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Description:

layer 0.10.3150493728

Layer - Metadata Store for Production ML

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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Getting Started
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!
Reporting bugs
You have a bug, a request or a feature? Let us know on Slack or open an issue
Contributing code
Do you want to help us build the best metadata store? Check out the Contributing Guide
Learn more

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