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1.2

----------------
MLflow 1.2 includes the following major features and improvements:

- Experiments now have editable tags and descriptions (1630, 1632, 1678, ankitmathur-db)
- Search latency has been significantly reduced in the SQLAlchemyStore (1660, t-henri)

**More features and improvements**

- Backend stores now support run tag values up to 5000 characters in length. Some store implementations may support longer tag values (1687, ankitmathur-db)
- Gunicorn options can now be configured for the ``mlflow models serve`` CLI with the ``GUNICORN_CMD_ARGS`` environment variable (1557, LarsDu)
- Jsonnet artifacts can now be previewed in the UI (1683, ankitmathur-db)
- Adds an optional ``python_version`` argument to ``mlflow_install`` for specifying the Python version (e.g. "3.5") to use within the conda environment created for installing the MLflow CLI. If ``python_version`` is unspecified, ``mlflow_install`` defaults to using Python 3.6. (1722, smurching)


**Bug fixes and documentation updates**

- [Tracking] The Autologging feature is now more resilient to tracking errors (1690, apurva-koti)
- [Tracking] The ``runs`` field in in the ``GetExperiment.Response`` proto has been deprecated & will be removed in MLflow 2.0. Please use the ``Search Runs`` API for fetching runs instead (1647, dbczumar)
- [Projects] Fixed a bug that prevented docker-based MLflow Projects from logging artifacts to the ``LocalArtifactRepository`` (1450, nlaille)
- [Projects] Running MLflow projects with the ``--no-conda`` flag in R no longer requires Anaconda to be installed (1650, spadarian)
- [Models/Scoring] Fixed a bug that prevented Spark UDFs from being loaded on Databricks (1658, smurching)
- [UI] AJAX requests made by the MLflow Server Frontend now specify correct MIME-Types (1679, ynotzort)
- [UI] Previews now render correctly for artifacts with uppercase file extensions (e.g., ``.JSON``, ``.YAML``) (1664, ankitmathur-db)
- [UI] Fixed a bug that caused search API errors to surface a Niagara Falls page (1681, dbczumar)
- [Installation] MLflow dependencies are now selected properly based on the target installation platform (1643, akshaya-a)
- [UI] Fixed a bug where the "load more" button in the experiment view did not appear on browsers in Windows (1718, Zangr)


Small bug fixes and doc updates (1663, 1719, dbczumar; 1693, max-allen-db; 1695, 1659, smurching; 1675, jdlesage; 1699, ankitmathur-db; 1696, aarondav; 1710, 1700, 1656, apurva-koti)

1.1

----------------
MLflow 1.1 includes several major features and improvements:

In MLflow Tracking:

- Experimental support for autologging from Tensorflow and Keras. Using ``mlflow.tensorflow.autolog()`` will enable automatic logging of metrics and optimizer parameters from TensorFlow to MLflow. The feature will work with TensorFlow versions ``1.12 <= v < 2.0``. (1520, 1601, apurva-koti)
- Parallel coordinates plot in the MLflow compare run UI. Adds out of the box support for a parallel coordinates plot. The plot allows users to observe relationships between a n-dimensional set of parameters to metrics. It visualizes all runs as lines that are color-coded based on the value of a metric (e.g. accuracy), and shows what parameter values each run took on. (1497, Zangr)
- Pandas based search API. Adds the ability to return the results of a search as a pandas dataframe using the new ``mlflow.search_runs`` API. (1483, 1548, max-allen-db)
- Java fluent API. Adds a new set of APIs to create and log to MLflow runs. This API contrasts with the existing low level ``MlflowClient`` API which simply wraps the REST APIs. The new fluent API allows you to create and log runs similar to how you would using the Python fluent API. (1508, andrewmchen)
- Run tags improvements. Adds the ability to add and edit tags from the run view UI, delete tags from the API, and view tags in the experiment search view. (1400, 1426, Zangr; 1548, 1558, ankitmathur-db)
- Search API improvements. Adds order by and pagination to the search API. Pagination allows you to read a large set of runs in small page sized chunks. This allows clients and backend implementations to handle an unbounded set of runs in a scalable manner. (1444, sueann; 1437, 1455, 1482, 1485, 1542, aarondav; 1567, max-allen-db; 1217, mparkhe)
- Windows support for running the MLflow tracking server and UI. (1080, akshaya-a)

