Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/unionai-oss/unionml
UnionML: the easiest way to build and deploy machine learning microservices
https://github.com/unionai-oss/unionml
hacktoberfest machine-learning mlops
Last synced: 2 months ago
JSON representation
UnionML: the easiest way to build and deploy machine learning microservices
- Host: GitHub
- URL: https://github.com/unionai-oss/unionml
- Owner: unionai-oss
- License: apache-2.0
- Archived: true
- Created: 2021-11-17T18:11:10.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-06T13:36:29.000Z (about 1 year ago)
- Last Synced: 2024-10-03T06:44:27.855Z (4 months ago)
- Topics: hacktoberfest, machine-learning, mlops
- Language: Python
- Homepage: https://www.union.ai/unionml
- Size: 14.7 MB
- Stars: 336
- Watchers: 4
- Forks: 43
- Open Issues: 84
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-production-machine-learning - UnionML - oss/unionml.svg?style=social) - UnionML is an open source MLOps framework that aims to reduce the boilerplate and friction that comes with building models and deploying them to production. (Deployment and Serving)
README
UnionML
The easiest way to build and deploy machine learning microservices
---
[](https://pypi.org/project/unionml/)
[](https://pypi.org/project/unionml/)
[](https://unionml.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/unionai-oss/unionml/actions/workflows/build.yml)
[](https://pypistats.org/packages/unionml)
[](https://github.com/orgs/unionai-oss/projects/1/views/4)
**UnionML** is an open source MLOps framework that aims to reduce the boilerplate and friction
that comes with building models and deploying them to production.
You can create **UnionML Apps** by defining a few core methods that are automatically bundled
into ML microservices, starting with model training and offline and online prediction.
Built on top of [Flyte](https://docs.flyte.org/en/latest/), UnionML provides a high-level
interface for productionizing your ML models so that you can focus on curating a better dataset
and improving your models.
To learn more, check out the 📖 [Documentation](https://unionml.readthedocs.io).
## Installing
Install using conda:
```bash
conda install -c conda-forge unionml
```
Install using pip:
```bash
pip install unionml
```
## A Simple Example
Create a `Dataset` and `Model`, which together form a **UnionML App**:
```python
from unionml import Dataset, Model
from sklearn.linear_model import LogisticRegression
dataset = Dataset(name="digits_dataset", test_size=0.2, shuffle=True, targets=["target"])
model = Model(name="digits_classifier", init=LogisticRegression, dataset=dataset)
```
Define `Dataset` and `Model` methods for training a hand-written digits classifier:
```python
from typing import List
import pandas as pd
from sklearn.datasets import load_digits
from sklearn.metrics import accuracy_score
@dataset.reader
def reader() -> pd.DataFrame:
return load_digits(as_frame=True).frame
@model.trainer
def trainer(
estimator: LogisticRegression,
features: pd.DataFrame,
target: pd.DataFrame,
) -> LogisticRegression:
return estimator.fit(features, target.squeeze())
@model.predictor
def predictor(
estimator: LogisticRegression,
features: pd.DataFrame
) -> List[float]:
return [float(x) for x in estimator.predict(features)]
@model.evaluator
def evaluator(
estimator: LogisticRegression,
features: pd.DataFrame,
target: pd.DataFrame
) -> float:
return float(accuracy_score(target.squeeze(), predictor(estimator, features)))
```
And that's all ⭐️!
By defining these four methods, you've created a minimal **UnionML App** that you can:
- [Execute locally](https://unionml.readthedocs.io/en/latest/index.html#train-and-predict-locally) to debug and iterate on your code.
- [Serve Seamlessly with FastAPI](https://unionml.readthedocs.io/en/latest/index.html#serve-seamlessly-with-fastapi) for online prediction.
- [Deploy on a Flyte Cluster](https://unionml.readthedocs.io/en/latest/deploying.html) to scale your model training and schedule offline prediction jobs.
## Contributing
All contributions are welcome 🤝 ! Check out the [contribution guide](https://unionml.readthedocs.io/en/latest/contributing.html) to learn more about how to contribute.
### Gitpod
[](https://gitpod.io/#https://github.com/unionai-oss/unionml)