ds-cache-cleaner 0.2.0

Clean up cached data from ML/data science libraries

ds-cache-cleaner

Clean up cached data from ML/data science libraries.

Supported Caches

• HuggingFace Models - ~/.cache/huggingface/hub (models)
• HuggingFace Datasets (Hub) - ~/.cache/huggingface/hub (datasets)
• Transformers - ~/.cache/huggingface/transformers
• HF Datasets - ~/.cache/huggingface/datasets
• ir_datasets - ~/.ir_datasets
• datamaestro (cache) - ~/datamaestro/cache (partial downloads, processing)
• datamaestro (data) - ~/datamaestro/data (downloaded datasets)

Installation

pip install ds-cache-cleaner

Or with uv:

uv pip install ds-cache-cleaner

Usage

List caches

ds-cache-cleaner list

Show cache entries

ds-cache-cleaner show
ds-cache-cleaner show -c "HuggingFace Hub"

Clean caches

# Interactive mode
ds-cache-cleaner clean

# Clean specific cache
ds-cache-cleaner clean -c "HuggingFace Hub"

# Clean all without prompting
ds-cache-cleaner clean --all

# Dry run
ds-cache-cleaner clean --dry-run

Interactive TUI

ds-cache-cleaner tui

Library Integration

ML libraries can integrate with ds-cache-cleaner to provide rich metadata about their cached data. This enables better descriptions, accurate last-access times, and more.

Metadata Format

The metadata is stored in a ds-cache-cleaner/ folder inside each cache directory:

~/.cache/mylib/
├── ds-cache-cleaner/
│   ├── lock                    # Lock file for concurrent access
│   ├── information.json        # Cache info and parts list
│   └── part_models.json        # Entries for "models" part
└── ... (actual cache data)

Using the CacheRegistry API

from ds_cache_cleaner import CacheRegistry

# Initialize once for your library
registry = CacheRegistry(
    cache_path="~/.cache/mylib",
    library="mylib",
    description="My ML Library cache",
)

# Register a part (e.g., models, datasets)
registry.register_part("models", "Downloaded model weights")

# When downloading a new model
registry.register_entry(
    part="models",
    path="bert-base",  # relative path within cache
    description="BERT base model",
    size=438_000_000,
)

# When accessing an existing entry (updates last_access time)
registry.touch("models", "bert-base")

# When deleting an entry (removes from metadata)
registry.remove("models", "bert-base")

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
hatch run test

# Lint
hatch run lint:check

# Format
hatch run lint:fix

License

MIT