Model comprises of model file/folder and some metadata. Each Model can have multiple versions. In essence they are just Artifacts with special type model

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Upload Model Version

You can automatically save and version model files/folder using thelog_model method. The basic usage looks like follows 
from truefoundry.ml import get_client
  
client = get_client()
model_version = client.log_model(
    ml_repo="name-of-the-ml-repo",
    name="name-for-the-model",
    model_file_or_folder="path/to/model/file/or/folder/on/disk",
    framework=<None or Framework object (see below)>
)

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Framework Agnostic

Any file or folder can be saved as model by passing it in model_file_or_folder and framework can be set to None.
This is an example of storing an sklearn model. To log a model we start a run and then give our model a name and pass in the model saved on disk and the framework instance. 
from truefoundry.ml import get_client, SklearnFramework, infer_signature

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

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)
joblib.dump(clf, "sklearn-pipeline.joblib")

client = get_client()
client.create_ml_repo(  # This is only required once
     name="my-classification-project",
     # This controls which bucket is used.
     # You can get this from Integrations > Blob Storage.
     storage_integration_fqn='<storage_integration_fqn>'
)
model_version = client.log_model(
     ml_repo="my-classification-project",
     name="my-sklearn-model",
     description="A simple sklearn pipeline",
     model_file_or_folder="sklearn-pipeline.joblib",
     framework=SklearnFramework(),
     metadata={"accuracy": 0.99, "f1": 0.80},
)
print(model_version.fqn)
This will create a new model my-sklearn-model under the ml_repo and the first version v1 for my-sklearn-model.
Once created the model version files are immutable, only fields like description, framework, metadata can be updated using CLI or UI.
Once created, a model version has a fqn (fully qualified name) which can be used to retrieve the model later - E.g. model:truefoundry/my-classification-project/my-sklearn-model:1 Any subsequent calls tolog_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 ml_repo.

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

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Get Model Version and Download

You can first get the model using the fqn and then download the logged model using the fqn and then use thedownload() function. From here on you can access the files at download_info.download_dir 
import os
import tempfile
import joblib
from truefoundry.ml import get_client


client = get_client()
model_version = client.get_model_version_by_fqn(
     fqn="model:truefoundry/my-classification-project/my-sklearn-model:1"
)

# Download the model to disk
temp = tempfile.TemporaryDirectory()
download_info = model_version.download(path=temp.name)
print(download_info.model_dir)

# Deserialize and Load
model = joblib.load(
     os.path.join(download_info.model_dir, "sklearn-pipeline.joblib")
)

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FAQs

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What are the frameworks supported by the log_model method?

Following framework classes are available in truefoundry.ml
  • FastAIFramework
  • GluonFramework
  • H2OFramework
  • KerasFramework
  • LightGBMFramework
  • ONNXFramework
  • PaddleFramework
  • PyTorchFramework
  • SklearnFramework
  • SpaCyFramework
  • StatsModelsFramework
  • TensorFlowFramework
  • TransformersFramework
  • XGBoostFramework

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Update Model Version

You may want to update fields like description, framework, metadata on an existing model version.
You can do so with the .update() call on the Model Version instance. E.g. 
from truefoundry.ml import get_client, SklearnFramework


client = get_client()
model_version = client.get_model_version_by_fqn(
    "model:truefoundry/my-classification-project/my-sklearn-model:1"
)
model_version.description = "This is my updated description"
model_version.metadata = {"accuracy": 0.98, "f1": 0.85}
model_version.framework = SklearnFramework(
    model_filepath="sklearn-pipeline.joblib",
    serialization_format="joblib"
)
# Updates the model fields for existing model version.
model_version.update()