open(LARGE)

Storing, versioning, and downloading files from S3 made as easy as using open() in Python. Caching included.

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 the deletion of large files, making it easy for them to eat-up the LFS quota and explode the size of your repos.

Solution

pip install open-large

Simple example

from open_large import LargeFile

LargeFile.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 LargeFile("test.txt", "w", keep_last_n=3) as f:
    for i in range(100000):
        f.write('test\n')

# By default the latest version is returned
# but an optional `version` keyword argument can be provided as well
with LargeFile("test.txt", "r") as f:
    print(f.readlines()[0])

Automatically creates a file, writes to it, uploads it to S3, and then queries the most recent version of it. 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 reading and writing is supported along with the different keywords open() accepts.

The local cache can be configured with these properties:

LargeFile.cache_path = Path('.cache')
LargeFile.max_cache_size = "30 GB"

I only need a path

In case you only need a path to the "remote" file, this pattern can be applied:

path_to_model = LargeFile("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:

LargeFile("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 LargeFile("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.

Command-line example

The package can be used as a module from the command-line to give you more flexibility.

Setup

Create an .ini file (or use ~/.aws/credentials). It may look like this:

[DEFAULT]
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

Just like in example secrets.

Print the expected options

python3 -m open_large --help

Upload some files

python3 -m open_large --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.

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.

python3 -m open_large --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

python3 -m open_large --secrets ~/.aws/credentials --delete my_first_file.json