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
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()
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
FAQs
Layer AI SDK
We found that layer demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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