In MLflow Projects:

- Experimental support to run Docker based MLprojects in Kubernetes. Adds the first fully open source remote execution backend for MLflow projects. With this, you can leverage elastic compute resources managed by kubernetes for their ML training purposes. For example, you can run grid search over a set of hyperparameters by running several instances of an MLproject in parallel. (1181, marcusrehm, tomasatdatabricks, andrewmchen; 1566, stbof, dbczumar; 1574 dbczumar)


**More features and improvements**

In MLflow Tracking:

- Paginated “load more” and backend sorting for experiment search view UI. This change allows the UI to scalably display the sorted runs from large experiments. (1564, Zangr)
- Search results are encoded in the URL. This allows you to share searches through their URL and to deep link to them. (1416, apurva-koti)
- Ability to serve MLflow UI behind ``jupyter-server-proxy`` or outside of the root path ``/``. Previous to MLflow 1.1, the UI could only be hosted on `/` since the Javascript makes requests directly to ``/ajax-api/...``. With this patch, MLflow will make requests to ``ajax-api/...`` or a path relative to where the HTML is being served. (1413, xhochy)

In MLflow Models:

- Update ``mlflow.spark.log_model()`` to accept descendants of pyspark.Model (1519, ankitmathur-db)
- Support for saving custom Keras models with ``custom_objects``. This field is semantically equivalent to custom_objects parameter of ``keras.models.load_model()`` function (1525, ankitmathur-db)
- New more performant split orient based input format for pyfunc scoring server (1479, lennon310)
- Ability to specify gunicorn server options for pyfunc scoring server built with `mlflow models build-docker`. 1428, lennon310)

**Bug fixes and documentation updates**

- [Tracking] Fix database migration for MySQL. ``mlflow db upgrade`` should now work for MySQL backends. (1404, sueann)
- [Tracking] Make CLI ``mlflow server`` and ``mlflow ui`` commands to work with SQLAlchemy URIs that specify a database driver. (1411, sueann)
- [Tracking] Fix usability bugs related to FTP artifact repository. (1398, kafendt; 1421, nlaille)
- [Tracking] Return appropriate HTTP status codes for MLflowException (1434, max-allen-db)
- [Tracking] Fix sorting by user ID in the experiment search view. (1401, andrewmchen)
- [Tracking] Allow calling log_metric with NaNs and infs. (1573, tomasatdatabricks)
- [Tracking] Fixes an infinite loop in downloading artifacts logged via dbfs and retrieved via S3. (1605, sueann)
- [Projects] Docker projects should preserve directory structure (1436, ahutterTA)
- [Projects] Fix conda activation for newer versions of conda. (1576, avinashraghuthu, smurching)
- [Models] Allow you to log Tensorflow keras models from the ``tf.keras`` module. (1546, tomasatdatabricks)

Small bug fixes and doc updates (1463, mateiz; 1641, 1622, 1418, sueann; 1607, 1568, 1536, 1478, 1406, 1408, smurching; 1504, LizaShak; 1490, acroz; 1633, 1631, 1603, 1589, 1569, 1526, 1446, 1438, apurva-koti; 1456, Taur1ne; 1547, 1495, aarondav; 1610, 1600, 1492, 1493, 1447, tomasatdatabricks; 1430, javierluraschi; 1424, nathansuh; 1488, henningsway; 1590, 1427, Zangr; 1629, 1614, 1574, 1521, 1522, dbczumar; 1577, 1514, ankitmathur-db; 1588, 1566, stbof; 1575, 1599, max-allen-db; 1592, abaveja313; 1606, andrewmchen)

1.0

----------------
MLflow 1.0 includes many significant features and improvements. From this version, MLflow is no longer beta, and all APIs except those marked as experimental are intended to be stable until the next major version. As such, this release includes a number of breaking changes.

