daman 0.2.0

A simple package that allows for the direct use of data stored and managed on a cloud storage service

daman

description

daman aims at providing a relatively simple solution to the challenge of sharing data between various multiple individuals/machines in the context of a python-development workflow. This solution relies on cloud storage and allows to control the amount of local disc space used for data management.

setup & configure

Install

This package is available through pip and can directly be installed as follows by running pip install daman.

cloud provider

AWS: S3 Bucket

Should your AWS account not yet configured on your current machine, retrieve the following information:

• access_key_id
• secret_access_key

using dm_aws

you can then simply running the following command:

dm_aws --access_key_id <access_key_id> --secret_access_key <secret_access_key>

manually

Alternatively you can create a file at ~/.aws/credentials and type in the following :

[default]
aws_access_key_id = <access_key_id>
aws_secret_access_key = <secret_access_key>

configure daman

To finalise the setup phase, it is required to run to provide the following information:

• storage_name: Name of the bucket to be used
• service: Type of cloud storage used (Currently only aws is available).
• local_dir [Optional]: local directory to store data in. default is ~/.daman/data/
• allocated_space [Optional]: Disc space to allocate to local directory. By default no limit is set.

dm_configure --storage_name <storage_name>
--service <service>

how to

upload

Currently it is only possible to push python object to daman data manager.

python: push

dm.push(
    obj,
    key=key,
    meta=None,
    local=True,
    force=False,
    persist=False)

Input

• obj: object - Any pickle serialisable object.
• key: str - name under which to store the object. will be used to retrieve the object.
• meta [OPTIONAL]: object - Any pickle serialisable meta information to store with the object.
• local [OPTIONAL]: bool - If set to True also adds the uploaded data to your local registery.
• force [OPTIONAL]: bool - If key is already in use, force must be set to True in order to force the overwriting of the already stored object.
• persist [OPTIONAL]: bool - If set to True ensures the file will not be deleted unless manually requested.

Output - None

download

python: pull

obj, meta = dm.push(
    key=key,
    force=False,
    persist=False)

Input

• key: str - key under which the data is stored.
• force [OPTIONAL]: bool - when set to True downloads the dataset from cloud service ignoring local version.
• persist [OPTIONAL]: bool - If set to True ensures the file will not be deleted unless manually requested.
• memory_only [OPTIONAL]: bool - is set to True only loads the data into memory and does not keep a local version unless already available.

Output

• obj: object stored object.
• meta: object store meta data.

terminal: dm_pull

dm_pull --help

usage: dm_pull [-h] --key {} [--force] [--persist]

Sets up daman package.

optional arguments:
  -h, --help  show this help message and exit
  --key {}    key of the file to delete
  --force     When provided forces the download even when the file is already
              available.
  --persist   When provided ensures that the downloaded file is always kept on
              disc on manually deleted.

delete

python: delete

obj, meta = dm.delete(
    key=key,
    local=True,
    remote=False)

Input

• key: str - key under which the data to delete is stored.
• local [OPTIONAL]: bool - if set to True will delete local file.
• remote [OPTIONAL]: bool - if set to True will delete remote version of the requested key.

Output - None

terminal: dm_delete

dm_delete --help

Sets up daman package.
optional arguments:
  -h, --help  show this help message and exit
  --key {}    key of the file to delete
  --remote    When provided, deletes the requested file on the cloud service
              as well.

usage: dm_delete [-h] --key {} [--remote]