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| 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. |
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| This guide will show you how to: |
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| - Change the cache directory. |
| - Control how a dataset is loaded from the cache. |
| - Clean up cache files in the directory. |
| - Enable or disable caching. |
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| The default cache directory is `~/.cache/huggingface/datasets`. Change the cache location by setting the shell environment variable, `HF_DATASETS_CACHE` to another directory: |
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| ``` |
| $ export HF_DATASETS_CACHE="/path/to/another/directory" |
| ``` |
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| 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: |
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| ```py |
| >>> from datasets import load_dataset |
| >>> dataset = load_dataset('LOADING_SCRIPT', cache_dir="PATH/TO/MY/CACHE/DIR") |
| ``` |
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| Similarly, you can change where a metric is cached with the `cache_dir` parameter: |
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| ```py |
| >>> from datasets import load_metric |
| >>> metric = load_metric('glue', 'mrpc', cache_dir="MY/CACHE/DIRECTORY") |
| ``` |
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| 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: |
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| ```py |
| >>> from datasets import load_dataset |
| >>> dataset = load_dataset('squad', download_mode='force_redownload') |
| ``` |
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| Refer to [`DownloadMode`] for a full list of download modes. |
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| Clean up the cache files in the directory with [`Dataset.cleanup_cache_files`]: |
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| ```py |
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| >>> dataset.cleanup_cache_files() |
| 2 |
| ``` |
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| 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`]: |
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| ```py |
| >>> updated_dataset = small_dataset.map(add_prefix, load_from_cache_file=False) |
| ``` |
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| 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. |
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| Disable caching on a global scale with [`disable_caching`]: |
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| ```py |
| >>> from datasets import disable_caching |
| >>> disable_caching() |
| ``` |
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| 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. |
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| <Tip> |
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| If you want to reuse a dataset from scratch, try setting the `download_mode` parameter in [`load_dataset`] instead. |
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| </Tip> |
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| You can also avoid caching your metric entirely, and keep it in CPU memory instead: |
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| ```py |
| >>> from datasets import load_metric |
| >>> metric = load_metric('glue', 'mrpc', keep_in_memory=True) |
| ``` |
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| <Tip warning={true}> |
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| Keeping the predictions in-memory is not possible in a distributed setting since the CPU memory spaces of the various processes are not shared. |
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| </Tip> |
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| <a id='load_dataset_enhancing_performance'></a> |
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| ## Improve performance |
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| Disabling the cache and copying the dataset in-memory will speed up dataset operations. There are two options for copying the dataset in-memory: |
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| 1. Set `datasets.config.IN_MEMORY_MAX_SIZE` to a nonzero value (in bytes) that fits in your RAM memory. |
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| 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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