Major features, improvements, and breaking changes
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- Support for recording, querying, and visualizing metrics along a new “step” axis (x coordinate), providing increased flexibility for examining model performance relative to training progress. For example, you can now record performance metrics as a function of the number of training iterations or epochs. MLflow 1.0’s enhanced metrics UI enables you to visualize the change in a metric’s value as a function of its step, augmenting MLflow’s existing UI for plotting a metric’s value as a function of wall-clock time. (1202, 1237, dbczumar; 1132, 1142, 1143, smurching; 1211, 1225, Zangr; 1372, stbof)
- Search improvements. MLflow 1.0 includes additional support in both the API and UI for searching runs within a single experiment or a group of experiments. The search filter API supports a simplified version of the ``SQL WHERE`` clause. In addition to searching using run's metrics and params, the API has been enhanced to support a subset of run attributes as well as user and `system tags <https://mlflow.org/docs/latest/tracking.html#system-tags>`_. For details see `Search syntax <https://mlflow.org/docs/latest/search-syntax.html#syntax>`_ and `examples for programmatically searching runs <https://mlflow.org/docs/latest/search-syntax.html#programmatically-searching-runs>`_. (1245, 1272, 1323, 1326, mparkhe; 1052, Zangr; 1363, aarondav)
- Logging metrics in batches. MLflow 1.0 now has a ``runs/log-batch`` REST API endpoint for logging multiple metrics, params, and tags in a single API request. The endpoint useful for performant logging of multiple metrics at the end of a model training epoch (see `example <https://github.com/mlflow/mlflow/blob/bb8c7602dcb6a3a8786301fe6b98f01e8d3f288d/examples/hyperparam/search_hyperopt.py#L161>`_), or logging of many input model parameters at the start of training. You can call this batched-logging endpoint from Python (``mlflow.log_metrics``, ``mlflow.log_params``, ``mlflow.set_tags``), R (``mlflow_log_batch``), and Java (``MlflowClient.logBatch``). (1214, dbczumar; see 0.9.1 and 0.9.0 for other changes)
- Windows support for MLflow Tracking. The Tracking portion of the MLflow client is now supported on Windows. (1171, eedeleon, tomasatdatabricks)
- HDFS support for artifacts. Hadoop artifact repository with Kerberos authorization support was added, so you can use HDFS to log and retrieve models and other artifacts. (1011, jaroslawk)
- CLI command to build Docker images for serving. Added an ``mlflow models build-docker`` CLI command for building a Docker image capable of serving an MLflow model. The model is served at port 8080 within the container by default. Note that this API is experimental and does not guarantee that the arguments nor format of the Docker container will remain the same. (1329, smurching, tomasatdatabricks)
- New ``onnx`` model flavor for saving, loading, and evaluating ONNX models with MLflow. ONNX flavor APIs are available in the ``mlflow.onnx`` module. (1127, avflor, dbczumar; 1388, 1389, dbczumar)
- Major breaking changes:

- Some of the breaking changes involve database schema changes in the SQLAlchemy tracking store. If your database instance's schema is not up-to-date, MLflow will issue an error at the start-up of ``mlflow server`` or ``mlflow ui``. To migrate an existing database to the newest schema, you can use the ``mlflow db upgrade`` CLI command. (1155, 1371, smurching; 1360, aarondav)
- [Installation] The MLflow Python package no longer depends on ``scikit-learn``, ``mleap``, or ``boto3``. If you want to use the ``scikit-learn`` support, the ``MLeap`` support, or ``s3`` artifact repository / ``sagemaker`` support, you will have to install these respective dependencies explicitly. (1223, aarondav)
- [Artifacts] In the Models API, an artifact's location is now represented as a URI. See the `documentation <https://mlflow.org/docs/latest/tracking.html#artifact-locations>`_ for the list of accepted URIs. (1190, 1254, dbczumar; 1174, dbczumar, sueann; 1206, tomasatdatabricks; 1253, stbof)

