Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/MachineLearningSystem/nexus
https://github.com/MachineLearningSystem/nexus
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/MachineLearningSystem/nexus
- Owner: MachineLearningSystem
- License: other
- Fork: true (uwsampl/nexus)
- Created: 2022-07-05T08:29:35.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2022-02-11T03:06:57.000Z (over 2 years ago)
- Last Synced: 2024-07-27T16:44:57.395Z (2 months ago)
- Size: 1.11 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-AI-system - Nexus: a GPU cluster engine for accelerating DNN-based video analysis SOSP'19
README
Nexus
=====
[](https://hub.docker.com/repository/docker/abcdabcd987/nexus)
Nexus is a scalable and efficient serving system for DNN applications on GPU
cluster.
## SOSP 2019 Paper
* Check out our SOSP 2019 paper [here](https://doi.org/10.1145/3341301.3359658).
* Check out the [Google Drive](https://drive.google.com/open?id=104UqrlNrfJoQnGdkxTQ56mfxSBFyJTcr) that contains a sample of video dataset.
## Building Nexus
See [BUILDING.md](BUILDING.md) for details.
## Docker and Examples
We provide a [Docker image](https://hub.docker.com/repository/docker/abcdabcd987/nexus)
so that you can try Nexus quickly. And there is an example that goes step by
step on how to run Nexus with a simple example application. We recommend you to
take a look [here](examples/README.md).
## Deployment
### Download Model Zoo
Nexus publishes public model zoo on our department-hosted GitLab. To download,
you need to install [Git LFS](https://git-lfs.github.com/) first. Then, run:
```bash
git clone https://gitlab.cs.washington.edu/syslab/nexus-models
cd nexus-models
git lfs checkout
```
### Run the Profiler
Nexus is a profile-based system. So before running Nexus, make sure you have
profiled all the GPUs. To profile a certain model on a certain GPU, run:
```bash
nexus/tools/profiler/profiler.py --gpu_list=GPU_INDEX --gpu_uuid \
--framework=tensorflow --model=MODEL_NAME \
--model_root=nexus-models/ --dataset=/path/to/datasets/
```
The profile will be saved to the `--model_root` directory.
See [examples](examples/README.md) for more concrete usage.
### Run Nexus
To run Nexus, you need to run the **scheduler** first, then spawn a **backend** for each
GPU card, and finally run the Nexus **frontend** of your application.
See [examples](examples/README.md) for more concrete usage.