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Cache management

When you download a dataset, the processing scripts and data are stored locally on your computer. The cache allows 🤗 Datasets to avoid re-downloading or processing the entire dataset every time you use it.

This guide 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 cache directory is `~/.cache/huggingface/datasets`. Change the cache location by setting the shell environment variable, `HF_DATASETS_CACHE` to another directory:

    $ export HF_DATASETS_CACHE="/path/to/another/directory"

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('LOADING_SCRIPT', cache_dir="PATH/TO/MY/CACHE/DIR")

Similarly, you can change where a metric is cached with the `cache_dir` parameter:

    >>> from datasets import load_metric
    >>> metric = load_metric('glue', 'mrpc', cache_dir="MY/CACHE/DIRECTORY")

##
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 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.

If you want to reuse a dataset from scratch, try setting the `download_mode` parameter in load_dataset() instead.

You can also avoid caching your metric entirely, and keep it in CPU memory instead:

    >>> from datasets import load_metric
    >>> metric = load_metric('glue', 'mrpc', keep_in_memory=True)

Keeping the predictions in-memory is not possible in a distributed setting since the CPU memory spaces of the various processes are not shared.

##
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.

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