Cache management
When you download a dataset, the processing scripts and data are stored locally on your computer. The cache allows π€ 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 datasets.load_dataset() with the download_mode
parameter. By default, π€ 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 datasets.DownloadMode for a full list of download modes.
Cache files
Clean up the cache files in the directory with datasets.Dataset.cleanup_cache_files():
# Returns the number of removed cache files
>>> dataset.cleanup_cache_files()
2
Enable or disable caching
If youβre 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=False
in datasets.Dataset.map():
>>> updated_dataset = small_dataset.map(add_prefix, load_from_cache=False)
In the example above, π€ 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 datasets.disable_caching():
>>> from datasets import disable_caching
>>> disable_caching()
When you disable caching, π€ 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 datasets.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:
Set
datasets.config.IN_MEMORY_MAX_SIZE
to a nonzero value (in bytes) that fits in your RAM memory.Set the environment variable
HF_DATASETS_IN_MEMORY_MAX_SIZE
to a nonzero value. Note that the first method takes higher precedence.