Update release version to 0.6.0 (mlflow#458)

CHANGELOG.rst

@@ -1,6 +1,65 @@
 Changelog
 =========
 
+0.6.0 (2018-09-10)
+------------------
+
+MLflow 0.6.0 introduces several major features:
+
+- A Java client API, available on Maven
+- Support for saving and serving SparkML models as MLeap for low-latency serving
+- Support for tagging runs with metadata, during and after the run completion
+- Support for deleting (and restoring deleted) experiments
+
+In addition to these features, there are a host of improvements and bugfixes to the REST API, Python API, tracking UI, and documentation. The `examples/ <https://github.com/mlflow/mlflow/tree/master/examples>`_ subdirectory has also been revamped to make it easier to jump in, and examples demonstrating multistep workflows and hyperparameter tuning have been added.
+
+Breaking changes:
+
+We fixed a few inconsistencies in the the ``mlflow.tracking`` API, as introduced in 0.5.0:
+
+- ``MLflowService`` has been renamed ``MlflowClient`` (#461, @mparkhe)
+- You get an ``MlflowClient`` by calling ``mlflow.tracking.MlflowClient()`` (previously, this was ``mlflow.tracking.get_service()``) (#461, @mparkhe)
+- ``MlflowService.list_runs`` was changed to ``MlflowService.list_run_infos`` to reflect the information actually returned by the call. It now returns a ``RunInfo`` instead of a ``Run`` (#334, @aarondav)
+- ``MlflowService.log_artifact`` and ``MlflowService.log_artifacts`` now take a ``run_id`` instead of ``artifact_uri``. This now matches ``list_artifacts`` and ``download_artifacts``  (#444, @aarondav)
+
+Features:
+
+- Java client API added with support for the MLflow Tracking API (analogous to ``mlflow.tracking``), allowing users to create and manage experiments, runs, and artifacts. The release includes a `usage example <https://github.com/mlflow/mlflow/blob/master/mlflow/java/client/src/main/java/org/mlflow/tracking/samples/QuickStartDriver.java>`_ and `Javadocs <https://mlflow.org/docs/latest/java_api/index.html>`_. The client is published to Maven under ``mlflow:mlflow`` (#380, #394, #398, #409, #410, #430, #452, @aarondav)
+- SparkML models are now also saved in MLeap format (https://github.com/combust/mleap), when applicable. Model serving platforms can choose to serve using this format instead of the SparkML format to dramatically decrease prediction latency. SageMaker now does this by default (#324, #327, #331, #395, #428, #435, #438, @dbczumar)
+- [API] Experiments can now be deleted and restored via REST API, Python Tracking API, and MLflow CLI (#340, #344, #367, @mparkhe)
+- [API] Tags can now be set via a SetTag API, and they have been moved to ``RunData`` from ``RunInfo`` (#342, @aarondav)
+- [API] Added ``list_artifacts`` and ``download_artifacts`` to ``MlflowService`` to interact with a run's artifactory (#350, @andrewmchen)
+- [API] Added ``get_experiment_by_name`` to Python Tracking API, and equivalent to Java API (#373, @vfdev-5)
+- [API/Python] Version is now exposed via ``mlflow.__version__``.
+- [API/CLI] Added ``mlflow artifacts`` CLI to list, download, and upload to run artifact repositories (#391, @aarondav)
+- [UI] Added icons to source names in MLflow Experiments UI (#381, @andrewmchen)
+- [UI] Added support to view ``.log`` and ``.tsv`` files from MLflow artifacts UI (#393, @Shenggan; #433, @whiletruelearn)
+- [UI] Run names can now be edited from within the MLflow UI (#382, @smurching)
+- [Serving] Added ``--host`` option to ``mlflow serve`` to allow listening on non-local addressess (#401, @hamroune)
+- [Serving/SageMaker] SageMaker serving takes an AWS region argument (#366, @dbczumar)
+- [Python] Added environment variables to support providing HTTP auth (username, password, token) when talking to a remote MLflow tracking server (#402, @aarondav)
+- [Python] Added support to override S3 endpoint for S3 artifactory (#451, @hamroune)
+- MLflow nightly Python wheel and JAR snapshots are now available and linked from https://github.com/mlflow/mlflow (#352, @aarondav)
+
+Bug fixes and documentation updates:
+
+- [Python] ``mlflow run`` now logs default parameters, in addition to explicitly provided ones (#392, @mparkhe)
+- [Python] ``log_artifact`` in FileStore now requires a relative path as the artifact path (#439, @mparkhe)
+- [Python] Fixed string representation of Python entities, so they now display both their type and serialized fields (#371, @smurching)
+- [UI] Entry point name is now shown in MLflow UI (#345, @aarondav)
+- [Models] Keras model export now includes Tensorflow graph explicitly to ensure the model can always be loaded at deployment time (#440, @tomasatdatabricks)
+- [Python] Fixed issue where FileStore ignored provided Run Name (#358, @adrian555)
+- [Python] Fixed an issue where any ``mlflow run`` failing printed an extraneous exception (#365, @smurching)
+- [Python] uuid dependency removed (#351, @antonpaquin)
+- [Python] Fixed issues with remote execution on Databricks (#357, #361, @smurching; #383, #387, @aarondav)
+- [Docs] Added `comprehensive example <https://github.com/mlflow/mlflow/tree/master/examples/multistep_workflow>`_ of doing a multistep workflow, chaining MLflow runs together and reusing results (#338, @aarondav)
+- [Docs] Added `comprehensive example <https://github.com/mlflow/mlflow/tree/master/examples/hyperparam>`_ of doing hyperparameter tuning (#368, @tomasatdatabricks)
+- [Docs] Added code examples to ``mlflow.keras`` API (#341, @dmatrix)
+- [Docs] Significant improvements to Python API documentation (#454, @stbof)
+- [Docs] Examples folder refactored to improve readability. The examples now reside in ``examples/`` instead of ``example/``, too (#399, @mparkhe)
+- Small bug fixes and doc updates (#328, #363, @ToonKBC; #336, #411, @aarondav; #284, @smurching; #377, @mparkhe; #389, gioa; #408, @aadamson; #397, @vfdev-5; #420, @adrian555; #459, #463, @stbof)
+
+
 0.5.2 (2018-08-24)
 ------------------
 

mlflow/version.py

@@ -1,4 +1,4 @@
 # Copyright 2018 Databricks, Inc.
 
 
-VERSION = '0.6.0.dev0'
+VERSION = '0.6.0'