EdgeServe
所属分类：中间件编程
开发工具：Python
文件大小：0KB
下载次数：0
上传日期：2023-03-15 21:24:26
上 传 者：
sh-1993
说明： EdgeServe是一个去中心化的模型服务系统，它在边缘c中优化数据移动和模型放置...
(EdgeServe is a decentralized model serving system that optimizes data movements and model placements within an edge cluster.)
文件列表:
LICENSE (1069, 2021-04-14)
reports/ (0, 2021-04-14)
reports/optimizer.pdf (330855, 2021-04-14)
src/ (0, 2021-04-14)
src/device.py (2536, 2021-04-14)
src/local_consumer_cloud.py (505, 2021-04-14)
src/local_producer_cloud.py (1107, 2021-04-14)
src/main.py (3018, 2021-04-14)
src/optimizer.py (1018, 2021-04-14)
# EdgeServe
EdgeServe is a decentralized model serving system that optimizes data movements and model placements within an edge cluster.
## Installation
```bash
git clone https://github.com/swjz/EdgeServe.git
cd EdgeServe
pip3 -r requirements.txt
pip3 install -e .
```
For how to set up an Apache Pulsar server in docker, please refer to [Apache Pulsar Doc](https://pulsar.apache.org/docs/2.11.x/getting-started-docker/).
## Usage
### Working as a data source node
```python
from edgeserve.data_source import DataSource
node = 'pulsar://server-address:6650'
def stream():
while True:
yield sensor.read()
with DataSource(stream(), node, source_id='data1', topic='topic_in') as ds:
# every next() call sends an example to the message queue
next(ds)
```
### Working as a prediction node
```python
from edgeserve.compute import Compute
node = 'pulsar://server-address:6650'
def task(data1, data2, data3, data4):
# actual prediction work here
return aggregate_and_predict(data1, data2, data3, data4)
with Compute(task, node, worker_id='worker1', topic_in='topic_in', topic_out='topic_out', min_interval_ms=30) as compute:
# every next() call consumes a message from the queue and runs task() on it
# the result is sent to another message queue
next(compute)
```
### Working as a destination node
```python
from edgeserve.materialize import Materialize
node = 'pulsar://server-address:6650'
def final_process(y_pred):
# reformat the prediction result, materialize and/or use it for decision making
make_decisions(y_pred)
with Materialize(final_process, node, topic='topic_out') as materialize:
# every next() call consumes a message from the prediction output queue and runs final_process() on it
next(materialize)
```
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