Log Models

You can automatically serialize, save and version model objects as using the log_model method.

This is an example of serializing and storing an sklearn model. To log a model we start a run and then give our model a name and pass in the model object and the framework name.

import mlfoundry
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

client = mlfoundry.get_client()
run = client.create_run(project_name="iris-demo")
X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
y = np.array([1, 1, 2, 2])
clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
clf.fit(X, y)

model_version = run.log_model(
    name="iris-classifier", 
    model=clf, 
    framework="sklearn", 
    description="sample iris pipeline model",
    metadata={"accuracy": 0.99, "f1": 0.80},
    step=1,  # step number, useful when using iterative algorithms like SGD
)
print(model_version.fqn)
run.end()

This will create a new model iris-demo under the project and the first version v1 for iris-classifier. Once created the model version is immutable.

Once created, a model version has a fqn (fully qualified name) which can be used to retrieve the model later - E.g. model:truefoundry/user/iris-demo/iris-classifier:1

Any subsequent calls to log_model with the same name would create a new version of this model - v2, v3 and so on.

The logged model can be found in the dashboard in the Models tab under your project.

You can view the details of each model version from there on.

FAQs

What are the frameworks supported by the log_model method?

This method supports, "sklearn", "tensorflow", "pytorch", "keras", "xgboost", "lightgbm", "fastai", "h2o", "spacy", "statsmodels", "gluon", "paddle". These options are also available as a enum - mlfoundry.ModelFramework

How can I load back a logged model?

You can first get the model using the fqn and then use the load() function to deserialize and load the logged model.

import mlfoundry

client = mlfoundry.get_client()
model_version = client.get_model("<fqn-of-your-model>") # e.g. "model:truefoundry/user/iris-demo/iris-classifier:1"
model = model_version.load()

How can I download a logged model to disk?

You can first get the model using the fqn and then use the download() function to download a logged model.

import mlfoundry

client = mlfoundry.get_client()
model_version = client.get_model("<fqn-of-your-model>") # e.g. "model:truefoundry/user/iris-demo/iris-classifier:1"
download_info = model_version.download(path="path/to/your/location")
print(download_info)

From here on you can access the files at download_info.download_dir