- The affected methods are:

- Python: ``<model-type>.load_model``, ``azureml.build_image``, ``sagemaker.deploy``, ``sagemaker.run_local``, ``pyfunc._load_model_env``, ``pyfunc.load_pyfunc``, and ``pyfunc.spark_udf``
- R: ``mlflow_load_model``, ``mlflow_rfunc_predict``, ``mlflow_rfunc_serve``
- CLI: ``mlflow models serve``, ``mlflow models predict``, ``mlflow sagemaker``, ``mlflow azureml`` (with the new ``--model-uri`` option)

- To allow referring to artifacts in the context of a run, MLflow introduces a new URI scheme of the form ``runs:/<run_id>/relative/path/to/artifact``. (1169, 1175, sueann)

- [CLI] ``mlflow pyfunc`` and ``mlflow rfunc`` commands have been unified as ``mlflow models`` (1257, tomasatdatabricks; 1321, dbczumar)
- [CLI] ``mlflow artifacts download``, ``mlflow artifacts download-from-uri`` and ``mlflow download`` commands have been consolidated into ``mlflow artifacts download`` (1233, sueann)
- [Runs] Expose ``RunData`` fields (``metrics``, ``params``, ``tags``) as dictionaries. Note that the ``mlflow.entities.RunData`` constructor still accepts lists of ``metric``/``param``/``tag`` entities. (1078, smurching)
- [Runs] Rename ``run_uuid`` to ``run_id`` in Python, Java, and REST API. Where necessary, MLflow will continue to accept ``run_uuid`` until MLflow 1.1. (1187, aarondav)

