How to use LargeFiles#
The functions save_model and @use_model wrap LargeFile instances. Hence, besides configuring LargeFile, users have few reasons to use LargeFiles directly.
Motivation#
Often, 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 large files, making it easy for them to eat-up the LFS quota and explode the size of your repos.
DVC is a viable alternative; however, it requires users to learn to use one more CLI tool.
Using LargeFile-s directly (usually not needed)
LargeFile doesn't require users to learn too much new. It is a nearly exact copy of Python's built-in open() function, with which users are undoubtedly already familiar.
Simple example#
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 three 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])
- 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.
The local cache can be configured with these properties:
I only need a path#
In case you only need a path to the (proxy of the) remote file, this pattern can be applied:
This will first download the file/folder into your local cache folder. Then, it returns a
Pathobject to the local version. Which can be turned into a string withstr(path_to_model).
The same approach works for uploads:
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:
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#
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.
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 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.
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.