Cache management

When you download a dataset from Hugging Face, the data are stored locally on your computer. Files from Hugging Face are stored as usual in the huggingface_hub cache, which is at ~/.cache/huggingface/hub by default. See the Hub cache documentation for more details and how to change its location.

The Hub cache allows 🤗 Datasets to avoid re-downloading dataset files from Hugging Face every time you use them.

🤗 Datasets also has its own cache to store datasets converted in Arrow format (the format used by Dataset objects).

This guide focuses on the 🤗 Datasets cache and will show you how to:

• Change the cache directory.
• Control how a dataset is loaded from the cache.
• Clean up cache files in the directory.
• Enable or disable caching.

Cache directory

The default 🤗 Datasets cache directory is ~/.cache/huggingface/datasets. Change the cache location by setting the shell environment variable, HF_HOME to another directory:

$ export HF_HOME="/path/to/another/directory/datasets"

When you load a dataset, you also have the option to change where the data is cached. Change the cache_dir parameter to the path you want:

>>> from datasets import load_dataset
>>> dataset = load_dataset('username/dataset', cache_dir="/path/to/another/directory/datasets")

Download mode

After you download a dataset, control how it is loaded by load_dataset() with the download_mode parameter. By default, 🤗 Datasets will reuse a dataset if it exists. But if you need the original dataset without any processing functions applied, re-download the files as shown below:

>>> from datasets import load_dataset
>>> dataset = load_dataset('squad', download_mode='force_redownload')

Refer to DownloadMode for a full list of download modes.

Cache files

Clean up the Arrow cache files in the directory with Dataset.cleanup_cache_files():

# Returns the number of removed cache files
>>> dataset.cleanup_cache_files()
2

Enable or disable caching

If you’re using a cached file locally, it will automatically reload the dataset with any previous transforms you applied to the dataset. Disable this behavior by setting the argument load_from_cache_file=False in Dataset.map():

>>> updated_dataset = small_dataset.map(add_prefix, load_from_cache_file=False)

In the example above, 🤗 Datasets will execute the function add_prefix over the entire dataset again instead of loading the dataset from its previous state.

Disable caching on a global scale with disable_caching():

>>> from datasets import disable_caching
>>> disable_caching()

When you disable caching, 🤗 Datasets will no longer reload cached files when applying transforms to datasets. Any transform you apply on your dataset will be need to be reapplied.

Improve performance

Disabling the cache and copying the dataset in-memory will speed up dataset operations. There are two options for copying the dataset in-memory:

1. Set datasets.config.IN_MEMORY_MAX_SIZE to a nonzero value (in bytes) that fits in your RAM memory.

2. Set the environment variable HF_DATASETS_IN_MEMORY_MAX_SIZE to a nonzero value. Note that the first method takes higher precedence.