An example MLFlow project
You can Notebook see the Databricks code here: https://github.com/noahgift/mlflow-project-best-practices/blob/main/XGBoost-fake-news-automl.ipynb
curl \
-u token:$DATABRICKS_TOKEN \
-X POST \
-H "Content-Type: application/json; format=pandas-records" \
[email protected] \
https://adb-2951765055089996.16.azuredatabricks.net/model/Fake-News/1/invocations
- Create a databricks config:
touch ~/.databrickscfg
- Put in host and token
- Query jobs
databricks jobs list --output JSON | jq
4. List clusters
databricks clusters list --output JSON | jq
- List contents of DBFS
databricks fs ls dbfs:/
You need to set the tracking URI.
export MLFLOW_TRACKING_URI=databricks
from pprint import pprint
from mlflow.tracking import MlflowClient
client = MlflowClient()
for rm in client.list_registered_models():
pprint(dict(rm), indent=4)
CLI version
mlflow artifacts download --artifact-uri models:/<name>/<version|stage>
To use Python do the following:
from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository
model_uri = MlflowClient.get_model_version_download_uri(model_name, model_version)
ModelsArtifactRepository(model_uri).download_artifacts(artifact_path="")
- Databricks Tensorflow run
- Feature Store
- Model Serving
https://docs.databricks.com/applications/mlflow/model-serving.html
Run it with mlserve
mlflow models serve --model-uri /workspaces/mlflow-project-best-practices/tf-model