etcetera 0.0.5

Sharing datasets via cloud storage

etcetera

Dataset sharing via cloud storage (S3, Google Storage)

Mental model

A dataset is an immutable collection of files organized in directories (e.g train/, val/).

A dataset can have a meta.json file, which is a collection of arbitraty key/value pairs.

Dataset can be local or remote. Local datasets are kept in ~/.etc/. Remote datasets are tgz files stored in cloud storage.

PyPI package etcetera provides:

• a command-line utility etc
• Python package etcetera

Using Command Line

etc -h
usage: etc [-h] {ls,register,pull,push,purge} ...

etcetera: managing cloud-hosted datasets

positional arguments:
  {ls,register,pull,push,purge}
                        command
    ls                  List datasets
    register            Register directory as a dataset
    pull                Pull dataset from repository
    push                Push dataset to the repository
    purge               Purge local dataset

optional arguments:
  -h, --help            show this help message and exit

Using Python

import etcetera as etc

dataset = etc.dataset('flower', auto_install=True)
dataset.keys()
>> { 'test', 'train' }

for filename in dataset['train'].iterdir():
    print(filename)
>> "~/.etc/flower/train/data00001.txt"
>> "~/.etc/flower/train/data00002.txt"

dataset.meta
>> {}
dataset.root
>> "~/.etc/flower"

Configuration

~/.etc.toml contains configuration for the service in TOML format. Example:

url = "s3://my-bucket"

Another example:

url = "s3://my-bucket"
public = false
aws_access_key_id = Axxxx
aws_secret_access_key = Axxx
endpoint_url = https://s3.amazonaws.com

A configuration file is required for remote operations (pull, push, ls -r).

URL value is required. The rest is optional.

• url: URL of the remote repository. For example, s3://my-bucket.
• public: set to true if you want push to create publicly-readable cloud files. Default is false.
• aws_access_key_id, aws_secret_access_key, endpoint_url: configuration files to access AWS api. If not set, the defaults from global AWS config will be used.

Command-line example

etc ls
etc ls -r
etc pull MNIST
etc register <directory> as SuperMNIST

Creating a dataset

A dataset must have:

1. data directory (non-empty)
2. data directory must not have any files, only sub-directories (we call them "partitions")

Optional:

1. meta.json
2. README.md
3. other sub-directories, for example assets/

A minimal dataset example

sample/
    data/
        train/
            data00001.json
            data00002.json
            data00003.json

A general dataset example

sample/
    README.md
    meta.json
    assets/
        Analysis.ipynb
        DataCleanup.ipynb
    data/
        train/
            data00001.json
            ...
        test/
            test00001.json
            ...
        val/
            val00001.json
            ...