Other breaking changes
~~~~~~~~~~~~~~~~~~~~~~

CLI:

- The ``--file-store`` option is deprecated in ``mlflow server`` and ``mlflow ui`` commands. (1196, smurching)
- The ``--host`` and ``--gunicorn-opts`` options are removed in the ``mlflow ui`` command. (1267, aarondav)
- Arguments to ``mlflow experiments`` subcommands, notably ``--experiment-name`` and ``--experiment-id`` are now options (1235, sueann)
- ``mlflow sagemaker list-flavors`` has been removed (1233, sueann)

Tracking:

- The ``user`` property of ``Run``s has been moved to tags (similarly, the ``run_name``, ``source_type``, ``source_name`` properties were moved to tags in 0.9.0). (1230, acroz; 1275, 1276, aarondav)
- In R, the return values of experiment CRUD APIs have been updated to more closely match the REST API. In particular, ``mlflow_create_experiment`` now returns a string experiment ID instead of an experiment, and the other APIs return NULL. (1246, smurching)
- ``RunInfo.status``'s type is now string. (1264, mparkhe)
- Remove deprecated ``RunInfo`` properties from ``start_run``. (1220, aarondav)
- As deprecated in 0.9.1 and before, the ``RunInfo`` fields ``run_name``, ``source_name``, ``source_version``, ``source_type``, and ``entry_point_name`` and the ``SearchRuns`` field ``anded_expressions`` have been removed from the REST API and Python, Java, and R tracking client APIs. They are still available as tags, documented in the REST API documentation. (1188, aarondav)

Models and deployment:

- In Python, require arguments as keywords in ``log_model``, ``save_model`` and ``add_to_model`` methods in the ``tensorflow`` and ``mleap`` modules to avoid breaking changes in the future (1226, sueann)
- Remove the unsupported ``jars`` argument from spark.log_model`` in Python (1222, sueann)
- Introduce ``pyfunc.load_model`` to be consistent with other Models modules. ``pyfunc.load_pyfunc`` will be deprecated in the near future. (1222, sueann)
- Rename ``dst_path`` parameter in ``pyfunc.save_model`` to ``path`` (1221, aarondav)
- R flavors refactor (1299, kevinykuo)

- ``mlflow_predict()`` has been added in favor of ``mlflow_predict_model()`` and ``mlflow_predict_flavor()`` which have been removed.
- ``mlflow_save_model()`` is now a generic and ``mlflow_save_flavor()`` is no longer needed and has been removed.
- ``mlflow_predict()`` takes ``...`` to pass to underlying predict methods.
- ``mlflow_load_flavor()`` now has the signature ``function(flavor, model_path)`` and flavor authors should implement ``mlflow_load_flavor.mlflow_flavor_{FLAVORNAME}``. The flavor argument is inferred from the inputs of user-facing ``mlflow_load_model()`` and does not need to be explicitly provided by the user.

Projects:

- Remove and rename some ``projects.run`` parameters for generality and consistency. (1222, sueann)
- In R, the ``mlflow_run`` API for running MLflow projects has been modified to more closely reflect the Python ``mlflow.run`` API. In particular, the order of the ``uri`` and ``entry_point`` arguments has been reversed and the ``param_list`` argument has been renamed to ``parameters``. (1265, smurching)

R:

- Remove ``mlflow_snapshot`` and ``mlflow_restore_snapshot`` APIs. Also, the ``r_dependencies`` argument used to specify the path to a packrat r-dependencies.txt file has been removed from all APIs. (1263, smurching)
- The ``mlflow_cli`` and ``crate`` APIs are now private. (1246, smurching)

Environment variables:

- Prefix environment variables with "MLFLOW_" (1268, aarondav). Affected variables are:

- [Tracking] ``_MLFLOW_SERVER_FILE_STORE``, ``_MLFLOW_SERVER_ARTIFACT_ROOT``, ``_MLFLOW_STATIC_PREFIX``
- [SageMaker] ``MLFLOW_SAGEMAKER_DEPLOY_IMG_URL``, ``MLFLOW_DEPLOYMENT_FLAVOR_NAME``
- [Scoring] ``MLFLOW_SCORING_SERVER_MIN_THREADS``, ``MLFLOW_SCORING_SERVER_MAX_THREADS``

More features and improvements
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- [Tracking] Non-default driver support for SQLAlchemy backends: ``db+driver`` is now a valid tracking backend URI scheme (1297, drewmcdonald; 1374, mparkhe)
- [Tracking] Validate backend store URI before starting tracking server (1218, luke-zhu, sueann)
- [Tracking] Add ``GetMetricHistory`` client API in Python and Java corresponding to the REST API. (1178, smurching)
- [Tracking] Add ``view_type`` argument to ``MlflowClient.list_experiments()`` in Python. (1212, smurching)
