Bump version and update changelog for 0.3.0 release (#167)

CHANGELOG.rst

@@ -1,6 +1,30 @@
 Changelog
 =========
 
+0.3.0 (2018-07-18)
+------------------
+
+Breaking changes:
+
+- [MLflow Server] Renamed ``--artifact-root`` parameter to ``--default-artifact-root`` in ``mlflow server`` to better reflect its purpose (#165, @aarondav)
+
+Features:
+
+- Spark MLlib integration: we now support logging SparkML Models directly in the log_model API, model format, and serving APIs (#72, @tomasatdatabricks)
+- Google Cloud Storage is now supported as an artifact storage root (#152, @bnekolny)
+- Support asychronous/parallel execution of MLflow runs (#82, @smurching)
+- [SageMaker] Support for deleting, updating applications deployed via SageMaker (#145, @dbczumar)
+- [SageMaker] Pushing the MLflow SageMaker container now includes the MLflow version that it was published with (#124, @sueann)
+- [SageMaker] Simplify parameters to SageMaker deploy by providing sane defaults (#126, @sueann)
+- [UI] One-element metrics are now displayed as a bar char (#118, @cryptexis)
+
+Bug fixes:
+
+- Require gitpython>=2.1.0 (#98, @aarondav)
+- Fixed TensorFlow model loading so that columns match the output names of the exported model (#94, @smurching)
+- Fix SparkUDF when number of columns >= 10 (#97, @aarondav)
+- Miscellaneous bug and documentation fixes from @emres, @dmatrix, @stbof, @gsganden, @dennyglee, @anabranch, @mikehuston, @andrewmchen, @juntai-zheng
+
 0.2.1 (2018-06-28)
 ------------------
 
@@ -19,7 +43,7 @@ This is a patch release fixing some smaller issues after the 0.2.0 release.
   - ``--artifact-root`` to allow storing artifacts at a remote location, S3 only right now (#78, @mateiz)
   - Server now runs behind gunicorn to allow concurrent requests to be made (#61, @mateiz)
 
-- Tensorflow integration, we now support logging Tensorflow Models directly in the log_artifacts API, Model format, and serving APIs (#28, @juntai-zheng)
+- Tensorflow integration: we now support logging Tensorflow Models directly in the log_model API, model format, and serving APIs (#28, @juntai-zheng)
 - Added ``experiments.list_experiments`` as part of experiments API (#37, @mparkhe)
 - Improved support for unicode strings (#79, @smurching)
 - Diabetes progression example dataset and training code (#56, @dennyglee)

docs/source/python_api/mlflow.spark.rst

@@ -0,0 +1,8 @@
+mlflow.spark
+===============
+
+.. automodule:: mlflow.spark
+    :members:
+    :undoc-members:
+    :show-inheritance:
+

mlflow/models/__init__.py

@@ -45,7 +45,7 @@ def log(cls, artifact_path, flavor, **kwargs):
 
         :param artifact_path: Run-relative path identifying this model.
         :param flavor: Flavor module / object to save the model with. The module / object must have
-        save_model function which will persist the model as a valid MLflow model.
+          save_model function which will persist the model as a valid MLflow model.
         :param kwargs: Extra args passed to the model flavor.
         """
         with TempDir() as tmp:

mlflow/version.py

@@ -1,4 +1,4 @@
 # Copyright 2018 Databricks, Inc.
 
 
-VERSION = '0.2.1'
+VERSION = '0.3.0'