawl.
  2. parse_issue_annotation.py: To validate user-submitted labels.
  3. merge_annotations_to_hf.py: To target the correct HuggingFace repositories.

5. Source APIs

Defines the official solar observatories we crawl for data.

data:
source_apis:
- name: NASA SDAC
- name: SOHO LASCO
- name: SDO HMI
...

6. Annotation Strategy

annotations:
merge_strategy: append
clear_after_merge: true
  • merge_strategy: Currently set to append, meaning new user labels are added to our datasets without overwriting historical ones.
  • clear_after_merge: (Boolean) If true, the local annotations/ directory is reset after a successful push to HuggingFace.