- [Tracking] Dictionary values provided to ``mlflow.log_params`` and ``mlflow.set_tags`` in Python can now be non-string types (e.g., numbers), and they are automatically converted to strings. (1364, aarondav)
- [Tracking] R API additions to be at parity with REST API and Python (1122, kevinykuo)
- [Tracking] Limit number of results returned from ``SearchRuns`` API and UI for faster load (1125, mparkhe; 1154, andrewmchen)
- [Artifacts] To avoid having many copies of large model files in serving, ``ArtifactRepository.download_artifacts`` no longer copies local artifacts (1307, andrewmchen; 1383, dbczumar)
- [Artifacts][Projects] Support GCS in download utilities. ``gs://bucket/path`` files are now supported by the ``mlflow artifacts download`` CLI command and as parameters of type ``path`` in MLProject files. (1168, drewmcdonald)
- [Models] All Python models exported by MLflow now declare ``mlflow`` as a dependency by default. In addition, we introduce a flag ``--install-mlflow`` users can pass to ``mlflow models serve`` and ``mlflow models predict`` methods to force installation of the latest version of MLflow into the model's environment. (1308, tomasatdatabricks)
- [Models] Update model flavors to lazily import dependencies in Python. Modules that define Model flavors now import extra dependencies such as ``tensorflow``, ``scikit-learn``, and ``pytorch`` inside individual _methods_, ensuring that these modules can be imported and explored even if the dependencies have not been installed on your system. Also, the ``DEFAULT_CONDA_ENVIRONMENT`` module variable has been replaced with a ``get_default_conda_env()`` function for each flavor. (1238, dbczumar)
- [Models] It is now possible to pass extra arguments to ``mlflow.keras.load_model`` that will be passed through to ``keras.load_model``. (1330, yorickvP)
- [Serving] For better performance, switch to ``gunicorn`` for serving Python models. This does not change the user interface. (1322, tomasatdatabricks)
- [Deployment] For SageMaker, use the uniquely-generated model name as the S3 bucket prefix instead of requiring one. (1183, dbczumar)
- [REST API] Add support for API paths without the ``preview`` component. The ``preview`` paths will be deprecated in a future version of MLflow. (1236, mparkhe)

Bug fixes and documentation updates
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- [Tracking] Log metric timestamps in milliseconds by default (1177, smurching; 1333, dbczumar)
- [Tracking] Fix bug when deserializing integer experiment ID for runs in ``SQLAlchemyStore`` (1167, smurching)
- [Tracking] Ensure unique constraint names in MLflow tracking database (1292, smurching)
- [Tracking] Fix base64 encoding for basic auth in R tracking client (1126, freefrag)
- [Tracking] Correctly handle ``file:`` URIs for the ``-—backend-store-uri`` option in ``mlflow server`` and ``mlflow ui`` CLI commands (1171, eedeleon, tomasatdatabricks)
- [Artifacts] Update artifact repository download methods to return absolute paths (1179, dbczumar)
- [Artifacts] Make FileStore respect the default artifact location (1332, dbczumar)
- [Artifacts] Fix ``log_artifact`` failures due to existing directory on FTP server (1327, kafendt)
- [Artifacts] Fix GCS artifact logging of subdirectories (1285, jason-huling)
- [Projects] Fix bug not sharing ``SQLite`` database file with Docker container (1347, tomasatdatabricks; 1375, aarondav)
- [Java] Mark ``sendPost`` and ``sendGet`` as experimental (1186, aarondav)
- [Python][CLI] Mark ``azureml.build_image`` as experimental (1222, 1233 sueann)
- [Docs] Document public MLflow environment variables (1343, aarondav)
- [Docs] Document MLflow system tags for runs (1342, aarondav)
- [Docs] Autogenerate CLI documentation to include subcommands and descriptions (1231, sueann)
- [Docs] Update run selection description in ``mlflow_get_run`` in R documentation (1258, dbczumar)
- [Examples] Update examples to reflect API changes (1361, tomasatdatabricks; 1367, mparkhe)

Small bug fixes and doc updates (1359, 1350, 1331, 1301, 1270, 1271, 1180, 1144, 1135, 1131, 1358, 1369, 1368, 1387, aarondav; 1373, akarloff; 1287, 1344, 1309, stbof; 1312, hchiuzhuo; 1348, 1349, 1294, 1227, 1384, tomasatdatabricks; 1345, withsmilo; 1316, ancasarb; 1313, 1310, 1305, 1289, 1256, 1124, 1097, 1162, 1163, 1137, 1351, smurching; 1319, 1244, 1224, 1195, 1194, 1328, dbczumar; 1213, 1200, Kublai-Jing; 1304, 1320, andrewmchen; 1311, Zangr; 1306, 1293, 1147, mateiz; 1303, gliptak; 1261, 1192, eedeleon; 1273, 1259, kevinykuo; 1277, 1247, 1243, 1182, 1376, mparkhe; 1210, vgod-dbx; 1199, ashtuchkin; 1176, 1138, 1365, sueann; 1157, cclauss; 1156, clemens-db; 1152, pogil; 1146, srowen; 875, 1251, jimthompson5802)

