https://github.com/layerai-archive/sdk

Metadata store for Production ML
https://github.com/layerai-archive/sdk

collaboration data-science data-versioning deep-learning experiment-tracking hyperparameter-optimization hyperparameter-tuning keras machine-learning mlops model-versioning python pytorch reinforcement-learning sklearn tensorflow

Last synced: 11 days ago
JSON representation

Metadata store for Production ML

• Host: GitHub
• URL: https://github.com/layerai-archive/sdk
• Owner: layerai-archive
• License: apache-2.0
• Archived: true
• Created: 2022-04-25T17:42:30.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-09-29T10:35:48.000Z (over 2 years ago)
• Last Synced: 2025-01-05T03:37:26.871Z (26 days ago)
• Topics: collaboration, data-science, data-versioning, deep-learning, experiment-tracking, hyperparameter-optimization, hyperparameter-tuning, keras, machine-learning, mlops, model-versioning, python, pytorch, reinforcement-learning, sklearn, tensorflow
• Language: Python
• Homepage: https://layer.ai
• Size: 2.22 MB
• Stars: 89
• Watchers: 7
• Forks: 7
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Security: SECURITY.md

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## Layer - Metadata Store for Production ML

![Layer - Metadata store for production ML](https://app.layer.ai/assets/layer_metadata_store.png)

[Layer](https://layer.ai) helps you build, train and track all your machine learning project metadata including ML models and datasets with semantic versioning, extensive artifact logging and dynamic reporting with local↔cloud training

**[Start for Free now!](https://app.layer.ai/login?returnTo=%2Fgetting-started)**

## Getting Started

Install Layer:

```shell

pip install layer --upgrade

```

Login to your free account and initialize your project:

```python

import layer

layer.login()

layer.init("my-first-project")

```

Decorate your training function to register your model to Layer:

```python

from layer.decorators import model

@model("my-model")

def train():

    from sklearn import datasets

    from sklearn.svm import SVC

    iris = datasets.load_iris()

    clf = SVC()

    clf.fit(iris.data, iris.target)

    return clf

train()

```

Now you can fetch your model from Layer:

```python

import layer

clf = layer.get_model("my-model:1.1").get_train()

clf

# > SVC()

```

[**🚀 Try in Google Colab now!**](https://colab.research.google.com/github/layerai/examples/blob/main/tutorials/add-models-to-layer/how_to_add_models_to_layer.ipynb)

## Reporting bugs

You have a bug, a request or a feature? Let us know on [Slack](https://bit.ly/layercommunityslack) or [open an issue](https://github.com/layerai/sdk/issues/new/choose)

## Contributing code

Do you want to help us build the best metadata store? Check out the [Contributing Guide](https://github.com/layerai/sdk/blob/main/CONTRIBUTING.md)

## Learn more

- Join our [Slack Community ](https://bit.ly/layercommunityslack) to connect with other Layer users

- Visit the [examples repo](https://github.com/layerai/examples) for more inspiration

- Browse [Community Projects](https://layer.ai/community) to see more use cases

- Check out the [Documentation](https://docs.layer.ai)

- [Contact us](https://layer.ai/contact-us) for your questions