Layer

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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

Start for Free now!

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