0.9.1

------------------
MLflow 0.9.1 is a patch release on top of 0.9.0 containing mostly bug fixes and internal improvements. We have also included a one breaking API change in preparation for additions in MLflow 1.0 and later. This release also includes significant improvements to the Search API.

Breaking changes:

- [Tracking] Generalized experiment_id to string (from a long) to be more permissive of different ID types in different backend stores. While breaking for the REST API, this change is backwards compatible for python and R clients. (1067, 1034 eedeleon)

More features and improvements:

- [Search][API] Moving search filters into a query string based syntax, with Java client, Python client, and UI support. This also improves quote, period, and special character handling in query strings and adds the ability to search on tags in filter string. (1042, 1055, 1063, 1068, 1099, 1106 mparkhe; 1025 andrewmchen; 1060 smurching)
- [Tracking] Limits and validations to batch-logging APIs in OSS server (958 smurching)
- [Tracking][Java] Java client API for batch-logging (1081 mparkhe)
- [Tracking] Improved consistency of handling multiple metric values per timestamp across tracking stores (972, 999 dbczumar)

Bug fixes and documentation updates:

- [Tracking][Python] Reintroduces the parent_run_id argument to MlflowClient.create_run. This API is planned for removal in MLflow 1.0 (1137 smurching)
- [Tracking][Python] Provide default implementations of AbstractStore log methods (1051 acroz)
- [R] (Released on CRAN as MLflow 0.9.0.1) Small bug fixes with R (1123 smurching; 1045, 1017, 1019, 1039, 1048, 1098, 1101, 1107, 1108, 1119 tomasatdatabricks)

Small bug fixes and doc updates (1024, 1029 bayethiernodiop; 1075 avflor; 968, 1010, 1070, 1091, 1092 smurching; 1004, 1085 dbczumar; 1033, 1046 sueann; 1053 tomasatdatabricks; 987 hanyucui; 935, 941 jimthompson5802; 963 amilbourne; 1016 andrewmchen; 991 jaroslawk; 1007 mparkhe)

0.9.0.1

--------------------
Bugfix release (PyPI only) with the following changes:

- Rebuilt MLflow JS assets to fix an issue where form input was broken in MLflow 0.9.0 (identified
in 1056, 1113 by shu-yusa, timothyjlaurent)

0.9.0

------------------

Major features:

- Support for running MLflow Projects in Docker containers. This allows you to include non-Python dependencies in their project environments and provides stronger isolation when running projects. See the `Projects documentation <https://mlflow.org/docs/latest/projects.html>`_ for more information. (#555, marcusrehm; 819, mparkhe; 970, dbczumar)
- Database stores for the MLflow Tracking Server. Support for a scalable and performant backend store was one of the top community requests. This feature enables you to connect to local or remote SQLAlchemy-compatible databases (currently supported flavors include MySQL, PostgreSQL, SQLite, and MS SQL) and is compatible with file backed store. See the `Tracking Store documentation <https://mlflow.org/docs/latest/tracking.html#storage>`_ for more information. (756, AndersonReyes; 800, 844, 847, 848, 860, 868, 975, mparkhe; 980, dbczumar)
- Simplified custom Python model packaging. You can easily include custom preprocessing and postprocessing logic, as well as data dependencies in models with the ``python_function`` flavor using updated ``mlflow.pyfunc`` Python APIs. For more information, see the `Custom Python Models documentation <https://mlflow.org/docs/latest/models.html#custom-python-models>`_. (791, 792, 793, 830, 910, dbczumar)
- Plugin systems allowing third party libraries to extend MLflow functionality. The `proposal document <https://gist.github.com/zblz/9e337a55a7ba73314890be68370fa69a>`_ gives the full detail of the three main changes:

- You can register additional providers of tracking stores using the ``mlflow.tracking_store`` entrypoint. (881, zblz)
- You can register additional providers of artifact repositories using the ``mlflow.artifact_repository`` entrypoint. (882, mociarain)
- The logic generating run metadata from the run context (e.g. ``source_name``, ``source_version``) has been refactored into an extendable system of run context providers. Plugins can register additional providers using the ``mlflow.run_context_provider`` entrypoint, which add to or overwrite tags set by the base library. (913, 926, 930, 978, acroz)

