This repository contains source code for Stream Processing Architecture for Resource Subtle Environments (or just Sparse for short). Additionally, sample applications utilizing Sparse for deep learning can be found in examples directory.
pip install sparse-framework
The repository includes example applications (in the examples directory). The applications are tested tested with the following devices and the following software:
| Device | JetPack version | Python version | PyTorch version | Docker version | Base image | Docker tag suffix |
|---|---|---|---|---|---|---|
| Jetson AGX Xavier | 5.0 preview | 3.8.10 | 1.12.0a0 | 20.10.12 | nvcr.io/nvidia/l4t-pytorch:r34.1.0-pth1.12-py3 | jp50 |
| Lenovo ThinkPad | - | 3.8.12 | 1.11.0 | 20.10.15 | pytorch/pytorch:1.11.0-cuda11.3-cudnn8-runtime | amd64 |
Follow these instructions to run the example applications with Kubernetes.
Follow the instructions below to update PyPi index after a new version has been released.
- Update version number in
pyproject.toml - Update PyPA's
buildpackage by running:
python3 -m pip install --upgrade build
- Build project package by running:
python3 -m build
- Update PyPA's
twinepackage by running:
python3 -m pip install --upgrade twine
- Upload the built package by running:
python3 -m twine upload dist/*
The following article introduces this design (corresponds to version v1.0.0-rc2):
@INPROCEEDINGS{10403079,
author={Vainio, Antero and Mudvari, Akrit and Kiedanski, Diego and Tarkoma, Sasu and Tassiulas, Leandros},
booktitle={2023 IEEE 7th International Conference on Fog and Edge Computing (ICFEC)},
title={Fog Computing for Deep Learning with Pipelines},
year={2023},
volume={},
number={},
pages={64-72},
keywords={Training;Program processors;Computational modeling;Pipelines;Hardware;Task analysis;Edge computing;fog computing;stream processing systems;deep learning},
doi={10.1109/ICFEC57925.2023.00017}}