Layer 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

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Install Layer:

pip install layer --upgrade

Login to your free account and initialize your project:

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

Decorate your training function to register your model to Layer:

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:

import layer clf = layer.get_model("my-model:1.1").get_train() clf # > SVC()

🚀 Try in Google Colab now!

You have a bug, a request or a feature? Let us know on Slack or open an issue

Do you want to help us build the best metadata store? Check out the Contributing Guide

• Join our Slack Community to connect with other Layer users
• Visit the examples repo for more inspiration
• Browse Community Projects to see more use cases
• Check out the Documentation
• Contact us for your questions