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how-to-guides/large-file/large-file.md

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+# How to use LargeFiles
+
+The functions [save_model][great_ai.use_model] and [@use_model][great_ai.use_model] wrap LargeFile instances. Hence, besides configuring [LargeFile](/reference/large-file), users have few reasons to use LargeFiles directly.
+
+## Motivation
+
+Oftentimes, especially when working with data-heavy applications, large files can proliferate in a repository. Version controlling them is an obvious next step, however, GitHub's git LFS implementation [doesn't support deleting](https://docs.github.com/en/repositories/working-with-files/managing-large-files/removing-files-from-git-large-file-storage#git-lfs-objects-in-your-repository) large files, making it easy for them to eat-up the LFS quota and explode the size of your repos.
+
+[DVC](https://dvc.org/) is a viable alternative, however, it requires users to learn to use one more CLI tool.
+
+??? note "Using LargeFile-s directly (usually not needed)"
+    LargeFile doesn't require users to learn too much new. It is a nearly exact copy of the built-in `open()` function of Python with which users are certainly already familiar.
+
+    ## Simple example
+
+    ```python
+    from great_ai.large_file import LargeFileS3
+
+    LargeFileS3.configure_credentials({
+        "aws_region_name": "your_region_like_eu-west-2",
+        "aws_access_key_id": "YOUR_ACCESS_KEY_ID",
+        "aws_secret_access_key": "YOUR_VERY_SECRET_ACCESS_KEY",
+        "large_files_bucket_name": "create_a_bucket_and_put_its_name_here",
+    })
+
+    # Creates a new version and deletes the older version 
+    # leaving the 3 most recently used intact
+    with LargeFileS3("test.txt", "w", keep_last_n=3) as f:
+        for i in range(100000):
+            f.write('test\n')
+
+    # The latest version is returned by default
+    # but an optional `version` keyword argument can be provided as well
+    with LargeFileS3("test.txt", "r") as f:  #(1)
+        print(f.readlines()[0])
+    ```
+
+    1.  In this case, the latest version is already in the local cache, no download is required.
+
+    ### More details
+
+    `LargeFile` behaves like an opened file (in the background it is a temp file after all). Binary reads and writes are supported along with the [different keywords `open()` accepts](https://docs.python.org/3/library/functions.html#open){ target=_blank }.
+
+    The local cache can be configured with these properties:
+
+    ```python
+    LargeFileS3.cache_path = Path('.cache')
+    LargeFileS3.max_cache_size = "30 GB"
+    ```
+
+    #### I only need a path
+
+    In case you only need a path to the (proxy of the) remote file, this pattern can be applied:
+
+    ```python
+    path_to_model = LargeFileS3("folder-of-my-bert-model", version=31).get()
+    ```
+
+    > This will first download the file/folder into your local cache folder. Then, it returns a `Path` object to the local version. Which can be turned into a string with `str(path_to_model)`.
+
+    The same approach works for uploads:
+
+    ```python
+    LargeFileS3("folder-of-my-bert-model").push('path_to_local/folder_or_file')
+    ```
+
+    > This way both regular files and folders can be handled. The uploaded file is called **folder-of-my-bert-model**, the local name is ignored.
+
+    Lastly, all version of the remote object can be deleted by calling `LargeFileS3("my-file").delete()`. It will still reside in your local cache afterwards; its deletion will happen next time your local cache has to be pruned.
+
+## From the command-line 
+
+The main reason for using the `large-file` or `python3 -m great_ai.large_file` commands is to upload or download models from the terminal. For example, when building a docker image, it is best-practice to cache the referred models.
+
+### Setup
+
+Create an .ini file (or use *~/.aws/credentials*). It may look like this:
+
+```ini
+aws_region_name = your_region_like_eu-west-2
+aws_access_key_id = YOUR_ACCESS_KEY_ID
+aws_secret_access_key = YOUR_VERY_SECRET_ACCESS_KEY
+large_files_bucket_name = my_large_files
+```
+
+### Upload some files
+
+```sh
+large-file --backend s3 --secrets secrets.ini \
+    --push my_first_file.json folder/my_second_file my_folder
+```
+
+> Only the filename is used as the S3 name, the rest of the path is ignored.
+
+!!! important "Using MongoDB"
+    The possible values for `--backend` are `s3`, `mongo`, and `local`. The latter doesn't need credentials, it only versions and stores your files in a local folder. MongoDB on the other hand requires a `mongo_connection_string` and a `mongo_database` to be specified. For storing large files, it uses the [GridFS](https://www.mongodb.com/docs/manual/core/gridfs){ target=_blank } specification.
+
+### Download some files to the local cache
+
+This can be useful when building a Docker image for example. This way, the files can already reside inside the container and need not be downloaded later.
+
+```sh
+large-file --backend s3 --secrets ~/.aws/credentials \
+    --cache my_first_file.json:3 my_second_file my_folder:0
+```
+
+> Versions may be specified by using `:`-s.
+
+### Delete remote files
+
+```sh
+large-file --backend s3 --secrets ~/.aws/credentials \
+    --delete my_first_file.json
+```