https://github.com/asgersvenning/pyremotedata
This repository contains the python module "pyRemoteData" which handles high-bandwidth data transfer with LFTP.
https://github.com/asgersvenning/pyremotedata
data-transfer lftp python pytorch sftp
Last synced: 2 months ago
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This repository contains the python module "pyRemoteData" which handles high-bandwidth data transfer with LFTP.
- Host: GitHub
- URL: https://github.com/asgersvenning/pyremotedata
- Owner: asgersvenning
- License: mit
- Created: 2023-11-08T12:55:31.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2026-02-19T12:10:02.000Z (4 months ago)
- Last Synced: 2026-02-19T16:34:42.214Z (4 months ago)
- Topics: data-transfer, lftp, python, pytorch, sftp
- Language: Python
- Homepage: https://asgersvenning.com/pyremotedata/
- Size: 4.66 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# `pyRemoteData`
`pyRemoteData` is a module developed for scientific computation using the remote storage platform [ERDA](https://erda.au.dk/) (Electronic Research Data Archive) provided by Aarhus University IT, as part of my PhD at the Department of Ecoscience at Aarhus University.
It can be used with **any** storage facility that supports SFTP and LFTP, but is only tested on a minimal SFTP server found at [atmoz/sftp](https://hub.docker.com/r/atmoz/sftp) and on the live AU ERDA service which runs on MiG (Minimum intrusion Grid - [SourceForge](https://sourceforge.net/projects/migrid/)/[GitHub](https://github.com/ucphhpc/migrid-sync)) developed by [SCIENCE HPC Centre at Copenhagen University](https://science.ku.dk/english/research/research-e-infrastructure/science-hpc-centre/).
## Capabilities
In order to facility high-throughput computation in a cross-platform setting, `pyRemoteData` handles data transfer with multithreading and asynchronous data streaming using thread-safe buffers.
## Use-cases
If your storage facility supports SFTP and LFTP, and you need high-bandwidth data streaming for analysis, data migration or other purposes such as model-training, then this module may be of use to you.
Experience with SFTP or LFTP is not necessary, but you must be able to setup the required SSH configurations.
See **Automated** for details on how to avoid having to set up SSH configuration.
## Setup
A more user-friendly setup process, which facilitates both automated as well as interactive setup is currently in development. (**TODO**: Finish and describe the setup process)
### Installation
The package is available on PyPI, and can be installed using pip:
```bash
pip install pyremotedata
```
### Interactive
Simply follow the popup instructions that appear once you load the package for the first time.
### Automated
The automatic configuration setup relies on setting the correct environment variables **BEFORE LOADING THE PACKAGE**:
* `PYREMOTEDATA_REMOTE_USERNAME` : Should be set to your username on your remote service.
* `PYREMOTEDATA_REMOTE_URI` : Should be set to the URI of the endpoint for your remote service (e.g. for ERDA it is "io.erda.au.dk").
* `PYREMOTEDATA_REMOTE_DIRECTORY` : If you would like to set a default working directory, that is not the root of your remote storage, then set this to that (e.g. "/MY_PROJECT/DATASETS") otherwise simply set this to "/".
* `PYREMOTEDATA_AUTO` : Should be **set to "yes"** to disable interactive mode. If this is not set, or set to anything other than "yes" (not case-sensitive), while any of the prior environment variables are unset an error will be thrown.
The recommended way to avoid any SSH or environment variables setup is to use:
```py
from pyremotedata.implicit_mount import IOHandler
with IOHandler(lftp_settings = {'sftp:connect-program' : 'ssh -a -x -i '}, user = , remote = ) as io:
...
```
Here `keyfile` is probably something like `~/.ssh/id_rsa`.
### Example
If you want to test against a mock server simply follow the instructions in tests/README.
If you have a remote storage facility that supports SFTP and LFTP, then you can use the following example to test the functionality of the module:
```python
# Set the environment variables (only necessary in a non-interactive setting)
# If you are simply running this as a Python script,
# you can omit these lines and you will be prompted to set them interactively
import os
os.environ["PYREMOTEDATA_REMOTE_USERNAME"] = "username"
os.environ["PYREMOTEDATA_REMOTE_URI"] = "storage.example.com"
os.environ["PYREMOTEDATA_REMOTE_DIRECTORY"] = "/MY_PROJECT/DATASETS"
os.environ["PYREMOTEDATA_AUTO"] = "yes"
from pyremotedata.implicit_mount import IOHandler
handler = IOHandler()
with handler as io:
print(io.ls())
local_file = io.download("/remote/file/or/directory")
# The configuration is persistent, but can be removed using the following:
from pyremotedata.config import remove_config
remove_config()
```
## Issues
This module is certainly not maximally efficient, and you may run into network- or OS-specific issues. Any and all feedback and contributions is highly appreciated.