testbed/huggingface__datasets/docs/source/cache.mdx

# 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:

```py
>>> 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:

```py
>>> 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:

```py
>>> 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`]:

```py
# 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`]:

```py
>>> 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`]:

```py
>>> 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.

<Tip>

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

</Tip>

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

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

<Tip warning={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.

</Tip>

<a id='load_dataset_enhancing_performance'></a>

## 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.