- Support for HTTP authentication to the Tracking Server in the R client. Now you can connect to secure Tracking Servers using credentials set in environment variables, or provide custom plugins for setting the credentials. As an example, this release contains a Databricks plugin that can detect existing Databricks credentials to allow you to connect to the Databricks Tracking Server. (938, 959, 992, tomasatdatabricks)


Breaking changes:

- [Scoring] The ``pyfunc`` scoring server now expects requests with the ``application/json`` content type to contain json-serialized pandas dataframes in the split format, rather than the records format. See the `documentation on deployment <https://mlflow.org/docs/latest/models.html#deploy-a-python-function-model-as-a-local-rest-api-endpoint>`_ for more detail. (960, dbczumar) Also, when reading the pandas dataframes from JSON, the scoring server no longer automatically infers data types as it can result in unintentional conversion of data types (916, mparkhe).
- [API] Remove ``GetMetric`` & ``GetParam`` from the REST API as they are subsumed by ``GetRun``. (879, aarondav)


More features and improvements:

- [UI] Add a button for downloading artifacts (967, mateiz)
- [CLI] Add CLI commands for runs: now you can ``list``, ``delete``, ``restore``, and ``describe`` runs through the CLI (720, DorIndivo)
- [CLI] The ``run`` command now can take ``--experiment-name`` as an argument, as an alternative to the ``--experiment-id`` argument. You can also choose to set the ``_EXPERIMENT_NAME_ENV_VAR`` environment variable instead of passing in the value explicitly. (889, 894, mparkhe)
- [Examples] Add Image classification example with Keras. (743, tomasatdatabricks )
- [Artifacts] Add ``get_artifact_uri()`` and ``_download_artifact_from_uri`` convenience functions (779)
- [Artifacts] Allow writing Spark models directly to the target artifact store when possible (808, smurching)
- [Models] PyTorch model persistence improvements to allow persisting definitions and dependencies outside the immediate scope:
- Add a ``code_paths`` parameter to ``mlflow.pytorch.save_model`` and ``mlflow.pytorch.log_model`` to allow external module dependencies to be specified as paths to python files. (842, dbczumar)
- Improve ``mlflow.pytorch.save_model`` to capture class definitions from notebooks and the ``__main__`` scope (851, 861, dbczumar)
- [Runs][R] Allow client to infer context info when creating new run in fluent API (958, tomasatdatabricks)
- [Runs][UI] Support Git Commit hyperlink for Gitlab and Bitbucket. Previously the clickable hyperlink was generated only for Github pages. (901)
- [Search][API] Allow param value to have any content, not just alphanumeric characters, ``.``, and ``-`` (788, mparkhe)
- [Search][API] Support "filter" string in the ``SearchRuns`` API. Corresponding UI improvements are planned for the future (905, mparkhe)
- [Logging] Basic support for LogBatch. NOTE: The feature is currently experimental and the behavior is expected to change in the near future. (950, 951, 955, 1001, smurching)


Bug fixes and documentation updates:

- [Artifacts] Fix empty-file upload to DBFS in ``log_artifact`` and ``log_artifacts`` (895, 818, smurching)
- [Artifacts] S3 artifact store: fix path resolution error when artifact root is bucket root (928, dbczumar)
- [UI] Fix a bug with Databricks notebook URL links (891, smurching)
- [Export] Fix for missing run name in csv export (864, jimthompson5802)
- [Example] Correct missing tensorboardX module error in PyTorch example when running in MLflow Docker container (809, jimthompson5802)
- [Scoring][R] Fix local serving of rfunc models (874, kevinykuo)
- [Docs] Improve flavor-specific documentation in Models documentation (909, dbczumar)

Small bug fixes and doc updates (822, 899, 787, 785, 780, 942, hanyucui; 862, 904, 954, 806, 857, 845, stbof; 907, 872, smurching; 896, 858, 836, 859, 923, 939, 933, 931, 952, dbczumar; 880, zblz; 876, acroz; 827, 812, 816, 829, jimthompson5802; 837, 790, 897, 974, 900, mparkhe; 831, 798, aarondav; 814, sueann; 824, 912, mateiz; 922, 947, tomasatdatabricks; 795, KevYuen; 676, mlaradji; 906, 4n4nd; 777, tmielika; 804, alkersan)

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